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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT Signal and Image Processing Letters International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
Aria Hendrawan, Aria
Universitas Semarang
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Journal : Journal of Information Systems and Informatics

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LSTM Forecasting and K-Means Clustering for Passenger Mobility Management at Bus Terminals Khairunnisa, Hasna Rizqia; Hendrawan, Aria
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1159

Abstract

Rapid urban population growth has increased the need for efficient public transportation systems, particularly at bus terminals as major mobility hubs. To address operational challenges such as traffic congestion and limited infrastructure, this study proposes an innovative data-driven approach. A hybrid model is applied, integrating Long Short-Term Memory (LSTM) for passenger volume forecasting and K-Means Clustering for mobility pattern segmentation at the Jepara Bus Terminal. Monthly passenger data was utilized, and the K-Means method was applied to group monthly mobility patterns into three categories: low, medium, and high. The optimal cluster selection (k=3) was based on the highest Silhouette score of 0.785, providing clear seasonal insights. Analysis results indicate that September is the peak mobility period, while months like January and February fall into the low category. Furthermore, an LSTM model was trained to predict future passenger volumes. The model's performance was carefully validated and proven accurate, with a Mean Squared Error (MSE) of 0.0304 and a Root Mean Squared Error (RMSE) of 0.1745. These findings confirm that the model is reliable in capturing complex passenger movement patterns. Overall, this study concludes that the combination of LSTM and K-Means is an effective solution for supporting proactive decision-making. The results of this study can assist terminal managers in optimizing resource allocation and formulating more adaptive operational strategies, thereby contributing to the development of a more responsive and efficient intelligent transportation system.
Co-Authors Adiuntoro, Alwan Agho, Davit Cany Agusta Praba Ristadi Pinem April Firman Daru Awaliyah, Rafika Farkhul Basworo Ardi Pramono Cholil, Saifur Rohman Christine Dewi Christioko, B.Very Dimas Dwi Budiarjo Febrian Wahyu Christanto Henny Indriyawati Huizen, Lenny Margaretta Khairunnisa, Hasna Rizqia Khoiriyah, Rofiatul Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Luqman Hakim Margaretta Huizen, Lenny Maulana, Charis Maulida, Innayah Nur Wakhidah Oky Dwi Nurhayati Pinem, Agusta Praba Ristadi Pradana, Muhamamd Surya Jati Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Rahmat Gernowo Rohman Cholil, Saifur Saifur Rohman Cholil Saputri, Anita Nur Widdia Sekar Sari, Mega Siti Asmiatun, Siti Soiful Hadi Susanto Susanto Titin Winarti Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit