IMPLEMENTATION OF DATA MINING WITH CLASSIFICATION AND FORECASTING METHOD USE MODEL GAUSSIAN NAÏVE BAYES FOR BUILDING STORE (STUDI CASE: TB SINAR JAYA)
In the construction industry, the building materials business is a necessity, one of the businesses that is currently growing rapidly is the property business. The public’s need for a place to live becomes a business opportunity sought by the public. The Industry 4.0 transformation brought building materials stores to the online market, one of which is Tokopedia. TB. Sinar Jaya has a dataset of inventory and sales with a total of approximately 15,000 data rows which are updated each month. With large amounts of data, data mining and machine learning methods are needed in data management. 5 years rapid development of TB. Sinar Jaya has not been without problems, such as competition with online stores that offer lower prices than offline stores and a lack of strong marketing strategy. In this case, TB. Sinar Jaya wants help in making marketing strategy decisions by utilizing information system technology and minimizing existing problems. Based on these problems, it is necessary to implement data mining and machine learning gaussian algorithms naïve bayes to find out the average prices available on Tokopedia to increase sales and carry out classification, forecasting and TSA (Time Series Analysis) at TB Sinar Jaya. Based on the results of testing/research, the gaussian naive Bayes algorithm has good accuracy results with an accuracy level of 0.71 and gains insight, that is, for potential buyers they can do Wishlist efficiency, and for segments that generate profits below 50% of the total profit the researcher recommends carrying out a campaign program according to customer profile in order to improve the resulting profitability.
P. N. Ardiansa, M. N. Tentua, and M. Fairuzabadi, “PERAMALAN PENJUALAN MENGGUNAKAN DATA MINING BERBASIS WEB,” Jurnal Dinamika Informatika, vol. 7, pp. 57–64, 2018.
E. Sutoyo and A. Almaarif, “Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 95–101, Feb. 2020, doi: 10.29207/resti.v4i1.1502.
A. Ambarwari, Q. Jafar Adrian, and Y. Herdiyeni, “Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 117–122, Feb. 2020, doi: 10.29207/resti.v4i1.1517.
K. Wabang, Oky Dwi Nurhayati, and Farikhin, “Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 872–876, Nov. 2022, doi: 10.29207/resti.v6i5.4498.
A. P. Wibawa et al., “Naïve Bayes Classifier for Journal Quartile Classification,” International Journal of Recent Contributions from Engineering, Science & IT (iJES), vol. 7, no. 2, p. 91, Jun. 2019, doi: 10.3991/ijes.v7i2.10659.
B. S. Prakoso, D. Rosiyadi, H. S. Utama, and D. Aridarma, “Klasifikasi Berita Menggunakan Algoritma Naive Bayes Classifer Dengan Seleksi Fitur Dan Boosting,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 2, pp. 227–232, Aug. 2019, doi: 10.29207/resti.v3i2.1042.
S. Saputra, A. Yudhana, and R. Umar, “Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 3, pp. 412–420, Jun. 2022, doi: 10.29207/resti.v6i3.4062.
SUHENDRA and I. RANGGADARA, “Naive Bayes Algorithm with Chi Square and NGram Feature for Reviewing Laptop Product on Amazon Site,” International Research Journal of Computer Science, vol. 4, pp. 28–33, 2017.
A. R. Lubis, M. Lubis, Al-Khowarizmi, and D. Listriani, “Big Data Forecasting Applied Nearest Neighbor Method,” in 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC), Aug. 2019, pp. 116–120. doi: 10.1109/ICSECC.2019.8907010.
Z. Bi, Y. Han, C. Huang, and M. Wang, “Gaussian Naive Bayesian Data Classification Model Based on Clustering Algorithm,” in Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019), 2019. doi: 10.2991/masta-19.2019.67.
A. Nugroho and Y. Religia, “Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 504–510, Jun. 2021, doi: 10.29207/resti.v5i3.3067.
Mila Desi Anasanti, Khairunisa Hilyati, and Annisa Novtariany, “The Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 832–839, Nov. 2022, doi: 10.29207/resti.v6i5.4419.
S. Sendari, I. A. E. Zaeni, D. C. Lestari, and H. P. Hariyadi, “Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm,” Knowledge Engineering and Data Science, vol. 3, no. 1, pp. 50–59, Aug. 2020, doi: 10.17977/um018v3i12020p50-59.
I. Santoso, Windu Gata, and Atik Budi Paryanti, “Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Sentimen Analisis Komisi Pemilihan Umum,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 3, pp. 364–370, Dec. 2019, doi: 10.29207/resti.v3i3.1084.
Evi Purnamasari, D. P. Rini, and Sukemi, “Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 3, pp. 469–475, Jun. 2020, doi: 10.29207/resti.v4i3.1833.
D. M. Tarigan, Dian Palupi Rini, and Samsuryadi, “Feature Selection in Classification of Blood Sugar Disease Using Particle Swarm Optimization (PSO) on C4.5 Algorithm,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 3, pp. 569–575, Jun. 2020, doi: 10.29207/resti.v4i3.1881.
Copyright (c) 2022 Dwi Ade Handayani Capah
This work is licensed under a Creative Commons Attribution 4.0 International License.