Jurnal Teknik Informatika (Jutif)

Clustering And Classification Of Toddler Stunting Risk Using K-Means And Naïve Bayes: A Case Study At Kembaran 1 Community Health Center

2025-10-31T23:08:08+00:00

Stunting continues to be a significant public health concern in Indonesia, with a frequency of 17.25% at Kembaran 1 Public Health Center, highlighting ongoing difficulties in early childhood nutrition and growth surveillance. This work seeks to assess and forecast stunting risk in toddlers by employing K-Means clustering and Naïve Bayes classification to enhance early detection precision. The K-Means method was utilized on 1,168 toddler growth records to categorize stunting features, whereas the Davies–Bouldin Index (DBI) was employed to evaluate cluster quality. The ideal cluster was attained at k = 8, yielding a DBI value of 4.353, indicating compact and distinctly differentiated clusters. The Naïve Bayes classifier subsequently predicted stunting potential with an accuracy of 93.56%, accurately categorizing 218 out of 233 test examples, yielding precision, recall, and F1-score values for the “short” class of 97.41%, 94.95%, and 96.18%, respectively. The findings indicate that the hybrid model successfully combines unsupervised and supervised learning, improving stunting prediction accuracy and cluster interpretability. The research provides a data-centric framework for localized stunting surveillance, aiding community health centers in formulating targeted early treatments and mitigating long-term developmental hazards.

Sentiment Analysis Using Bidirectional Encoder Representations from Transformers for Indonesian Stock Price Prediction with Long Short-Term Memory and Gated Recurrent Unit Models

2025-11-24T03:28:10+00:00

The advancement of artificial intelligence based market analytics has driven the need for stock price prediction models capable of representing market behavior both technically and psychologically. This study aims to improve stock price forecasting in the Indonesian capital market by integrating sentiment analysis with deep learning time-series models. It evaluates whether public sentiment can contribute to enhancing prediction accuracy when combined with historical stock data. Textual sentiments were extracted using IndoBERT and converted into positive, negative, and neutral scores, which were then merged with historical stock prices. These data were modeled using LSTM, GRU, and a hybrid LSTM–GRU architecture. Model evaluation was conducted using MSE, MAE, RMSE, and MAPE metrics across six Indonesian stocks ANTM, BBCA, BBRI, SCMA, TLKM, and UNVR. The hybrid LSTM–GRU model produced the lowest prediction errors for BBCA and BBRI, with MSE scores of 0.151 and 1022.062, respectively. GRU delivered the best performance for highly volatile stocks, such as SCMA MAPE 1.65% and UNVR MAPE 0.51%, while LSTM demonstrated the most stable performance for TLKM with an MSE of 606.93 and RMSE of 24.63. Across all cases, sentiment scores improved model responsiveness, particularly during price spikes ANTM mid-2025 and price declines BBRI early year. The integration of sentiment significantly enhances prediction relevance by combining psychological market indicators with technical price trends. This framework provides more reliable decision-making support for investors, strengthens algorithmic trading strategies in Indonesia, and contributes to intelligent financial analytics that reflect local market behavior.

Deep Learning-Based Recognition of Indonesian Sign Language (BISINDO) Alphabetic Gestures Using Skeletal Feature Extraction and LSTM

2025-10-06T03:37:51+00:00

Communication is a fundamental aspect of human life, and for the deaf community, sign language serves as the primary medium of interaction. In Indonesia, the Indonesian Sign Language (BISINDO) is widely used, however, research on automatic BISINDO recognition remains limited due to the scarcity of representative datasets. This study presents the development of a BISINDO recognition system based on deep learning by integrating the Long Short-Term Memory (LSTM) architecture with the MediaPipe Holistic framework. To address data limitations, a custom dataset comprising 866 BISINDO alphabetic gesture videos was collected, involving recordings from both expert and non-expert signers to capture stylistic variations. Extracted skeletal landmark features were processed through a three-layer LSTM network followed by dense layers for sequential modeling and classification. Experimental results show that the proposed model achieved a validation accuracy of approximately 93%, outperforming static image–based methods and demonstrating the effectiveness of skeletal features in representing dynamic gestures. The model also exhibited real-time applicability with promising performance, although challenges such as misclassification of visually similar gestures and dataset imbalance remain. This study contributes to the underexplored field of BISINDO recognition by providing a baseline system and dataset, and further advances the domains of computer vision and human–computer interaction within informatics through an inclusive, data-driven framework for Indonesian Sign Language recognition and future AI-assisted accessibility technologies.

