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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Sosial dan Sains
Fadhlirobby, Faiz
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Astronomy Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN YOGYAKARTA Utari, Dina Tri; Palage, Ghalang Rambu Putera; Fadhlirobby, Faiz; Nuswantoro, Artheta Bimo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2765-2776

Abstract

Precise rainfall classification is most important for meteorological forecasting and disaster risk mitigation, particularly in regions such as Yogyakarta, which are vulnerable to extreme weather events. Although previous studies have examined rainfall classification through the lens of meteorological variables, a notable lack of research has systematically evaluated the effectiveness of diverse machine learning algorithms for categorizing rainfall types within this specific locale. This study aims to rectify this gap by incorporating essential weather variables, specifically temperature, humidity, atmospheric pressure, and precipitation, into predictive models that utilize K-Nearest Neighbors (KNN), decision trees, and logistic regression techniques. Among the evaluated models, the decision tree demonstrated the highest degree of accuracy across both training and testing datasets. An examination of feature significance indicated that precipitation emerged as the most pivotal variable, aligning with the fundamental physical mechanisms associated with rainfall. This study contributes significantly to the evolving field of weather informatics by illustrating the utility of machine learning approaches in classifying regional rainfall. However, the parameters of this research are limited to specific meteorological variables and do not account for spatial or temporal variations, which could potentially influence the model’s broader applicability. Future research endeavors could augment this framework by integrating remote sensing data and methodologies for spatiotemporal modeling.
Analysis of the Impact of Fiscal Incentives on the Development of Electric Vehicles in Indonesia's Green Economy Transition (2020-2024) Puspita, Dyah Hanum; Meshanayagi, Salsabila Raisha; Fadhlirobby, Faiz; Pasya, Revaya Rizqia; Pertiwi, Elyana Ade
Jurnal sosial dan sains Vol. 5 No. 10 (2025): Jurnal Sosial dan Sains
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsosains.v5i10.32563

Abstract

Accelerating the transition to a green economy is the main demand for developing countries such as Indonesia amid the increasing impact of climate change. This study analyzes the effectiveness of fiscal incentive policies regulated in Presidential Regulation No. 79 of 2023 on increasing the use of battery-based electric vehicles as part of the national clean energy strategy. The study was conducted using a quantitative descriptive approach with secondary data from the Central Statistics Agency, the Ministry of Energy and Mineral Resources, and the 2021–2030 Rencana Usaha Penyediaan Tenaga Listrik (RUPTL). The (PCA) method is used to assess sectoral contributions to variations in carbon emissions, while the ANOVA test is applied to identify significant differences between years. The results of the analysis show that there is a surge in the adoption of electric vehicles after the policy is implemented, but CO₂ emissions still fluctuate highly, especially in the industrial and electricity sectors. The highest score of the first key component (PC1) in 2021 indicates a pivotal point in the changing sectoral dynamics towards green transformation, but the misalignment in the following years highlights the need for cross-policy integration. Thus, the effectiveness of fiscal incentives is not enough to rely solely on the demand for electric vehicles but also requires energy system reform and the readiness of economic structures to adopt low-emission technologies.  
Co-Authors Elyana Ade Pertiwi Meshanayagi, Salsabila Raisha Nuswantoro, Artheta Bimo Palage, Ghalang Rambu Putera Pasya, Revaya Rizqia Puspita, Dyah Hanum Utari, Dina Tri