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All Journal AUSTENIT JIKO (Jurnal Informatika dan Komputer) Al-Madrasah: Jurnal Ilmiah Pendidikan Madrasah Ibtidaiyah Jambura Journal of Informatics Aptisi Transactions on Technopreneurship (ATT) Jurnal Ilmiah Manajemen Publik dan Kebijakan Sosial ADI Journal on Recent Innovation (AJRI) Indonesia Berdaya Journal of Sustainable Tourism and Entrepreneurship Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Valuasi : Jurnal Ilmiah Ilmu Manajemen dan Kewirausahaan JURNAL LENTERA AKUNTANSI Manajemen dan Kewirausahaan Bussman Journal : Indonesian Journal of Business and Management Indonesian Journal of Business Analytics (IJBA) Toba: Journal of Tourism, Hospitality, and Destination Jurnal Ilmiah Teknik Informatika dan Komunikasi Eduvest - Journal of Universal Studies Jurnal Abdimas Sosek (Jurnal Pengabdian dan Pembardayaan Masyarakat) International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Social & Economic Bulletin Ekspresi: Publikasi Kegiatan Pengabdian Indonesia Green Health: Journal of Health Sciences, Nursing and Nutrition Data : Journal of Information Systems and Management
Harlis Setiyowati
Universitas Pradita
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Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction Harriz, Muhammad Alfathan; Akbariani, Nurhaliza Vania; Setiyowati, Harlis; Santoso, Handri
Jambura Journal of Informatics VOL 5, NO 1: APRIL 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v5i1.18814

Abstract

This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.
Predicting Supply Chain Risks Using Machine Learning for Resilient Operations Widayanti, Riya; Setiyowati, Harlis; Yusup, Muhamad; Rodriguez, Marta
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1376

Abstract

Rising supply chain disruptions highlight increasing vulnerabilities in global logistics networks caused by geopolitical conflicts, fluctuating demand, transportation failures, and environmental instability. These challenges reveal the limitations of conventional risk assessment approaches that rely heavily on manual analysis and historical data. Machine Learning (ML) offers a promising approach to enhance predictive intelligence and support more accurate decision making in complex supply chain environments. This study aims to develop and evaluate a Machine Learning based risk prediction model capable of identifying potential supply chain disruptions and enabling early detection of critical risk factors in global logistics operations. A quantitative experimental approach was employed using supply chain datasets integrated with disruption indicators from international logistics activities. The dataset consisted of more than 5,000 operational records collected between 2018 and 2024. Several machine learning algorithms were implemented and compared, including Random Forest, Gradient Boosting, and Support Vector Machines. Experimental results indicate that the Gradient Boosting algorithm achieved the highest predictive performance with an accuracy of 94.2%. The model successfully identified key determinants of supply chain risk, including demand variability, supplier reliability, and transportation delays. These findings confirm that machine learning based predictive models can enhance supply chain resilience by enabling early risk detection and supporting proactive decision making in global logistics operations.
Co-Authors Akbariani, Nurhaliza Vania Amroni Amroni, Amroni Aprillia Eka Lestari Aryanto, Puput Astuti, Eka Dian Azuddin, Muna Basuki, Sucipto Carlos Perez Chandra, Ray Cicilia S Bangun Dazk, Erick Dewa Gede Satriawan Dewi Kusumaningsih Edi Junaedi Edi Junaedi Enden Suryati Firdaus Firdaus G Thippanna Gazali Rahman Handri Santoso Harimurti Wulandjani Ikhsan, Ramiro Santiago Indah Sulistyoningsih Irfan Maulana Irfan Maulana, Irfan Jaka Suwita Jhon Hendri Konita Lutfiyah Kurniadjaya, Morgan Lestari, Aprillia Eka Lie, Shania Luh Putu Puji Trisnawati Mayatopani, Hendra Muhamad Yusup Muhammad Alfathan Harriz Nurhaliza Vania Akbariani Nurhaliza Vania Akbariani Nurhaliza Vania Akbariani Nurjannah Nurjannah Pasha, Lukita Qurotul Aini Ratna Setiawati Rina Rina Rinny Meidiyustiani Risma Haris, Risma Riya Widayanti Rizki Kurniawan Rodriguez, Marta Santoso, Handri Sembiring, Rinawati Seno Hadi Saputro Setiawati, Ratna Shania Lie Soni Hartanto Supriyanto, Bambang Eko Surjo Widodo Suryo Widodo Sutapa, Widasapta Thalib, Supriadi V. Lilik Hariyanto Vaher, Kristina Wibowo, Shesilia Zakaria, Noor Azura