Optimizing Automatic Irrigation Duration for Grapevines in Greenhouses Using Multiple Linear Regression Analysis

2025-09-03T12:57:34+00:00

Greenhouses offer a controllable microclimate for high‑value horticulture, yet manual irrigation and single‑sensor threshold rules remain inefficient and error‑prone for grapevine cultivation in tropical conditions. This study designs and implements an Internet‑of‑Things (IoT) automatic irrigation system that employs an interpretable multiple linear regression (MLR) model as the decision core, using air temperature and soil moisture—acquired via DHT11 and capacitive soil‑moisture sensors—to estimate irrigation duration in real time. The model is trained on greenhouse measurements and deployed for low‑latency edge inference to actuate valves with duration‑to‑volume conversion, enabling precise and adaptive water delivery. Experimental evaluation shows strong predictive performance (MSE = 0.15, MAPE = 1.44%, R² = 0.98), indicating high accuracy and reliable generalization for operational control. The primary contributions are: (i) a lightweight, explainable regression formulation tailored to tropical grapevines that outperforms single‑parameter baselines; (ii) an end‑to‑end, edge‑deployable IoT pipeline that reduces computational and energy costs while maintaining real‑time autonomy; and (iii) an engineering blueprint that is scalable and maintainable for smallholder contexts. The impact for Informatics/Computer Science lies in demonstrating a practical ML‑on‑the‑edge reference design—combining interpretable modeling, sensor fusion, and actuation—that advances sustainable computing for precision agriculture, improves resource efficiency, and supports robust, replicable deployment of smart‑irrigation systems in data and power‑constrained environments.

Enhancing Diagnostic Accuracy of Polycystic Ovary Syndrome Classification in Ultrasound Images Using a Hybrid Deep Learning Model of VGG16 and AlexNet

2025-07-20T09:03:22+00:00

Diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound (USG) imaging still faces a major challenge in the form of inter-observer variability, which can lead to inconsistent diagnostic outcomes and increase the risk of misclassification. This limitation highlights the urgent need for an automated artificial intelligence (AI)–based system capable of performing ultrasound image classification with greater objectivity, accuracy, and consistency. This study aims to develop an automated PCOS classification model based on a hybrid Convolutional Neural Network (CNN) architecture that integrates VGG16 and AlexNet through a feature concatenation mechanism, following preprocessing and data augmentation steps to enhance model generalization. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and specificity as key metrics. Experimental results demonstrate that the VGG16–AlexNet hybrid model achieved the best performance, with an accuracy of 98.26%, precision of 97.90%, recall of 97.90%, F1-score of 97.90%, and specificity of 98.52%. These results outperform other hybrid configurations such as VGG16–MobileNetV2, VGG16–ResNet50, and VGG16–InceptionV3, each of which achieved accuracies above 96%. These findings confirm that combining the feature depth of VGG16 with the computational efficiency of AlexNet enables more comprehensive extraction of spatial and textural patterns in ultrasound images. Consequently, the proposed hybrid model offers a promising AI-driven diagnostic support system that not only enhances the accuracy of PCOS detection but also assists clinicians in making faster, more objective, and consistent medical decisions.

Geodetically-Enhanced Hybrid GRU with Adaptive Dropout and Dynamic L2 Regularization for Earthquake Parameter Prediction in Indonesia

2025-11-26T10:46:53+00:00

Earthquake prediction remains challenging due to the nonlinear behavior and uncertainty of seismic activity. This study introduces a geodetically-enhanced hybrid GRU model integrating adaptive dropout and dynamic L2 regularization to improve robustness and accuracy in earthquake magnitude prediction. In addition to seismic sequence data, slip-rate values derived from scalar moment distribution were incorporated as a domain-informed feature to represent tectonic strain accumulation across Indonesia. The dataset consisted of BMKG records from 2010–2025 and was processed through outlier removal, normalization, temporal reshaping, and feature integration. The proposed model was evaluated against multiple deep learning baselines including CNN-1D, LSTM, standard GRU, Transformer-based models, and Neural ODE architectures. Performance assessment used RMSE, MAE, and R² metrics. The resulting hybrid GRU achieved improved predictive accuracy with an RMSE of 0.5176, MAE of 0.3973, and an R² score of 0.5997, outperforming both CNN-1D and standard GRU baselines. The integration of slip-rate features contributed to reduced prediction variance across tectonically active zones. These findings demonstrate that combining geodetic information with adaptive regularization strategies improves generalization and model stability for seismic forecasting. The approach offers potential applicability for rapid early-warning scenarios requiring low latency and reliable prediction accuracy.

Optimizing Heart Disease Classification Using C4.5, Random Forest, and XGBoost with ANOVA, Chi-Square, and AdaBoost

2025-11-10T12:24:58+00:00

Heart disease remains one of the leading causes of mortality worldwide, underscoring the need for accurate and scalable prediction models within clinical informatics. This study proposes a leakage-safe machine learning pipeline combining stratified splitting, SMOTE-based imbalance handling, and in-fold feature selection using ANOVA, Chi-Square, and AdaBoost-assisted ranking to enhance classification performance on a large heart-disease dataset consisting of 10,000 samples and 21 attributes. Three widely used algorithms, C4.5, Random Forest, and XGBoost, were evaluated to determine the optimal model-feature selection configuration for structured medical data. The results demonstrate that feature relevance contributes more significantly to predictive performance than increasing model complexity, with Random Forest achieving the highest accuracy, precision, recall, and F1-Score at 98.43% when combined with Chi-Square or ANOVA feature selection. C4.5 showed the greatest relative improvement, rising from 76.52% to 97.57% using AdaBoost-assisted selection, while XGBoost improved from 66.32% to 94.88% after statistical filtering. The dominant features identified such as CRP, BMI, blood pressure, fasting glucose, LDL, triglycerides, and homocysteine align with well-established cardiovascular biomarkers, supporting clinical validity. This research provides an important contribution to computer science by demonstrating an efficient and scalable hybrid FS-boosting framework capable of reducing unnecessary model complexity, improving generalization, and supporting low-latency deployment in clinical decision-support systems. The findings highlight the potential of structured-data machine learning to strengthen digital health diagnostics in resource-limited environments.

