Analisis Peramalan Produksi Tanaman Kelapa Sawit Menggunakan Metode Arima pada PTPN Kebun Sukamaju
DOI:
https://doi.org/10.58812/jbmws.v3i03.1537Kata Kunci:
Peramalan Produksi, ARIMA, Manajemen OperasionalAbstrak
Industri kelapa sawit Indonesia memainkan peran penting dalam sektor ekonomi dan sosial dengan memberikan kontribusi signifikan terhadap pendapatan nasional. Penelitian ini berfokus pada peramalan produksi kelapa sawit di PTPN Kebun Sukamaju, Jawa Barat, menggunakan model ARIMA untuk mendukung manajemen operasional. Data sekunder produksi bulanan dari 2016 hingga 2023 dianalisis dengan pendekatan mixed methods, menggunakan perangkat lunak R-Studio. Model ARIMA(1,1,3)(1,0,1)[12] dipilih sebagai model terbaik setelah melalui identifikasi, estimasi parameter, dan pemeriksaan diagnostik. Hasil peramalan menunjukkan penurunan produksi dengan nilai MAPE sebesar 12,49%.
Referensi
Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP using ARIMA models. Reports on Economics and Finance, 5(1), 35–47. https://doi.org/10.12988/ref.2019.81023
Afeef, M., Ihsan, A., & Zada, H. (2018). Forecasting Stock Prices through Univariate ARIMA Modeling. NUML International Journal of Business & Management ISSN, 13(2), 2410–5392.
AYIK, U., & ERKAL, G. (2021). Forecasting of Unemployment and Economic Growth for Turkey: ARIMA Model Application. Turkish Journal of Forecasting, 05(1), 12–22. https://doi.org/10.34110/forecasting.917300
BPS. (2023). Ekspor Minyak Kelapa Sawit Menurut Negara Tujuan Utama, 2012-2022. Retrieved from https://www.bps.go.id/id/statistics-table/1/MTAyNiMx/ekspor-minyak-kelapa-sawit-menurut-negara-tujuan-utama--2012-2022.html
Diksa, I. G. B. N. (2022). Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach. Jurnal Matematika, Statistika Dan Komputasi, 18(2), 237–250. https://doi.org/10.20956/j.v18i2.18542
Edwards, R. B., Edmonds, E., Burke, P., Cameron, L., Falcon, W., Gollin, D., … Suryadarma, D. (2019). Export agriculture and rural poverty: evidence from Indonesian palm oil. Dartmouth College: Hanover, Germany, 1–25.
Fauziah, Ningsih, Y. I., & Setiarini, E. (2019). Jurnal Ilmiah Ekonomi dan Bisnis, 10(1): 61-67 Analisis Peramalan (Forecasting) Penjualan Jasa Pada Warnet Bulian City di Muara Bulian. Jurnal Ilmiah Ekonomi Dan Bisnis, 10(1), 61–67.
