Analisis Prediksi Kecepatan Angin di Kabupaten Pekalongan dengan Algoritma Decision Tree Regression

Noviana Safira; Abdul Hakim Prima Yuniarto

doi:10.62048/qjms.v2i2.88

Authors

Noviana Safira Program Studi Fisika, Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan, Pekalongan, Indonesia
Abdul Hakim Prima Yuniarto Sekolah Tinggi Teknik Wiworotomo Purwokerto

DOI:

https://doi.org/10.62048/qjms.v2i2.88

Keywords:

Decision Tree, Machine Learning, PLTB, Predictions, Wind Speed

Abstract

The coastal characteristics of Pekalongan Regency indicate substantial wind potential, prompting the need for studies on renewable energy utilization. This study aims to forecast wind speed and estimate the electrical power that can be generated using a Decision Tree Regression algorithm. Eleven years of historical climate data (2013–2023) from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) were used to build the model. Evaluation results show a Mean Squared Error (MSE) of 4.108 and a coefficient of determination (R²) of 0.049, indicating the model has limited predictive performance. The 2024 wind speed forecast ranges from 3.8 to 7 m/s, with an average of 4.5 m/s. This wind speed translates into estimated electrical power ranging from 844 to 5,277 watts, averaging 1,599 watts per month, equivalent to a potential monthly energy output of 191.88 kWh. This study concludes that while there is potential for small-scale Wind Power Plant (PLTB) development, such as for public street lighting, the accuracy of the predictive model needs to be significantly improved for more critical applications.

References

Adistia, N. A., Nurdiansyah, R. A., Fariko, J., Vincent, V., & Simatupang, J. W. (2020). Potensi energi panas bumi, angin, dan biomassa menjadi energi listrik di Indonesia. TESLA: Jurnal Teknik Elektro, 22(2), 105. https://doi.org/10.24912/tesla.v22i2.9107

Andiyantama, M. Q., Zahira, I., & Irawan, A. (2021). Prediksi energi listrik kincir angin berdasarkan data kecepatan angin menggunakan LSTM. JITCE (Journal of Information Technology and Computer Engineering), 5(1), 1–7. https://doi.org/10.25077/jitce.5.01.1-7.2021

Ardiana, J. D. P., Y., & S. (2023). Analisis potensi energi angin sebagai PLTB di Pantai Watu Ulo Jember menggunakan data BMKG. Jurnal Pendidikan, Sains dan Teknologi, 2(4), 962–965. https://doi.org/10.47233/jpst.v2i4.1313

Chairunisa, G., Najib, M. K., Nurdiati, S., Imni, S. F., Sanjaya, W., Andriani, R. D., Henriyansah, Putri, R. S. P., & Ekaputri, D. (2024). Life expectancy prediction using decision tree, random forest, gradient boosting, and XGBoost regressions. Jurnal Sintak, 2(2), 71–82. https://doi.org/10.62375/jsintak.v2i2.249

Demolli, H., Dokuz, A. S., Ecemis, A., & Gokcek, M. (2019). Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management, 198, 111823. https://doi.org/10.1016/j.enconman.2019.111823

Fadila, M. S., Fitri, L., Dasuha, P., Fauzi, R., Matematika, P. S., Sains, F., & Selatan, L. (2024). Prediksi kecepatan angin harian Kota Bandar Lampung menggunakan recurrent neural network (RNN). Prosiding Seminar Nasional Sains dan Teknologi Seri 02, 1(2), 95–100.

Fahim, M., Sharma, V., Cao, T. V., Canberk, B., & Duong, T. Q. (2022). Machine learning-based digital twin for predictive modeling in wind turbines. IEEE Access, 10, 14184–14194. https://doi.org/10.1109/ACCESS.2022.3147602

Illahi, P. K., Viana, A. R., Permata, N. F. M., & Pratama, M. Y. (2023). Penerapan algoritma decision tree dan regresi linear untuk klasifikasi kanker payudara. SENTIMAS: Seminar Nasional Penelitian dan Pengabdian Masyarakat, 86–92. https://journal.irpi.or.id/index.php/sentimas

Karaman, Ö. A. (2023). Prediction of wind power with machine learning models. Applied Sciences (Switzerland), 13(20), 11455. https://doi.org/10.3390/app132011455

Puspita Sari, A., Hakim, E. A., Arman Prasetya, D., Arifuddin, R., & Dani, P. (2021). Sistem prediksi kecepatan dan arah angin menggunakan bidirectional long short-term memory. Seminar Keinsinyuran Program Studi Program Profesi Insinyur, 1(1), 6–16. https://doi.org/10.22219/skpsppi.v1i0.4196

Putri, I. P., Terttiaavini, T., & Arminarahmah, N. (2024). Analisis perbandingan algoritma machine learning untuk prediksi stunting pada anak. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(1), 257–265. https://doi.org/10.57152/malcom.v4i1.1078

Rahman, A., Sugiyarto, I., Faddillah, U., & Sudarsono, B. (2022). Prediksi kecepatan angin untuk mengetahui sumber energi alternatif menggunakan regresi ANN (Studi kasus Provinsi Aceh tahun 2022). Journal of Information Technology and Computer Science (INTECOMS), 5(2). https://journal.ipm2kpe.or.id/index.php/INTECOM/article/download/4990/2974

Retnoningsih, E., & Pramudita, R. (2020). Mengenal machine learning dengan teknik supervised dan unsupervised learning menggunakan Python. Bina Insani ICT Journal, 7(2), 156. https://doi.org/10.51211/biict.v7i2.1422

Ryan, M., & Alfiandy, S. (2022). Prediksi kecepatan angin 12 jam ke depan menggunakan automatic weather observing system (AWOS) berbasis regresi linear. Buletin GAW Bariri, 3(2), 1–7. https://doi.org/10.31172/bgb.v3i2.63

Nurjanah, S., Purbolingga, Y., Putri, D. M., Rahmawati, A., Fahrizal, F., & Akramunnas, B. W. (2024). Prediksi kecepatan angin untuk mengetahui potensi sumber energi alternatif menggunakan model regresi lasso: Studi kasus Kota Makassar pada tahun 2024. Jurnal Penelitian Rumpun Ilmu Teknik, 3(1), 278–288. https://doi.org/10.55606/juprit.v3i1.3501

Triyandi, E., Risma, P., Kusumanto, R., Dewi, T., & Oktarina, Y. (2021). Pembangkit energi listrik hybrid mini menggunakan turbin angin sumbu vertikal Savonius sebagai sumber energi alternatif. Journal of Applied Smart Electrical Network and Systems, 2(2), 49–56. https://doi.org/10.52158/jasens.v2i2.200

Wardhana, R. G., Wang, G., & Sibuea, F. (2023). Penerapan machine learning dalam prediksi tingkat kasus penyakit di Indonesia. Journal of Information System Management (JOISM), 5(1), 40–45. https://doi.org/10.24076/joism.2023v5i1.1136

Yeganeh-Bakhtiary, A., Eyvazoghli, H., Shabakhty, N., Kamranzad, B., & Abolfathi, S. (2022). Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios. Complexity, 2022, 8451812. https://doi.org/10.1155/2022/8451812

Yusuf, M., Setyanto, A., & Aryasa, K. (2022). Analisis prediksi curah hujan bulanan wilayah Kota Sorong menggunakan metode multiple regression. Jurnal Sains Komputer & Informatika (J-SAKTI), 6(1), 405–417.s