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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Jurnal Review Pendidikan dan Pengajaran (JRPP) Journal of Robotics and Control (JRC) Jurnal Tecnoscienza JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Computer Science and Information Technology (CoSciTech) Komatika: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistim Informasi dan Teknologi The Eastasouth Journal of Information System and Computer Science Jurnal SUTASOMA Journal of Moeslim Research Technik
I Putu Dody Suarnatha
Information System Study Program, Faculty Of Science And Technology, Universitas Tabanan, Bali, Indonesia
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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PENGEMBANGAN SISTEM INFORMASI GEOGRAFIS (SIG) UNTUK ANALISIS SPASIAL DALAM PENGAMBILAN KEPUTUSAN Rahmawati, Lailia; Febrian , Wenny Desty; Fachruzzaki, Fachruzzaki; Mardiyati, Sri; Lengam, Rino; Suarnatha, I Putu Dody
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 2 (2024): Volume 7 No. 2 Tahun 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i2.26929

Abstract

Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Geografis (SIG) yang dapat digunakan dalam analisis spasial untuk mendukung pengambilan keputusan. Melalui studi literatur yang mendalam, penelitian ini mengeksplorasi konsep dasar SIG, teknologi terkini, dan metode analisis spasial yang canggih. Temuan penelitian mengidentifikasi pentingnya integrasi teknologi sensor, pemrosesan data berbasis cloud, dan pemanfaatan kecerdasan buatan dalam pengembangan SIG. Konsep analisis spasial yang canggih juga diperkenalkan, mencakup pemodelan spasial, analisis overlay, dan teknik keterkaitan spasial. Integrasi SIG dalam pengambilan keputusan menjadi fokus utama, dengan penekanan pada pengembangan model dan algoritma yang dapat meningkatkan efisiensi dan ketepatan keputusan. Studi literatur juga mencatat tantangan, seperti kompleksitas data dan interoperabilitas, yang dapat diatasi melalui standarisasi data geografis dan penggunaan platform terintegrasi. Penelitian ini memberikan kontribusi baru terhadap pemahaman SIG, menghadirkan pemahaman yang lebih mendalam tentang potensi dan batasannya. Implikasi teoritis mencakup pengayaan konsep SIG, sementara implikasi praktis memberikan panduan berharga bagi pengembang sistem dan pengambil keputusan. Saran penelitian mencakup fokus pada pengembangan model prediktif spasial dan eksplorasi lebih lanjut terkait integrasi SIG dengan teknologi baru.
ALGORITHMIC INTELLIGENCE IN ENGINEERING DESIGN: INTEGRATING MACHINE LEARNING WITH PHYSICAL MODELING Erwis, Fauzi; Fujita, Miku; Suarnatha, I Putu Dody; Wilson, Amanda
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3467

Abstract

Increasing complexity in engineering systems demands design methodologies that balance computational efficiency, predictive accuracy, and physical reliability. Traditional physics-based simulations ensure mechanistic consistency but are computationally expensive, while purely data-driven machine learning models offer speed yet often lack interpretability and physical compliance. Integrating algorithmic intelligence with physical modeling has therefore emerged as a promising paradigm in advanced engineering design. This study aims to develop and evaluate a hybrid framework that integrates machine learning algorithms with governing physical equations to enhance design performance, robustness, and computational efficiency. A mixed-methods computational design was employed using 15,000 high-fidelity simulation datasets across structural, aerodynamic, and thermal engineering cases. Three modeling configurations—physics-based models, data-driven models, and hybrid physics-informed machine learning models—were comparatively analyzed using performance metrics including mean squared error, R², runtime efficiency, robustness testing, and constraint violation indices. Statistical analyses were conducted to determine significance of performance differences. Hybrid models achieved superior balance, reaching R² = 0.97 with significantly reduced runtime compared to physics-based simulations (p < 0.001), while maintaining substantially lower physical constraint violations than purely data-driven models. Sensitivity and uncertainty analyses confirmed enhanced robustness under parameter perturbation. Algorithmic intelligence integrated with physical modeling represents an epistemologically coherent and practically effective approach, advancing engineering design toward trustworthy, efficient, and physically consistent computational frameworks.
Co-Authors Andik Wijanarko, Andik Arnes Yuli Vandika Dhanar Intan Surya Saputra Effendy, Femmy Erwis, Fauzi Fachruzzaki, Fachruzzaki Febrian , Wenny Desty Fujita, Miku I Gede Aris Gunadi I Gede Jana Adi Putra I Made Agus Oka Gunawan I Made Agus Widiana Putra I Putu Buda Suyasa Indrayanti, Ni Made Ayu Sonya Judijanto, Loso Kadek Laksmi Damayanthi Lengam, Rino Mahardika, Fajar Moeis, Dikwan Muhamad Ziaul Haq Nalis Hendrawan Ni Gusti Ayu Putu Raka Elyfiani Wardani Ni Putu Erni Asih Nursalim Putra, I Gede Jana Adi Rahmawati, Lailia Roy Mubarak Sitaresmi Wahyu Handani, Sitaresmi Wahyu Sri Mardiyati Wilson, Amanda