Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing In Product Sales Prediction

Franklin Fong; Randy Ciptady; Anita Anita

doi:10.36378/jtos.v8i1.4437

Franklin Fong Fakultas Sains dan Teknologi, Universitas Prima Indonesia
Randy Ciptady Fakultas Sains dan Teknologi, Universitas Prima Indonesia
Anita Anita universitas prima indonesia

DOI: https://doi.org/10.36378/jtos.v8i1.4437

Keywords: Sales Forecast Deadstock, ARRES, Grid Search, Inventory Management

Abstract

Effective inventory management is one of the keys to a company's success, especially in the retail and distribution sectors that are highly dependent on product availability according to market demand. One common problem faced in inventory management is deadstock, which is a condition where a product is not sold for a long time, causing a buildup of goods and financial losses. This problem is generally caused by inaccuracy in predicting product sales needs. This study aims to overcome this problem by implementing the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm combined with the Grid Search optimization method to improve the accuracy of sales predictions. By utilizing the Sales Data Analysis dataset from Kaggle, the algorithm is implemented in a web-based system using Python and Flask. The results showed that the combination of Grid Search and ARRES was able to significantly increase prediction accuracy, as indicated by a decrease in the MAPE value from 2.845% (ARRES only ) to 0.877% (Grid Search + ARRES). This proves that the proposed method can help companies manage stock more efficiently, reduce the risk of deadstock, and increase the effectiveness of product sales planning

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References

. R. A. Rizkyah and B. Prabowo, “Implementasi Pengelolaan Persediaan dan Distribusi Pada Divisi Logistik PDAM Surya Sembada Kota Surabaya,” KARYA J. Pengabdi. Kpd. Masy., vol. 4, no. 2, pp. 210–214, 2024.

. E. F. Rayo, A. C. P. Inaray, and B. Lule, “Capacity Strategies a Comparative Perspective in Manufacturing vs Service Industries,” J. Inform. Ekon. Bisnis, vol. 5, no. 4, pp. 1445–1452, 2023, doi: 10.37034/infeb.v5i4.759.

. Jamaludin, R. A. Permana, Marlina, and S. Sahara, “Prediksi Persediaan Barang Menggunakan Indikator Moving Average Studi Kasus Department Store,” J. Teknol. Sist. Inf., vol. 5, no. 1, pp. 171–180, 2024, doi: 10.35957/jtsi.v5i1.7811.

. Y. Saraswati, F. Fauziah, and N. D. Nathasia, “Prediksi Stok Persediaan Barang Menggunakan Algoritma Apriori Dan Metode Single Moving Average (SMA),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 8, no. 2, pp. 692–703, 2023, doi: 10.29100/jipi.v8i2.3933.

. F. Lestari and Rustandi, “Penerapan Metode Economic Order Quantity dan Just in Time Guna Meningkatkan Optimasi Pengendalian Persediaan Produk,” J. Bisnisman Ris. Bisnis dan Manaj., vol. 5, no. 03, pp. 44–64, 2024, doi: 10.52005/bisnisman.v5i03.190.

. B. Harto et al., Transformasi Bisnis di Era Digital (Teknologi Informasi dalam Mendukung Transformasi Bisnis di Era Digital), no. August. 2023.

. B. S. Adicahya, S. Wulandari, and D. Avianto, “Metode Neural Network Dalam Prediksi Jumlah Penumpang Kereta Api Berbasis Web,” J. Inf. Syst. Res., vol. 6, no. 1, pp. 302–314, 2024, doi: 10.47065/josh.v6i1.6001.

. R. J. Djami and Y. W. A. Nanlohy, “Peramalan Indeks Harga Konsumen di Kota Ambon Menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Double Exponential Smoothing,” Var. J. Stat. Its Appl., vol. 4, no. 1, pp. 1–14, 2022.

. R. Romindo, J. J. Pangaribuan, and O. P. Barus, “Penerapan Algoritma Adaptive Response Rate Exponential Smoothing Terhadap Business Intelligence System,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 565–575, 2023, doi: 10.47065/bits.v5i2.3955.

. N. A. Pramudhyta and M. S. Rohman, “Perbandingan Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 19, 2024, doi: 10.30865/mib.v8i1.6965.

. T. A. Prasetyo et al., “Sales forecasting of marketing using adaptive response rate single exponential smoothing algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 1, pp. 423–432, 2023, doi: 10.11591/ijeecs.v31.i1.pp423-432.

