Perbandingan Double Exponential Smoothing, Single Exponential Smoothing dan MA terhadap Peramalan Jumlah Pelanggan Di Gendis Jowo
DOI:
https://doi.org/10.70609/jusifor.v4i2.8896Keywords:
Peramalan, Jumlah Pelanggan, Exponential Smoothing, Moving AverageAbstract
Gendis Jowo experiences fluctuations in the number of nasi box customers, which lead to suboptimal stock management and operational inefficiency, thereby requiring a forecasting approach to predict customer numbers more accurately. This study applies three forecasting methods Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Moving Average (MA)—with the aim of determining the most accurate method for forecasting the next period’s customer count. Historical data from January 2022 to August 2025 were analyzed, with SES and DES parameters optimized using the Optimal ARIMA approach, and accuracy evaluated through MAPE, MAD, and MSD. The results show that the Moving Average method with a length of 4 (MA4) provides the highest accuracy with the lowest error values, making it the best-performing model. Based on the MA4 method, the number of customers for the next period is predicted to be 1,065.88, and this result can be used to plan stock requirements, packaging needs, and operational activities more effectively.
References
[1] E. Siswanto, E. Satria Wibawa, and Z. Mustofa, “Implementasi Aplikasi Sistem Peramalan Persedian Barang Menggunakan Metode Single Moving Average Berbasis Web,” vol. 14, no. 2, pp. 224–233, 2021, [Online]. Available: http://journal.stekom.ac.id/index.php/elkompage224
[2] J. N. Aziza, “Perbandingan Metode Moving Average, Single Exponential Smoothing, dan Double Exponential Smoothing Pada Peramalan Permintaan Tabung Gas LPG PT Petrogas Prima Services,” 2022.
[3] A. S. Anggraeni, R. Chandra Utama, and D. C. Wati, “PENGHALUSAN EKSPONENSIAL DAN DEKOMPOSISI SAHAM APPLE.INC,” 2022. [Online]. Available: https://doi.org/
[4] D. Novanda and R. Hidayati, “Prediction of The Number of Pulmonary Tuberculosis Disease Using The Moving Average Forecasting Method And Time Series Decomposition,” Antivirus : Jurnal Ilmiah Teknik Informatika, vol. 18, no. 1, pp. 37–45, May 2024, doi: 10.35457/antivirus.v18i1.3468.
[5] A. Asrirawan, S. U. Permata, and M. I. Fauzan, “Pendekatan Univariate Time Series Modelling untuk Prediksi Kuartalan Pertumbuhan Ekonomi Indonesia Pasca Vaksinasi COVID-19,” Jambura Journal of Mathematics, vol. 4, no. 1, pp. 86–103, Jan. 2022, doi: 10.34312/jjom.v4i1.11717.
[6] G. Putra and A. R. Maulud, “Peramalan Kebutuhan Batubara Menggunakan Metode Single Exponential Smoothing di PT. Solusi Bangun Andalas”, [Online]. Available: www.jurnal.utu.ac.id/joptimalisasi
[7] N. H. A. S. Al Ihsan, H. H. Dzakiyah, and F. Liantoni, “Perbandingan Metode Single Exponential Smoothing dan Metode Holt untuk Prediksi Kasus COVID-19 di Indonesia,” Ultimatics : Jurnal Teknik Informatika, vol. 12, no. 2, pp. 89–94, Dec. 2020, doi: 10.31937/ti.v12i2.1689.
[8] V. I. Octaviani, H. Maulana, and A. N. Sihananto, “Pembuatan Sistem Prediksi Penjualan Dan Persediaan Barang Menggunakan Metode Double Exponential Smoothing Dan Economic Order Quantity (EOQ),” Jurnal Sistem Informasi dan Informatika (JUSIFOR), vol. 2, no. 2, pp. 102–111, Dec. 2023, doi: 10.33379/jusifor.v2i2.3407.
[9] A. Nurul Maulidaniar and E. Widodo, “Perbandingan Metode Peramalan Double Exponential Smoothing dan Triple Exponential Smoothing Pada Penjualan Indihome di Wilayah Telekomunikasi Cirebon,” Emerging Statistics and Data Science Journal, vol. 1, no. 2, 2023.
[10] Y. Farida, D. A. Sulistiani, and N. Ulinnuha, “PERAMALAN INDEKS PEMBANGUNAN MANUSIA (IPM) KABUPATEN BOJONEGORO MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING BROWN,” Teorema: Teori dan Riset Matematika, vol. 6, no. 2, Sep. 2021, doi: 10.25157/teorema.v6i2.5521.
[11] E. Sefry et al., “Analisis Peramalan Persediaan Barang Menggunakan Metode Moving Average Dan Exponential Smoothing Pada CV. Sanjaya Bangun Pratama.”
[12] E. H. Sihotang, Dahlan Abdullah, and Nunsina, “PREDIKSI JUMLAH PASIEN RAWAT INAP MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING DI RSU CUT MEUTIA KOTA LHOKSEUMAWE,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 10, no. 2, pp. 270–279, Jul. 2025, doi: 10.36341/rabit.v10i2.6060.
[13] I. Shobori and E. Suryana, “PREDIKSI PENJUALAN BARANG MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING PADA DUO SISTER HIJAB BENGKULU,” 2024. [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
[14] B. G. Prianda and E. Widodo, “Comparison of Seasonal ARIMA and Extreme Learning Machine Methods in Forecasting on the Number of Foreign Tourist to Bali,” Barekeng, vol. 15, no. 4, pp. 639–650, Dec. 2021, doi: 10.30598/barekengvol15iss4pp639-650.
[15] N. Chaerunnisa and D. A. Momon, “PERBANDINGAN METODE SINGLE EXPONENTIAL SMOOTHING DAN MOVING AVERAGE PADA PERAMALAN PENJUALAN PRODUK MINYAK GORENG DI PT TUNAS BARU LAMPUNG,” 2021.
[16] S. Defiyanti, B. Nurina Sari, and T. Nur Padilah, “Optimasi Pertanian Padi: Peramalan Curah Hujan Berbasis Arima Untuk Penentuan Waktu Tanam Yang Tepat,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1377–1384, Dec. 2024, doi: 10.25126/jtiik.2024118682.
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