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All Journal TEKNIK INFORMATIKA Jurnal Simetris Speed - Sentra Penelitian Engineering dan Edukasi CESS (Journal of Computer Engineering, System and Science) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Network Engineering Research Operation [NERO] Jurnal Komtika (Komputasi dan Informatika) JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Adimas : Jurnal Pengabdian Kepada Masyarakat KOMPUTEK Journal of Information Systems and Informatics GANESHA: Jurnal Pengabdian Masyarakat Formosa Journal of Computer and Information Science NERO (Networking Engineering Research Operation) Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat Jurnal ilmiah teknologi informasi Asia Jurnal Komtika (Komputasi dan Informatika) Mathematics, Informatics, Knowledge, And Information Research
Mohammad Bhanu Setyawan
Universitas Muhammadiyah Ponorogo
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : mathematics informatics knowledge and information research

 1 
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Identifikasi Jenis Tanaman Rimpang (Zingiberaceae) Rani Dwi Kartikasari; Mohammad Bhanu Setyawan; Fauzan Masykur; Adi Fajaryanto Cobantoro
MIKIR : Mathematics, Informatics, Knowledge And Information Research Vol. 1 No. 1 (2025): OCTOBER
Publisher : PT Mekar Research and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rhizomes (Zingiberaceae) are modified plant stems that grow horizontally beneath the soil surface and can produce shoots and new roots from their nodes. Rhizome plants (Zingiberaceae) are known as ginger or spice plants. This research article discusses the identification of rhizome plant species using Convolutional Neural Network (CNN) algorithm with VGG19 architecture, involving a total of 10 classes of data samples. The rhizome images underwent data preprocessing, resizing them from 500 x 500 to 200 x 200 pixels. During the model design phase, three different scenarios were tested, considering variations in dataset proportions, number of epochs, and batch sizes. The results of the three scenarios showed that the second scenario performed the best, achieving an accuracy of 90%, a loss of 0.285, precision of 93%, recall of 89%, and F1-Score of 91%. The first scenario obtained an accuracy of 88%, and the third scenario achieved an accuracy of 82%. However, when applying the model to test images and achieving the highest accuracy of 90% during training, the accuracy dropped to 40% when evaluated on 100 testing data. This drop in accuracy can be attributed to several factors, including noise in the dataset used and insufficient amount of training data, leading to the model being less effective in learning and recognizing data patterns.
Student Engagement Detection Based on Visual Behavior Indicators Using YOLOv8 on a Public Classroom Dataset Mohammad Bhanu Setyawan; Angga Prasetyo; Fauzan Masykur
MIKIR : Mathematics, Informatics, Knowledge And Information Research Vol. 2 No. 2 (2026): JUNE
Publisher : PT Mekar Research and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65475/sser8h18

Abstract

Student engagement is an important indicator for evaluating the quality of the learning process; however, its measurement in conventional classrooms still largely relies on subjective and labor-intensive manual observation. This study aims to establish a reproducible baseline for student engagement detection based on visual behavioral indicators using the YOLOv8 model on a public dataset. The working dataset was constructed from two subsets of the Student Class Behavior (SCB) Dataset and restructured into five behavioral classes: hand_raising, reading, writing, bowing_head, and turn_head, resulting in 9,274 image-label pairs split into 6,491 training, 1,854 validation, and 929 test samples. The experiment used YOLOv8n with an image size of 416, a batch size of 8, and 50 effective epochs in Google Colab. Performance was evaluated using precision, recall, mAP@0.5, and mAP@0.5:0.95. The results show that the model achieved a precision of 0.4429, a recall of 0.5393, mAP@0.5 of 0.4630, and mAP@0.5:0.95 of 0.3211. The best class-level performance was observed for writing (AP 0.635) and hand_raising (AP 0.597), while bowing_head (AP 0.288) and turn_head (AP 0.332) remained comparatively weak. These findings indicate that YOLOv8n is feasible as a reproducible baseline for visual student behavior detection, although annotation refinement, comparative experiments, and architectural optimization are still required to strengthen the scientific contribution and the feasibility of real-world classroom deployment
Co-Authors ., Sugianti Aang Kisnu Darmawan Agustin, Nely Dwi Angga Prasetyo Angga Prasetyo Arief Rahman Yusuf Ariyanti Aslan Alwi Bimantoro, Alfin Dien Cobantoro, Adi Fajaryanto Cobantoro, Adi Fajaryanto DWI RAHMAWATI Elok Putri Nimasari Fauzan Fasykur Fauzan Masykur Hardiyan Oktavianto Hoiriyah, . Ida Widaningrum, Ida Ida Yeni Rahmawati Ismail Abdulrazzaq Zulkarnain Ismail Abdurrozaq Jamilah Karaman Karaman, Jamilah Kuntang Winangun Miftahudin Agung Budi Wibowo Mita Bela Franciska Mohammad Waail Al Wajieh Munirah Munirah Navila Uliya Sahidah Nurfitri, Khoiru Oktavianto, Hardiyan Permana Putra, Angga Dian Prasetyo, Angga Rani Dwi Kartikasari Siti Khoirul Bariyah Sugianti, Sugianti Susi Darihastining Sussolaikah, Kelik Tri Wahyono Tri Yandi Yovi Litanianda, Yovi Zulkarnain, Ismail Abdurrozaq