A Comparative Study of Generalized Linear Mixed Model and Mixed Effects Random Forest for Analyzing Data with Outliers

2025-11-11T15:54:05+00:00

This study compares MERF and GLMM-NB in analyzing hierarchical data and focusing on the role of residual outliers and the application of winsorization. A two-stage analytical pipeline was implemented: (1) winsorization to reduce extreme residual values, and (2) model training using MERF and GLMM-NB. The dataset comes from the 2021 National Socio-Economic Survey (Susenas) in West Java Province, measuring tobacco consumption intensity. Two statistical approaches are compared, MERF and GLMM with a Negative Binomial distribution (GLMM-NB). Models were trained under two conditions: without winsorization (WIN0) and with two-sided 5% winsorization (WIN5). Winsorization was applied to the training data, and the test data were adjusted using thresholds from the training set. Model performance was assessed using Root Mean Squared Error (RMSE) and the train-test ratio. Under WIN0, GLMM recorded an RMSE of 49.65 for training and 42.27 for testing, while MERF achieved 35.96 and 39.94, respectively. After WIN5, GLMM showed a larger error reduction, with RMSE values of 34.90 (train) and 30.20 (test), while MERF dropped to 26.63 (train) and 28.64 (test). These results indicate that MERF provides higher predictive accuracy, whereas GLMM benefits more from winsorization. Household expenditure, employment status, age, and gender consistently emerged as key variables linked to tobacco consumption intensity. This study is the first to compare MERF and GLMM-NB with winsorization using Indonesia’s hierarchical data. The analytical framework helps inform public health policies aligned with SDG 3: Good Health and Well-being, particularly in reducing tobacco-related health risks.

Evaluating SMOTE Performance for Imbalanced Multi-Label Sentiment Classification in MLSE Usability Testing of Mobile App Reviews

2025-11-05T09:52:53+00:00

Imbalanced data poses a significant challenge in multi-label classification tasks, especially when combining sentiment analysis with usability testing of mobile application reviews. This study investigates the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification performance on a multi-label dataset consisting of 10,000 Indonesian language user reviews from the Google Play store. The classification labels represent a combination of usability criteria and sentiment polarity, with strong imbalance observed across several classes. Three machine learning algorithms SVM, Decision Tree, and Random Forest were evaluated on datasets of increasing sizes (1,000 to 10,000 entries), each tested under both original and SMOTE-balanced conditions using stratified 10-fold cross-validation with accuracy and F1-score as the primary metrics. Experimental results show that SMOTE significantly improves the performance of Decision Tree mainly on smaller datasets but exhibits inconsistent gains as the dataset grows, provides modest and stable improvements for Random Forest, and negatively impacts SVM, whose performance remains consistently better without SMOTE. This study concludes that SMOTE is not a universally effective solution and must be applied selectively based on model characteristics. These findings contribute to the Machine Learning for Software Engineering (ML4SE) domain and the field of informatics by highlighting the importance of aligning resampling techniques with algorithmic behaviour when dealing with highly imbalanced multi-label text classification tasks.

Improving Imbalanced Data Classification Using Stacked Ensemble Learning with Naïve Bayes Variants and Random Forest

2025-10-02T22:34:49+00:00

Classification in imbalanced and heterogeneous datasets poses significant challenges in informatics, particularly in agricultural domains where minority classes are often underrepresented and feature redundancy affects model performance. This research aims to improve classification performance by developing a stacked ensemble learning framework that integrates probabilistic and tree-based learners to address class imbalance and enhance model interpretability. The framework combines Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), and Random Forest (RF) as base learners with Logistic Regression as the meta-learner. Feature selection was performed using Chi-Square and ReliefF to identify the most relevant predictors, while SMOTE was applied to balance the dataset. Two ensemble configurations were evaluated: Ensemble A (GNB + MNB) and Ensemble B (GNB + RF). Experimental results demonstrate that Ensemble B achieved 97% accuracy and a macro F1-score of 0.97, with a 5.7% accuracy improvement over the best individual classifier and an 18% improvement in minority-class recall. The integration of probabilistic and tree-based models within a stacked architecture provides an interpretable and effective solution for data-driven decision systems in informatics, particularly valuable for domains requiring both high accuracy and model explainability in handling imbalanced datasets.

Exploring Ensemble Architectures on Lung X-Ray Multi-Class Image for Classification Using Convolutional Neural Network and Random Forest

2025-07-15T15:01:26+00:00

The lungs are vital organs that play an important role in the respiratory and circulatory systems. Early detection of lung diseases through medical images, especially Chest X-Ray (CXR), is still a challenge due to the limited amount of data and complexity in image interpretation. This research aims to develop an effective image classification approach for lung disease detection by comparing two main methods: direct training using Convolutional Neural Network (CNN) and a hybrid method involving feature extraction from CNN model, feature selection using Chi-Square method, and classification using Random Forest algorithm.

