Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Transportasi dan Logistik Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Alfan Baharuddin
Politeknik Keselamatan Transportasi Jalan
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences Transportation Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Evaluasi Kinerja Terminal Leuwipanjang Kota Bandung Della Vina Fudla; Siti Maimunah; Alfan Baharuddin
Jurnal Teknologi Transportasi dan Logistik Vol. 2 No. 1 (2021): Juni 2021
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Poltrada Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52920/jttl.v2i`1.31

Abstract

Bertambahnya jumlah penduduk dan penggunaan kendaraan pribadi pada Kota Bandung mempengaruhi pengoperasian dan kinerja Terminal Leuwipanjang Kota Bandung dengan Tipe A yaitu kelas pada terminal, penilaian pelayanan dan kinerja terminal. Tujuan pada penelitian ini untuk mengetahui kriteria kelas terminal, perbaikan pelayanan dan mengetahui kinerja yang perlu perbaikan dalam operasional di Terminal Leuwipanjang untuk itu dilakukan penentuan kelas terminal sesuai peraturan dari SK.6251 tahun 2017 tentang pedoman teknik penetapan kriteria kelas terminal, penilaian pelayanan dan kinerja terminal dilakukan analisis metode IPA (Importance Performance Analysis) dengan menggunakan google form dengan jumlah pertanyaan tentang pelayanan sebanyak 46 dengan penilaian kepuasan dan kepentingan yang disebarkan kepada pengguna terminal dimana yang masuk kuadran I dan II yang menjadi prioritas utama untuk perbaikan pelayanan sedangkan pada kuadran III dan IV tidak perlu perbaikan pelayanannya dan penentuan kelas terminal dilakukan penentuan 5 indikator yang disesuaikan dengan kondisi eksisting. Hasil analisis kelas terminal sesuai SK.6251 tahun 2017 maka Terminal Leuwipanjang masuk pada kategori kelas 2, hasil metode IPA yang perlu prioritas utama dalam perbaikan dan pelayanan yang masuk pada kuadran I dan II dan penilaian kinerja berdasarkan PM 132 tahun 2015 tentang penyelengaraan terminal penumpang angkutan jalan.
YOLO-Based Real-Time Artificial Intelligence Traffic Counting for Urban Transportation Monitoring in Surakarta: Implications for SDG 11 Bambang Istiyanto; Yan El Rizal Unzilatirrizqi D; Alfan Baharuddin; Pipit Rusmandani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.152

Abstract

Objective: To develop and evaluate an artificial intelligence (AI)-based traffic counting system using the YOLO (You Only Look Once) deep learning algorithm to provide accurate and real-time traffic volume data for urban transportation management. Method: Employing a deep learning approach by implementing the YOLO algorithm for vehicle detection and traffic counting. Traffic video data from road objects in Surakarta City were processed to identify and classify various vehicle types. The AI-generated traffic counting results were then compared with manual traffic survey data to assess the system’s accuracy and effectiveness. Results: The findings indicate that the proposed AI-based traffic counting system can accurately detect and classify multiple vehicle categories, including cars, motorcycles, trucks, buses, bicycles, and Bajaj. The traffic-counting data produced by the system were highly readable and reliable. Comparison with manual traffic surveys showed that the AI-generated results were very similar while requiring significantly less time and human resources. The system achieved nearly 100% consistency with the available secondary traffic volume data, demonstrating its effectiveness in monitoring urban traffic conditions. Novelty: Application of the YOLO deep learning algorithm for automated traffic counting in the urban road environment of Surakarta City. The proposed system provides a practical and efficient alternative to conventional manual traffic surveys by delivering accurate, real-time traffic data with minimal human intervention, thereby supporting more effective urban transportation planning and management. These contributions are also relevant to SDG 11 (Sustainable Cities and Communities) by enabling data-driven traffic monitoring and facilitating smarter, more sustainable urban mobility management.
YOLO-Based Real-Time Artificial Intelligence Traffic Counting for Urban Transportation Monitoring in Surakarta: Implications for SDG 11 Bambang Istiyanto; Yan El Rizal Unzilatirrizqi D; Alfan Baharuddin; Pipit Rusmandani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.152

Abstract

Objective: To develop and evaluate an artificial intelligence (AI)-based traffic counting system using the YOLO (You Only Look Once) deep learning algorithm to provide accurate and real-time traffic volume data for urban transportation management. Method: Employing a deep learning approach by implementing the YOLO algorithm for vehicle detection and traffic counting. Traffic video data from road objects in Surakarta City were processed to identify and classify various vehicle types. The AI-generated traffic counting results were then compared with manual traffic survey data to assess the system’s accuracy and effectiveness. Results: The findings indicate that the proposed AI-based traffic counting system can accurately detect and classify multiple vehicle categories, including cars, motorcycles, trucks, buses, bicycles, and Bajaj. The traffic-counting data produced by the system were highly readable and reliable. Comparison with manual traffic surveys showed that the AI-generated results were very similar while requiring significantly less time and human resources. The system achieved nearly 100% consistency with the available secondary traffic volume data, demonstrating its effectiveness in monitoring urban traffic conditions. Novelty: Application of the YOLO deep learning algorithm for automated traffic counting in the urban road environment of Surakarta City. The proposed system provides a practical and efficient alternative to conventional manual traffic surveys by delivering accurate, real-time traffic data with minimal human intervention, thereby supporting more effective urban transportation planning and management. These contributions are also relevant to SDG 11 (Sustainable Cities and Communities) by enabling data-driven traffic monitoring and facilitating smarter, more sustainable urban mobility management.
Co-Authors Bambang Istiyanto Della Vina Fudla Dewantoro, Yan El Rizal Unzilatirrizqi Pipit Rusmandani, Pipit Siti Maimunah