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JISKa (Jurnal Informatika Sunan Kalijaga)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 25275836     EISSN : 25280074     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Library & Information Science
JISKa (Jurnal Informatika Sunan Kalijaga) adalah jurnal yang mencoba untuk mempelajari dan mengembangkan konsep Integrasi dan Interkoneksi Agama dan Informatika yang diterbitkan oleh Departemen Teknik Informasi UIN Sunan Kalijaga Yogyakarta. JISKa menyediakan forum bagi para dosen, peneliti, mahasiswa dan praktisi untuk menerbitkan artikel penelitiannya, mengkaji artikel dari para kontributor, dan teknologi baru yang berkaitan dengan informatika dari berbagai disiplin ilmu
Arjuna Subject : -
Articles 241 Documents
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Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification Ahmad Fauzi; Achmad Lutfi Fuadi; Agus Heri Yunial
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5860

Abstract

The automated categorization of brain cancers from MRI is essential for improving diagnostic precision. Traditional Convolutional Neural Networks (CNNs) are proficient in local feature extraction but are constrained in their ability to capture long-range spatial relationships, hence impairing performance on intricate malignancies. We propose a hybrid parallel architecture that merges a CNN with a Vision Transformer (ViT) to combine local and global feature modeling. We assessed our dual-branch model in comparison to a conventional CNN baseline using a curated dataset of 15,000 MRI images categorized into three classes: glioma, meningioma, and pituitary. The hybrid model exhibited enhanced performance, attaining 98.40% accuracy and 0.0783 loss, in contrast to the baseline's 97.40% accuracy and 0.1187 loss. The substantial decrease in misclassifications was validated by additional metrics, such as enhanced recall for the meningioma category. The integration of local and global variables produces a more precise, stable, and generalizable classification framework, demonstrating significant potential as a basis for dependable AI-driven Clinical Decision Support Systems (CDSS) in neuroradiology.
Evaluasi Pemanfaatan Online Compiler dan Autograder Sebagai Pendukung Aktivitasi Praktikum Pemograman Mahasiswa Muhammad Galih Wonoseto; Muhammad Jibril
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5864

Abstract

The automation of programming practicum assessment using an online compiler has become increasingly important to address the limitations of manual evaluation, such as time inefficiency, subjectivity, and the lack of automated feedback support in conventional e-learning systems. This study aims to evaluate the implementation of an online compiler and autograder in a programming practicum through interviews and questionnaires with 91 respondents, comprising lecturers and students. The online compiler and autograder were implemented using the Moodle platform, integrated with the CodeRunner plugin and the JOBE server to automate code evaluation and provide real-time feedback to students. User acceptance was assessed using the Technology Acceptance Model (TAM), which includes three dimensions: perceived ease of use, perceived usefulness, and system acceptance. The evaluation results indicate positive scores across all dimensions, with 78.75% for ease of use, 78.09% for usefulness, and 76.33% for system acceptance. These findings demonstrate that the proposed system effectively improves the assessment process and shows strong potential for adoption in programming education environments.
Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes Fakhar Muhammad Hidayat; Cahyo Crysdian; Tri Mukti Lestari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5912

Abstract

Manual identification of road damage using simple measuring tools is considered inefficient, subjective, and time-consuming, hindering the infrastructure repair process. This study aims to optimize automatic road crack detection by combining edge detection for feature extraction and Gaussian Naive Bayes (GNB) classification. This research utilizes the Road Surface Classification Dataset (RSCD), consisting of 1000 concrete road images with balanced class proportions. The research process includes image acquisition, segmentation, and preprocessing using the Sobel filter to extract edge features and erosion to refine crack representation. Statistical features in the form of black pixel count and edge length are extracted as model inputs. Experiments were conducted using three data split scenarios (70:30, 80:20, 90:10) validated with the K-Fold Cross Validation method. The test results show that the 90:10 data split scenario yields the most optimal and stable performance, achieving 88% accuracy, 91.67% precision, 84.62% recall, and an F1-Score of 88%. This study optimises the balance between computational efficiency and detection accuracy through a lightweight hybrid approach that integrates edge-based feature extraction with probabilistic classification.
Pengaruh Orientasi Citra MRI pada Klasifikasi Tumor Otak Berbasis GLCM dan SVM Yoza Setya Febriyanti; Okta Qomaruddin Aziz; Suhartono Suhartono
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5935