To overcome data imbalance and increase variation, data augmentation techniques such as rotation, vertical and horizontal flipping, and zooming are used. Four popular CNN architectures are used in training, namely VGG16, ResNet-50, InceptionV3, and MobileNet. After training, features are extracted and stored in .csv format. Next, feature selection using the Chi-Square method and classification with Random Forest are performed.

The experimental results show that direct CNN training achieves high accuracy, with MobileNet reaching the highest performance at 98.83%. However, this approach requires significant computational resources and longer training time. In contrast, the hybrid method offers competitive accuracy with lower computational demands. The findings highlight the potential of combining deep learning and traditional machine learning to create efficient, accurate, and resource-friendly medical image classification systems. This research has significant implications for supporting early diagnosis of lung diseases, reducing diagnostic workload for medical professionals, and enabling the development of deployable AI-assisted healthcare solutions in resource-limited settings.

Optimization of MobileNet SSD Using Pruning, Quantization, and Transfer Learning for Real-Time Vehicle Detection in IoT-Based Security Systems

2025-11-28T07:15:09+00:00

Security is a critical requirement in modern public and private environments, especially in systems that rely on resource-constrained IoT devices. This research aims to optimize the MobileNet SSD (Single Shot MultiBox Detector) model to achieve fast and reliable real-time vehicle and human detection on low-power hardware. The proposed optimization pipeline integrates three techniques: pruning to reduce network redundancy, quantization to accelerate inference and decrease memory usage, and transfer learning using six relevant object classes (person, car, motorcycle, bicycle, bus, and truck). Experiments were conducted on a Raspberry Pi 5 equipped with a camera and local dashboard interface. The optimized MobileNet SSD v2 model achieved a mean Average Precision (mAP) of 0.724 and mAP@0.5 of 0.951, while improving inference speed from 21 FPS to over 24 FPS. These results indicate a balanced trade-off between accuracy, speed, and resource efficiency, enabling stable real-time performance on constrained IoT platforms. The findings contribute to the body of knowledge in embedded and edge AI by demonstrating how integrated model-level optimization can significantly enhance deep learning inference on low-power systems, offering scientific and practical implications for smart surveillance and intelligent traffic monitoring.

A Locally Grounded Retrieval-Augmented LLM-Based Chatbot for Bilingual Stunting Prevention Consultation among Health Cadres in Indonesia

2025-11-21T14:10:56+00:00

Stunting remains a major public health challenge in Indonesia, affecting 21.6% of children under five nationally and 18.34% in Nusa Tenggara Barat (NTB), which strains the capacity of health cadres to deliver timely and accurate nutrition education. This study aims to develop a consultation chatbot by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to provide context-aware stunting prevention guidance. A total of 45 journal articles and 7 books were curated to construct 7,642 question–answer pairs using a RAG-based pipeline. Text preprocessing involved segmentation, embedding, and Byte Pair Encoding tokenization, followed by fine-tuning a LLaMA 3 model on an NVIDIA L4 GPU. Model performance was evaluated using ROUGE and BERTScore metrics, complemented by a small pilot usability assessment. The RAG-integrated model achieved a ROUGE-1 score of 81.03% and a BERTScore F1 of 93.48%, consistently outperforming baseline models. These findings demonstrate the potential of RAG-enhanced LLMs to support scalable and accessible health informatics solutions for empowering health cadres in resource-limited and rural settings.

Optimizing E-commerce Personalization through Hybrid Decision Tree–Nearest Neighbor Recommendation Integration

2025-11-04T22:52:44+00:00

Single-method recommendation systems face critical limitations: content-based filtering suffers from overspecialization while collaborative filtering struggles with data sparsity and cold-start problems. This research introduces an innovative hybrid recommendation framework that synthesizes Content-Based Filtering (CBF) utilizing Decision Trees with Collaborative Filtering (CF) employing Nearest Neighbor algorithms. Our approach addresses the inherent limitations of singular recommendation methodologies by integrating product attribute analysis with collective user behavior patterns. We conducted comprehensive evaluations using a shopping behavior dataset comprising 3,900 consumer records with diverse demographic and product interaction data. Our findings reveal that an asymmetric hybrid configuration—weighted at 70% for CBF and 30% for CF—achieves optimal performance with a Root Mean Square Error (RMSE) of 0.7422. The system incorporates an interactive user interface that facilitates a natural shopping experience: browsing available items, receiving personalized recommendations, and providing explicit feedback on suggested products. Through feature importance analysis, we identified key product attributes that significantly influence recommendation quality, including size variations and specific color preferences. The hybrid approach demonstrates 42% greater category diversity and 37% more recommendation diversity compared to pure content-based filtering, while maintaining superior accuracy metrics. Our research contributes to understanding optimal hybrid architectures and provides practical insights for implementing effective personalization strategies in real-world e-commerce environments.

Deep Learning Based MobileNet Optimization For High Accuracy Classification Of Toddler Stunting

2025-11-04T22:24:34+00:00

This study aims to develop and optimize a MobileNet-based deep learning model for toddler stunting classification using whole-body images. A progressive optimization strategy was applied through three scenarios: (1) a baseline MobileNet feature-extraction model, (2) an optimized fine-tuned model, and (3) a final model enhanced with an adaptive ReduceLROnPlateau scheduler. Using a private dataset of 571 images, the proposed model achieved significant improvements—from 97.47% accuracy in the baseline model to a perfect 100% accuracy, precision, recall, and F1-score in the final scenario. These results highlight the novelty of this study, namely the use of whole-body images combined with progressive MobileNet optimization, which substantially outperforms prior studies relying solely on facial image analysis. The proposed approach demonstrates strong potential as a highly accurate and efficient computational tool for clinical stunting screening.

Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images

2025-11-11T03:13:20+00:00

This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.

Performance Analysis of Traditional Machine Learning Classifiers on LSTM-Extracted Features for Indonesian Sign Language System Recognition

2025-08-03T22:04:36+00:00

Recognizing affix gestures in the Indonesian Sign Language System (SIBI) remains challenging due to subtle visual differences in hand shape and movement, often resulting in lower classification accuracy compared to other categories. This study aims to evaluate whether lightweight traditional and hybrid classifiers can provide competitive performance to deep learning models for SIBI recognition. Using a dataset of 21,351 gesture videos covering four categories (Affix, Alphabet, Number, and Word), features were extracted from MediaPipe keypoints and processed as frozen LSTM embeddings. Six classifiers (Random Forest, K-Nearest Neighbors, Naïve Bayes, Multilayer Perceptron, Support Vector Machine, and Hidden Markov Model) were evaluated with 5-fold stratified cross-validation using accuracy, precision, recall, and F1-score, with statistical significance tested through Friedman and Nemenyi analyses. Results show that MLP and RF achieved high performance in Alphabet, Number, and Word categories (above 96 percent accuracy), while Affix remained the most difficult, with MLP reaching 81.17 percent, outperforming the 68.17 percent from a prior BiLSTM model. This study provides a benchmark for hybrid model implementation in sign language recognition, showing that while traditional classifiers on deep features are effective and computationally lighter for general gestures, deep architectures remain superior for capturing the fine-grained temporal nuances critical for complex categories like affixes.

Augmentation Strategy and Hyperparameter Optimization Using Optuna for Potato Leaf Disease Classification in Uncontrolled Environment

2025-09-03T13:11:13+00:00

Image-based classification of potato leaf diseases presents a significant challenge, particularly when data are collected in uncontrolled field environments. While Convolutional Neural Networks (CNNs) and Computer Vision have been widely used for plant disease identification, most previous studies relied on laboratory datasets with uniform lighting and backgrounds, limiting their real-world applicability. This study proposes an integrated framework that combines data augmentation, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and automated hyperparameter optimization through Optuna to enhance the robustness and accuracy of CNN-based models. A total of 3,076 high-resolution potato leaf images representing seven disease classes were evaluated across five CNN architectures and three training scenarios. The MobileNetV3-Large model achieved the best baseline performance with an accuracy of 0.863 and F1-score of 0.868, while Optuna-based optimization further improved performance to 0.895 accuracy, 0.913 precision, 0.906 recall, and 0.904 F1-score, demonstrating the effectiveness of adaptive optimization in improving model generalization. The integration of augmentation, SMOTE, and Optuna resulted in an intelligent and efficient system resilient to environmental variability, showing strong potential for automatic early detection of potato leaf diseases in real agricultural settings. This research contributes to the advancement of Informatics and Artificial Intelligence by promoting adaptive computer vision approaches for smart agriculture and real-world image-based diagnostic systems.

Rule-Based Expert System for Personalized GERD Food Recommendations Using Forward Chaining and Certainty Factor in Indonesia

2025-11-26T07:57:01+00:00

This study develops a personalized and transparent rule-based expert system to support dietary decision-making for Indonesian patients with gastroesophageal reflux disease (GERD), addressing a critical gap in existing expert-system applications that focus mainly on diagnosis rather than daily diet management. The system integrates knowledge derived from the Indonesian GERD Consensus (2022) and its 2024 addendum with local nutritional evidence to construct if–then rules that classify foods into safe, limit, and avoid categories. A forward-chaining inference engine processes user-specific inputs—including symptoms, trigger sensitivities, eating behaviors, and dietary restrictions—while the Certainty Factor (CF) model quantifies confidence levels to accommodate individual tolerance variability. The system was implemented using Python and deployed through a Gradio-based wizard interface, enabling stepwise data collection and producing Top-N food recommendations with explainable “reason traces.” Functional evaluations across mild, moderate, and severe profiles demonstrated consistent alignment with national dietary guidelines, steering users toward low-fat, non-spicy, soft-textured, and clear-broth menu options, while eliminating high-risk trigger foods. Preliminary expert validation indicated high agreement with guideline principles, emphasizing the system’s interpretability and practical relevance. This research contributes to the field of health informatics by operationalizing forward chaining and CF for personalized dietary support, offering an auditable and computationally efficient alternative to black-box recommendation systems. Future developments include expanding the food dataset, refining CF calibration, and conducting structured clinical validation to enhance performance and applicability in real-world mHealth environments.

