Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit
-
Raja Indra Ramoza
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Siska Kurnia Gusti Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Lestari Handayani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Siti Ramadhani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.
Downloads
References
Fackrurrozi, A. Junaedi, and D. Derajat Matra, “Manajemen Pemanenan Kelapa Sawit (Elaeis guineensis Jacq.) di Kebun Rambutan, Serdang Bedagai, Sumatera Utara,” Bul. Agrohorti, vol. 7, no. 3, pp. 319–328, 2019, doi: 10.29244/agrob.v7i3.30259.
F. Insani, I. Harani, S. Sanjaya, and Yusra, “Peramalan Produksi Tandan Buah Segar (TBS) Kelapa Sawit dengan Regresi Linear Dan Algoritma Genetik (Studi Kasus : PT. Peputra Masterindo),” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 11, 2019, no. 11, pp. 262–269. [Online]. Available: https://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/7910
F. Irawan, S. Sumijan, and Y. Yuhandri, “Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average,” J. Inf. dan Teknol., vol. 3, no. 4, pp. 251–256, 2021, doi: 10.37034/jidt.v3i4.162.
Amriana, A. A. Kasim, and Maghfirat, “Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic,” Ilk. J. Ilm., vol. 12, no. 3, pp. 236–244, 2020, doi: 10.33096/ilkom.v12i3.619.236-244.
J. Adhiva, Mustakim, S. A. Putri, and S. G. Setyorini, “Prediksi Hasil Produksi Kelapa Sawit Menggunakan Model Regresi pada PT . Perkebunan Nusantara V,” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), 2020, pp. 155–162.
M. Rifqi and Suharjito, “Deteksi Kematangan Tandan Buah Segar (TBS) Kelapa Sawit Berdasarkan Komposisi Warna Menggunakan Deep Learning,” J. Tek. Inform., vol. 14, no. 2, pp. 125–134, 2021, doi: 10.15408/jti.v14i2.23295.
S. R. Widyawati, R. Efendi, N. P. Riau, S. D. Riantiza, Z. Aini, and R. Susanti, “Model Fuzzy Linear Regression untuk Peramalan Produksi Kelapa Sawit ( Studi Kasus : PT. Perkebunan III Medan ),” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), 2020, pp. 582–590. [Online]. Available: http://repository.uin-suska.ac.id/id/eprint/64283
Ipan, Syaripuddin, and D. A. Nohe, “Perbandingan Model Chen Dan Model Lee pada Metode Fuzzy Time Series untuk Peramalan Produksi Kelapa Sawit Provinsi Kalimantan Timur,” in Prosiding Seminar Nasional Matematika dan Statistika, 2022, pp. 81–95. [Online]. Available: http://jurnal.fmipa.unmul.ac.id/index.php/SNMSA/article/view/899
R. N. Puspita, “Perbandingan Metode Double Exponential Smoothing Dan Triple Exponential Smoothing Pada Peramalan Nilai Ekspor Di Indonesia,” Jambura J. Probab. Stat., vol. 3, no. 2, pp. 141–150, 2022, doi: 10.34312/jjps.v3i2.15590.
G. A. N. Pongdatu, E. Abinowi, and S. Wahyuddin, “Peramalan Transaksi Penjualan dengan Metode Holt-Winter ’ S Exponential Smoothing,” J. Ilm. Teknol. Inf. Terap., vol. 6, no. 3, pp. 228–233, 2020, doi: 10.33197/jitter.vol6.iss3.2020.438.
Alfajriani, M. Wati, and N. Puspitasari, “Penerapan Metode Fuzzy Time Series Chen dan Hsu dalam Memprediksi Kunjungan Wisatawan di Museum Mulawarman,” JURTI J. Rekayasa Teknol. Inf., vol. 4, no. 2, pp. 144–153, 2020, doi: 10.30872/jurti.v4i2.5802.
A. D. Selasakmida, Tarno, and T. Wuryandari, “Perbandingan Metode Double Exponential Smoothing Holt dan Fuzzy Time Series Chen untuk Peramalan Harga Paladium,” J. Gaussian, vol. 10, no. 3, pp. 325–336, 2021, doi: 10.14710/j.gauss.10.3.325-336.
