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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 471 Documents
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Penerapan PAM Clustering untuk Analisis Pola dan Optimasi Pemesanan Tiket Angkutan Umum syaffina amalia ashari; Liza Fitria; Rizalul Akram
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10031

Abstract

Public transportation is a primary mode of intercity travel in Aceh Tamiang, yet operators face challenges in balancing fleet availability with fluctuating ticket demand. This study aims to analyze ticket booking patterns for buses and minibuses (Hiace) using the Partitioning Around Medoids (PAM) clustering method to provide a basis for operational optimization. The research applies the Knowledge Discovery in Databases (KDD) framework to historical booking data from the local Transportation Agency. The PAM algorithm effectively grouped the 15 main routes into three distinct clusters: Cluster 1 (12 routes) with stable-medium demand, Cluster 2 (2 strategic city routes) with high demand, and Cluster 3 (1 inter-province route) with very high demand. These results successfully identify hidden demand patterns, with the final clustering achieving a low Total Cost of 2.0. The findings directly translate into actionable recommendations for differential scheduling and fleet allocation, demonstrating the practical value of data mining in public transport management.

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