Integrasi Naïve Bayes dalam Sistem Klasifikasi Permintaan Pakan Ayam: Strategi Data-Driven untuk Efisiensi Stok

Anggi Malinda; Suhirman Suhirman

doi:10.29408/edumatic.v9i3.32628

Authors

Anggi Malinda Program Studi Informatika, Universitas Teknologi Yogyakarta https://orcid.org/0009-0006-6031-0256
Suhirman Suhirman Program Studi Teknologi Informasi, Universitas Teknologi Yogyakarta https://orcid.org/0009-0008-7273-0586

DOI:

https://doi.org/10.29408/edumatic.v9i3.32628

Keywords:

data-driven strategies, decision support systems, demand forecasting, machine learning, naïve bayes

Abstract

Frequent fluctuations in chicken feed demand cause stock imbalances and operational cost wastage. This study aims to develop an end-to-end decision support system (DSS) based on the Naïve Bayes algorithm to classify feed demand patterns and provide distribution recommendations through an interactive dashboard. The integration of this system is an innovation that is rarely applied in the chicken feed agribusiness in Indonesia. The type of research used is System Development Research. The company's historical sales data for the past year (covering the variables of date/month, product name, and sales volume) was used as the main input. The system was developed using the Waterfall development model, which includes the stages of data collection, system design, implementation, and testing. Naïve Bayes modelling was carried out through the stages of data preprocessing, model training (80%), and testing (20%). Our findings are in the form of an interactive dashboard-based DSS that is ready to be used to improve stock management efficiency. System testing shows that the developed end-to-end architecture has been successfully implemented, with a classification accuracy rate of 99%. Analysis proves that the Naïve Bayes model shows high reliability in classifying three demand categories ('Low', 'Medium', 'High'), as evidenced by precision, recall, and F1-score values above 97% for all categories. This system can be used as an effective and practical decision-making tool.

References

Ajide, S. O. (2025). Data-driven approaches for supply chain resilience: a review of predictive analytics, optimization, and risk modeling. International Journal of Engineering Technology Research & Management, 9(9), 240–260.

Ayu, I., SF, A. F., & Fahamsyah, M. H. (2023). Metode Demand Forecasting dalam menjalankan manajemen operasi pada industri manufaktur Demand Forecasting method in carrying out operations management in the manufacturing industry. EKOMABIS: Jurnal Ekonomi Manajemen Bisnis, 3(2), 127–136. https://doi.org/10.37366/ekomabis.v3i02.286

Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 1–25. https://doi.org/10.1080/13675567.2020.1803246

Fernando, P., & Pratiwi, M. P. (2025). Penerapan Metode Fifo (First In First Out) dalam Merancang Sistem Pergudangan Berbasis Web. Computer and Science Industrial Engineering (COMASIE), 12(4), 72–81.

Hamoud, A. K., Hussein, M. K., Alhilfi, Z., & Sabr, R. H. (2021). Implementing data-driven decision support system based on independent educational data mart. International Journal of Electrical and Computer Engineering, 11(6), 5301–5314. https://doi.org/10.11591/ijece.v11i6.pp5301-5314

Hidayat, M. T., Suarna, N., & Rahaningsih, N. (2023). Implementasi Algoritma Naïve Bayes Untuk Prediksi Persediaan Barang Pt. Dilmoni Citra Mebel Indonesia. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 693–699. https://doi.org/10.36040/jati.v7i1.6310

Izzathohir, K. M., & Yulianton, H. (2024). Sistem Aplikasi Penjualan Gula Aren Berbasis Web Menggunakan Framework Flask. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 163–169. https://doi.org/10.35870/jtik.v8i1.1332

Kumar, P., Javeed, D., Islam, A. K. M. N., & Robert, X. (2025). DeepSecure : A computational design science approach for interpretable threat hunting in cybersecurity decision making. Decision Support Systems, 188(114351), 1–15. https://doi.org/10.1016/j.dss.2024.114351

Munthohar, A. D. A., Pribadi, T., & Sulistiawan, A. (2024). Penerapan Metode Naive Bayes Untuk Prediksi Stok Barang Bangunan Di Toko Bangunan Rejo Mulyo. Multidisciplinary Applications of Quantum Information Science (Al-Mantiq), 4(1), 1–7.

Nebri, M. A., Moussaid, A., & Bouikhalene, B. (2024). Forecasting livestock feed sales using machine learning techniques: an analysis of the Moroccan market. Indonesian Journal of Electrical Engineering and Computer Science, 35(2), 1139–1150. https://doi.org/10.11591/ijeecs.v35.i2.pp1139-1150

Nurmumpuni, D. A., Farisy, F. S., & Putri, N. M. (2025). Implementasi Naïve Bayes Dalam Memprediksi Permintaan Bahan Baku Di PT. Miyasaka Indonesia. IKRAM: Jurnal Ilmu Komputer Al Muslim, 4(1), 23–29.

Peretz, O., Koren, M., & Koren, O. (2024). Engineering Applications of Artificial Intelligence Naive Bayes classifier – An ensemble procedure for recall and precision enrichment. Engineering Applications of Artificial Intelligence, 136(108972), 1–12. https://doi.org/10.1016/j.engappai.2024.108972

Romadhoni, M. N., Anisa, N., & Winarsih, S. (2025). Kinerja Naive Bayes dan SVM pada Data Survei Tidak Seimbang : Studi Klasifikasi Kepuasan Masyarakat. Edumatic : Jurnal Pendidikan Informatika, 9(2), 382–391. https://doi.org/10.29408/edumatic.v9i2.30185

Shaffitri, L. R., Wahida, Perdana, R. P., Ilham, N., & Suryana, E. A. (2024). Implementasi kebijakan usaha pakan untuk mendukung pengembangan industri perunggasan. Analisis Kebijakan Pertanian, 22(1), 1–15. https://doi.org/10.21082/akp.v22n1.2024.1-15

Sheikhkhoshkar, M., El-haouzi, H. B., Aubry, A., Hamzeh, F., & Rahimian, F. (2025). A data-driven and knowledge-based decision support system for optimized construction planning and control. Automation in Construction, 173(106066), 1–22. https://doi.org/10.1016/j.autcon.2025.106066

Sheth, V., Tripathi, U., & Sharma, A. (2023). A Comparative Analysis of Machine Learning Algorithms for Classification Purpose. Procedia Computer Science, 215(2022), 422–431. https://doi.org/10.1016/j.procs.2022.12.044

Slam, B. E., Irawan, F., Efranda, N., & Herikson, R. (2024). Implementasi machine learning untuk klasifikasi. JIP Jurnal Informatika Polinema, 11(3), 305–310. https://doi.org/10.33795/jip.v11i3.7298

Sudrajat, A., Mulyani, N., & Marpaung, N. (2022). Sistem Pendukung Keputusan Penentuan Kelayakan Penangguhan Kredit Nasabah menggunakan Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 6(2), 205–214. https://doi.org/10.29408/edumatic.v6i2.6298

Syahroni, M. I. (2022). Prosedur Penelitian Kuantitatif. Jurnal Al-Mustafa, 2(3), 43–56. https://doi.org/10.62552/ejam.v2i3.50

Zulkifli, Asmawati.S, & Irianti, A. (2022). Penerapan Algoritma Naive Bayes dalam Memprediksi Persediaan Bahan Mebel (Studi Kasus Mebel Usaha Bersama Palipi Soreang). Journal of Computer and Information System ( J-CIS ), 5(1), 57–64. https://doi.org/10.31605/jcis.v5i1.1360