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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Noorminshah A. Iahad
Universiti Teknologi Malaysia
Computer Science & IT Engineering

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CAPBLAT: An Innovative Computer-Assisted Assessment for Problem-Based Learning Approach Muhammad Qomaruddin; Azizah Abdul Rahman; Noorminshah A. Iahad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 1: March 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i1.3

Abstract

This paper profiles the potential of developing and implementing Computer-Assisted Assessments (CAA) for helping lecturer in assessing students’ achievements on the Problem-Based Learning (PBL) Approach. PBL Assessment is typically formative; it includes delivery of feedback to the student, with the aim of improving their skills. The use of CAA in PBL gives advantages for both of lecturer and students by providing them with detailed formative feedback on their learning achievements compare to conventional assessment. It also reduces lectures tedious load by automating parts of the task of marking students’ work.  The methodology applied to this research was literature review and investigation of practitioners’ perception about assessment in PBL. The literatures showed that there are methods of assessments that have been used successfully in PBL, but the research selected five methods as PBL assessment framework; there are “Peer-assessment, Self-assessment, Group presentation, Individual activities, and Group report assessment”. The framework applied into the tool that encompasses the use of computer (called with CAPBLAT) for helping lecturer in assessing students’ achievements. In addition, the CAPBLAT helps to store assessment material, deliver assessment, and do auto-rating of the assessment result. The evaluation results concerning technology acceptance demonstrated that incorporating CAPBLAT can get better technology acceptance.
Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network Andi Riansyah; Irfan Eka Mahdy; Mochamad Abdul Basir; Noorminshah A. Iahad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3318-3325

Abstract

Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.
Co-Authors Azizah Abdul Rahman Basir, Mochamad Abdul Irfan Eka Mahdy Muhammad Qomaruddin Riansyah, Andi