. A. B. E. F. Firmanda, A. H. AS, A. Tholib, and J. X. Guterres, “Implementasi GridSearch dalam Meningkatkan Kinerja Model Support Vector Regression (SVR) utuk Prediksi Penjualan Produk pada Meuble Rohman Jaya,” J. Explor. IT Keilmuan dan Apl. Tek. Inform., vol. 16, no. 1, pp. 22–30, 2024.

. Andi, Thamrin, A. Susanto, E. Wijaya, and D. Djohan, “Analysis of the random forest and grid search algorithms in early detection of diabetes mellitus disease,” J. Mantik, vol. 7, no. 2, pp. 2685–4236, 2023, doi: 10.35335/mantik.v7i2.3981.

. N. Syahfitrri, Nonong Amalita, Dodi Vionanda, and Zamahsary Martha, “Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm,” UNP J. Stat. Data Sci., vol. 2, no. 1, pp. 32–39, 2024, doi: 10.24036/ujsds/vol2-iss1/145.

. W. A. Medyanti, M. Faisal, and H. Nurhayati, “Optimasi Metode Single Exponential Smoothing Dengan Grid Search Pada Prediksi Nilai Ekspor Migas,” SINTECH (Science Inf. Technol. J., vol. 7, no. 1, pp. 59–69, 2024, doi: 10.31598/sintechjournal.v7i1.1526.

. Nazli, R (2018) “Pemodelan Aplikasi Mobile Modul Perkuliahan Berbasis Client Server” Jurnal Teknologi dan Open Source1(1) 25-32

. Nazli, R. (2019). Pemodelan Aplikasi Mobile Pelayanan Publik Desa (Smart Village) Berbasis Cloud Computing.Jurnal Teknologi dan Open Source,2(2), 87-95

. Nazli, R (2020) “Pemodelan Aplikasi Pendukung Keputusan Makanan Pendamping Air Susu Ibu (Mpasi) Berbasis Android” Jurnal Teknologi dan Open Source3(2) 272-283.

. Sanuri. R, Muzzakkar. M & Nugraha D (2023).”Pengembangan Odoo Enterprise Resource Planning (ERP) Modul Audit Mutu Internal Sistem Penjaminan Mutu Dengan Metode Accelerated SAP”Jurnal Informatika Komputer, Bisnis dan Manajemen. Vol.21, No.3, September 2023. ISSN: 2715-2944

. Nopriandi, H., & Haswan, F. (2022). Analisis Klasterisasi Mahasiswa Baru dalam Memilih Program Studi dengan Menggunakan Algoritma K-Means. J. Inf. Syst. Res, 3(4), 666-671.

. Al-Hafiz, N. W., & Harianja, H. (2024). Design of an Internet of Things-Based automatic cat feeding control device (IoT). Jurnal Mandiri IT, 13(1), 161–169. https://doi.org/10.35335/mandiri.v13i1.322.

. Nofri Wandi Al-Hafiz, Helpi Nopriandi, & Harianja. (2024). Design of Rainfall Intensity Measuring Instrument Using IoT-Based Microcontroller. JURNAL TEKNOLOGI DAN OPEN SOURCE, 7(2), 202 - 211. https://doi.org/10.36378/jtos.v7i2.2898.

. PutriD. and Al-HafizN., “SISTEM INFORMASI SURAT KETERANGAN GANTI RUGI TANAH PADA KECAMATAN KUANTAN TENGAH MENGGUNAKAN WEBGIS”, Biner : Jurnal Ilmiah Informatika dan Komputer, vol. 2, no. 2, pp. 112-121, Jul. 2023.

. Al-Hafiz, N. W., & Chairani, S. (2022). PERANCANGAN APLIKASI PEMBELAJARAN INTERAKIF PADA MATERI PERLINDUNGAN DAN PENEGAKAN HUKUM BERBASIS ANDROID. JURNAL PERENCANAAN, SAINS DAN TEKNOLOGI (JUPERSATEK), 5(1), 1-5.

. Al Hafiz, N. W., & Siregar, M. H. (2021). GEOGRAPHIC INFORMATION SYSTEMS FOR THE DISTRIBUTION OF COMMUNITY SERVICE ACTIVITIES IN KUANTAN SINGINGI DISTRICT. INFOKUM, 10(1), 236-243.