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All Journal Rekayasa Mesin Jurnal Ilmiah FIFO JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) ScientiCO : Computer Science and Informatics Journal Cyber Security dan Forensik Digital (CSFD) Journal of Computer System and Informatics (JoSYC) Indonesia Berdaya International Journal of Engineering, Science and Information Technology Science Information System and Technology DIPANEGARA KOMPUTER TEKNOLOGI INFORMATIKA (DIPAKOMTI) Media of Computer Science Jurnal Pusat Kolaborasi Pengabdian Kepada Masyarakat Journal of Vocational, Informatics and Computer Education
Rima Ruktiari
Universitas Sembilanbelas November Kolaka
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Implementation of Green IT-Based Cloud Computing for Energy Efficiency in Technology Companies Judijanto, Loso; Wijaya, Hamid; Ismail, Rima Ruktiari; Vandika, Arnes Yuli
West Science Information System and Technology Vol. 3 No. 01 (2025): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v3i01.1846

Abstract

The growing demand for energy-efficient and sustainable solutions has positioned Green IT-based cloud computing as a pivotal strategy for technology companies aiming to balance operational efficiency with environmental stewardship. This study conducts a systematic literature review of 15 Scopus-indexed documents to explore the benefits, challenges, and strategies associated with the adoption of Green IT-based cloud computing. The findings reveal that these practices significantly enhance energy efficiency, reduce operational costs, and minimize carbon footprints. However, challenges such as high implementation costs, technological complexity, and intermittent renewable energy sources impede widespread adoption. Strategies including the use of AI and machine learning, collaborations with renewable energy providers, and the establishment of standardized policies are identified as effective solutions. This study contributes to the growing discourse on sustainable IT practices and provides a roadmap for technology companies aiming to integrate Green IT principles into their cloud computing operations.
FUEL-INJECTED MOTORCYCLE PERFORMANCE OPTIMIZATION UTILISING PERTALITE-ETHANOL BLENDS AND DEEP NEURAL NETWORK-BASED ECU FOR EFFICIENCY IMPROVEMENT AND EMISSION REDUCTION Yunus, La Ode Ichlas Syahrullah; Putri, Farika Tono; Ismail, Rima Ruktiari
Jurnal Rekayasa Mesin Vol. 16 No. 2 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i2.1946

Abstract

This study aims to optimize the performance of fuel-injected motorcycles through the application of a Deep Neural Network (DNN) in the Electronic Control Unit (ECU) and the use of ethanol-pertalite fuel blends. The ethanol blends used in the study were 0%, 5%, 10%, 15%, and 20%. Fuel consumption tests were conducted using the standard ECE/324 driving cycle, and emission tests were performed according to Euro 4 standards. Tests were conducted on a real track to evaluate fuel consumption performance and exhaust gas emissions. The results indicate that the 15% ethanol blend (E15) provided optimal engine efficiency, while the 20% ethanol blend (E20) resulted in the largest reduction in carbon monoxide (CO) and hydrocarbon (HC) emissions. Furthermore, the DNN model with 50 neurons and a Sigmoid activation function demonstrated the best balance between accuracy (R=0.9868) and generalization (MSE=0.3843) in optimizing ignition timing and injection timing. In conclusion, the ethanol blends and the application of DNN in the ECU have proven effective in enhancing fuel efficiency and reducing exhaust emissions, supporting the development of more sustainable transportation technologies.
Sistem Pakar Untuk Mendiagnosa Gangguan Somatisasi Menggunakan Metode K-Nearest Neighbors (KNN) Ismail, Rima Ruktiari; Wijaya, Hamid; Siregar, Juarni; Nugroho, Nurhasan
Jurnal Ilmiah FIFO Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i2.010

