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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA
Putu Desiana Wulaning Ayu
Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Indonesia
Computer Science & IT Control & Systems Engineering

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Classification of Infected Salmon Using CNN Deep Features and Optuna-Optimized SVM Agus Hendra Pradita; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.108820

Abstract

Fish diseases are a major challenge in the aquaculture industry, impacting productivity and the economy, particularly in salmon farming. This study aims to develop an image classification system for infected salmon using Convolution Neural Network (CNN) deep features approach and Support Vector Machine (SVM) classifier optimized with Optuna. The dataset consists of 1,208 images that were balanced through augmentation before being divided into 70% training data and 30% test data. Features were extracted from the middle layer of three pretrained CNN architectures: EfficientNetB1 (block6d_add), ResNet50 (conv4_block6_out), and VGG16 (block4_pool), then selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method to address high-dimensionality issues. An SVM classification model was trained using stratified 5-fold cross-validation, both with default parameters and hyperparameter optimization results from Optuna. The results show that the model with features from EfficientNetB1 tuned by Optuna achieved the highest accuracy of 99.34%, a significant improvement over the default model 98.23%. Meanwhile, ResNet50 and VGG16 achieved optimal accuracies of 98.23% and 98.89%, respectively, after tuning. This study contributes to the development of an adaptive and accurate early detection system for infected fish.
Usability Evaluation of SIMAK at Udayana University Using Outlier Identification with Webuse and Heuristic Methods Deppy librata; Putu Desiana Wulaning Ayu; Roy Rudolf Huizen
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.48807

Abstract

Universities require reliable and user-friendly academic information systems to support teaching, learning, and administrative processes. However, the usability of such systems often encounters obstacles that affect user satisfaction and operational efficiency. This study evaluates the usability of the Sistem Informasi Manajemen Akademik (SIMAK) at Udayana University using a combined Webuse questionnaire and Heuristic Evaluation approach. A total of 100 student responses were collected, and outlier identification using standard deviation analysis removed 34 inconsistent responses. This step was essential for preventing inflated Webuse scores and ensuring that the final dataset (n = 66) more accurately reflected typical user experiences.The Webuse results classify all usability dimensions in the “Excellent” category Content Organization & Readability (0.86), Navigation & Links (0.83), User Interface Design (0.84), and Performance & Effectiveness (0.81). However, the heuristic evaluation conducted by four expert evaluators identified 19 moderate to high-severity issues, revealing critical weaknesses in system responsiveness, interface consistency, and error prevention. These contrasting outcomes highlight that high perceived satisfaction does not necessarily align with expert-validated usability standards.The main contribution of this study lies in integrating outlier detection to refine questionnaire-based usability data, resulting in more valid interpretations. The findings offer practical recommendations for improving SIMAK’s performance, interface clarity, and error-handling mechanisms, while providing methodological insights for future usability evaluations.
Co-Authors Agus Hendra Pradita Dandy Pramana Hostiadi Deppy librata Roy Rudolf Huizen