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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Jurnal Bina Praja Bappenas Working Papers Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Buletin Poltanesa Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Lokomotif Abdimas: Jurnal Pengabdian kepada Masyarakat BACA: Jurnal Dokumentasi dan Informasi
Rianto, Yan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM Aldi, Kenny; Rianto, Yan
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6414

Abstract

The growing volume of global waste poses a significant challenge for effective waste management, particularly in developing countries where awareness and practices around waste sorting remain limited. This study aims to enhance elementary school students' understanding and efficiency in sorting organic and inorganic waste using an interactive, AI-powered educational tool. The proposed system, WasteWise, integrates YOLOv8 for real-time object detection and ESP32-CAM for capturing waste images. A pre-test and post-test experimental design was conducted to assess students’ performance before and after using the system. The results showed a notable improvement in sorting accuracy, increasing from 60% with manual sorting to 90% using the WasteWise system, alongside reduced sorting time. These findings highlight the system's potential not only as an automated waste classification tool but also as a cost-effective and engaging platform for promoting environmental awareness and digital literacy among young learners.
SISTEM DETEKSI BERITA PALSU DUA BAHASA MENGGUNAKAN TF-IDF DAN MULTINOMIAL NAIVE BAYES Septianto, Rheno; Rianto, Yan
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 1 (2026): Maret 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i1.550

Abstract

The rapid spread of misinformation poses a major threat to public trust and digital literacy. This study develops a bilingual fake news detection system capable of analyzing news content in English and Indonesian. The system uses two separate monolingual models trained independently on the WELFake dataset (English) and the Berita Hoax 2023 dataset (Indonesian). Each model applies text preprocessing techniques such as tokenization, stopword removal, and normalization before transforming the text using TF-IDF. The classification process utilizes the Multinomial Naïve Bayes algorithm, chosen for its efficiency in handling high-dimensional text data. The bilingual system integrates an automatic language detection module that selects the appropriate model based on the detected language. Evaluation results show that the English model achieves an accuracy of 86%, while the Indonesian model achieves an accuracy of 93%. These results indicate that the two-model bilingual approach provides reliable performance for multilingual fake news detection. This study contributes to practical solutions for misinformation mitigation, especially in multilingual environments like Indonesia.
Tinjauan Penerapan CNN dan YOLO pada Pengolahan Citra Agraria Medis Industri Cerdas Robby Saputra; Rianto, Yan; Kusuma, Muhammad Romadhona
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10988

Abstract

The Rapid growth of Artificial Intelligence (AI), particularly Deep Learning, is driving significant transformations in digital image processing in the argricultural, medical, and smart industrial sectors. Two approaches are most dominant in this research Convolutional Neural Network (CNN) for image classification and You Only Look Once (YOLO) for real-time object detection. The purpose of this reasearch is to systematically review the application, performance, and defense of CNN and YOLO in various domains with different data characteristics. The method used is a Systematic Literatur Review (SLR) of the latest relevant scientific publications, focusing on evaluation matrics such as accuracy, pression, recall, F1-score, and Mean Average Precision (mAP). The review results show that CNN excels in image classification tasks with a high level of accuracy, especially on data with relatively stabel visual patterns, while YOLO is more effective in applications that demand inference speed and direct object detection. However, several major limitations were found, including decreased performance in extreme lighting conditions, complex backgrounds, small objects, and visual similarity between classes. It is concluded that the choice of architecture must be adjusted to the characteristics of the data and application needs,
Co-Authors Abdullah, Ifnu Abdul Aziz Abdurrahman, Muhammad Farhan Aldi, Kenny Antonius Yadi Kuntoro Cipta Endyana Dedi Priansyah Emma Rochima Fabiyanto, Dedik Fahlapi, Riza Gata , Windu Hanggoro, Dimas Banu Dwi Ida Widianingsih Kusuma, Muhammad Romadhona Laela Kurniawati, Laela Lindung Parningotan Manik Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad Nasir Hamzah Nova, Lusindah Nugraha, Thomas Fajar Parningotan Manik , Lindung Prakoso Bhairawa Putera Prawira, Nurul Adi Rika Wulandari Robby Saputra Sadikin, Rifki Septiani, Riska Kurnia Septianto, Rheno Sinta Ningrum Sunendar, Nendi Supriyadi, Muhammad Suryanto Suryanto Zico Pratama Putra