PREDIKSI HARGA DAGING SAPI DI KOTA JAKARTA PUSAT MENGGUNAKAN LSTM DAN GRU
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Abstract
This study analyzes the performance of two algorithms, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting data from the PIHPS website, focusing on beef commodity prices. The dataset was divided into two proportions: 80:20 and 70:30, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The experimental results showed that GRU with 128 units and a 70:30 proportion achieved the best performance, with metrics of MAE at 170, RMSE at 390.2889, and R² at 0.902. The goal of this research is to determine the most suitable algorithm and unit configuration for this dataset. Future research is expected to integrate additional data with more complex models to improve prediction accuracy.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Adithya Putra, F., & Handhayani, T. (2025). PREDIKSI HARGA DAGING SAPI DI KOTA JAKARTA PUSAT MENGGUNAKAN LSTM DAN GRU. Jurnal Informatika Progres, 17(1), 12-21. https://doi.org/10.56708/progres.v17i1.438
References
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[18] W. Li, H. Wu, N. Zhu, Y. Jiang, J. Tan, and Y. Guo, “Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU),” Information Processing in Agriculture, vol. 8, no. 1, pp. 185–193, 2021.
[19] E. Novarida, Y. P. Pasrun, and M. N. Sutoyo, “Implementasi Metode Time Series Simple Moving Average untuk Prediksi Penjualan Multi-Produk,” Journal of Information Systems and Technology, vol. 1, no. 1, pp. 1–6, 2024.
[20] I. Amansyah, J. Indra, E. Nurlaelasari, and A. R. Juwita, “Prediksi Penjualan Kendaraan Menggunakan Regresi Linear: Studi Kasus pada Industri Otomotif di Indonesia,” Innovative: Journal Of Social Science Research, vol. 4, no. 4, pp. 1199–1216, 2024.
[21] H. D. E. Sinaga and N. Irawati, “Perbandingan double moving average dengan double exponential smoothing pada peramalan bahan medis habis pakai,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 4, no. 2, pp. 197–204, 2018.
[22] R. Al Kiramy, I. Permana, A. Marsal, M. R. Munzir, and M. Megawati, “Perbandingan Performa Algoritma RNN dan LSTM dalam Prediksi Jumlah Jamaah Umrah pada PT. Hajar Aswad: Comparison of RNN and LSTM Algorithm Performance in Predicting the Number of Umrah Pilgrims at PT. Hajar Aswad,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 4, pp. 1224–1234, 2024.
[23] H. T. S. Harita and O. M. Siregar, “Pengaruh Direct Marketing Dan Product Quality Terhadap Minat Beli Ulang Pada Live Streaming Marketing Tiktok,” Jurnal Ekonomi Kreatif Dan Manajemen Bisnis Digital, vol. 1, no. 2, pp. 171–184, 2022.
[24] W. D. Febrian, Z. Zulhaida, and A. Ilosa, “Analisis Pengaruh Lingkungan Kerja Terhadap Kinerja Pegawai Bank Riau Kepri Syariah Pekanbaru,” Syarikat: Jurnal Rumpun Ekonomi Syariah, vol. 2, no. 2, pp. 77–87, 2019.
[2] N. D. L. Imani, T. Tarno, and B. A. Saputra, “PREDIKSI HARGA DAGING SAPI DI KABUPATEN BREBES MENGGUNAKAN PEMODELAN ARFIMA DENGAN EFEK GARCH,” Jurnal Gaussian, vol. 12, no. 4, pp. 570–580, 2024.
[3] D. Sapi, “VOLATILITAS DAN TRANSMISI HARGA DAGING SAPI DI INDONESIA: STUDI KASUS DI JAKARTA, BANDUNG, SEMARANG DAN SURABAYA”.
[4] A. I. Hasibuan and S. Mulatsih, “Covid-19 dan Disparitas Harga Daging Sapi Indonesia,” Policy Brief Pertanian, Kelautan, dan Biosains Tropika, vol. 4, no. 1, pp. 175–178, 2022.
[5] B. C. Saragih, “Analisis Faktor-Faktor Yang Mempengaruhi Permintaan Daging Sapi Di Provinsi Dki Jakarta,” Agrista, vol. 11, no. 2, 2023.
[6] A. F. Fajriaty, “Analisis Efisiensi Pemasaran Daging Sapi di Kabupaten Cirebon,” Agrista, vol. 12, no. 1, 2024.
[7] M. A. Sholeh, “Perbandingan Model LSTM Dan GRU Untuk Memprediksi Harga Minyak Goreng di Indonesia,” Edusaintek: Jurnal Pendidikan, Sains dan Teknologi, vol. 9, no. 3, pp. 800–811, 2022.
