DEVELOPMENT OF HERBIFY APPLICATION WITH AI INTEGRATED UTILIZING YOLO V8 FOR OPTIMIZING HERBAL POTENTIAL IN INDONESIA

Ahmad Fajruddin Syauqi; Didik Dwi Prasetya

doi:10.52436/1.jutif.2024.5.4.2094

Ahmad Fajruddin Syauqi Informatics, Engineering Faculty, University Negeri Malang, Indonesia
Didik Dwi Prasetya Informatics, Engineering Faculty, University Negeri Malang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2094

Keywords: computer vision, herbal, mobile application, object detection, YOLOv8

Abstract

Indonesia is known as the home to 80% of the world's medicinal plant species, with an estimated 25,000-30,000 potential plants. However, this is in stark contrast to the current situation of limited access to herbal information, leading to restricted consumption and distribution of herbal products. The numerous digital platforms providing herbal data still fail to address this issue, as the information provided does not cater to the users' needs. Therefore, to address the current challenges in the Indonesian herbal industry, researchers developed an AI-integrated application called Herbify. The application was developed using the Agile Software Development Life Cycle method, chosen to meet user needs with a user-centered design approach. From this research, a mobile application with two main features, namely 'Herbalpedia' and 'Scanherbal,' was developed. Measurements through three methods: mAP matrix, usability tests, and user experience questionnaires (UEQ), yielded positive results. The measurement results show that the trained model achieved a 94.6% mAP with an inference time of 0.07965 seconds. Furthermore, the usability test results of the application show a 0% mission unfinished rate, with an average completion time of 10 seconds. The UEQ results indicate that the application has high usability, trustworthiness, and information quality. Based on these results, it can be concluded that Herbify has great potential to effectively optimize herbal potentials in Indonesia.

Downloads

Download data is not yet available.

References

B. A. Suliasih dan A. Mun’im, “Chemistry and Materials Review: Potensi dan Masalah dalam Pengembangan Kemandirian Bahan Baku Obat Tradisional di Indonesia,” Depok, 2022.

F. A. Hastuari, A. Sufanniyah, A. R. Dewi, E. F. Maghfiroh, dan S. Prajoko, “Konservasi Tanaman Obat Keluarga Unggulan Sebagai Bahan Jamu Tradisional,” Jurnal Pemberdayaan Masyarakat, vol. 2, no. 2, hlm. 58–67, Nov 2023, doi: 10.46843/jmp.v2i2.286.

Y. Y. S. Rahayu, T. Araki, dan D. Rosleine, “Factors affecting the use of herbal medicines in the universal health coverage system in Indonesia,” J Ethnopharmacol, vol. 260, Okt 2020, doi: 10.1016/j.jep.2020.112974.

S. B. Santoso, H. Lutfiyati, dan T. M. Kusuma, “Pemberdayaan Potensi Masyarakat Melalui Pengelolaan Kebun Tanaman Obat Keluarga,” Community Empowerment, vol. 6, no. 3, hlm. 391–397, Mar 2021, doi: 10.31603/ce.4044.

Muhammad Alfarizi, “Pengobatan Komplementer Alternatif Lokal dan Potensinya di Indonesia dalam Perspektif Kesehatan dan Ekonomi: Kajian Literatur Sistematik,” Salus Cultura: Jurnal Pembangunan Manusia dan Kebudayaan, vol. 2, no. 2, hlm. 138–150, 2022.

Muflih Muflih, Rahayu Widaryanti, dan Marselina Endah Hiswati, “EVALUASI KEMAMPUAN PEMANFAATAN HERBAL UNTUK MENINGKATKAN IMUNITAS DI MASA PANDEMI COVID-19 PADA KADER KESEHATAN,” dalam Strategi Mempertahankan Kualitas Penelitian dan Publikasi Di Era Pandemi, Yogyakarta : Universitas Respati Yogyakarta, Des 2021.

