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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Innovation in Educational and Cultural Research
Dandy Pramana Hostiadi
Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Indonesia
Computer Science & IT Control & Systems Engineering Education

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Lecturer Performance Measurement Based on Organizational Culture and Leadership Behavior Analysis Using Pearson Correlation Ni Luh Putri Srinadi; Luh Putu Wiwien Widhyastuti; Dandy Pramana Hostiadi; Candra Ahmadi
Journal of Innovation in Educational and Cultural Research Vol 5, No 1 (2024)
Publisher : Yayasan Keluarga Guru Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46843/jiecr.v5i1.1163

Abstract

The academic institution generally has management quality to measure lecturer performance. It can be estimated from two variables: leadership behavior and organizational culture. This variable needs to be analyzed to determine the relationship between its parameters and forms as correlations strength for identifying the lecturer's performance. This paper proposed a new approach to measure a correlation with two variables, namely the leadership variable and the organizational culture variable. Correlation measurement adopts the Pearson Correlation. It aims to define the correlation strength. Thus, it can show the quality of lecturer performance in an academic institution. Correlation measurement uses seven parameters on leadership variables and seven measurement parameters from organizational culture variables. The experiment shows that five parameter pairs in the two variables have a strong correlation. The highest positive correlation strength is obtained at 0.321 in routine evaluation with behavior parameters. The approaches can be used to determine the lecturer's reward.
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.
Co-Authors Agus Hendra Pradita Candra Ahmadi Luh Putu Wiwien Widhyastuti Ni Luh Putri Srinadi Putu Desiana Wulaning Ayu