Hybrid LSTM-CNN-GRU Deep Learning for Integrating IoT and Social Media Sentiment Analysis in Indonesian Higher Education Reputation Management

2025-11-10T12:23:36+00:00

Higher education institutions in Indonesia face critical challenges in managing digital reputation. Despite 85% of prospective students using social media for university research, only 23% of institutions have integrated monitoring systems, resulting in 67% experiencing undetected reputation crises with substantial financial losses. This research proposes a novel framework integrating IoT campus data with social media sentiment analysis using hybrid deep learning architecture. The system employs LSTM-CNN networks with multi-head attention mechanisms for sentiment classification and GRU networks for reputation trend prediction, enhanced with data fusion strategy. Data collected from 428 IoT sensors and 3.2 million social media posts across five Indonesian universities over six months underwent advanced preprocessing including Indonesian-specific slang normalization and Sastrawi stemming. The hybrid LSTM-CNN architecture with attention achieved 90.3% sentiment classification accuracy (Macro-F1: 0.903), significantly outperforming baseline methods including Naive Bayes (76.2%), traditional LSTM (84.5%), and IndoBERT (87.1%). IoT integration contributed 18.2% RMSE improvement in trend prediction (R²: 0.874). The early warning system predicted reputation crises with 85.7% precision and 82.4% recall, providing critical intervention windows averaging 14.3 days before incidents. The real-time dashboard achieved 98.5% availability with sub-3-second response time and excellent usability (SUS score: 82.4). This research contributes: (1) novel IoT-sentiment integration framework with demonstrated effectiveness, (2) context-aware deep learning architecture optimized for Indonesian language achieving state-of-the-art performance, (3) validated early warning system enabling proactive reputation management, and (4) practical implementation with significant improvements over existing methods, advancing educational data analytics and AI-based decision support systems.

Early Detection Of Melanoma Skin Cancer Using Gray Level Co-Occurrence Matrix And Ensemble Support Vector Machine

2025-11-24T08:51:22+00:00

Skin cancer is a major global health problem with incidence rates increasing every year. Melanoma, the most aggressive form of skin cancer, requires accurate early detection to reduce mortality risk. Conventional diagnostic methods such as visual examination and biopsy still face limitations in precision and consistency, highlighting the need for more objective and efficient technological approaches. This study proposes a classification method for melanoma using an ensemble of Support Vector Machine (SVM) and Random Forest (RF), supported by feature extraction through the Gray Level Co-occurrence Matrix (GLCM) and dimensionality reduction using Linear Discriminant Analysis (LDA). The research stages include image preprocessing using grayscale conversion to reduce data complexity, followed by GLCM-based texture feature extraction, and LDA transformation to enhance class separability. The classification model is developed using an ensemble voting mechanism that combines predictions from SVM and RF to produce a more stable and robust decision. Experimental results with a 60:40 train–test ratio show that the proposed method achieves an accuracy of 88.75%, outperforming each individual model tested. These findings indicate that the integration of GLCM–LDA features with the SVM-RF ensemble effectively improves melanoma detection performance. Overall, this study provides a significant contribution to the development of early detection systems in health informatics, offering potential improvements in patient safety and survival rates for individuals affected by skin cancer.

A Smart System for Non-Invasive Early Detection of Diabetes through Deep Learning-Based Nail Image Analysis and Expert Systems

2025-10-02T22:45:02+00:00

Public health in Indonesia faces significant challenges in the early detection of diseases, particularly in areas with limited medical services. Diabetes Mellitus can lead to serious complications, but its detection is often hindered by limited access to invasive and expensive diagnostic methods. This study aims to develop a non-invasive early detection system through nail image analysis using a deep learning method based on EfficientNet-B7 and a rule-based expert system. The system classifies nail images into five categories: Healthy, Beaus lines, Onycholysis, Onychomycosis, and Paronychia. The evaluation results show an accuracy of 97.11% on the test set, demonstrating excellent performance in detecting nail conditions associated with diabetes. The application of the expert system using Forward Chaining and Certainty Factor provides in-depth medical explanations for the model's predictions, making this system a potential solution for diabetes screening that is fast, affordable, and accessible across various healthcare facilities.

Prophet with Google Trends for Forecasting Train Passengers in Java

2025-11-24T02:52:15+00:00

As a popular transportation method for long-distance travel, trains were also a preferred choice during the homecoming period before Eid Al-Fitr, one of the major religious holidays in Indonesia. During this period, known locally as ‘mudik,’ millions of people travel from the urban cities back to their hometowns to celebrate with their families, creating a significant surge in transportation demand. However, since the holiday follows the Islamic calendar, which changes slightly every year, forecasting train passengers becomes tricky, thus requiring a different approach to achieve accurate predictions. This study utilizes the Prophet method to forecast train passengers in Java (excluding the Jabodetabek area) using the data from 2006 to 2024. We also incorporated the COVID-19 period as a fixed external regressor, along with external regressors from Google Trends data using the keywords ‘kereta api’, ‘mudik’, and ‘lebaran’, which are commonly searched by the public in relation to train travel and the Eid homecoming period. The results on the test set, 2024 data, showed that the word ‘mudik’ was the most effective in improving forecast accuracy, with a MAPE of 9.12 and RMSE of 797.76, a decrease of 11.57% and 9.34% compared to the updated baseline. This indicates that public search behavior around the term ‘mudik’ closely aligns with actual travel demand patterns. The findings of this study suggest that Prophet with external regressors are capable of forecasting train passengers and Google Trends can be a valuable addition for capturing data patterns related to specific phenomenon.

