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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

How to Cite

Citation Format
Tarwoto, Ismayanti, R., Utami, V. D., & Elfanza, V. C. (2025). Prediksi Potensi Suatu Wilayah Menggunakan Machine Learning. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(3), 802-809. https://doi.org/10.35870/jtik.v9i3.3480

References

15 References
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Author Biographies

Tarwoto, Universitas Amikom Purwokerto

Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.

Ratri Ismayanti, Universitas Amikom Purwokerto

Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.

Vita Dwi Utami, Universitas Amikom Purwokerto

Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.

Vellyn Chalista Elfanza, Universitas Amikom Purwokerto

Program Studi Sistem Komputer, Fakultas Ilmu Komputer, Universitas Amikom Purwokerto, Kabupaten Banyumas, Provinsi Jawa Tengah, Indonesia.

License

Copyright (c) 2025 Tarwoto, Ratri Ismayanti, Vita Dwi Utami, Vellyn Chalista Elfanza

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

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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