GC Arun, & JunHo Yeo. (2020). Rice Production of Nepal in 2030: A Forecast using Autoregressive Integrated Moving Average Model. Journal of South Asian Studies, 25(4), 31–58. https://doi.org/10.21587/jsas.2020.25.4.002
Hal, J. (2021). Penerapan Model SARIMA untuk Memprediksi Tingkat Inflasi di Indonesia Jurnal Sains Matematika dan Statistika Penerapan Model SARIMA untuk Memprediksi Tingkat Inflasi di Indonesia, (August). https://doi.org/10.24014/jsms.v7i2.13168
Harahap, F., Leduc, S., Mesfun, S., Khatiwada, D., Kraxner, F., & Silveira, S. (2020). Meeting the bioenergy targets from palm oil based biorefineries: An optimal configuration in Indonesia. Applied Energy, 278(September), 115749. https://doi.org/10.1016/j.apenergy.2020.115749
Hassan, H. M. (2020). Modelling GDP for Sudan using ARIMA. SSRN Electronic Journal, (101207). https://doi.org/10.2139/ssrn.3630099
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2023). Practical machine learning: Forecasting daily financial markets directions. Expert Systems with Applications, 233, 120840. https://doi.org/10.1016/J.ESWA.2023.120840
Hidayatullah, T. (2023). Analisis Empiris Produksi Kelapa Sawit Terhadap Tingkat Kesejahteraan Petani. Jurnal Budget : Isu Dan Masalah Keuangan Negara, 8(1), 156–175. https://doi.org/10.22212/jbudget.v8i1.152
Ishak, S. S., Abednego, M., Sari, D. M., Sabila, V. S., Khoirunnisa, K., Alvionita, M., & Muthoharoh, L. (2023). Indonesian Consumer Price Index Forecasting Using Autoregressive Integrated Moving Average. International Journal of Electronics and Communications Systems, 3(1), 33. https://doi.org/10.24042/ijecs.v3i1.18252
Krishna, V. V., & Kubitza, C. (2021). Impact of oil palm expansion on the provision of private and community goods in rural Indonesia. Ecological Economics, 179(August 2020), 106829. https://doi.org/10.1016/j.ecolecon.2020.106829
Kristanto, H., Tamsi, & Cuandra, F. (2022). Penerapan Manajemen Operasional dalam Meningkatkan Kinerja di Apple, Inc. YUME : Journal of Management, 5(3), 84–96. https://doi.org/10.37531/yume.vxix.457
Kubitza, C., Krishna, V. V., Alamsyah, Z., & Qaim, M. (2018). The Economics Behind an Ecological Crisis: Livelihood Effects of Oil Palm Expansion in Sumatra, Indonesia. Human Ecology, 46(1), 107–116. https://doi.org/10.1007/s10745-017-9965-7
Mahayana, I. B. B., Mulyadi, I., & Soraya, S. (2022). Peramalan Penjualan Helm dengan Metode ARIMA (Studi Kasus Bagus Store). Inferensi, 5(1), 45. https://doi.org/10.12962/j27213862.v5i1.12469
Mathew, S., & Murugesan, R. (2020). Indian natural rubber price forecast–An Autoregressive Integrated Moving Average (ARIMA) approach. Indian Journal of Agricultural Sciences, 90(February), 418–422. https://doi.org/https://doi.org/10.56093/ijas.v90i2.103067
Mgaya, J. F. (2019). Application of ARIMA models in forecasting livestock products consumption in Tanzania. Cogent Food and Agriculture, 5(1). https://doi.org/10.1080/23311932.2019.1607430
Mishra, P., Yonar, A., Yonar, H., Kumari, B., Abotaleb, M., Das, S. S., & Patil, S. G. (2021). State of the art in total pulse production in major states of India using ARIMA techniques. Current Research in Food Science, 4, 800–806. https://doi.org/10.1016/j.crfs.2021.10.009
Mokorimban, F. E., Nainggolan, N., & Langi, Y. A. R. (2021). Penerapan Metode Autoregressive Integrated Moving Average ( ARIMA ) dalam Model Intervensi Fungsi Step terhadap Indeks Harga Konsumen di Kota Manado. D’Cartesian Jurnal Matematika Dan Aplikasi, 10(2), 91–99.
Nasution, A. S., & Wulansari, I. Y. (2019). Analyzing Impacts of Renewable Energy Directive (RED) on Crude Palm Oil (CPO) Export and Forecasting CPO Export from Indonesia to European Union (EU) for 2019-2020 Using ARIMA Intervention Analysis, 98(Icot 2019), 131–135. https://doi.org/10.2991/icot-19.2019.28
Nurfadila, K., & Ilham Aksan. (2020). Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Penggunaan Harian Data Seluler. Journal of Mathematics: Theory and Applications, 2(1), 5–10. https://doi.org/10.31605/jomta.v2i1.749
Ozturk, S., & Ozturk, F. (2018). Forecasting Energy Consumption of Turkey by Arima Model. Journal of Asian Scientific Research, 8(2), 52–60. https://doi.org/10.18488/journal.2.2018.82.52.60
Palma, W. (2016). Time Series Analysis. (D. J. Balding, N. A. Cressie, G. M. Fitzmaurice, G. H. Givens, H. Goldstein, G. Molenberghs, … J. L. Teugels, Eds.) (1st ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.
