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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jambura Journal of Food Technology JSAI (Journal Scientific and Applied Informatics) JAGO TOLIS : Jurnal Agrokompleks Tolis Journal of Artificial Intelligence and Digital Business Jurnal Abdimas Terapan Jurnal Ilmu Komputer dan Sistem Informasi Journal Of Renewable Energy Engineering KETIK : Jurnal Informatika Journal Of Software Engineering And Communication Journal Of Fisheries Agribusiness Jurnal Cendekia Ilmiah PESHUM KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Kendali Teknik dan Sains JTPG (Jurnal Teknologi Pertanian Gorontalo)
Fuad Pontoiyo
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Physics Social Sciences Transportation Other

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Digitalisasi Koperasi Merah Putih dan Sistem Informasi Berbasis Web Untuk Meningkatkan Partisipasi Program Keluarga Berkualitas di Desa Tiohu Rosbin Pakaya; Nur Oktavin Idris; Fuad Pontoiyo
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 5 No. 2 (2025): Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v5i2.6410

Abstract

Optimization of participation in the quality family program and the digitalization of the Merah Putih cooperative in Tiohu Village is the main focus of this community service, based on the challenge of low public participation due to limited access to information and the still manual recording process. This study aims to develop a web-based information system to support the quality family program while also digitizing the cooperative, in order to sustainably increase community involvement in the village’s social and economic services. The methodology applied includes needs identification, system design and development using the PHP programming language and MySQL database, followed by socialization, participatory training, and monitoring and evaluation. The results show that the implemented digital system has succeeded in accelerating access to information, strengthening the service functions of the quality family program, and encouraging the cooperative’s economic independence. Implicitly, this activity contributes to the development of a digital technology-based empowerment model that is adaptive to the local context, while also providing a tangible impact in improving management efficiency and community participation
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.
Rancang Bangun Aplikasi Pemesanan Lapangan Olahraga Berbasis Dekstop dengan Pendekatan Object Oriented Programming Moh. Anggriawan Arif; Idris, Nur Oktavin; Pontoiyo, Fuad
Jurnal Kendali Teknik dan Sains Vol. 4 No. 1 (2026): Januari: Jurnal Kendali Teknik dan Sains
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jkts-widyakarya.v4i1.5983

Abstract

Manual management of sports field bookings is still widely practiced and often leads to scheduling conflicts, data recording errors, and low service efficiency. This study aimed to design and develop a desktop-based sports field booking application that automates the booking process and manages schedules in a structured manner. The research employed a system design and development method using an object-oriented programming (OOP) approach. Data were collected through direct observation of the booking process, interviews with field managers, and documentation of system requirements. The application was developed using the Python programming language with the PyQt5 framework for the graphical user interface and MySQL as the database management system. The results showed that the developed application is capable of managing field data, schedules, bookings, and user information in an integrated manner while reducing recording errors and minimizing scheduling conflicts. The application of OOP resulted in a modular, well-organized, and maintainable system structure. This application is expected to improve the efficiency and accuracy of sports field booking management and provide a practical solution for implementing a computerized booking system.
PEMANFAATAN PENGERING EFEK RUMAH KACA DALAM MENINGKATKAN KUALITAS KERUPUK KASUBI LONUO BUKIT ARANG Fuad Pontoiyo; Burhan Liputo; Yunita Djamalu
Jurnal Abdimas Terapan Vol. 4 No. 1 (2024): JURNAL ABDIMAS TERAPAN (NOVEMBER)
Publisher : Program Vokasi Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jat.v4i1.61

