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All Journal Jurnal Teknologi Transportasi dan Logistik Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Bambang Istiyanto
Politeknik Keselamatan Transportasi Jalan
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences Transportation Other

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Tinjauan Legalitas Pengusahaan Multimoda Transport Operator dalam Mewujudkan Efektivitas Sistem Logistik Nasional Ni Luh Darmayanti; I Wayan Arnaya; Bambang Istiyanto
Jurnal Teknologi Transportasi dan Logistik Vol. 4 No. 1 (2023): Juni 2023
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Poltrada Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52920/jttl.v4i1.128

Abstract

Transportasi multimoda (MTO) dapat diartikan sebagai metode pengangkutan kargo dari suatu lokasi ke lokasi lain menggunakan setidaknya dua atau lebih moda transportasi yang berbeda. Prinsip utama dari transportasi multimoda adalah hanya terdapat satu bill of lading meskipun melibatkan beberapa moda transportasi seperti udara, kereta api, darat, maupun laut. Kegiatan angkutan multimoda, sesuai dengan Peraturan Menteri No. 8 tahun 2012, hanya dapat diselenggarakan oleh badan usaha angkutan multimoda, baik badan usaha nasional maupun badan usaha asing. Dalam menyelenggarakan kegiatan angkutan multimoda, badan usaha MTO bertanggung jawab terhadap kegiatan penunjang angkutan multimoda yang meliputi pengurusan transportasi, pergudangan, konsolidasi muatan, penyediaan ruang muatan, serta kepabeanan untuk angkutan multimoda ke luar negeri dan ke dalam negeri. Bagi sebuah badan usaha, izin usaha atau legalitas merupakan salah satu bentuk ketaatan kepada hukum. Legalitas yang dimaksudkan disini adalah izin yang sah secara hukum terhadap segala kegiatan usaha yang dilaksanakan. Semua badan usaha MTO nasional harus mampu menyediakan jasa angkutan multimoda yang memenuhi standar keselamatan serta keamanan yang berlaku. Guna menjamin terwujudnya efektivitas dan efisiensi dalam penyelenggaraan sistem logistik nasional, maka perlu dipastikan bahwa setiap badan usaha penyelenggara MTO telah memenuhi aspek legalitas sesuai dengan ketentuan yang berlaku. Penelitian ini bertujuan untuk mengetahui tinjauan legalitas pengusahaan multimoda transport operator dalam mewujudkan efektivitas sistem logistik nasional guna mengetahui legalitas pengusahaan angkutan multimoda di daerah Denpasar. Teknik pengambilan data pada penelitian ini yaitu dengan studi literatur dan survey (Kuesioner dan Wawancara) yang kemudian akan dianalisis menggunakan metode analisis deskriptif kualitatif.
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 Alfan Baharuddin Dewantoro, Yan El Rizal Unzilatirrizqi I Wayan Arnaya Ni Luh Darmayanti Pipit Rusmandani, Pipit