Abstract

Brain tumors are a global health problem, ranking 12th as a cause of death. MRI is used in the diagnosis of brain tumors because of its ability to display soft tissue structures in detail, but manual interpretation of MRI images by radiologists is still subjective. Therefore, a more objective computer-based classification approach is needed. One factor that could potentially affect classification performance is the difference in MRI image orientation, namely axial, sagittal, and coronal. This study aims to analyze the effect of MRI image orientation on GLCM and SVM-based brain tumor classification. The preprocessing stage includes cropping, noise reduction, and resizing. Feature extraction was performed using GLCM with distance d = 1 at angles of 0°, 45°, 90°, and 135° with contrast, correlation, energy, and homogeneity features. Classification was performed using SVM with Linear, Polynomial, RBF, and Sigmoid kernels. The test results show that the axial orientation produces the highest accuracy of 78% with the Linear kernel, the sagittal orientation achieves an accuracy of 83% with the Polynomial kernel, and the coronal orientation provides the highest accuracy of 86% with the RBF kernel. These findings indicate that the orientation of MRI images affects the performance of texture-based brain tumor classification.
Optimasi Hyperparameter Ensemble Learning untuk Prediksi Perkembangan Penyakit Diabetes dengan Explainable AI David Suharjanto; Muhammad Syafiq Akmal; Nur Fikri Khuluq; Muh Naufal Muzhaffar; Maria Ulfah Siregar
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5953

Abstract

This research focuses on optimizing and assessing ensemble learning models for predicting diabetes progression by combining hyperparameter tuning and explainable artificial intelligence techniques. Experiments were conducted using the scikit-learn diabetes dataset, which contains 442 samples with ten numerical features representing patients’ clinical conditions. The data were split into 80% for training and 20% for testing. Two ensemble methods were explored: Random Forest Regressor (bagging) and XGBoost Regressor (boosting). Hyperparameter optimization was carried out using RandomizedSearchCV and BayesianSearchCV under a five-fold cross-validation scheme. Model performance was evaluated using MAE, MSE, RMSE, and R² metrics, while interpretability was examined through SHAP summary plots. The results indicate that BayesianSearchCV consistently delivered superior performance gains compared to random search. In particular, the optimized XGBoost model achieved an R² score of 0.5018, improving by 19.8% over the baseline model (R² = 0.4188), and reduced RMSE from 55.49 to 51.37. SHAP analysis showed that serum triglycerides, body mass index, and blood pressure were the most influential features. Overall, the findings suggest that Bayesian-based hyperparameter optimization can effectively improve ensemble regression performance in medical prediction tasks involving limited datasets.
Optimizing Iris Plant Classification with Ensemble Models and XAI: A Comprehensive Analysis of Model Performance Ahmad Subadri; Ishmah Afiyah; Fiki Sanora; Arya Indrawan; Maria Ulfah Siregar
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5967

Abstract

This study aims to improve the performance of Iris plant classification by integrating ensemble learning techniques with Explainable Artificial Intelligence (XAI) to achieve high accuracy while enhancing model interpretability. Random Forest, XGBoost, and AdaBoost algorithms are combined within a Voting Ensemble framework and evaluated using the Iris Plants dataset, which comprises 150 data samples distributed equally across three Iris species (50 samples per class: Iris setosa, Iris versicolor, and Iris virginica). The dataset exhibits a perfectly balanced class distribution, ensuring that no class imbalance correction was required. The Voting Ensemble model was evaluated using a hold-out test set (80:20 split) and further validated through 5-Fold Stratified Cross-Validation, yielding a mean cross-validation accuracy of 95.83% (±2.64%) and a test set accuracy of 93.33%. To enhance model transparency, the SHAP (SHapley Additive Explanations) method is applied to explain the contribution of each feature to the prediction outcomes. The Voting Ensemble model achieved an ROC AUC score of 0.9900 (macro-average), with Precision, Recall, and F1-Score each reaching 0.9333 (macro-average). Feature importance analysis reveals that petal length and petal width are the primary factors in the Iris species classification process. The strong correlation (r = 0.9991) between feature importance scores in the Random Forest model and SHAP values confirms the consistency and reliability of the model’s interpretability. These findings demonstrate that integrating ensemble learning with XAI not only improves predictive performance but also strengthens transparency and trust in machine learning models, particularly for plant classification tasks.
Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan Metode Multi-Layer Perceptron Laudza Atsila Prasetyo; Okta Qomaruddin Aziz; Tri Mukti Lestari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6022