Explainable Ensemble Learning for Depression Risk Classification Using Multidomain Behavioral Features

2025-07-15T23:56:47+00:00

Depression is a growing global health concern, particularly among adolescents and university students. Despite the availability of standardized assessments, delays in early detection remain a major barrier to effective treatment. Digital behavioral data holds considerable potential for mental health assessment, but its utilization remains limited due to the absence of integrated and interpretable computational models. This study presents an interpretable machine learning framework for classifying depression risk using multi-domain behavioral features extracted from simulated digital life datasets. Three public datasets were integrated and mapped to five psychological clusters based on DSM-5 criteria: self-regulation, negative affect, cognitive strain, comparison and avoidance, and sleep disturbance. Two ensemble classifiers, Random Forest and XGBoost, were applied and evaluated using 10-fold stratified cross-validation. Depression risk was categorized into three levels: Low, Medium, and High. The Random Forest model achieved the highest accuracy (81%) and macro-averaged F1-score (0.81), showing strong performance especially in identifying transitional Medium-risk users. To enhance transparency, both global and local model interpretations were performed using SHapley Additive exPlanations (SHAP). Results revealed that digital stressors such as excessive screen time and disrupted sleep patterns were prominent in high-risk classifications, while mood stability and mindfulness were protective factors in low-risk groups. The proposed framework offers a scalable and explainable for early depression screening by integrating psychological theory with artificial intelligence methods. The findings contribute to the field of behavioral informatics by demonstrating the practical value of interpretable models in enhancing the reliability, transparency, and applicability of digital mental health systems and personalized behavioral monitoring.

Geographic Information System for Land Suitability Mapping of Partner Farmers at Okiagaru Indonesia Agricoop Using Rule-Based System and Prototype Methodology

2025-11-28T03:51:07+00:00

Geographic Information System (GIS) for land suitability assessment integrates spatial and attribute data to evaluate and map areas for food crops and horticulture. The system applies parameters such as temperature, rainfall, water pH, clay CEC, organic carbon, and other soil characteristics, analyzed through a rule-based approach. Its main goal is to optimize agricultural land use by aligning crop selection with physical and environmental conditions. GIS-based analysis enables accurate digital mapping and categorizes land into highly suitable, moderately suitable, marginally suitable, and unsuitable classes, providing valuable insights for farmers, governments, and stakeholders in sustainable land management. System development employed the Prototype methodology, emphasizing iterative stages of requirement gathering, rapid design, prototype construction, user evaluation, and refinement. The Land Suitability GIS (SigKL) was tested at five partner-farmer sites in Cianjur and Sukabumi. Black Box Testing confirmed that all 20 functional features achieved a 100% success rate. The system supports the identification of potential new agricultural areas and offers recommendations for improving less productive land. The novelty of this research lies in integrating FAO (1976) classification with interactive digital mapping and locally tailored knowledge rules, enabling real-time accessibility. Unlike prior studies limited to static analysis, SigKL introduces an adaptive, rule-based GIS prototype with interactive visualization, directly supporting decision-making for sustainable agriculture. This innovation enhances transparency and accessibility, contributing to the Sustainable Development Goals (SDGs) related to food security and sustainable farming.

Enhancement Of The C4.5 Decision Tree Algorithm With Anova For Predicting Academic Achievement Of Students At Smpn.16 Kota Jambi

2025-11-10T12:25:52+00:00

This study aims to improve the accuracy of predicting student academic achievement by integrating the Analysis of Variance (ANOVA) method with the C4.5 Decision Tree algorithm. In the context of information systems, this research holds significant importance for the development of more reliable Decision Support Systems (DSS) or early warning systems in school environments. The research was conducted at SMPN 16 Jambi City using secondary data from three academic years (2022/2023-2024/2025) covering academic variables, attendance, and parental income. The main issue addressed was the limitations of the C4.5 algorithm in handling irrelevant features and unbalanced data, which, at the system implementation level, can lead to inaccurate recommendations or alerts.This research method employed a data mining approach with stages including data cleaning, numeric conversion, missing value imputation, formation of derived variables, and categorization of the target variable "Achievement." The initial C4.5 model produced 72.81% accuracy on the training data and 69.71% accuracy on cross-validation. After feature selection using ANOVA, one insignificant variable was removed, resulting in a hybrid C4.5+ANOVA model with nine key features. Test results showed an increase in accuracy to 80.44% on the training data and 73.66% on the cross-validation data, representing an improvement of 7.63 and 3.95 percentage points, respectively.This improvement in model performance directly translates to an enhancement in the quality of the information system's output, yielding more reliable reports and predictions for teachers and school management.

Analysis of Public Sentiment Indonesia’s Personal Data Protection Law: A Comparison of SVM and IndoBERT on X Platform

2025-10-31T23:20:48+00:00

The high number of data misuses, thefts, and leaks led to the enactment of the PDP Law, which regulates the rights and obligations of data owners and electronic system providers. The purpose of this study is to examine the public’s response to the implementation of the law through the X platform, using tweet harvest as a scraping tool, and to evaluate model performance through a comparative approach between SVM and BERT. The feature extraction used in this study is TF-IDF for SVM and BERT with IndoBERT. The accuracy results indicate that BERT is better with an accuracy of 86% compared to SVM with a training and test data ratio of 85:15. This advantage is because BERT can understand linguistic context that SVM cannot. On the other hand, SVM has advantages in computational efficiency and faster processing, making it a suitable choice in situations with limited computational resources.