[8] M. Marwondo and T. Hidayah, “Perbandingan Algoritma Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Prediksi Harga Emas Dunia,” In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism), vol. 21, no. 2, pp. 230–239, 2022.
[9] A. P. Meriani and A. Rahmatulloh, “Perbandingan Gated Recurrent Unit (Gru) Dan Algoritma Long Short Term Memory (Lstm) Linear Refression Dalam Prediksi Harga Emas Menggunakan Model Time Series,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, 2024.
[10] M. F. Rizkilloh and S. Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 1, pp. 25–31, 2022.
[11] A. Hanafiah, Y. Arta, H. O. Nasution, and Y. D. Lestari, “Penerapan Metode Recurrent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) Untuk Prediksi Harga Saham,” Bulletin of Computer Science Research, vol. 4, no. 1, pp. 27–33, 2023.
[12] F. Shahid, A. Zameer, and M. Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,” Chaos Solitons Fractals, vol. 140, p. 110212, 2020.
[13] A. D. Milniadi and N. O. Adiwijaya, “Analisis Perbandingan Model Arima Dan Lstm Dalam Peramalan Harga Penutupan Saham (Studi Kasus: 6 Kriteria Kategori Saham Menurut Peter Lynch),” SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, vol. 2, no. 6, pp. 1683–1692, 2023.
[14] S. Sautomo and H. F. Pardede, “Prediksi belanja pemerintah Indonesia menggunakan long short-term memory (LSTM),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 99–106, 2021.
[15] D. A. Aquinaldo, “Perbandingan Metode Multivariatif GRU dan VAR Berdasarkan Sentimen Investor dan Nilai Kurs Dollar Untuk Prediksi Harga Saham,” Innovative: Journal Of Social Science Research, vol. 3, no. 6, pp. 10238–10257, 2023.
[16] E. N. Waroi, A. Arief, and K. Khusnawi, “Prediksi Harga Laptop Menggunakan Algoritma GRU dan BILSTM,” Jurnal Sosial Teknologi, vol. 4, no. 7, pp. 408–424, 2024.
[17] K. Danil, “Pengenalan Jenis Kelamin dalam Lingkungan Multiaksen Menggunakan Metode Multi Layer Perceptron (MLP) dan Gated Recurrent Unit (GRU): Gender Recognition in a Multiaccent Environment Using Multi Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) Methods,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 3, pp. 803–811, 2024.
[18] W. Li, H. Wu, N. Zhu, Y. Jiang, J. Tan, and Y. Guo, “Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU),” Information Processing in Agriculture, vol. 8, no. 1, pp. 185–193, 2021.
[19] E. Novarida, Y. P. Pasrun, and M. N. Sutoyo, “Implementasi Metode Time Series Simple Moving Average untuk Prediksi Penjualan Multi-Produk,” Journal of Information Systems and Technology, vol. 1, no. 1, pp. 1–6, 2024.
[20] I. Amansyah, J. Indra, E. Nurlaelasari, and A. R. Juwita, “Prediksi Penjualan Kendaraan Menggunakan Regresi Linear: Studi Kasus pada Industri Otomotif di Indonesia,” Innovative: Journal Of Social Science Research, vol. 4, no. 4, pp. 1199–1216, 2024.
[21] H. D. E. Sinaga and N. Irawati, “Perbandingan double moving average dengan double exponential smoothing pada peramalan bahan medis habis pakai,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 4, no. 2, pp. 197–204, 2018.
[22] R. Al Kiramy, I. Permana, A. Marsal, M. R. Munzir, and M. Megawati, “Perbandingan Performa Algoritma RNN dan LSTM dalam Prediksi Jumlah Jamaah Umrah pada PT. Hajar Aswad: Comparison of RNN and LSTM Algorithm Performance in Predicting the Number of Umrah Pilgrims at PT. Hajar Aswad,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 4, pp. 1224–1234, 2024.
[23] H. T. S. Harita and O. M. Siregar, “Pengaruh Direct Marketing Dan Product Quality Terhadap Minat Beli Ulang Pada Live Streaming Marketing Tiktok,” Jurnal Ekonomi Kreatif Dan Manajemen Bisnis Digital, vol. 1, no. 2, pp. 171–184, 2022.
[24] W. D. Febrian, Z. Zulhaida, and A. Ilosa, “Analisis Pengaruh Lingkungan Kerja Terhadap Kinerja Pegawai Bank Riau Kepri Syariah Pekanbaru,” Syarikat: Jurnal Rumpun Ekonomi Syariah, vol. 2, no. 2, pp. 77–87, 2019.