Peramalan Permintaan Produk Bakso Frozen FA Menggunakan Metode Time Series

Irin Ramdhani; Raodah Raodah; Erniyani Erniyani

doi:10.56211/factory.v4i2.1248

Authors

Irin Ramdhani Universitas Negeri Makassar, Makassar
Raodah Raodah Universitas Negeri Makassar, Makassar
Erniyani Erniyani Universitas Negeri Makassar, Makassar

DOI:

https://doi.org/10.56211/factory.v4i2.1248

Keywords:

Peramalan; Permintaan; UMKM

Abstract

Peramalan permintaan menjadi aspek penting dalam mendukung keberlangsungan usaha mikro, kecil, dan menengah (UMKM), khususnya pada sektor pangan beku. Fluktuasi permintaan yang tidak menentu seringkali menimbulkan permasalahan dalam perencanaan produksi, baik berupa kelebihan stok yang meningkatkan biaya penyimpanan maupun kekurangan stok yang mengakibatkan hilangnya peluang penjualan. Penelitian ini bertujuan menganalisis penerapan metode deret waktu, yaitu Moving Average, Weighted Moving Average, dan Single Exponential Smoothing, untuk meramalkan permintaan produk Bakso Frozen Fatimah Asri. Data yang digunakan berupa catatan penjualan bulanan tahun 2024. Evaluasi akurasi model dilakukan menggunakan tiga ukuran kesalahan, yaitu Mean Absolute Deviation (MAD), Mean Squared Error (MSE), dan Mean Absolute Percentage Error (MAPE). Hasil analisis menunjukkan bahwa metode Single Exponential Smoothing menghasilkan nilai MAD dan MAPE terendah, masing-masing sebesar 46,129 dan 11,216%, sehingga memberikan hasil peramalan yang lebih mendekati data aktual. Sementara itu, metode Weighted Moving Average memiliki nilai MSE terendah sebesar 6636,046, sehingga dapat mengurangi dampak kesalahan besar. Dengan demikian, Single Exponential Smoothing dipandang sebagai metode terbaik untuk digunakan dalam peramalan permintaan jangka pendek produk Bakso Frozen FA. Penerapan metode ini diharapkan mampu membantu UMKM dalam meningkatkan akurasi perencanaan produksi, mengoptimalkan persediaan, serta mendukung pengambilan keputusan strategis yang berorientasi pada kepuasan konsumen dan efisiensi biaya operasional.

Downloads

Download data is not yet available.

References

M. Maslim And K. Arinanda, “Motorcycle Parts Sales Forecasting Using Auto-Regressive Integrated Moving Average Model,” International Journal Of Computer Theory And Engineering, Vol. 12, No. 1, Pp. 28–31, 2020. DOI: https://doi.org/10.7763/IJCTE.2020.V12.1259

R. Asrianto, M. Kom, And Y. Anggraini, “Prediksi Produksi Roti Coklat Berdasarkan Data Trends Google Menggunakan Metode Double Exponential Smoothing (Studi Kasus : Rotte Bakery),” 2021. DOI: https://doi.org/10.37859/seis.v1i1.2821

P. Sandora And F. Fauzi, “Inventory Management Analysis For Light Steel Assembly Optimization In Cv. Nira Jaya Abadi, East Lampung,” Social: Jurnal Inovasi Pendidikan Ips, Vol. 3, No. 4, Pp. 175–180, 2023. DOI: https://doi.org/10.51878/social.v3i4.3071

F. L. Sari, “Demand Forecasting Of Trading Companies Based On Time Sequence,” In 2nd Annual Management, Business And Economic Conference (Ambec 2020), Atlantis Press, 2021, Pp. 158–161. DOI: https://doi.org/10.2991/aebmr.k.210717.033