Pangaribuan, J. J., Fanny, F., Barus, O. P., & Romindo, R. (2023). Prediksi Penjualan Bisnis Rumah Properti Dengan Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA). Jurnal Sistem Informasi Bisnis, 13(2), 154–161. https://doi.org/10.21456/vol13iss2pp154-161
Panjaitan, H., Prahutama, A., & Sudarno, S. (2018). PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang). Jurnal Gaussian, 7(1), 96–109. https://doi.org/10.14710/j.gauss.v7i1.26639
Pathak, M. R., & Kapadia, J. M. (2021). Indian Stock Market Predictive Efficiency using the ARIMA Model. Srusti Management Review, 14(1), 10–15.
Prasetyono, R. I., & Anggraini, D. (2021). Analisis Peramalan Tingkat Kemiskinan Di Indonesia Dengan Model Arima. Jurnal Ilmiah Informatika Komputer, 26(2), 95–110. https://doi.org/10.35760/ik.2021.v26i2.3699
Purnama, D. I. (2021). Peramalan Curah Hujan Di Kabupaten Parigi Moutong Menggunakan Model Seasonal Autoregressive Integrated Moving Average (SARIMA). Jurnal Ilmiah Matematika Dan Terapan, 18(2), 136–147. https://doi.org/10.22487/2540766x.2021.v18.i2.15652
Purwanza, S. W., Wardhana, A., Mufidah, A., Renggo, Y. R., Hudang, A. K., Setiawan, J., & Darwin. (2022). Metodologi Penelitian Kuantitatif, Kualitatif dan Kombinasi. Media Sains Indonesia.
Rahim, N. F., Othman, M., & Sokkalingam, R. (2018). A Comparative Review on Various Method of Forecasting Crude Palm Oil Prices. Journal of Physics: Conference Series, 1123(1). https://doi.org/10.1088/1742-6596/1123/1/012043
Rahimi, Z. H., & Khashei, M. (2018). A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting. Computers and Industrial Engineering, 118, 44–53. https://doi.org/10.1016/j.cie.2018.02.023
Ridwan Harimansyah, F., & Tukhas Shilul Imaroh. (2020). Aircraft Spare Parts Inventory Management Analysis on Airframe Product Using Continuous Review Methods. Dinasti International Journal of Management Science, 2(1), 81–90. https://doi.org/10.31933/dijms.v2i1.528
Rizkya, I., Syahputri, K., Sari, R. M., Siregar, I., & Utaminingrum, J. (2019). Autoregressive Integrated Moving Average (ARIMA) Model of Forecast Demand in Distribution Centre. IOP Conference Series: Materials Science and Engineering, 598(1). https://doi.org/10.1088/1757-899X/598/1/012071
Ruml, A., Chrisendo, D., Iddrisu, A. M., Karakara, A. A., Nuryartono, N., Osabuohien, E., & Lay, J. (2022). Smallholders in agro-industrial production: Lessons for rural development from a comparative analysis of Ghana’s and Indonesia’s oil palm sectors. Land Use Policy, 119(May), 106196. https://doi.org/10.1016/j.landusepol.2022.106196
Salwa, N., Tatsara, N., Amalia, R., & Zohra, A. F. (2018). Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 21–31. https://doi.org/10.24815/jda.v1i1.11874
Santika, T., Wilson, K. A., Budiharta, S., Law, E. A., Poh, T. M., Ancrenaz, M., … Meijaard, E. (2019). Does oil palm agriculture help alleviate poverty? A multidimensional counterfactual assessment of oil palm development in Indonesia. World Development, 120, 105–117. https://doi.org/10.1016/j.worlddev.2019.04.012
Selvam, S. K., Rajendran, C., & Sankaralingam, G. (2024). A linear programming-based bi-objective optimization for forecasting short univariate time series. Decision Analytics Journal, 10(January), 100400. https://doi.org/10.1016/j.dajour.2024.100400