S. H. Soroya, A. Farooq, K. Mahmood, J. Isoaho, dan S. e. Zara, “From information seeking to information avoidance: Understanding the health information behavior during a global health crisis,” Inf Process Manag, vol. 58, no. 2, Mar 2021, doi: 10.1016/j.ipm.2020.102440.

X. Sun, H. Qian, Y. Xiong, Y. Zhu, Z. Huang, dan F. Yang, “Deep learning-enabled mobile application for efficient and robust herb image recognition,” Sci Rep, vol. 12, no. 1, Des 2022, doi: 10.1038/s41598-022-10449-9.

V. Kakani, V. H. Nguyen, B. P. Kumar, H. Kim, dan V. R. Pasupuleti, “A critical review on computer vision and artificial intelligence in food industry,” Journal of Agriculture and Food Research, vol. 2. Elsevier B.V., 1 Desember 2020. doi: 10.1016/j.jafr.2020.100033.

Jadhav Chitrarekha A., Vikhe Dattaprasad N., dan Dr. Jadhav R. S., “Global and domestic market of herbal medicines: A review,” Research Journal of Science and Technology, vol. 12, no. 4, hlm. 327–330, Jan 2020.

S. Naeem dkk., “The classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach,” Agronomy, vol. 11, no. 2, Feb 2021, doi: 10.3390/agronomy11020263.

K. K. Mak dan M. R. Pichika, “Artificial intelligence in drug development: present status and future prospects,” Drug Discovery Today, vol. 24, no. 3. Elsevier Ltd, hlm. 773–780, 1 Maret 2019. doi: 10.1016/j.drudis.2018.11.014.

B. R. Hussein, O. A. Malik, W.-H. Ong, dan J. W. F. Slik, “Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review,” Ecol Inform, vol. 69, hlm. 101641, 2022, doi: https://doi.org/10.1016/j.ecoinf.2022.101641.

O. A. Malik, N. Ismail, B. R. Hussein, dan U. Yahya, “Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region,” Plants, vol. 11, no. 15, 2022, doi: 10.3390/plants11151952.

A. M. Sari, D. Yani, dan D. Suryani, “Perancangan Aplikasi Mobile Pengenalan Tanaman Obat Herbal Berbasis Android,” vol. 2, no. 7, hlm. 466–470, 2021, [Daring]. Tersedia pada: https://ejurnal.seminar-id.com/index.php/tin

A. Hinderks, F. J. Domínguez Mayo, J. Thomaschewski, dan M. J. Escalona, “Approaches to manage the user experience process in Agile software development: A systematic literature review,” Inf Softw Technol, vol. 150, Okt 2022, doi: 10.1016/j.infsof.2022.106957.

S. Al-Saqqa, S. Sawalha, dan H. Abdelnabi, “Agile software development: Methodologies and trends,” International Journal of Interactive Mobile Technologies, vol. 14, no. 11, hlm. 246–270, 2020, doi: 10.3991/ijim.v14i11.13269.

R. Jamian dan H. Taha, “Analisis keperluan kebolehgunaan aplikasi mudah alih terhadap sikap, minat dan pengetahuan asas matematik tahun 4 Need analysis of mobile application usability specifications for mathematics year 4: constructs of attitude, interest and basic knowledge,” JURNAL PENDIDIKAN SAINS & MATEMATIK MALAYSIA, vol. 10, no. 1, 2020.

D. Larson Kaligis dan R. R. Fatri, “PENGEMBANGAN TAMPILAN ANTARMUKA APLIKASI SURVEI BERBASIS WEB DENGAN METODE USER CENTERED DESIGN,” 2020. [Daring]. Tersedia pada: https://jurnal.umj.ac.id/index.php/just-it

R. C. Martin, Clean Architecture: A Craftsman’s Guide to Software Structure and Design. dalam Martin, Robert C. Prentice Hall, 2018. [Daring]. Tersedia pada: https://books.google.co.id/books?id=8ngAkAEACAAJ

D. Esteban, S. Rodriguez, A. E. Rojas, H. Florez, dan D. Sanchez, “Towards a Clean Architecture for Android Apps using Model Transformations,” 2022. [Daring]. Tersedia pada: https://developer.android.com/jetpack

