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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi Pertanian Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JTET (Jurnal Teknik Elektro Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal JIKO (Jurnal Informatika dan Komputer) Compiler Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Information Systems for Public Health
Agus Harjoko
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health

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Journal : International Journal of Artificial Intelligence Research

 1 
Intelligent Traffic Monitoring Systems: Vehicle Type Classification Using Support Vector Machine Ika Candradewi; Agus Harjoko; Bakhtiar Alldino Ardi Sumbodo
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.229 KB) | DOI: 10.29099/ijair.v5i1.201

Abstract

In the automation of vehicle traffic monitoring system, information about the type of vehicle, it is essential because used in the process of further analysis as management of traffic control lights. Currently, calculation of the number of vehicles is still done manually. Computer vision applied to traffic monitoring systems could present data more complete and update.In this study consists of three main stages, namely Classification, Feature Extraction, and Detection. At stage vehicle classification used multi-class SVM method to evaluate characteristics of the object into eight classes (LV-TK, LV-Mobil, LV-Mikrobis, MHV-TS, MHV-BS, HV-LB, HV- LT, MC). Features are obtained from the detection object, processed on the feature extraction stage to get features of geometry, HOG, and LBP in the detection stage of the vehicle used MOG method combined with HOG-SVM to get an object in the form of a moving vehicle and does not move. SVM had the advantage of detail and based statistical computing. Geometry, HOG, and LBP characterize complex and represents an object in the form of the gradient and local histogram.The test results demonstrate the accuracy of the calculation of the number of vehicles at the stage of vehicle detection is 92%, with the parameters HOG cellSize 4x4, 2x2 block size, the son of vehicle classification 9. The test results give the overall mean recognition rate 91,31 %, mean precision rate 77,32 %, and mean recall rate 75,66 %. 
Evaluation Of A Feature-Concatenated Model For Multiclass Diagnosis Of Pulmonary Diseases on An Imbalanced Dataset Ajitomo, Wahyu; Tyas, Dyah Aruming; Harjoko, Agus
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1519

Abstract

Lung diseases such as pneumonia, tuberculosis, and COVID-19 pose serious global health challenges, particularly in X-ray image classification where class distribution is often imbalanced. To address this issue, this study proposes a hybrid model based on concatenated CNN architectures and applies class weighting using focal loss multiclass. The dataset consists of 7,135 X-ray images divided into four main classes: pneumonia, tuberculosis, COVID-19, and normal. Focal loss with a gamma parameter of 2.0 is employed to enhance the model’s focus on minority classes. Evaluation results show that combined models such as DenseNet121 + VGG16 and VGG16 + ResNet50 achieve F1-scores of up to 0.87, outperforming single models. Grad-CAM visualizations also indicate that the combined models can recognize pathological areas more comprehensively and accurately. This approach proves effective in improving the accuracy and sensitivity of AI-based diagnostic systems.
Co-Authors Agus Wahyu Widodo, Agus Wahyu Ahmad Ashari Ajitomo, Wahyu Alabid, Noralhuda N. Aldino Ardi S, Bakhtiar Anak Agung Istri Ngurah Eka Karyawati Andi Dharmawan Andi Sunyoto Andiko Putro Suryotomo Aniati Murni Arymurthy Anny Kartika Sari Ashar Punto Nurwendo Azhari, Azhari Bakhtiar Alldino Ardi Sumbodo Bernard Renaldy Suteja Budi Rahardjo Budi Rahmani Dyah Aruming Tyas Edy Winarno Elizabeth Nurmiyati Tamatjita Enny Itje Sela Feri Wibowo Gamma Kosala Hadi Santoso Helna Wardhana Hermawan Syahputra I Gede Aris Gunadi I Putu Adi Pratama Ika Arfiani Ika Candradewi Ika Candradewi, Ika Ika Sudirahayu Ikhwan Ruslianto Iwan Budi Nugroho Jumanto Jumanto, Jumanto Khabib Mustofa Kusrini Kusrini La Ode Hasnuddin S. Sagala Latifah, Husnul Lilik Sutiarso Lukman Awaludin Mahmuddin Yunus Maimunah Maimunah Maura Widyaningsih Much Aziz Muslim Muhammad Anis Al Hilmi Muhammad Shahid Ardi Munakhir Mudjosemedi Murinto Murinto Mursid Wahyu Hananto Nicodemus Mardanus Setiohardjo Nora Idiawati Norman Yazid Novrido Charibaldi Nugraha, Faizal Widya Pradana, Gregorius Adi Pujiastuti, Asih Raden Sumiharto Rahmad Hidayat Rahmi Hidayati Retantyo Wardoyo Rocky Yefrenes Dillak Rudiati Evi Masithoh Sajid, Syahmi Salman Aliaji Septia Rani Setyo Nugroho Slamet Santosa Slamet Santoso Sri Hartati Sri Hartati Sri Hartati Sri Kusumadewi Sri Suwarno Tia Widiana Tri Kuntoro Priyambodo Tri Wahyu Supardi Wawan Kurniawan Winarko, Edi Winarno Winarno Yustina Retno Zaenal Abidin