Published: 2025-07-01
Prediksi Potensi Suatu Wilayah Menggunakan Machine Learning
DOI: 10.35870/jtik.v9i3.3480
Tarwoto, Ratri Ismayanti, Vita Dwi Utami, Vellyn Chalista Elfanza
- Tarwoto: Universitas Amikom Purwokerto , Indonesia
- Ratri Ismayanti: Universitas Amikom Purwokerto , Indonesia
- Vita Dwi Utami: Universitas Amikom Purwokerto , Indonesia
- Vellyn Chalista Elfanza: Universitas Amikom Purwokerto , Indonesia
Article Metrics
- Views 0
- Downloads 0
- Scopus Citations
- Google Scholar
- Crossref Citations
- Semantic Scholar
- DataCite Metrics
-
If the link doesn't work, copy the DOI or article title for manual search (API Maintenance).
Abstract
Electricity has become a basic need for some human beings because all activities are almost related to electricity. Indonesia has several power plant projects and the largest power plant is generated from PLTU which can have an impact that we feel is greenhouse gas emissions and bad air pollution and also relies heavily on coal while the natural resources are not renewable with this fact if we reduce the use of coal it will be a boomerang for Indonesia itself. This research aims to predict areas that can potentially become Solar Power Plants with a machine learning regression model approach. So hopefully this research can be a reference in the development of Solar Power Plants in Indonesia. The methods used are Linear Regression (LR), Lasso Regression (LR), Ridge Regression (RR), and Support Vector Regression (SVR). The R2 coefficients for solar radiation were 0.924; 0.910; 0.917; 0.949; and 0.987, respectively.
Keywords
Liniear Regression ; Lasso Regression ; Ridge Regression ; Support Vector Regression
Article Metadata
Peer Review Process
This article has undergone a double-blind peer review process to ensure quality and impartiality.
Indexing Information
Discover where this journal is indexed at our indexing page to understand its reach and credibility.
Open Science Badges
This journal supports transparency in research and encourages authors to meet criteria for Open Science Badges by sharing data, materials, or preregistered studies.
How to Cite
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 3 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i3.3480
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Tarwoto
Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.
Ratri Ismayanti
Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.
Vita Dwi Utami
Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.
-
-
-
-
Caraka, R. E., Yasin, H., & Basyiruddin, A. W. (2017). Peramalan crude palm oil (CPO) menggunakan support vector regression kernel radial basis. J. Mat, 7(1), 43. https://doi.org/10.24843/jmat.2017.v07.i01.p81.
-
Faadhilah, A., & Nugroho, H. (2024). Pemetaan Daerah Rawan Longsor di Kabupaten Bandung Barat menggunakan Metode Machine Learning dengan Teknik SVM. Rekayasa Hijau: Jurnal Teknologi Ramah Lingkungan, 8(2), 185-199. https://doi.org/10.26760/jrh.v8i2.185-199.
-
Fitrianah, D., Dwiasnati, S., & Baihaqi, K. A. (2021). Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes. Faktor Exacta, 14(2), 92-99. http://dx.doi.org/10.30998/faktorexacta.v14i2.9297.
-
Gufron, H., Rusirawan, D., & Widyawati, L. (2022). Forecasting Produksi Energi PLTS 1 kWp Menggunakan Mesin Pembelajaran Dengan Algoritma Support Vector Machine. Jurnal Tekno Insentif, 16(2), 79-91. https://doi.org/10.36787/jti.v16i2.843.
-
-
Isnaeni, A. Y., & Prasetyo, S. Y. J. (2022). Klasifikasi Wilayah Potensi Risiko Kerusakan Lahan Akibat Bencana Tsunami Menggunakan Machine Learning. Jurnal Teknik Informatika Dan Sistem Informasi, 8(1), 33-42. https://doi.org/10.28932/jutisi.v8i1.4056.
-
-
Muzakir, A., & Wulandari, R. A. (2016). Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree. Scientific Journal of Informatics, 3(1), 19-26. https://doi.org/10.15294/sji.v3i1.4610.
-
Noor, M. A., Batubara, R. G., Sewoyo, K. C., Fikri, B. A., & Ardalova, R. (2022). Analisis Prediksi Investasi dengan Machine Learning dan Determinan Investasi di Tingkat Regional Provinsi Indonesia. Jurnal Manajemen Perbendaharaan, 3(2), 90-115. https://doi.org/10.33105/jmp.v3i2.414.
-
Suthar, R., Abhijith, T., Sharma, P., & Karak, S. (2023). Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells. Solar Energy, 250, 119-127. https://doi.org/10.1016/j.solener.2022.12.029.
-
-
Wang, H., Liu, Y., Zhou, B., Li, C., Cao, G., Voropai, N., & Barakhtenko, E. (2020). Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Conversion and Management, 214, 112909. https://doi.org/10.1016/j.enconman.2020.112909.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.