M. Brilliant, K. Lestari, and H. Oktaria, “Peramalan Pola Jumlah Nasabah Menggunakan Metode Arima, Holt-Winters Exponential Smoothing, Fuzzy Time Series (Study Kasus: PT.AIA Sunrise Agency),” J. Softw. Eng. Technol., vol. 2, no. 2, pp. 8–18, 2022, [Online]. Available: https://journal.instidla.ac.id/index.php/seat/article/view/61
A. Yunus, M. Akbar, and Andri, “Data Mining Untuk Memprediksi Hasil Produksi Buah Sawit pada PT Bumi Sawit Sukses (BSS) Menggunakan Metode K-Nearest Neighbor,” Bina Darma Conf. Comput. Sci., pp. 198–207, 2019.
I. W. A. S. Darma, I. P. E. G. Gunawan, and N. P. Sutramiani, “Peramalan Jumlah Kunjungan Wisatawan Menggunakan Triple Exponential Smoothing,” J. Ilm. Merpati Menara Penelit. Akad. Teknol. Inf., vol. 8, no. 3, pp. 211–221, 2020, doi: 10.24843/JIM.2020.v08.i03.p06.
M. H. Nasution, S. Anwar, A. Fitri, and A. F. Zohra, “Peramalan Jumlah Ikan Tuna/Madidihang ( Yellowfin tuna ) yang Didaratkan di PPS Kutaraja Kota Banda Aceh dengan Metode Triple Exponential Smoothing,” Samakia J. Ilmu Perikan., vol. 10, no. 1, pp. 8–14, 2019, doi: 10.35316/jsapi.v10i1.231.
Rahmawati, Yuniza, A. Novia, and Zukrianto, “Prediksi Jumlah Wisatawan di Kota Pekanbaru pada Tahun 2019- 2023 Dengan Menggunakan Metode Fuzzy Time Series Chen,” THETA J. Pendidik. Mat., vol. 2, no. 1, pp. 36–44, 2020, [Online]. Available: https://journal.umbjm.ac.id/index.php/THETA/article/view/512
D. N. Adli, “Prediksi Harga Jagung Menggunakan Metode Fuzzy Time Series Dengan Atau Tanpa Menggunakan Markov Chain,” J. Nutr. Ternak Trop., vol. 4, no. 1, pp. 49–54, 2021, doi: 10.21776/ub.jnt.2021.004.01.6.
D. Suryani, W. I. S, and M. Y. Riandi, “Perencanaan Kebutuhan Penjualan Kayu pada TPK Rejosari KPH Malang Mengunakan Metode Triple Exponential Smoothing,” in Seminar Informatika Aplikatif Polinema, 2020, pp. 270–273. [Online]. Available: http://repota.jti.polinema.ac.id/353/
M. Layakana and S. Iskandar, “Penerapan Metode Double Moving Average dan Double Eksponential Smoothing dalam Meramalkan Jumlah Produksi Crude Palm Oil (CPO) pada PT Perkebunan Nusantara IV Unit Dolok Sinumbah,” KARISMATIKA Kumpul. Artik. Ilmiah, Inform. Stat. Mat. dan Apl., vol. 6, no. 1, pp. 44–53, 2020, doi: 10.24114/jmk.v6i1.19309.
A. Krisma, M. Azhari, and P. P. Widagdo, “Perbandingan Metode Double Exponential Smoothing Dan Triple Exponential Smoothing Dalam Parameter Tingkat Error Mean Absolute Percentage Error ( MAPE ) dan Means Absolute Deviation ( MAD ),” in Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi), 2019, no. I, pp. 81–87. [Online]. Available: https://e-journals.unmul.ac.id/index.php/SAKTI/article/view/2303
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit
Pages: 614-624
Copyright (c) 2023 Raja Indra Ramoza, Siska Kurnia Gusti, Lestari Handayani, Siti Ramadhani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
