Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression

Aura Azzahra, M. Afdal, Mustakim Mustakim, Rice Novita

Abstract


PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.

Full Text:

PDF

References


T. Imron, R. S. S. Nazli, and S. Raharja, “Strategi Pengembangan Pemasaran Batu Andesit ( Studi Kasus pada PT Duta Keluarga Imfaco , Bogor Jawa Barat ),” Manaj. IKM, vol. 13, no. 2, pp. 127–136, 2018.

S. T. Simamora, S. C. Wahyono, and S. S. Siregar, “Identifikasi Batuan Andesit Menggunakan Metode Geolistrik 2D,” J. Fis. Unand, vol. 9, no. 4, pp. 487–494, 2020.

A. B. Santoso and H. Sidiq, “Perhitungan Sumberdaya Batuan Breksi Andesit Berdasarkan Ukuran Fragmen Dengan Menggunakan Metode Geolistrik (Studi Kasus Lahan 52 Ha, Desa Mekarsari, Kecamatan Merak, Kabupaten Cilegon, Provinsi Banten),” KURVATEK, vol. 2, no. 1, pp. 39–44, 2017.

H. J. Sebah and A. Nugroho, “Prediksi Tingkat Produksi Batu Quarry Andesite Dengan Metode ARIMA Artikel Ilmiah,” 2023.

S. Saadah, F. Z. Z, and H. H. Z, “Support Vector Regression (SVR) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR/USD,” J. Comput. Sci. Informatics Eng., vol. 5, no. 1, pp. 85–92, 2021, doi: 10.29303/jcosine.v5i1.403.

Q. Quan, Z. Hao, H. Xifeng, and L. Jingchun, “Research on water temperature prediction based on improved support vector regression,” Neural Comput. Appl., vol. 4, 2020, doi: 10.1007/s00521-020-04836-4.

R. E. Cahyono and J. P. Sugiono, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen,” J. Teknol. Inf. dan Multimed., vol. 1, no. 2, pp. 106–116, 2019, doi: 10.35746/jtim.v1i2.22.

S. Saadah, F. Z. Z, and H. H. Z, “Support Vector Regression ( SVR ) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR / USD,” J-COSINE, vol. 5, no. 1, pp. 85–92, 2021.

A. Widiarni and Mustakim, “Penerapan Algoritma Support Vector Regression dalam Memprediksi Produksi dan Produktivitas Kelapa Sawit,” J. MEDIA Inform. BUDIDARMA, vol. 7, no. April, pp. 864–872, 2023, doi: 10.30865/mib.v7i2.6089.

C. F. F. Purwoko, Sediono, T. Saifudin, and M. F. F. Mardianto, “Prediksi Harga Ekspor Non Migas di Indonesia Berdasarkan Metode Estimator Deret Fourier dan Support Vector Regression,” INFERENSI, vol. 6, no. 1, pp. 45–55, 2023, doi: 10.12962/j27213862.v6i1.15558.

P. Edastama, A. S. Bist, and A. Prambudi, “Implementation Of Data Mining On Glasses Sales Using The Apriori Algorithm,” Int. J. Cyber IT Serv. Manag., vol. 1, no. 2, pp. 159–172, 2021.

J. Yang et al., “Brief introduction of medical database and data mining technology in big data era,” WiILEY, no. August 2019, pp. 1–13, 2020, doi: 10.1111/jebm.12373.

E. A. Rady and A. S. Anwar, “Informatics in Medicine Unlocked Prediction of kidney disease stages using data mining algorithms,” Informatics Med. Unlocked, vol. 15, no. March, p. 100178, 2019, doi: 10.1016/j.imu.2019.100178.

A. Rahmadeyan and Mustakim, “Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19,” J. Nas. Teknol. dan Sist. Inf., vol. 9, no. 1, pp. 21–32, 2023, doi: 10.25077/TEKNOSI.v9i1.2023.21-32.

P. Bhola and S. Bhardwaj, “Estimation of solar radiation using support vector regression,” J. Inf. Optim. Sci., vol. 40, no. 2, pp. 339–350, 2019, doi: 10.1080/02522667.2019.1578093.

Mustakim, A. Buono, and I. Hermadi, “Performance Comparison Between Support Vector Regression And Artificial Neural Network For Prediction Of Oil Palm Production,” J. Ilmu Komput. dan Inf. (Journal Comput. Sci. Information), vol. 1, no. 9, pp. 1–8, 2016.

Y. Yang, J. Che, C. Deng, and L. Li, “Sequential grid approach based support vector regression for short-term electric load forecasting,” Appl. Energy, vol. 238, no. January, pp. 1010–1021, 2019, doi: 10.1016/j.apenergy.2019.01.127.

S. Zahara, Sugianto, and M. B. Ilmiddafiq, “Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 357–363, 2021.

L. Wiranda and M. Sadikin, “Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk PT . Metiska Farma,” J. Nas. Pendidik. Tek. Inform., vol. 8, no. 3, pp. 184–196, 2019.

N. M. Asrah, M. E. Nor, S. N. A. Rahim, and W. K. Leng, “Time Series Forecasting of the Number of Malaysia Airlines and AirAsia Passengers,” J. Phys. Conf. Ser., vol. 995, no. 1, 2018, doi: 10.1088/1742-6596/995/1/012006.

D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput. J., vol. 97, p. 105524, 2019, doi: 10.1016/j.asoc.2019.105524.

R. Ocviani, Mustakim, Rusliyawati, M. Muharrom, I. Ahmad, and Sepriano, “Classification of Student Graduation Using

Backpropagation Neural Network with Features Selection and Dimensions Reduction,” in 2023 5th International Conference on Cybernetics and Intelligent System (ICORIS), 2023, pp. 1–5. doi: 10.1109/ICORIS60118.2023.10352284.

S. Pratista, A. Nazir, I. Iskandar, E. Budianita, and I. Afrianty, “Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 456–465, 2023, doi: 10.47065/bits.v5i2.4260.

S. Agnesti, A. Nazir, I. Iskandar, E. Budianita, and I. Afrianty, “Perbandingan Algoritma Triple Exponential Smoothing dan Support Vector Regression dalam Prediksi Pemakaian Obat di Puskesmas,” Sist. J. Sist. Inf., vol. 12, no. 3, pp. 996–1006, 2023.




DOI: https://doi.org/10.32520/stmsi.v13i5.4155

Article Metrics

Abstract view : 31 times
PDF - 11 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.