Abstract

Lonuo Village is located in Tilongkabila District, Bone Bolango Regency, Gorontalo. Kasubi crackers are a typical snack produced by one of the Lonuo Bukit Arang Kasubi Crackers UKMs. Kasubi crackers are made from cassava, tapioca flour, baking soda, and brown sugar, while other production requirements are cooking oil, LPG gas, gasoline, firewood, raffia rope, and plastic packaging. Meanwhile, other supporting tools in the kasubi cracker production process are plastic plates, steamer frames, steaming pans, molding tanks, sweet potato grinding machines, and para-paras as attachments for manual drying. The manual drying place used by IKM consists of 3 bamboo-based lamps measuring 7 x 13 meters for the entire cracker drying place. The drying process using bamboo sheets has many disadvantages, including drying time which takes 4 to 5 drying hours in sunny weather, unpredictable weather, less hygienic, and tends to be contaminated with bacteria because the location of the IKM is opposite the location of the Final Processing Site (TPA) for waste disposal. Bone Bolango Regency. The Kasubi Lonuo Bukit Arang cracker IKM was established in 2000 and has been continued by Santian Pillow since 2020. This IKM has more than 6 (six) permanent and non-permanent employees with daily raw material production of 1 () sack of cassava Working time from grating the coconut to drying takes 8 to 9 hours, namely from 08.00 to 15.00 WITA with the resulting cracker output being 2448 crackers per day and packaged in one hanging containing 10 crackers and priced at IDR. 11,000 per hanging. The method used in this activity is the preparation stage, implementation of activities, and program sustainability plans. The alternative drying tool that will be socialized in this activity is a greenhouse drying tool in the form of a rectangular prism with the help of energy from sunlight. While serving this community group, the service team packages activities from lecture presentations on introducing tools, questions, and answers, how to make tools, how to use tools, and how to maintain tools. From the results of this activity, it was agreed that the next activity would focus more on making cracker products using biomass stoves or stoves fueled by used oil and greenhouse effect dryers.
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.
INOVASI TEKNOLOGI SURYA GENERASI BARU UNTUK KONVERI ENERGI, INTEGRASI TERMAL, PENGERINGAN, DESALINASI, DAN BANGUNAN CERDAS Sarinah Pakpahan; Fuad Pontoiyo; Jamal Darusalam Giu
Journal Of Renewable Energy Engineering Vol. 4 No. 1 (2026): Journal Of Renewable Energy Engineering (April)
Publisher : Program Vokasi-Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jree.v4i1.68

Abstract

This article presents a comprehensive Systematic Literature Review (SLR) of cutting-edge advancements in solar energy technology, mapping the paradigm shift from single-component efficiency to multi-functional system integration. Utilizing a data corpus from JREE and PLTS comprising high-repute publications from the 2023–2026 period, this study identifies five primary innovation clusters: (1) optimization of photovoltaic (PV) systems and perovskite solar cells; (2) integration of thermal energy storage utilizing Phase Change Materials (PCM); (3) hybrid solar drying applications for the agro-industrial sector; (4) desalination technologies based on interfacial evaporation; and (5) smart building implementation via data-driven control systems. In response to critical reviews, this article strengthens its methodological foundation by providing technical justification for selecting the data corpus, ensuring it is representative of global trends, and mitigating the limitations of abstract-based analysis through an in-depth examination of key studies. Specific focus is placed on the role of Artificial Intelligence (AI), as this review transitions from a descriptive narrative to a systematic analysis of the requirements for interpretable and robust models in system monitoring. The analytical results indicate that the synergy between advanced materials such as nanofluids and metamaterials—and multi-objective optimization algorithms is pivotal to achieving decarbonization targets. Overall, this review provides a roadmap for researchers in developing solar systems that are not only technically efficient but also economically viable and sustainable, supported by a comprehensive reference list that reinforces the reliability of this data synthesis.
ANALISIS PERFORMA DAN EVALUASI CAPACITY FACTOR PADA PEMBANGKIT LISTRIK TENAGA SURYA (PLTS) 2 MWp SUMALATA GORONTALO Gabriel Stella Sinay; Mohammad Lanto Kamil Amali; Fuad Pontoiyo; Sarinah Pakpahan
Journal Of Renewable Energy Engineering Vol. 4 No. 1 (2026): Journal Of Renewable Energy Engineering (April)
Publisher : Program Vokasi-Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jree.v4i1.84

