Application of Time Series For Palm Oil Production Prediction At PT. Dwie Warna Karya

PENERAPAN TIME SERIES UNTUK PREDIKSI PRODUKSI MINYAK KELAPA SAWIT DI PT. DWIE WARNA KARYA

Authors

  • Elsa Monica Putri STMIK Palangka Raya
  • Veny Cahya Hardita STMIK Palangkaraya
  • Catharina Elmayantie STMIK Palangka Raya

Keywords:

Machine Learning, Palm Oil, SARIMA, Time Series

Abstract

Fluctuations in palm oil production at PT. Dwie Warna Karya negatively impact the company's efficiency and profitability. This study aims to implement the Time Series method using the SARIMA model to accurately predict palm oil production, enabling the company to make better decisions in production planning and operations. This research employs a quantitative approach with descriptive and predictive analysis, utilizing data collected through interviews, literature studies, and historical production documentation. The SARIMA (1,1,1)(1,1,1)12_{12} model is identified as the most suitable for forecasting palm oil production over the next 12 months. The model indicates that production is influenced by previous values, requires first-order differencing to address trends, and includes a random component affected by prior forecasting errors, both in the short-term and seasonal patterns. This SARIMA model enhances forecasting accuracy and serves as a valuable reference for production planning, inventory management, and strategic decision-making.

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Author Biographies

Elsa Monica Putri, STMIK Palangka Raya

Veny Cahya Hardita, STMIK Palangkaraya

Catharina Elmayantie, STMIK Palangka Raya

References

Durrah, F. I., Yulia, Parhusip, T. P., & Rusyana, A. (2018). Peramalan Jumlah Penumpang Pesawat Di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average). Journal of Data Analysis, 1-11.

Levia, D., & Mhubaligh. (2023). Analisis Proses Produksi CPO Untuk Mengidentifikasi Faktor-Faktor Yang Mempengaruhi Kualitas Mutu CPO. Jurnal Teknologi dan Manajemen Industri Terapan, 82-89.

Nazar, R. (2024). Implementasi Pemrograman Python Menggunakan Google Colab. Jurnal Informatika dan Komputer (JIK), 50-56.

Ruhiat, D., & Effendi, A. (2018). Pengaruh Faktor Musiman Pada Pemodelan Deret Waktu Untuk Peramalan Debit Sungai Dengan Metode Sarima. Jurnal Teori dan Riset Matematika (TEOREMA), 117-128.

Soen, G. I., Marlina, & Renny. (2022). Implementasi Cloud Computing dengan Google Colaboratory pada Aplikasi Pengolah Data Zoom Participants. JITU : Journal Informatic Technology And Communicatione, 24-30.

Suseno, & Wibowo, S. (2023). Penerapan Metode ARIMA dan SARIMA Pada Peramalan Penjualan Telur Ayam Pada PT Agromix Lestari Group. Jurnal Teknologi dan Manajemen Industri Terapan , 33-40.

Tokan, L. F., & Hermawan, A. (2023). Implementasi Model SARIMA Untuk Memprediksi Produksi Minyak Kelapa Sawit. Jurnal Fasilkom, 456-563.

Wahyudi, A., & Mujilahwati, S. (2023). Implementasi Metode Time Series untuk Prediksi dan Monitoring Pendapatan Masla Delivery Berbasis Website. Joutica, 1-6.

Wibowo, A. (2018). Model Peramalan Indeks Harga Konsumen Kota Palangka Raya Menggunakan Seasonal ARIMA (SARIMA). Jurnal Teori dan terapan Matematika, 17-24.

Yudha, E. P., & Bagaskara, F. (2024). Analisis Daya Saing Ekspor Kelapa Sawit (CPO) Indonesia dan Malaysia di India. Agroinfo Galuh Jurnal Ilmiah Mah, 1212-1227.

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Published

2025-05-30

How to Cite

Elsa Monica Putri, Veny Cahya Hardita, & Catharina Elmayantie. (2025). Application of Time Series For Palm Oil Production Prediction At PT. Dwie Warna Karya : PENERAPAN TIME SERIES UNTUK PREDIKSI PRODUKSI MINYAK KELAPA SAWIT DI PT. DWIE WARNA KARYA . Jurnal Sains Komputer Dan Teknologi Informasi, 7(2), 9–14. Retrieved from https://journal.umpr.ac.id/index.php/jsakti/article/view/9446