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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan International Journal of Advances in Data and Information Systems
Budi Sulistiyo Jati
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Multi-Task Learning for Traffic Sign Recognition using Multi-Scale Convolutional Neural Networks Akbar, Mutaqin; Susilawati, Indah; Jati, Budi Sulistiyo; Alamsyah, Nur
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1406

Abstract

Traffic signs are an essential component of road infrastructure. According to the Department of Transportation, Indonesia has over 300 distinct traffic signs, categorized based on their functions and purposes. TSR systems have been widely integrated into various intelligent transportation technologies, such as Driver Assistance Systems (DAS), Advanced Driver Assistance Systems (ADAS), and Autonomous Driving Systems (ADS). The output generated by TSR serves as a critical input for DAS, ADAS, ADS, and other intelligent systems. This article presents a CNN-based classification for traffic sign recognition using multi-task learning (MTL), focusing on traffic signs in Indonesia. The dataset was collected from direct capture with the help of a cellphone camera, indirect capture by utilizing screenshots on a digital map application, and they are captured from several different angles, during the day and at night. The proposed CNN architecture incorporates multi-scale within an MTL framework. The use of a multi-scale approach will hopefully enhance the model’s ability to recognize traffic signs in varied and complex environments. And the integration of MTL will enable the model to handle multiple related tasks concurrently, sharing learned features across tasks. During the training stage, the MS-CNN outperformed a standard CNN model by demonstrating lower initial loss, higher starting accuracy, and achieving 100% accuracy by the 8th epoch with a minimal error rate of just 0.003. In the testing stage, the model achieved exceptional results, as shown by the confusion matrix, it successfully classified all traffic sign types (10 classes) and accurately categorized each sign into one of two categories—warning or prohibition. All performance metrics, including precision, recall, and F1-score, reached 100% for both output tasks, confirming the robustness and reliability of the model.
Pemanfaatan teknologi kecerdasan buatan untuk guru kinderstation school Jati, Budi Sulistiyo; Akbar, Mutaqin; Susilawati, Indah
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 3 (2024): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i3.25728

Abstract

AbstrakKemajuan dalam bidang teknologi informasi dan komunikasi telah menyebabkan perubahan besar dalam berbagai aspek kehidupan, terutama dalam bidang pendidikan. Era pendidikan dewasa ini ditandai oleh integrasi teknologi canggih seperti AI, machine learning, dan internet of things (IoT) dalam proses pembelajaran. Untuk mengatasi permasalahan tersebut, program pengabdian kepada masyarakat ini dirancang untuk meningkatkan pemahaman dan keterampilan praktis guru dan pengajar di Kinderstation School Yogyakarta dalam memanfaatkan teknologi AI dalam proses pembelajaran. Kegiatan pelatihan dilaksanakan dalam dua kali pertemuan (10 Juli 2024 dan 17 Juli 2024), dimana pertemuan pertama membahas mengenai pengenalan teknologi AI dan pertemuan kedua merupakan pelatihan penggunaan teknologi AI dalam dunia pendidikan. Sebelum pertemuan pertama diberikan pre-test untuk mengukur tingkat pemahaman awal para peserta mengenai teknologi AI dalam dunia pendidikan. Nilai rata-rata (mean) berkisar antara 2,24 hingga 3,53, menunjukkan bahwa sebagian besar peserta belum memiliki pemahaman yang mendalam tentang AI. Kemudian setelah pertemuan kedua, diberikan lagi post-test untuk mengukur tingkat pemahaman para peserta setelah pelatihan. Nilai rata-rata (mean) yang berkisar antara 4,06 hingga 4,35 menunjukkan peningkatan pemahaman dan keyakinan yang cukup besar di kalangan peserta. Hasil pengukuran pada pre-test dan post-test menunjukkan bahwa pelatihan pemanfaatan teknologi AI dalam dunia pendidikan berhasil meningkatkan pemahaman, keyakinan, dan kesiapan peserta dalam mengadopsi AI. Peningkatan di semua aspek menunjukkan bahwa pelatihan telah memberikan dampak positif yang signifikan. Kata kunci: guru; kecerdasan buatan; pelatihan; teknologi. AbstractInformation and communication technology advances have brought significant changes in various aspects of life, including education. Today's educational era is characterized by integrating advanced technology such as AI, machine learning, and the internet of things (IoT) in education. To overcome these problems, this community service program is designed to increase the understanding and practical skills of Kinderstation School Yogyakarta teachers in utilizing AI technology in the learning process. The workshops were carried out in two meetings (10 July 2024 and 17 July 2024), where the first meeting discussed the introduction of AI technology, and the second meeting was workshop on the use of AI technology in education. Before the first meeting, a pre-test was given to measure participants' initial understanding of AI technology in education. The mean scores ranged from 2.24 to 3.53, indicating that most participants do not yet have a deep understanding of AI. Then, after the second meeting, post-test was given to measure participants' knowledge after the workshop. The mean values ranging from 4.06 to 4.35 indicate a significant increase in understanding and confidence among participants. The measurement results in the pre-test and post-test show that workshops on the use of AI technology in education increased participants' understanding, confidence, and readiness to adopt AI. The increase in all aspects shows that the workshop has had a significant positive impact. Keywords: artificial intelligence; teacher; technology; workshop.
Co-Authors Indah Susilawati Mutaqin Akbar Nur Alamsyah