IMPLEMENTASI METODE RANDOM FOREST UNTUK MEMPREDIKSI PENJUALAN PRODUK
Abstract
This study aims to predict product sales at CV Pelumas Murni Keluarga using the Random Forest method to overcome sales fluctuations that impact stock management and production planning. Uncertainty in sales forecasting can cause excess or shortage of stock, thus hampering the company's growth. This method was chosen because of its advantages in handling complex data and producing accurate predictions. The study was conducted quantitatively through observation and collection of automotive lubricant sales data from January to June 2023. Data was analyzed using the Google Colab application to implement the Random Forest model. The process involves data preprocessing, model building, and evaluation using out-of-bag data. The results of the study show that the Random Forest method is able to significantly increase the accuracy of sales predictions, providing a stronger foundation in developing sales strategies and inventory management. Thus, this study is expected to help CV Pelumas Murni Keluarga in optimizing operational efficiency and increasing profitability.
References
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[3] V. M. M. Siregar, Sinaga, Kalvin, Sirait, Erwin, Manalu, Andi and Purba, Arifin Tua, "Sistem pendukung keputusan pemilihan tenaga pendidik terbaik menggunakan metode complex proportional assessment," TEKINKOM, vol. 7, no. 1, pp. 310-317, 2024.
[4] A. Nurlifa and S. Kusumadewi, "Sistem Peramalan Jumlah Penjualan Menggunakan Metode Moving Average Pada Rumah Jilbab Zaky," JURNAL INOVTEK POLBENG - SERI INFORMATIKA, vol. 2, no. 1, 2017.
[5] S. R. Tangahu and M. H. Koniyo, "Penerapan Metode DESB dan EOQ untuk Prediksi Penjualan dan Persediaan Mobil," Jambura Journal of Informatics, vol. 3, no. 1, pp. 29-43, 2021.
[6] R. H. Hirzi, U. Hidayaturrohman, Kertanah, M. H. Amaly and R. Satriawan, "Prediksi Jumlah Wisatawan Menggunakan Metode Random Forest, Single Exponential Smoothing dan Double Exponential Smoothing," Jambura Journal of Probability and Statistics, vol. 4, no. 1, pp. 47-55, 2023.
[7] D. Z. H. Iskandar and Y. Ramdhani, "Optimasi Parameter Random Forest menggunakan Grid Search Untuk Analisis Time Series," PETIR: Jurnal Pengkajian dan Penerapan Teknik Informatika, vol. 16, no. 2, pp. 267-277, 2023.
[8] S. Amaliah, M. Nusrang and Aswi, "Penerapan Metode Random Forest Untuk Klasifikasi Varian Minuman Kopi Di Kedai Kopi Konijiwa Bantaeng," VARIANSI: Journal of Statistics and Its Application on Teaching and Research, vol. 4, no. 2, pp. 121-127, 2022.
[9] H. Marlina, Elmayati, A. Zulius and H. O. L. Wijaya, "Penerapan Algoritma Random Forest Dalam Klasifikasi Penjurusan Di SMA Negeri Tugumulyo," BRAHMANA: Jurnal Penerapan Kecerdasan Buatan, vol. 4, no. 2, pp. 138-143, 2023.
[10] R. Supriyadi, W. Gata, N. Maulidah and A. Fauzi, "Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah," Jurnal Ilmiah Ekonomi dan Bisnis, vol. 13, no. 2, pp. 67-75, 2020.
[11] A. P. Abriantoro and J. R. Khana, "Optimasi Mix Design Beton melalui Teknologi Machine Learning," Jurnal Rekayasa Infrastruktur, vol. 9, no. 2, pp. 94-107, 2023.
[12] K. R. Liyadi, H. Pratiwi, P. Aditya and M. I. Sa’ad, "Penerapan Metode Single Moving Average Dalam Peramalan Persediaan Bahan Pangan," BRAHMANA: Jurnal Penerapan Kecerdasan Buatan, vol. 4, no. 1, pp. 72-80, 2022.
[13] F. Ramayanti, D. Vionanda, D. Permana and Zilrahmi, "Application of Random Forest to Identify for Poor Households in West Sumatera Province," UNP Journal of Statistics and Data Science, vol. I, no. 2, pp. 97-104, 2023.
[14] Khodijah and Sriyanto, "Perbandingan Kinerja Algoritma C4.5. Naive Bayes Dan Random Forest Dalam Prediksi Penyakit Jantung," Jurnal Teknika, vol. 17, no. 2, pp. 419-426, 2023.
[15] Y. N. Dewi and F. A. Sariasih, "Metode Sample Bootstrapping untuk Meningkatkan Performa," Jurnal Teknik Informatika, vol. 12, no. 1, pp. 1-10, 2019.
[16] I. T. S. Kaontole, A. L. E. Rumayar and M. M. Kumaat, "Analisis Karakteristik dan Tingkat Pelayanan Arus Pejalan Kaki (Studi Kasus: Jl. Suprapto – Jl. Lembong)," TEKNO, vol. 21, no. 84, pp. 627-638, 2023.
[17] R. Sugeng, R. Sebriani, Muliana, Syamsuddin, M. Abbas and V. Hadrianti, "Analisis Pengaruh Bi Rate Dan Inflasi Terhadap Indeks Saham Syariah Indonesia (ISSI) Periode 2017-2020," Jurnal Ilmiah Ekonomi Islam, vol. 10, no. 01, pp. 166-176, 2024.
[18] T. Muhayat, Jayanta and N. Chamidah, "Prediksi harga Smartphone menggunakan Algoritma Multiple Linear Regression," Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), pp. 506-525, 2022.
[19] Ronaldo and L. Utama, "Pengaruh Mediasi Kompetensi Jaringan dalam Kompetensi Kewirausahaan pada Pertumbuhan UMKM di Tanah Abang," Jurnal Manajerial dan Kewirausahaan, vol. 06, no. 01, pp. 161-169, 2024.
[20] D. A. Rhamadhani and E. E. D. Saputri, "Analisa Model Machine Learning dalam Memprediksi Laju Produksi Sumur Migas 15/9-F-14H," Journal of Sustainable Energy Development, vol. 1, no. 1, pp. 48-55, 2023.
Published
2024-12-31
How to Cite
BARUS, Ertina Sabarita; DARMANTO, Darmanto.
IMPLEMENTASI METODE RANDOM FOREST UNTUK MEMPREDIKSI PENJUALAN PRODUK.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 591-600, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1510>. Date accessed: 26 apr. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1510.
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