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All Journal Jurnal Teknologi dan Manajemen Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL ILMIAH INFORMATIKA Computer Based Information System Journal Compiler Jurnal Informasi dan Komputer Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter Ar-Rasyid: Jurnal Pendidikan Agama Islam Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Puan Indonesia Jurnal Desain dan Analisis Teknologi Jurnal Pengabdian Ibnu Sina Jurnal Pengabdian Barelang JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Harman, Rika
Universitas Putera Batam
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : Compiler

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Baseline Evaluation of Backpropagation Artificial Neural Network for Visual Image-Based Vehicle Type Classification Harman, Rika; Riadi, Imam; Fadlil, Abdul
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3210

Abstract

The increasing number of vehicles in urban areas requires technology-based solutions for efficient transportation management. This study proposes a vehicle classification model using Artificial Neural Networks (ANN) with the backpropagation algorithm, based on digital image data. The model is a feedforward neural network comprising an input layer, a hidden layer with 64 sigmoid-activated neurons, and an output layer with 7 softmax-activated neurons. The dataset, sourced from Roboflow Inc., consists of 16,185 images across eight vehicle classes: Hummer, Toyota Innova, Hyundai Creta, Suzuki Swift, Audi, Mahindra Scorpio, Rolls Royce, and Tata Safari. The data is split 80:20 for training and testing. Input features include vehicle dimensions, dominant RGB color, number of axles, and license plate detection. The model is trained using gradient descent and categorical crossentropy loss. Evaluation results show 85% validation accuracy at epoch 28 and 100% test accuracy. Precision, recall, and F1-score indicate strong performance, though minor errors occur in visually similar classes. These findings demonstrate that backpropagation-based ANN is effective for vehicle classification and can be applied in systems such as automatic parking and traffic monitoring
Co-Authors Abdul Fadlil Alfannisa Annurullah Fajrin Algifanri Maulana, Algifanri Alyshen, Alyshen Ambarita, Tri Mey Wendelina Amrizal Amrizal Amrizal Amrizal Amrizal Andarwati, Alifia Putri Andini, Mayada Dwi Arnomo , Sasa Ani Arnomo, Sasa Ani Ayu Wulandari Azwanti, Nurul Calvin Darmansah Darmansah, D Darmansah, Darmansah Devina Desliana Siagian Elisa, Erlin Ellvan Reisyer, Kevin Ellvan Reisyer Evan Rosiska Fredy, Julian Handoko, Koko Harina, Harina Hendra Hendra Hendra Purnama Ika Purnama Sari Imam Riadi kok seng, vincent kiu Lase, Hellina Ofnani Narti Eka Putria Nofriadi Nofriadi Nopriadi Nopriadi Novri Adhiatma Ong, Ricky Panjaitan, Tulus Bobi Saen Evansen Pardede, Grace Theresia Rahmat Fauzi Reisyer, Kevin Ellvan Rosiska, Evan Sarah Astiti S.Kom., M.MT Sepentia, Nirpan Sigalingging, Iska Heriyati Simanjutak, Pastima Syahril Effendi Syaputra, Wendy Telutci, Telutci Tri Driano, Welly Tukino, Tukino tukino, tukino Vilona Fitri Alfionita ZAHARA, MUTHIA Zai, Onesimo Zetli, Sri