PENERAPAN ALGORTMA NAÏVE BAYES DALAM MENGOPTIMALKAN MANAJEMEN INVENTORI PADA PT SOALA GOGO NATAMA
DOI:
https://doi.org/10.54314/jssr.v9i2.6121Keywords:
Naïve Bayes, inventory management, classification, data mining, inventory predictionAbstract
The advancement of information technology has encouraged companies to manage data more effectively in decision-making processes, including inventory management. Problems such as overstock, deadstock, and stockout frequently occur due to the lack of data-driven management based on historical transactions. This study aims to analyze and classify inventory demand levels using the Naïve Bayes algorithm to optimize inventory management at PT Soala Gogo Natama. This research employs a quantitative descriptive method with a case study approach. The dataset consists of 300 observations divided into training data (210) and testing data (90) with 9 variables. The analysis process includes data preprocessing, implementation of the Gaussian Naïve Bayes algorithm using Python, and model evaluation using accuracy, confusion matrix, and classification report. The results indicate that the model successfully classifies data into three categories: low, medium, and high, with an accuracy of 78.89%. Model evaluation shows good performance in the low and high classes, while misclassification occurs in the medium class due to overlapping characteristics between classes. This suggests that the model is effective in identifying data patterns but has limitations when handling data with similar distributions. In conclusion, the Naïve Bayes algorithm is effective in supporting inventory management decision-making, particularly in determining stock priorities based on historical data.
Downloads
References
Damanik, M. R., R. L. Manik, and M. Khadafi, “Metode Penelitian Kuantitatif: Konsep, Jenis, Tahapan, dan Kelebihan,” J. Intelek Insa. Cendikia, vol. 2, no. 7, pp. 13479–13496, 2025.
Dewi, C. K., D. Hartanti, and A. Farida, “Penerapan Algoritma Apriori pada Sistem Manajemen Persediaan (Studi Kasus Toko
Berkah Batam),” Adopsi Teknol. dan Sist. Inf., vol. 4, no. 2, pp. 40–46, 2025.
Erni, W., Kamarudin, “Klasifikasi Stok Barang Menggunakan Naive Bayes Untuk Optimalisasi Persediaan Toko Ahmad Adam,” J. Ilm. Tek. Mesin, Elektro dan Komput., 2025.
Fauziah, S. Sabrina Nur, “Analisis Manajeman Persediaan Barang Dagang Dalam Meningkatkan Efektivitas Pendapatan Pada Apotek K-24 Pameungpeuk Kabupaten Bandung,” COSTING J. Econ. Bus. Account., vol. 8, pp. 2950–2960, 2025.
Ilma Fahmi Aziza, S. L. Z. R. et al., Metodologi Penelitian: Pendekatan Kualitatif dan Kuantitatif. 2024.
Kartiko, B. A., A. Wibowo, M. Kom, M. Kom, A. A. Permana, and M. Kom, “Sistem Pendukung Keputusan Penentuan Penerima Beasiswa Dengan Metode Simple Additive Weighting di SMPN 19 Tangerang,” JIKA, pp. 41–53, 2021.
Kurniawan, R., D. Saputra, & T. Hidayat, “Penerapan algoritma naïve bayes dalam klasifikasi data penjualan untuk pengambilan keputusan persediaan barang,” Jurnal Sistem Informasi, vol. 16, no. 1, pp. 2020.
Maidelwita, Y., R. Nopiah, F. Purnama, and S. Indah, Konsep Penelitian Kuantitatif. 2024.
Nir, U., I. K. Gede, D. Putra, and I. P. Arya, “Implementasi Algoritma Apriori untuk Menemukan Pola Pembelian Konsumen pada Perusahaan Retail,” J. Ilm. Teknol. dan Komput., vol. 1, no. 2, 2020.
Pasaribu, V. L. D., Menciptakan Daya Saing Melalui Informasi Teknologi. 2025.
Putri, H. J., and S. Murhayati, “Metode Pengumpulan Data Kualitatif,” J. Pendidik. Tambusai, vol. 9, pp. 13074–13086, 2025.
Putri, N. A., & A. Rahman, “Implementasi algoritma naïve bayes untuk klasifikasi tingkat persediaan barang pada UMKM,” Jurnal RESTI, vol. 7, no. 4, pp. 812–820, 2023.
Raden Johnny Hadi Raharjo, E. P., Alya Nur Azizah, Adi Bimantoro, Vandra Renanda Zulfian, “Penerapan Enterprise Resource Planning Dalam Supply Chain Management Pada Minimarket Family Cukir,” J. Ilm. Wahana Pendidik., vol. 9, no. 16, pp. 670–683, 2023.
Rito, G. M., Penerapan Data Mining Di Berbagai Bidang. 2023.
Sari, M. M., “Klasifikasi Data Nasabah Kredit Menggunakan Data Mining Dengan Algoritma Decision Tree,” J. Glob.Multidiscip., vol. 3, no. 3, pp.5095–5100, 2025.
Sjahruddin, H., Metodologi Penelitian Ilmiah. 2024.
Suryaningsih, C., Metode Penelitian Ilmiah Modern. 2025.
Undari Sulung, M. M., “Memahami Sumber Data Penelitian: Primer, Sekunder, dan Tersier,” J. Edu Res. Indones., vol. 5, no. September, pp. 110–116, 2024.
Waruwu, M., M. A. Pendidikan, U. Kristen, and S. Wacana, “Pendekatan Penelitian Pendidikan: Metode Penelitian Kualitatif, Kuantitatif dan Mixed Method,” J. Pendidik. Tambusai, vol. 7, pp. 2896–2910, 2023.
Yusuf Ramadhan Nasution, I. H. S., Suhardi, “Penerapan Algoritma Klasifikasi Naïve Bayes Untuk Analisis Sentimen Tentang Pemilu 2024,” J. Elektronika dan Komput., vol. 17, no. 2, pp. 495–502, 2024.
T. D. A. N. Penerapannya, Metode Penelitian Kualitatif: Teori dan Penerapannya. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Andriyan Jonatan Matondang, Eben Ezer Seluya Malau, Siti Aisyah, Amalia, Muhammad Radhi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
