Article Per Year (5 Year)
p-Index From 2021 - 2026
2.911
P-Index
This Author published in this journals
All Journal Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Journal of Applied Smart Electrical Network and System (JASENS) Bulletin of Community Engagement Asian Journal Science and Engineering Jurnal 7 Samudra Politeknik Pelayaran Surabaya CSRID Journal of Soft Computing Exploration
Imam Sutrisno
Unknown Affiliation
Religion Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Social Sciences Transportation Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Automatic identification system big data‑driven maritime traffic density prediction in surabaya port using PCA and k‑means clustering Arfianto, Afif Zuhri; Haj, Muhammad Izzul; Muhammad Khoirul Hasin; Noorman Rinanto; Imam Sutrisno; Dimas Pristovani Riananda; Dwi Sasmita Aji Pambudi
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.22

Abstract

The management of maritime traffic directly determines the level of operational efficiency and safety achievable at major ports, including Tanjung Perak in Surabaya, which serves as a critical logistics node for eastern Indonesia. This study presents a comprehensive analysis of maritime traffic density prediction using Automatic Identification System (AIS) big data combined with Principal Component Analysis (PCA) and K-Means clustering techniques. The dataset comprises 1,173 vessel movements recorded in December 2025, encompassing various vessel types, port operations, and voyage characteristics. Through dimensionality reduction using PCA and unsupervised clustering with K-Means, we identified 10 distinct traffic patterns representing different operational profiles. The analysis revealed significant temporal patterns, with peak traffic occurring at 14:00 (79 vessels) and lowest traffic at 02:00 (18 vessels). The clustering results achieved a silhouette score of 0.3863, effectively segmenting vessels based on voyage distance, capacity, speed, draught, and temporal features. The results of this research offer practical guidance for port authorities seeking to improve resource allocation, traffic management, and operational efficiency based on empirical evidence.
Co-Authors Adi Rahmad Ramadhan Adianto Afianto, Semeru Agus Khumaidi Agus Khumaidi Agus Prawoto Ahmad Putra Akbara, Febrian Antonius Edy Kristiyono Ardiansyah Arfianto, Afif Zuhri Ari Wibawa Budi Santosa Aulia Rahma Annisa Aziz Nugroho Basuki Rahmat Boedi Herijono Daisy Dwijati Kumala Ratna Antariksih Darrylivan Putra Hadisyah, Rafli Didik Dwi Suharso Didik Sukoco Dimas Pristovani Riananda Dwi Sasmita Aji Pambudi Edy Prasetyo Hidayat Fahmi Umasangadji Fais Hamzah Faris Nofandi Fiqrotin Nur Asita Fiqrotin Nur Asita Haj, Muhammad Izzul Hasin, Muhammad Khoirul Ii Munadhif Isa Rachman ISKANDAR Joessianto Eko Poetro Joko Endrasmono Marshelino Chandra Suhartono Mat Syai’in Mochammad Rifai Mohammad Abu Jami'in Muhammad Khoirul Hasin MUHAMMAD NIAL ALANUS SHOLAH Mustika Kurnia Mayangsari Nafi Almuzani Naufal Hibban, Naufal Hibban Perwi Darmajanti Petrisia Widyasari Sudarmadji Pingky, Ginda Pingky Ramadhani Pranowo Sidi Projek Priyonggo Sumangun Lukitadi Rinanto, Noorman Rini Indarti Ryan Yudha Adhitya Shofa Dai Robbi Siti Zaenab, Siti Zaenab Surya Dharma Trisnowati Rahayu Urip Mudjiono Zindhu Maulana Ahmad Putra