Setiawan, D. A., Wahyuningsih, S., & Goejantoro, R. (2019). Peramalan Produksi Kelapa Sawit Menggunakan Winter’s dan Pegel’s Exponential Smoothing dengan Pemantauan Tracking Signal. Jambura Journal of Mathematics, 2(1), 1–14. https://doi.org/10.34312/jjom.v2i1.2320
Siami-Namini, S., & Namin, A. S. (2018). Forecasting Economics and Financial Time Series: ARIMA vs. LSTM, 1–19. Retrieved from http://arxiv.org/abs/1803.06386
Smith, D. C., Thumm, E. B., Anderson, J., Kissler, K., Reed, S. M., Centi, S. M., … Barton, A. J. (2023). Sudden Shift to Telehealth in COVID-19: A Retrospective Cohort Study of Disparities in Use of Telehealth for Prenatal Care in a Large Midwifery Service. Journal of Midwifery and Women’s Health, 8, 1–12. https://doi.org/10.1111/jmwh.13601
Syam, A. R. P. (2022). Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States. Jurnal Matematika, Statistika Dan Komputasi, 18(2), 224–236. https://doi.org/10.20956/j.v18i2.18460
Vebrianto, R., Thahir, M., Putriani, Z., Mahartika, I., Ilhami, A., & Diniya. (2020). Mixed methods Research: Trends and Issues in Research Methodology. Bedelau: Journal of Education and Learning, 1(2), 63–73. https://doi.org/10.55748/bjel.v1i2.35
Wahjono, W. (2021). Peran Manajemen Operasional dalam Menunjang Keberlangsungan Kegiatan Perusahaan. Jurnal Ilmiah Infokam, 17(2), 114–120. https://doi.org/10.53845/infokam.v17i2.302
Wei Yee, K., & Banu Samsudin, H. (2021). Comparison between Artificial Neural Network and ARIMA Model in Forecasting Palm Oil Price in Malaysia. International Journal of Scientific Engineering and Science, 5(11), 12–15. Retrieved from http://ijses.com/
Yani, T. A. R., Wahyuningsih, S., & Siringoringo, M. (2022). Optimasi Parameter Pemulusan Pada Metode Peramalan Double Exponential Smoothing Holt Menggunakan Golden Section ( Studi Kasus : NTPT Provinsi Kalimantan Timur Tahun 2014-2019 ) Optimization of Smoothing Parameters in the Double Exponential Smoothing Holt. Jurnal Eksponensial, 13(1), 51–56. Retrieved from http://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/880%0Ahttp://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/download/880/361
Yonar, H., Yonar, A., Tekindal, M. A., & Tekindal, M. (2020). Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods. Eurasian Journal of Medicine and Oncology, 4(2), 160–165. https://doi.org/10.14744/ejmo.2020.28273
Yonar, H., Yonar, A., Tekindal, M., Çevrimli, M. B., Alkan, H., İnanç, Z. S., … Tekindal, M. A. (2020). Analyzing COVID-19 outbreak for Turkey and Eight Country with Curve Estimation Models, Box-Jenkins (ARIMA), Brown Linear Exponential Smoothing Method, Autoregressive Distributed Lag (ARDL) and SEIR Models. Eurasian Journal of Veterinary Sciences, (Covid-19 Special Issue), 142–155. https://doi.org/10.15312/eurasianjvetsci.2020.304
Yudi, Y. (2018). Peramalan Penjualan Mesin Industri Rumah Tangga Dengan Metode Fuzzy Time Series Ruey Chyn Tsaur. Jurnal Informatika Kaputama (JIK), 2(1), 53–59. https://doi.org/10.59697/jik.v2i1.432
Zainul, M. (2019). Buku Manajemen Operasional. Deepublish (1st ed.). Yogyakarta: Penerbit Deepublish (Grup Penerbit CV BUDI UTAMA). Retrieved from www.deepublish.co.id
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