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All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Scientific Journal of Informatics International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal TAM (Technology Acceptance Model) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Building of Informatics, Technology and Science Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal TAM (Technology Acceptance Model)
Y. Suhendro
Darmajaya Informatics And Business Institute
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Transportation Other

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Decision Support System for Aircraft Takeoff and Landing Using Mamdani Fuzzy Logic Based on Weather Parameters Armansyah, Armansyah; Irianto, Suhendro Yusuf
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7464

Abstract

Aviation safety is highly influenced by weather conditions, particularly during take-off and landing, necessitating an accurate feasibility assessment. Traditional manual methods rely on subjective judgment, making them prone to inconsistencies and errors. This study proposes a decision support system utilizing Mamdani fuzzy logic to process real-time meteorological data from the Radin Inten II station and assess take-off and landing feasibility. The system evaluates key weather parameters, including wind speed, wind direction, visibility, precipitation, and cloud height. Testing 31 data samples from BMKG, the system achieved an accuracy of 96.77%, with 30 out of 31 outputs matching standard aviation criteria. These results indicate that the system significantly improves decision-making reliability. The Mamdani fuzzy logic approach proves effective in interpreting complex weather data and generating consistent, data-driven recommendations to support safe aircraft operations.
Lithology Prediction Using Deep Learning Artificial Neural Network and Schlumberger Resistivity Inversion Data at Eastern Lampung Ramadhan, M Fitrah; Irianto, Suhendro Yusuf; Farduwin, Alhada
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37652

Abstract

The Schlumberger geoelectric method has been extensively employed in earth resource exploration due to its capability to identify variations in subsurface resistivity. However, the manual interpretation of geoelectric data inversion results is often subjective and time-consuming. This study aims to automate the lithology identification process by utilizing deep learning techniques, particularly Artificial Neural Networks (ANN), based on the inverted resistivity parameters obtained through the IPI2Win software. The Schlumberger configuration geoelectric data were obtained from survey reports provided by the Ministry of Public Works and Housing (Kementerian Pekerjaan Umum dan Perumahan Rakyat/ PUPR), which conducted geoelectric measurements in East Lampung Regency, Lampung Province, Indonesia. The ANN algorithm demonstrated an average accuracy of 90% in predicting lithology based on resistivity patterns resulting from Schlumberger inversion. Outperforming Support Vectorr Machine (SVM) (87%) and XGBoost (88%). These results confirm the initial hypothesis that ANN can effectively capture the complex relationships between resistivity values and rock types. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data.
Co-Authors - Nurjoko - Winarsih -, Meiliza -, Nisar A.A. Ketut Agung Cahyawan W Adimas, Adimas Agung Pradana Agus Rahardi Agus Rahardi, Agus Ahmad Rofi’i Ali, Rionaldi Andreas Perdana Apri Ramadhan, Apri ARDIANSYAH ARDIANSYAH Arman Suryadi Karim, Arman Suryadi Armansyah Armansyah Aziz, RZ. Abdul Dian Sari Dona Yuliawati Dwi Handoko Effendi, Mukhammad Khoirul Eko Hendrawan Endang Astitin Enrico Findley Farduwin, Alhada Fauzi, Chairani Fitria - Fitria - Fitria Fitria handoyo widi nugroho Hendra Kurniawan Herwanto, Riko Ida Ayu Puspita Sari Indera Irianto . Irwanto, Ika Krisdiyanto, Wawan Lukmanul Hakim M Said Hasibuan Muhammad Galih Ramaputra Muktiawan, Danang Ade Nisar - Nursiyanto Ochi Marshella Febriani Onno W. Purbo Ramadhan, M Fitrah Rian Yunandar Ribut Yulianto Rio Kurniawan Roffi, Ahmad Safitri, Egi Septiawan, Yuda Sken Jian Tao Sri Karnila Sri Karnila Sri Karnila Sriyanto Sriyanto Sriyanto Sriyanto Sushanty Saleh Vitriani, Yelfi Wahyu Nugroho Wenni Puspa Sari Yudiansyah, Lepi Astra