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem pakar yang mampu mendiagnosa gangguan somatisasi menggunakan metode K-Nearest Neighbors (KNN). Gangguan somatisasi merupakan kondisi psikologis yang sulit didiagnosis karena gejalanya yang bersifat fisik namun berasal dari masalah psikologis. Ketidakjelasan gejala ini sering kali mengarah pada pemeriksaan medis yang tidak diperlukan dan mahal, menambah beban bagi pasien dan sistem kesehatan. KNN dipilih karena kemampuannya untuk melakukan klasifikasi dengan membandingkan data uji dengan data pelatihan berdasarkan kedekatan menggunakan Euclidean Distance. Euclidean Distance digunakan untuk mengukur jarak terpendek antara dua titik dalam ruang fitur, yang dihitung dengan mengakar kuadrat dari jumlah perbedaan kuadrat antara nilai-nilai fitur dari dua titik tersebut. Hasil penelitian menunjukkan bahwa sistem pakar yang dikembangkan memiliki akurasi yang tinggi, yaitu mencapai 92,5%, yang mengindikasikan bahwa metode KNN dengan Euclidean Distance efektif dalam mendiagnosa gangguan somatisasi. Faktor-faktor seperti pemilihan nilai K yang optimal dan normalisasi data berperan penting dalam keberhasilan sistem ini. Kontribusi signifikan dari penelitian ini adalah pembuktian bahwa KNN dapat diimplementasikan secara efektif dalam sistem pakar untuk mendukung tenaga medis dalam melakukan diagnosis gangguan somatisasi dengan akurasi yang tinggi dan keandalan yang baik.
Optimization of Fertilizer and Pesticide Efficiency Based on Hybrid Artificial Intelligence to Increase Rice Production Hamid Wijaya; Rima Ruktiari; Rizal Fani
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.725

Abstract

Purpose – This study aims to develop a Hybrid Artificial Intelligence model integrating Artificial Neural Network (ANN) and Genetic Algorithm (GA) to optimize fertilizer and pesticide efficiency while improving rice production under diverse agricultural conditions. The research addresses the limitations of conventional agricultural input management, which often relies on generalized cultivation practices and leads to inefficient resource utilization and environmental degradation. Methods – The study employed a quantitative predictive and optimization-based experimental design using 1,080 rice cultivation records collected from Southeast Sulawesi Province, Indonesia. The dataset included agronomic and environmental variables such as soil pH, rainfall, pest infestation intensity, fertilizer dosage, pesticide dosage, and rice yield. ANN was utilized to predict rice production patterns, while GA was implemented to optimize fertilizer and pesticide dosage combinations. Model performance was evaluated using MAPE, MSE, RMSE, MAE, and coefficient of determination (R²). Findings – The ANN model demonstrated strong predictive capability with a MAPE value of 3.27%, RMSE of 0.22, and R² value of 0.53, indicating its ability to capture complex non-linear relationships among cultivation variables. Furthermore, the hybrid ANN-GA model successfully optimized agricultural input usage by reducing fertilizer dosage by 76.61% and pesticide dosage by 66.32%, while increasing predicted rice production from 4.28 tons/ha to 6.09 tons/ha. These results indicate that hybrid AI systems can improve agricultural efficiency and support sustainable rice production management. Research implications – This study contributes theoretically to the advancement of hybrid AI applications in precision agriculture by integrating predictive learning and adaptive optimization within a unified framework. Practically, the findings provide an intelligent decision-support model that may assist farmers and agricultural stakeholders in improving productivity, reducing excessive chemical input usage, and promoting environmentally sustainable farming practices. Originality – The originality of this study lies in its contribution to the advancement of hybrid AI applications in precision agriculture through the integration of predictive learning and adaptive optimization within a unified framework.
Co-Authors Agus Sugiharto Aini Nurul Al Jum'ah, Muhammad Na'im alders paliling Arnes Yuli Vandika Asriani, Ika Ben Rahman Dirman Erfan Hasmin Fifto Nugroho Hamid Wijaya Isfauzi Hadi Nugroho Judijanto, Loso La Ode Ichlas Syahrullah Yunus Muh. Na’im Al Jum’ah Muhammad Na'im Al Jum'ah Muliyadi Muliyadi Muliyadi Mutmainnah Muchtar Nur Fajriah Muchlis, Nur Fajriah Nur Hayni Nurdin Nurdin Nurhasan Nugroho Nurul Mega Saraswati Nurwijayanti Kusumaningrum Piu, Sri Wahyuningsih Putri, Farika Tono Rahmat Karim Rahmi Rahmi Rasna, Rasna Rito Cipta sigitta Hariyono Rizal Fani Sarimuddin, Sarimuddin Septiana, Asyifa Imanda Siregar, Juarni Sunyanti Syaban, Kharis Wijaya, Hamid