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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi dan Sistem Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Abdimas Indonesia Journal Of Artificial Intelligence And Software Engineering Journal of Computer Science and Information Technology Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal Jurnal Sistem Informasi dan Teknologi Informasi
Andrianingsih
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Spatial Analysis of Random Forest Classification Model for Availability Mapping of Sports Facilities in Jakarta Candra, Hansen; Andrianingsih, Andrianingsih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6556

Abstract

This research analyzes the distribution of sports facilities in DKI Jakarta Province using spatial modeling and Machine Learning Random Forest algorithm in order to support Indonesia Emas 2045. The goal is to classify areas based on the level of availability of sports facilities into low, sufficient, and high categories, and evaluate the accuracy of the Random Forest algorithm in the classification. CRISP-DM methodology is used in this research, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data analyzed includes spatial sub-district areas and attributes of sports facilities in DKI Jakarta. Random Forest was chosen because of its ability to classify complex data and identify feature importance. The results show that the distribution of sports facilities is uneven, with low categories more in Central Jakarta and North Jakarta, while high categories are scattered in other areas. Random Forest accuracy reached 89%, with high precision and recall in the high category.
Rainfall Classification Based on El-Niño and La-Niña Climate Phenomenon Using Naive Bayes Classifier Algorithm Erlinda, Mely; Andrianingsih, Andrianingsih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6552

Abstract

As a tropical country, Indonesia faces significant challenges due to global climate phenomena such as El Niño and La Niña that impact rainfall patterns. This research aims to classify daily rainfall in major Indonesian cities such as, DKI Jakarta, Surabaya, Medan, Makassar, and Bandung, into three main categories, namely moderate rain, extreme rain, and no rain. In addition, it identifies climate conditions based on El Niño, La Niña, and Normal categories by applying the Naïve Bayes Classifier algorithm. In this study, the CRISP-DM (Cross-Industry Standard Process for Data Mining) method was used as a framework for processing daily rainfall data for the period January to December 2023, obtained from BMKG. The analysis results show that the Naïve Bayes Classifier algorithm has high performance with 93.15% accuracy, 98% precision, 93% recall, and 94% F1-score. Further analysis, this study found that El Niño causes a significant decrease in rainfall, while La Niña increases extreme rainfall, especially in Makassar and Medan. This research contributes to the development of rainfall classification models that can help the government to anticipate the impacts of climate change and improve the efficiency of water resources management in urban areas.
Co-Authors Adinda, Saskia Agung Triayudi Ahmad Taufik Aldisa, Rima Tamara Aldjawad, Mohamad Alhadi, Alhadi Alhakim, Rahmat Ambi Muhammad Dzuhri, Ambi Muhammad Annisa Annisa Asrul Sani Buhori, Kiki Burhani, Hanif Rahman Candra, Hansen Chafidin, Afiyan Nur Dhieka Avrilia Lantana Elok Ainur Latif Endah Tri Esti Handayani Erlinda, Mely Erliza Hambali Fakrurrozi, Fakrurrozi Fauziah Fauziah Firzatullah, Raden Muhamad Hindarto, Djarot Ina Agustina Indra Lukmana Irzan Busrayan Iskandar Fitri, Iskandar Kaeren, Kaeren Kusuma, Rega Andhika Maharani, Anggita Putri Mochamad Hariadi Muhammad Nurdin Muhammad Taufiq Muhammad, Taufik Mutia Pamikatsih Nur Hayati Pratama, Wildan Ikbar Putra, Gifari Yudiya Ramadhan, Jonathan Aditya Ratih Titi Komalasari Riyantoro Riyantoro Sakti Ade Prastyo, Anggoro Bimma Sari Ningsih Septi Andryana setiawan, Danis Shalihati, Ira Diana Siti Aisyah Soepriyono, Gatot Subkhi, Akhmad Yunus Suparman, Kodim Susanto, Arman Rama Syaiful Syaiful Wahyuddin, M Iwan Wahyuddin, Mohammad Iwan WIJANARKO, SIGIT Wiratama Putra, Tobby Zuraidah, Arika