W. Isnaini And A. Sudiarso, “Demand Forecasting In Small And Medium Enterprises (Smes) Ed Aluminium Yogyakarta Using Causal, Time Series, And Combined Causal-Time Series Approaches,” In Matec Web Of Conferences, Edp Sciences, Sep. 2018. Doi: 10.1051/Matecconf/201820401004. DOI: https://doi.org/10.1051/matecconf/201820401004

F. Rohman Hariri, “Metode Least Square Untuk Prediksi Penjualan Sari Kedelai Rosi,” Jurnal Simetris, Vol. 7, No. 2, 2016. DOI: https://doi.org/10.24176/simet.v7i2.788

M. Khoirunnisa And S. Nurriyah, “Analisis Efisiensi Biaya Persediaan Dimsum Frozen Food Di Toko Urban Request Food, Bekasi,” Sharia Agribusiness Journal, Vol. 1, No. 2, Pp. 121–134, 2021. DOI: https://doi.org/10.15408/saj.v1i2.20788

S. Kuswandi Et Al., Perancangan Teknik Industri. [Online]. Available: Www.Globaleksekutifteknologi.Co.Id

W. Andani And N. Satyahadewi, “Peramalan Nilai Tukar Rupiah Terhadap Dolar As Menerapkan Arima, Var Dan Random Forest,” Cendekia: Jurnal Ilmu Pengetahuan, Vol. 5, No. 1, Pp. 204–216, 2025. DOI: https://doi.org/10.51878/cendekia.v5i1.4305

P. Suwignjo, L. Panjaitan, A. Baihaqy, And A. Rusdiansyah, “Predictive Analytics To Improve Inventory Performance: A Case Study Of An Fmcg Company,” Operations And Supply Chain Management: An International Journal, Vol. 16, No. 2, Pp. 293–310, 2023. DOI: https://doi.org/10.31387/oscm0530390

A. Zuhri Et Al., “Enhancing The Inventory Management Through Demand Forecasting”, Doi: 10.47772/Ijriss. DOI: https://doi.org/10.47772/IJRISS

F. M. Puspita, N. A. Primadani, And E. Susanti, “Application Of Material Requirement Planning With Arima Forecasting And Fixed Order Quantity Method In Optimizing The Inventory Policy Of Raw Materials Of Sederhana Restaurant In Palembang,” In 5th Sriwijaya Economics, Accounting, And Business Conference (Seabc 2019), Atlantis Press, 2020, Pp. 71–76. DOI: https://doi.org/10.2991/aebmr.k.200520.014

Olamide Raimat Amosu, Praveen Kumar, Yewande Mariam Ogunsuji, Segun Oni, And Oladapo Faworaja, “Ai-Driven Demand Forecasting: Enhancing Inventory Management And Customer Satisfaction,” World Journal Of Advanced Research And Reviews, Vol. 23, No. 2, Pp. 708–719, Aug. 2024, Doi: 10.30574/Wjarr.2024.23.2.2394. DOI: https://doi.org/10.30574/wjarr.2024.23.2.2394

I. Rizkya, K. Syahputri, R. M. Sari, I. Siregar, And J. Utaminingrum, “Autoregressive Integrated Moving Average (Arima) Model Of Forecast Demand In Distribution Centre,” In Iop Conference Series: Materials Science And Engineering, Iop Publishing, 2019, P. 012071. DOI: https://doi.org/10.1088/1757-899X/598/1/012071

P. Rahardja, “Pengantar Ilmu Ekonomi: Mikroekonomi Dan Makroekonomi,” 2008.

S. Makridakis, S. C. Wheelwright, And R. J. Hyndman, Forecasting Methods And Applications. John Wiley & Sons, 2008.

H. Setijowati, “Desain Strategi ‘Covi Srondol’ Dalam Rangka Akselerasi Jawa Tengah Corporate University,” Cendekia: Jurnal Ilmu Pengetahuan, Vol. 5, No. 1, Pp. 249–263, 2025. DOI: https://doi.org/10.51878/cendekia.v5i1.4437