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All Journal Jurnal Informatika Upgris Jurnal Nasional Komputasi dan Teknologi Informasi JSAI (Journal Scientific and Applied Informatics) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem Komputer dan Informatika (JSON) Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian Journal of Artificial Intelligence and Digital Business Jurnal Ilmu Komputer dan Sistem Informasi Journal Of Software Engineering And Communication KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Nur Oktavin Idris
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Transportation

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Sistem Pendukung Keputusan Seleksi Tenaga Fasilitator Lapangan BSPS Menggunakan Metode Multi Factor Evaluation Process Nur Oktavin Idris; A. Mulawati Mas Pratama; Muliati Badaruddin
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5303

Abstract

Field facilitator workers shall guide the Self-Help Housing Stimulant Assistance Program (BSPS) recipients intensively, either in technical matters or the fund use realization process during construction terms. Guidance is given from planning, implementing a construction, and forming an assistance recipient community group that can construct decent housing independently. Accordingly, field facilitator workers are imperative to guide the BSPS recipient community. Public Works and Spatial Planning Office in Gorontalo is still selecting field facilitator workers manually, bringing on assessment errors in the selection process, e.g., selecting field facilitator workers that do not fulfill the requirement. Additionally, the sheer number of applicants overwhelms staff as they have to face off piling files of applicants. It brings about a longer selection process. As such, a systematic selection process based on the determined criteria is important. Later, it can act as a reference. It is called a decision-supporting system. Our decision-supporting system aims to help make decisions to select field facilitator workers guiding the BSPS recipient community using the Multi-Factor Evaluation Process (MFEP) method. The results point out Bambang achieved the highest final score of 87.675. Hence, MFEP in the supporting system is a recommended method to make decisions to select field facilitator workers guiding the BSPS recipient community in building decent housing.
Evaluasi Model Machine Learning untuk Prediksi Harga Mobil dengan Perbandingan Ensemble dan Regresi Linear Nur Oktavin Idris; Fuad Pontoiyo
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 1 (2025): Januari 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i1.181

Abstract

Car price prediction is a major challenge in the automotive industry because it is influenced by various factors, such as technical specifications, fuel type, and transmission system. This research aims to evaluate and compare the performance of linear regression models and ensemble learning methods, namely Random Forest and Gradient Boosting, in predicting car prices. The dataset used comes from Kaggle, with 11,914 rows of data and 16 features. The research process includes the stages of data understanding, data preparation, modeling, and evaluation using the Mean Squared Error (MSE) and R-squared (R²) metrics. The research results show that the Gradient Boosting model has the best performance, with an R² value of 0.963868 and the lowest MSE compared to other models, followed by Random Forest with an R² of 0.899657. In contrast, linear regression showed lower performance, with an R² of 0.417905, indicating its limitations in handling non-linear relationships in the data. The prediction results from the best model show price estimates that are quite close to actual prices, although some improvements still need to be made through hyperparameter optimization. This research confirms that ensemble learning methods, especially Gradient Boosting, provide a more effective approach to predicting car prices than linear regression. This model has the potential to be applied in the automotive industry to improve the accuracy of vehicle price estimates for manufacturers, dealers, and consumers.
Analisis Regresi Linear dan Ensemble Learning Berbasis Kontribusi Fitur dalam Prediksi Harga Mobil Listrik Nur Oktavin Idris; Fuad Pontoiyo
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9891

Abstract

This study aims to analyze the performance of linear regression and ensemble learning methods in predicting electric vehicle prices based on technical specifications, as well as to examine the contribution of key features to the prediction results. The main challenge in electric vehicle price prediction lies in the high price variability driven by nonlinear relationships among technical attributes, which are difficult to capture using simple linear models. Linear regression was employed as a baseline model, while Random Forest and Gradient Boosting were used as ensemble learning approaches. The dataset was obtained from Kaggle and processed through data cleaning, categorical encoding, normalization, and an 80:20 train–test split. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R²). The results indicate that the Gradient Boosting model achieved the best performance, with an MSE of 8.63 and an R² of 0.891, outperforming both Random Forest and linear regression models. Feature contribution analysis reveals that vehicle acceleration time is the most influential factor in determining electric vehicle prices. These findings demonstrate that ensemble learning not only improves predictive accuracy but also provides analytical insights into the key technical factors shaping electric vehicle pricing.
Pemberdayaan Masyarakat melalui Sistem Informasi Digital Wisata Hiu Paus untuk Mendukung Pengelolaan Pariwisata Berkelanjutan di Desa Botubarani Nur Oktavin Idris; Rosbin Pakaya; Moh. Hidayat Koniyo; Fuad Pontoiyo
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 6 No. 2 (2026): Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v6i2.11942

Abstract

The whale shark ecotourism potential in Botubarani Village is an economic pillar for the local community, yet its management remains conventional. This triggers disorderly boat queues, vulnerable revenue-sharing records, and suboptimal conservation education. This community service aims to empower managers through the development of a website-based Digital Information System. The implementation method includes needs analysis (observation and interviews), ERD design, system development, implementation, and training. The result is a comprehensive digital platform featuring public information, digital registration, whale shark monitoring, and tourism management. The system's implementation has successfully reduced ticket service time, organized boat queues, and made financial data recapitulation more transparent. Evaluation using a Likert scale shows a very high level of user satisfaction regarding ease of use, interface design, and information clarity. In conclusion, this digital transformation strengthens effective, transparent, and sustainable ecotourism governance in Botubarani Village.
Ensemble Learning Models for Energy Efficiency Prediction in Smart Sustainable Systems Nur Oktavin Idris; Fuad Pontoiyo
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.11724

Abstract

The growing demand for energy-efficient buildings requires accurate predictive models to support sustainable development. However, conventional prediction approaches often struggle to capture the complex nonlinear relationships between building design characteristics and energy consumption, limiting prediction accuracy for practical building energy management. Therefore, developing more reliable predictive models has become an important challenge in supporting data-driven energy management. This study implemented and evaluated machine learning models, specifically ensemble learning methods, to predict building energy efficiency based on building design parameters. The study aimed to compare the predictive performance of conventional regression and ensemble learning models for estimating heating and cooling loads while identifying the most influential building design parameters affecting energy efficiency. The proposed methodology consisted of data preprocessing, model development using Linear Regression, Random Forest, and Gradient Boosting, followed by performance evaluation using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²). The results indicate that the ensemble learning models substantially outperform Linear Regression in predicting both heating and cooling loads. Random Forest achieved the best performance for heating load prediction, while Gradient Boosting performed best for cooling load prediction. Feature importance analysis showed that geometric parameters, including relative compactness, overall height, and surface area, had the greatest influence on energy efficiency. These findings demonstrate that ensemble learning not only improves prediction accuracy but also enhances model interpretability through feature importance analysis, providing valuable support for data-driven decision-making in smart and sustainable building systems.
Co-Authors Adam, Novita Andi Mulawati Mas Pratama Badaruddin, Muliati Farid Farid Farid Farid, F. Fuad Pontoiyo Maryati Kadir Thalib Moh. Hidayat Koniyo Nurjana Suleman Rapika Anwar Rosbin Pakaya Satriadi D. Ali Sri Oktavia Dai Suleman, Fitriyanti Suleman, Nurjana Utiarahman, Siti Andini Wiwik Handayani Zufrianto K Dunggio