A. Tashildar, N. Shah, R. Gala, T. Giri, dan P. Chavhan, “Application Development Using Flutter,” 2020. [Daring]. Tersedia pada: www.irjmets.com

W. P. Laksono, B. Satria, T. Wicaksana, A. Y. Wijasena, dan Y. Sahria, “Implementasi Clean Architecture Dalam Membangun Aplikasi Mobile Menggunakan Flutter,” Nusantara Journal of Multidisciplinary Science, vol. 2, no. 1, hlm. 173–180, 2024, [Daring]. Tersedia pada: https://jurnal.intekom.id/index.php/njms

D. Nafis Alfarizi, R. Agung Pangestu, D. Aditya, M. Adi Setiawan, dan P. Rosyani, “Penggunaan Metode YOLO Pada Deteksi Objek: Sebuah Tinjauan Literatur Sistematis,” 2023. [Daring]. Tersedia pada: https://jurnalmahasiswa.com/index.php/aidanspk

B. Lv, L. Wu, T. Huangfu, J. He, W. Chen, dan L. Tan, “Traditional Chinese Medicine Recognition Based on Target Detection,” Evidence-based Complementary and Alternative Medicine, vol. 2022, 2022, doi: 10.1155/2022/9220443.

I. P. W. Prasetia dan I Made Gede Sunarya, “Image Classification of Balinese Seasoning Base Genep Based on Deep Learning,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 13, no. 1, Mar 2024, doi: 10.23887/janapati.v13i1.67967.

G. Yang, J. Wang, Z. Nie, H. Yang, dan S. Yu, “A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention,” Agronomy, vol. 13, no. 7, Jul 2023, doi: 10.3390/agronomy13071824.

M. Tri Ananta, L. Fanani, I. Adista Sihombing, Z. Abidin, dan B. University, “User Experience Design for Information Technology Career Preparation Platform Using the Design Thinking Method,” 2023. [Daring]. Tersedia pada: www.jitecs.ub.ac.id

I. Maramba, A. Chatterjee, dan C. Newman, “Methods of usability testing in the development of eHealth applications: A scoping review,” International Journal of Medical Informatics, vol. 126. Elsevier Ireland Ltd, hlm. 95–104, 1 Juni 2019. doi: 10.1016/j.ijmedinf.2019.03.018.

R. Richard, A. Kusumadwiputra, dan A. Z. F. Suherman, “Usability Analysis on Health Tracking Application using User Experience Questionnaire in Jakarta Area,” PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic, vol. 11, no. 1, hlm. 149–158, Mar 2023, doi: 10.33558/piksel.v11i1.6929.

P. Weichbroth, “Usability of mobile applications: A systematic literature study,” IEEE Access, vol. 8, hlm. 55563–55577, 2020, doi: 10.1109/ACCESS.2020.2981892.

M. Ramadhani, A. Susanto, F. Mustofa, dan V. Tauda, “Design and User Experience Evaluation of Bersii Android-based Mobile Application User Interface,” MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 14, no. 2, hlm. 41–49, 2022.

I. A. M. Zin, Z. Ibrahim, D. Isa, S. Aliman, N. Sabri, dan N. N. A. Mangshor, “Herbal plant recognition using deep convolutional neural network,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 5, hlm. 2198–2205, Okt 2020, doi: 10.11591/eei.v9i5.2250.

M. S. Mustafa, Z. Husin, W. K. Tan, M. F. Mavi, dan R. S. M. Farook, “Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection,” Neural Comput Appl, vol. 32, no. 15, hlm. 11419–11441, 2020, doi: 10.1007/s00521-019-04634-7.

S. Roopashree dan J. Anitha, “DeepHerb: A Vision Based System for Medicinal Plants Using Xception Features,” IEEE Access, vol. 9, hlm. 135927–135941, 2021, doi: 10.1109/ACCESS.2021.3116207.