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This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Innovation in Educational and Cultural Research Journal of Applied Data Sciences
Dandy Pramana Hostiadi
Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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.
Multi Domain Feature Fusion and Boosting Based Learning for Robust Gallbladder Ultrasound Image Classification Gede Angga Pradipta; Pharan Chawaphan; Sutikno Sutikno; Putu Desiana Desiana Ayu; Dandy Pramana Hostiadi; Made Liandana
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1268

Abstract

Accurate identification of gallbladder conditions is critically important in the medical sector, as early detection of diseases such as gallstones, cholecystitis, carcinoma, and polyps can substantially improve treatment outcomes, reduce complications, and guide timely surgical or therapeutic interventions. Existing literature on gallbladder disease classification still presents notable gaps. Most prior works rely solely on single-domain feature extraction either deep CNN-based spatial descriptors or handcrafted statistical/texture features without exploiting the complementary strengths of multi-domain feature fusion. This study addresses these gaps by proposing a hybrid framework that combines advanced preprocessing, multi-domain feature fusion, feature selection, and ensemble classification. The preprocessing pipeline applies Non-Local Means (NLM) denoising, Contrast Limited Adaptive Histogram Equalization (CLAHE), frequency-domain low-pass filtering, and Gabor filtering to enhance image quality and highlight diagnostically relevant structures. Features are extracted by fusing deep spatial descriptors from a pretrained Inception V3 network with handcrafted statistical and texture-based features, including Gray Level Dependency Matrix (GLDM) measures. Dimensionality reduction is performed using ANOVA k-best selection to retain the most discriminative attributes. The refined features are classified using LightGBM, XGBoost, Histogram Gradient Boosting, and AdaBoost, enabling a comprehensive performance comparison. Experiments conducted on the balanced UIdataGB dataset (10,692 annotated images across nine diagnostic categories) demonstrate that LightGBM, XGBoost, and Histogram Gradient Boosting achieve near-perfect performance, with accuracies exceeding 98.6% and AUC values of 0.98 across all classes, while AdaBoost shows markedly lower discriminative capability.The results suggest that gradient boosting approaches are a promising option for multi-class gallbladder disease detection, particularly when combined with multi-domain feature fusion and appropriate preprocessing and feature selection techniques.
Co-Authors Agus Hendra Pradita Candra Ahmadi Gede Angga Pradipta Luh Putu Wiwien Widhyastuti Made Liandana Ni Luh Putri Srinadi Pharan Chawaphan Putu Desiana Desiana Ayu Putu Desiana Wulaning Ayu Sutikno Sutikno