Abstract

This research aims to evaluate the performance of the Multi-Layer Perceptron (MLP) for classifying eye diseases from fundus images in the ODIR dataset, which comprises four classes: Normal, Diabetic, Glaucoma, and Cataract. The methodology includes feature extraction using GLCM and Gabor, data pre-processing through cleaning, augmentation, and undersampling, and testing 16 model scenarios with variations in the number of hidden layers (2 and 3) and neuron configurations. The results show that data balance and dataset size are the most influential factors affecting model performance, with the best results achieved through the combination of undersampling and augmentation. The optimal architecture was obtained with the 64–32-neuron configuration, yielding a mean accuracy of 73.06%. Overall, this study concludes that combining a balanced dataset with a proportional MLP architecture significantly improves the model’s ability to classify eye diseases from fundus images.
Optimizing Financial Risk Prediction for Loan Approval Decisions Khalifatur Rauf; Adi Cahyo Kuswijayanto; Ella Kristiantini Susan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6071

Abstract

Accurate financial risk prediction is essential for effective loan approval decision-making, particularly in data-driven financial systems. This study investigates the influence of feature selection strategies on the performance of machine learning models for loan approval prediction using the publicly available Kaggle "Financial Risk for Loan Approval" synthetic dataset, which contains 20,000 applications. Experiments evaluated multiple feature selection paradigms, including filter-based, wrapper-based, embedded, and PCA-informed approaches across six classification models using stratified 10-fold cross-validation and imbalance-aware metrics. The results show that feature selection consistently improves predictive robustness and minority class recognition. Contrary to assumptions favoring complex models, Logistic Regression combined with Lasso regularization achieved the best overall predictive performance, yielding an ROC-AUC of 99.41% and an F1-score of 91.72%. Embedded feature selection methods provided the most favorable balance between accuracy and computational efficiency. These findings indicate that the effectiveness of feature selection depends heavily on its interaction with model complexity, providing empirical guidance for designing robust, interpretable financial risk prediction systems.
Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake Chalifa Chazar; Firhan Hafiansyah; Milda Gustiana Husada; Uung Ungkawa; Rizka Milandga Milenio
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6085

Abstract

Deepfake is a form of facial manipulation in videos that utilizes artificial intelligence-based models to generate highly realistic visual content. The increasing spread of Deepfake has the potential to cause misinformation, manipulate public opinion, and enable the misuse of digital content, making reliable detection systems increasingly necessary. This study develops a face-based Deepfake video detection system by combining spatial and temporal analysis within a unified processing framework. ResNeXt is employed to extract visual facial characteristics from each frame, while Long Short-Term Memory (LSTM) is utilized to learn facial pattern changes across frames in a video sequence. The dataset used is sourced from FaceForensics++, consisting of 1000 original videos and 1000 Deepfake videos. All data are processed through frame extraction and face detection stages. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that ResNeXt, as the baseline model, achieves 77.67% accuracy and 76.66% F1-score, while integrating LSTM improves system performance to 81.00% accuracy and 80.41% F1-score. These findings indicate that the utilization of temporal information contributes to improved stability and accuracy in face-based Deepfake video detection.
Analisis Ketahanan Lightweight Audio Spectrogram Transformer pada Identifikasi Pembicara Kondisi Berderau I Kadek Arya Sugianta; Gde Palguna Reganata
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6170

Abstract

The use of deep learning models for speaker identification on devices with limited computational resources requires significant architectural optimization. This study evaluates the performance and robustness of the Lightweight Audio Spectrogram Transformer (AST) architecture, which has been extremely compressed to 570,536 parameters. The proposed method uses low-resolution Mel-Spectrogram representations (64x64 pixels) as input for a global self-attention mechanism. Testing was conducted using a 5-Fold Cross Validation scheme on a dataset injected with non-stationary environmental noise from the ESC-50 corpus at various Signal-to-Noise Ratio (SNR) levels. Experimental results show that under ideal conditions, the model achieves a solid average validation accuracy of 70.86% ± 2.69% with a Macro Average F1-score of 0.68 ± 0.03. However, the model’s performance degrades sharply to 17.61% at an SNR of 5 dB and drops to 9.21% under extreme conditions at an SNR of 0 dB. These findings reveal a critical trade-off where radical parameter compression leads to the loss of spectral feature redundancy that acts as an implicit noise filter. This study concludes that while lightweight Transformer mechanisms are highly efficient for Edge AI, the integration of pre-processing modules or noise-robust training strategies is an absolute necessity to maintain identification integrity in noisy real-world environments.

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