The sentiment analysis result revealed that data protection, digital footprint and the institution's role were the most frequently discussed topics. Furthermore, periodic or real-time evaluations can be conducted on the public's response to the PDP Law to ensure it remains aligned and relevant to technological developments and societal needs.

Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier

2025-11-01T14:58:59+00:00

Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.

Comparative Analysis of GPT-2 Augmentation, ALBERT, and Similarity Measures for Cyberbullying Detection

2025-10-21T12:08:40+00:00

The effectiveness of cyberbullying detection is influenced by the availability of sufficient, diverse, and contextually rich training data, which is often limited in low-resource languages such as Indonesian. To address dataset limitations, researchers have extensively explored data augmentation (DA) as a promising approach to improving model performance. DA generates new data instances by applying transformations to existing data, thereby increasing both dataset size and variability. Prior studies have demonstrated that applying Easy Data Augmentation (EDA) with Support Vector Machine (SVM) classification improved cyberbullying detection performance, even when it faced challenges in capturing semantic and contextual nuances. In this paper, we investigated Indonesian DA methods using the Transformer-based GPT-2 model. The augmented sentences were evaluated and filtered based on context, semantics, diversity, and novelty, with similarity measures such as Euclidean Distance (ED), Cosine Similarity (CS), Jaccard Similarity (JS), and BLEU Score (BLS) ensuring the quality of the augmentation. Furthermore, we compared text classification performance using both SVM and the Transformer-based ALBERT model. Experimental results revealed that incorporating similarity measures and GPT-2 as a DA method failed to improve cyberbullying detection performance, potentially due to the semantic drift introduced by GPT-2 and the inadequacy of similarity measures in capturing nuanced contextual information. However, we found that ALBERT outperformed SVM as a classification model, achieving average F1-scores of 91.77% and 91.72%, respectively. This study contributes to the informatics field by exploring the potential of Transformer-based augmentation and similarity evaluation in enhancing low-resource text classification, while acknowledging the limitations in data quality and model adaptation.

Enhancement of YOLOv9 Model for Traffic Vehicle Detection using Augmentation Techniques

2025-08-04T16:25:36+00:00

Traffic vehicle detection is a crucial component in developing intelligent transportation systems, with object detection models like YOLO (You Only Look Once) often preferred for their speed and accuracy. However, challenges remain in detecting vehicles under diverse lighting conditions and small object scales, even with advanced models such as YOLOv9. To address these limitations, image augmentation techniques are employed to enhance model robustness by providing broader data variation. This study investigates the impact of multiple image augmentation methods on the YOLOv9t model for traffic vehicle detection. The techniques evaluated include Blur, Brightness Adjustment, Contrast Adjustment, Color Jitter, Cropping, Flipping, Noise Injection, Rotation, Scaling, and Zoom-In. Results reveal that Scaling and Brightness Adjustment significantly improve detection accuracy, achieving mAP50-95 values of 0.450 and 0.449, respectively. Conversely, methods such as Contrast Adjustment, Rotation, and Cropping produced unsatisfactory outcomes, with Contrast Adjustment performing the worst at only 0.167. Without augmentation, the baseline mAP50-95 was 0.378, emphasizing the vital role of augmentation in improving detection performance, especially under challenging conditions. These findings highlight the importance of selecting appropriate augmentation techniques to optimize YOLOv9t performance, with further improvements possible through combining multiple methods. Compared to approaches that solely focus on enhancing model architecture, the proposed augmentation-based strategy proves more effective in addressing real-world challenges, strengthening resilience against lighting variations and small object detection. This contribution supports the development of more accurate and reliable multilabel vehicle detection systems, advancing safer and more efficient intelligent transportation solutions.

Performance Evaluation of Gradient Boosting Techniques for Predicting Customer Purchase Decisions

2025-11-25T05:57:25+00:00

Customer purchase prediction remains a critical challenge in e-commerce and retail analytics, with significant implications for marketing strategies and business revenue. This research provides a detailed comparative evaluation of advanced gradient boosting techniques XGBoost, LightGBM, and CatBoost to predict customer purchasing behavior using review trends and demographic factors. The study employed a dataset of 100 customer records with attributes such as age, gender, review quality, and education level. Through systematic feature engineering, including age group categorization and categorical feature combinations, as well as addressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), all three models were trained and evaluated using default hyperparameters with optimal settings. The experimental results show that CatBoost achieved the best performance, with 78.26% accuracy, 0.8011 precision, 0.7826 recall, and a 0.7775 F1-score, outperforming LightGBM (73.91% accuracy) and XGBoost (60.87% accuracy). The evaluation includes confusion matrix analysis, precision–recall metrics, and visual comparisons across all performance dimensions. These findings provide valuable insights for practitioners selecting appropriate machine learning algorithms for customer purchase prediction tasks, particularly in scenarios involving limited datasets and categorical features. This research contributes to the growing body of literature on the use of gradient boosting techniques for predicting consumer behavior and offers important practical implications for e-commerce applications. These findings offer important contributions to machine learning applications in customer behavior prediction.