Abstract

Solar Power Plants (PLTs) are among the main pillars of Indonesia's renewable energy transition, especially in regions with high solar irradiation potential, such as Gorontalo. This study aims to evaluate the operational performance of the 2 MWp PLTS in Sumalata by calculating the Capacity Factor (CF). The data used includes monthly energy production and solar irradiation during the 2024 operational period. The research method is descriptive-quantitative, calculating the ratio between actual energy output and maximum installed capacity. The results showed that the highest energy production occurred in September at 247,640 kWh, while the lowest was in December at 124,200 kWh. The average Capacity Factor obtained was 15.03%. This value is significantly influenced by weather fluctuations, cloud cover, and solar panel cleanliness (soiling). Although the CF value is within the standard range for PLTS in tropical regions, optimization in preventive maintenance and regular panel cleaning is required to improve system efficiency. This study concludes that the Sumalata PLTS makes a vital contribution to the regional energy mix, but its efficiency remains highly dependent on local meteorological conditions.
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.
Agricultural Technology in Climate-Smart Maize Systems: Integrating Digital, Agronomic, Biological, and Inclusive Innovations Yunita Djamalu; Jumiati Ilham; Fuad Pontoiyo; Lanto Mohamad Kamil Amali
JTPG (Jurnal Teknologi Pertanian Gorontalo) Vol 11 No 1 (2026): JTPG (May)
Publisher : PROGRAM STUDI MESIN DAN PERALATAN PERTANIAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30869/jtpg.v11i1.1637

Abstract

Agricultural technology is reshaping maize systems, yet its contribution to sustainable intensification depends less on the novelty of individual tools than on their integration across genetics, sensing, agronomy, ecology, institutions, and value chains. This structured critical review synthesizes 70 Scopus-indexed studies published from 2021 to 2025 to assess how recent technologies influence productivity, resource-use efficiency, climate resilience, environmental performance, and farmer welfare. The evidence shows rapid progress in unmanned aerial vehicle imaging, hyperspectral sensing, machine learning, crop modeling, variable-rate management, precision irrigation, and digital extension. These tools improve diagnosis and prediction, but their agronomic value is realized only when linked to actionable management rules. Conservation agriculture, subsurface drip fertigation, optimized nitrogen placement, controlled-release fertilizers, biochar-based amendments, microbial inoculants, and stress-targeted nanomaterials can raise yield or reduce environmental burdens, although performance is strongly conditioned by soil, climate, formulation, and management. Genomic prediction, high-density genotyping, gene editing, and high-throughput phenotyping are also converging toward environment-specific breeding. Across smallholder contexts, adoption is shaped by profitability, credit, market access, organizational membership, risk, and the compatibility of technologies as a package; information alone is rarely sufficient. Important trade-offs include microplastic accumulation from film mulching, nitrate displacement after ammonia-control interventions, uncertain nanoparticle safety, and digital model transferability. The review proposes an integrated framework in which sensing, prediction, intervention, verification, and institutional delivery operate as a closed decision loop. Future research should prioritize interoperable data, multi-location validation, whole-system environmental accounting, affordability, and co-designed technology bundles that deliver measurable gains under real farm constraints across diverse production regions.
Co-Authors Abdul Haris PanaI Ahmad Faqih Ahmad Faqih Ahmad Faqih Ardiyanto Saleh Modjo Burhan Liputo Dewi Wahyuni K. Baderan Farid Farid Gabriel Stella Sinay Hasim Hasim Hasim Hasim Hasim, H Iswan Dunggio Jamal Darusalam Giu Jumiati Ilham La Alio La Alio Lanto Mohamad Kamil Amali Mahludin H. Baruwadi Marini Susanti Hamidun Moh. Anggriawan Arif Moh. Hidayat Koniyo Mohammad Lanto Kamil Amali Muh. Arfah Syam Mukhtar, Sigit Adi Nur Oktavin Idris Ramli Utina Rosbin Pakaya Sarinah Pakpahan Sukirman Rahim Syam, Muh Arfah Syam, Muh. Arfah Yunita Djamalu