PERANCANGAN SISTEM PREDIKSI VOLUME EKSPOR PISANG , KOPI DAN KELAPA SUMATERA UTARA KE MALAYSIA MENGGUNAKAN METODE ARIMA BERBASIS WEB
DOI:
https://doi.org/10.62712/juktisi.v5i1.870Kata Kunci:
Export Prediction, ARIMA, Web-Based System, , Bananas, Coffee,, Coconuts, North Sumatra, MalaysiaAbstrak
The adoption of information technology in predictive systems is increasingly popular in the agricultural export sector, leveraging historical data analysis to forecast global market fluctuations and optimize supply chains for commodities such as bananas, coffee, and coconuts. Exporters in North Sumatra face challenges related to fluctuating export volumes to Malaysia, influenced by seasonal factors, international price changes, weather dependency, and a lack of accurate data. This results in supply imbalances, economic losses, and difficulties in strategic planning. This research offers a solution by employing the Autoregressive Integrated Moving Average (ARIMA) method in the development of a web-based system to address these issues. ARIMA is a statistical time series model that combines autoregressive (AR) components for dependencies on previous values, integrated (I) components to handle non-stationarity through differencing, and moving average (MA) components to predict the influence of past errors; its seasonal variant (SARIMA) is applied to capture monthly harvest cycle patterns. The developed solution involves processing historical export data from 2019–2024 sourced from the Central Bureau of Statistics (BPS) via a Python Flask API with an automated ARIMA approach, integrated into a PHP CodeIgniter 4 web platform, providing interactive visualizations, real-time data updates, and easy user access. The expected outcomes from this system are more accurate export volume predictions, with a MAPE of approximately 19.02% and MAE of 180,755.28 tons on 2024 test data for bananas as a representative sample, which can support strategic decision-making, production efficiency, and enhanced competitiveness for banana, coffee, and coconut exports from North Sumatra to Malaysia.
Unduhan
Referensi
Alouw, J. C., & Wulandari, S. (2020). Present status and outlook of coconut development in Indonesia. IOP Conference Series: Earth and Environmental Science, 418(1), Article 012035. https://doi.org/10.1088/1755-1315/418/1/012035
Tampubolon, J., Ginting, A., Nainggolan, H. L., & Tarigan, J. R. (2023). Indonesian coffee development path: Production and international trade. Asian Journal of Agricultural Extension, Economics & Sociology, 41(12), 316–328. https://doi.org/10.9734/ajaees/2023/v41i122326
Aprianto, A., Hanum, C., & Mukhlis, M. (2023). Effectiveness of shade and cocopeat as a growing media for acclimatization of Barangan banana (Musa acuminata lin) plants. Jurnal Penelitian Pendidikan IPA, 9(7), 5560–5567. https://doi.org/10.29303/jppipa.v9i7.3996
Fitri, F. A., Syafrial, S., & Toiba, H. (2024). Determining factors of Indonesian coffee demand in the US market: Using LA/AIDS approach. Agro Bali: Agricultural Journal, 7(3), 760–773. https://doi.org/10.37637/ab.v7i3.1548
Novianti, T., Sari, A. M., Sari, L. K., & Asikin, Z. (2024). Competitiveness of Indonesia’s agricultural exports to China: Trends and strategic insights. Jurnal Manajemen & Agribisnis, 21(3), 374–374. https://doi.org/10.17358/jma.21.3.374
Goestjahjanti, F. S., Pasaribu, S. B., Novitasari, D., Azz, I. K. H., & Winanti, W. (2023). The role of export in boosting Indonesia’s GDP during crisis: Macroeconomic conditions. Etikonomi, 22(2), 369–388. https://doi.org/10.15408/etk.v22i2.29236
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.
Erlina, R., & Azhar, R. (2020). Forecasting model of agriculture commodity of value export of coffee: Application of ARIMA model. Jurnal Teknik Pertanian Lampung, 9(3), 257–263. https://doi.org/10.23960/jtep-l.v9i3.257-263
Nasirudin, S., Said, S., & Widodo, E. (2022). Peramalan jumlah produksi kopi di Jawa Timur pada tahun 2020-2021 menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Jurnal Agrium, 25(1), 34–43. https://doi.org/10.29103/agrium.v25i1.7370
Hasibuan, L. H., Musthofa, S., Putri, D. M., & Jannah, M. (2023). Comparison of seasonal time series forecasting using SARIMA and Holt-Winter’s exponential smoothing (Case study: West Sumatra export data). BAREKENG: Jurnal Ilmu Matematika dan Terapan, 17(3), 1773–1784. https://doi.org/10.30598/barekengvol17iss3pp1773-1784
Putri, N. H., Abidin, Z., & Situmorang, S. (2023). The prospects of Lampung’s pepper export to the global market: An analysis using the ARIMA model. Jurnal Habitat, 34(3), 289–298. https://doi.org/10.55981/habitat.2023.289
Fauzani, S. P., & Rahmi, D. (2023). Penerapan metode ARIMA dalam peramalan harga produksi karet di Provinsi Riau. Jurnal Teknologi dan Manajemen Industri Terapan, 2(4), 245–253.
Fathurachman, M., Susanto, T. D., & Wibowo, R. P. (2020). RESTful API implementation in making a master data planogram using the Flask framework. J-SAKTI (Jurnal Sains Komputer dan Informatika), 4(2), 516–526. https://doi.org/10.30645/j-sakti.v4i2.328
Ahmed, M. K., & Bello, A. H. (2024). A comparative analysis of performance optimization techniques for benchmarking PHP frameworks: Laravel and CodeIgniter. Dutse Journal of Pure and Applied Sciences, 10(3c), 284–295.
Al Azhar, M. F., & Harwahyu, R. (2023). Detection of SQL injection vulnerability in CodeIgniter framework using static analysis. Multitek Indonesia, 17(1), 69–78. https://doi.org/10.30591/multitek.v17i1.3975
Ichsandi, I., Yanto, W., Alhaq, H., Sari, R. S., & Juanda, M. (2025). Implementasi UML dalam desain sistem informasi program studi SI di Universitas Merangin. Impression: Jurnal Teknologi dan Informasi, 4(2), 224–237.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Muhammad Wahyu Hidayat Hidayat
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
