Published: 2025-07-01
Penerapan Data Mining Menggunakan Algoritma Single Moving Average pada Penjualan Mobil Honda
DOI: 10.35870/jtik.v9i3.3847
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Abstract
Data mining is a branch of artificial intelligence that is used to find patterns and information hidden in data. One of the common algorithms used in data mining is the Single Moving Average (SMA). The SMA algorithm can be used to analyze and predict trend data, such as sales, stocks, production, and so on. In this study, SMA will be used to provide forecasts on Honda car sales and find hidden patterns in them with the aim of finding out the dominant patterns in Honda car sales and preparing for all possible risks that will be obtained due to this forecasting system. The data used in this study is Honda car sales data from official Honda dealers, where data was collected as much as 90 data as a dataset, and 8 data as data to be tested from 2017 to 2023. The results of the study show that the SMA algorithm can be used to analyze Honda car sales data where the right order in this case is order 2 with a value obtained above 90% based on the results of calculations from MAPE and MSE. These results can be used by the Honda company to improve sales strategies and improve product quality in terms of inventory management.
Keywords
Data mining ; Single Moving Average Algorithm ; Forcast ; Honda car sales
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 3 (2025)
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Section: Computer & Communication Science
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i3.3847
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Tundo
Program Studi Teknik Informatika, Fakultas Teknik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Marcia Rizky Hamdala
Program Studi Teknik Elektro, Universitas 17 Agustus 1945 Jakarta, Kota Jakarta Utara, Daerah Khusus Ibukota Jakarta, Indonesia.
Andi Saidah
Program Studi Teknik Mesin, Universitas 17 Agustus 1945 Jakarta, Kota Jakarta Utara, Daerah Khusus Ibukota Jakarta, Indonesia.
Muhammad Nurdin
Program Studi Ketatalaksanaan Angkutan Laut dan Pelabuhan, Sekolah Tinggi Ilmu Pelayaran, Kota Jakarta Utara, Daerah Khusus Ibukota Jakarta, Indonesia.
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Alex, M. A. H., & Rahmawati, N. (2023). Application of the single moving average, weighted moving average and exponential smoothing methods for forecasting demand at boy delivery. Tibuana, 6(1), 32-37. https://doi.org/10.36456/tibuana.6.1.6442.32-37.
-
Anggraini, D., Putri, S. A., & Utami, L. A. (2020). Implementasi Algoritma Apriori Dalam Menentukan Penjualan Mobil Yang Paling Diminati Pada Honda Permata Serpong. J. Media Inform. Budidarma, 4(2), 302.
-
DO, D. T. R. C. T. (2022). Konsep Decision Tree Reptree Untuk Melakukan Optimasi Rule Dalam Fuzzy Inference System Tsukamoto. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 9(3).
-
Gani, A. P., Tundo, T., Akbar, R., & Sitompul, K. A. J. (2024). Peramalan Harga Saham Nvidia Dengan Metode Double Moving Average. Jurnal Ilmiah Informatika Komputer, 29(2), 154-166.
-
Hamidah, K., & Voutama, A. (2023). Analisis Faktor Tingkat Kebahagiaan Negara Menggunakan Data World Happiness Report dengan Metode Regresi Linier. Explore IT: Jurnal Keilmuan dan Aplikasi Teknik Informatika, 15(1), 1-7.
-
Insani, R. H., & Suropati, U. Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production. IJID (International Journal on Informatics for Development), 13(1), 448-459. https://doi.org/10.14421/ijid.2024.4663.
-
Juwanda, A., Barus, S. G., Prasetyo, T. A., Anggadha, F., & Prasvita, D. S. (2021). Analisa Peramalan Penjualan Mobil dengan Metode Autoregressive Integrated Moving Average (ARIMA). In Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya (Vol. 2, No. 2, pp. 96-102).
-
Mandala, A. R. D., Hidayat, F. R., Primadian, R., Sutopo, W., Yuniaristanto, Y., & Prianjani, D. (2022). Perbandingan Metode Trend Line Analysis dan Metode Jaringan Syaraf Tiruan Backpropagation untuk Peramalan Permintaan Koran. Performa: Media Ilmiah Teknik Industri, 21(2), 190-199.
-
Mauladi, K. F., & Jayyidah, I. I. (2022). Prediksi penjualan barang pada toko baby shop dengan algoritma single moving average (sma). JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 7(4), 1189-1197. https://doi.org/10.29100/jipi.v7i4.3220.
-
Mulyani, S., Hayati, D., & Sari, A. N. (2021). Analisis Metode Peramalan (Forecasting) Penjualan Sepeda Motor Honda Dalam Menyusun Anggaran Penjualan Pada Pt Trio Motor Martadinata Banjarmasin. Dinamika Ekonomi: Jurnal Ekonomi dan Bisnis, 14(1), 178-188.
-
Mustapa, R., Latief, M., & Rohandi, M. (2019, December). Double moving average method for predicting the number of patients with dengue fever in Gorontalo City. In International Conference on Education, Science and Technology (pp. 332-337). Redwhite Press.
-
Putra, D. P., Siregar, S. A., Fadillah, S. R., & Ningtyas, Z. K. (2024). PERAMALAN PENJUALAN MOBIL DENGAN MENERAPKAN METODE SINGLE MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING. Jurnal Pariwisata Bisnis Digital dan Manajemen, 3(2), 81-86. https://doi.org/10.33480/jasdim.v3i2.5631.
-
Tundo, T., Saifullah, S., Dharmawan, T., Junaidi, J., & Devia, E. (2023). Seasonal meat stock demand used comparison of performance smoothing-average forecasting.
-
Tundo, T., Yel, M. B., & Nugroho, A. Y. (2024). Forecasting Beef Production with Comparison of Linear Regression and DMA Methods Based on n-th Ordo 3. JTAM (Jurnal Teori dan Aplikasi Matematika), 8(4), 1133-1145. https://doi.org/10.31764/jtam.v8i4.24706.
-
Wiladibrata, M. I., & Rifai, N. A. K. (2022, August). Peramalan Produksi Mobil Menggunakan Metode Double Exponential Smoothing Dengan Algoritma Golden Section. In Bandung Conference Series: Statistics (Vol. 2, No. 2, pp. 507-511).
-
Yacob, G. S., Mulyana, D. I., & Lestari, S. (2025). Prediksi Produksi Sablon di Perusahaan Tomoinc dengan Perbandingan Metode Single Moving Average dan Single Exponential Smoothing. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 9(1), 59-67. https://doi.org/10.35870/jtik.v9i1.3018.
-
Yuliyanti, R., & Arliani, E. (2022). Peramalan jumlah penduduk menggunakan model arima. Jurnal Kajian dan Terapan Matematika, 8(2), 114-128.
-
Yuniarti, E. Forecasting Drug Demand Using The Single Moving Average At Prof. dr. IGNG Ngoerah Hospital. Majalah Farmaseutik, 19(3), 394-402. https://doi.org/10.22146/farmaseutik.v19i3.86207.
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