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Evaluasi Keamanan OTP Firebase pada Aplikasi Android: Perbandingan SAST dan IAST dalam Identifikasi Kerentanan Rahayuda, I Gede Surya; Santiari, Ni Putu Linda
JISKA (Jurnal Informatika Sunan Kalijaga) Early Access
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.4909

Application security is crucial for protecting user data from cyber threats, particularly in Android applications that utilize One-Time Password (OTP)-based authentication. This study evaluates the security of Firebase OTP via email using a combination of Static Application Security Testing (SAST) with Mobile Security Framework (MobSF) and Interactive Application Security Testing (IAST) with AppSweep. The results show that the combination of SAST and IAST is superior to single testing methods due to its wider detection coverage. SAST detects vulnerabilities in static code, while IAST identifies exploits in runtime. The testing showed significant improvements, with high-severity vulnerabilities decreasing from 3 cases in OTP-1 to zero in OTP-5, and the security score increasing from 43 (B) to 78 (A) in MobSF. Meanwhile, the number of vulnerabilities in AppSweep decreased from 14 to 9, with all high-severity vulnerabilities resolved. However, this study still has limitations, such as limited dataset coverage and potential bias from the testing tool. For further improvement, additional research can integrate artificial intelligence to automate vulnerability detection, as well as explore biometric-based authentication to enhance system security even further.

Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM Pratama, Naufal Adhi; Utomo, Danang Wahyu
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.4908

Diabetes mellitus is a metabolic disease characterized by elevated blood sugar levels due to impaired insulin secretion, insulin action, or both. The disease has a major impact on public health and contributes to high morbidity and mortality rates in many countries. Prevention and early detection are essential to reduce the adverse effects of this disease. This study aims to analyze and apply machine learning algorithms in detecting diabetes mellitus, focusing on the use of XGBoost and LightGBM algorithms. The dataset used in this study includes various features related to diabetes risk factors, such as age, gender, body mass index (BMI), hypertension, smoking history, and HbA1c and blood glucose levels. Preprocessing was performed to clean and balance the data using the SMOTE-Tomek technique. Next, the model was built and evaluated using the K-Fold cross-validation method to measure the accuracy and stability of the model. The results showed that the XGBoost model achieved 97.31% accuracy, while the LightGBM model produced 97.26% accuracy. Combining the two models through blending techniques resulted in an accuracy of 97.51%, indicating that the combination of models can improve prediction performance. This study shows the great potential of machine learning algorithms, especially XGBoost and LightGBM, in detecting diabetes mellitus accurately and efficiently. Hopefully, the results of this study can contribute to the development of decision support systems for more effective early diagnosis of diabetes.

Evaluasi Keamanan OTP Firebase pada Aplikasi Android: Perbandingan SAST dan IAST dalam Identifikasi Kerentanan Rahayuda, I Gede Surya; Santiari, Ni Putu Linda
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.4909

Application security is crucial for protecting user data from cyber threats, particularly in Android applications that utilize One-Time Password (OTP)-based authentication. This study evaluates the security of Firebase OTP via email using a combination of Static Application Security Testing (SAST) with Mobile Security Framework (MobSF) and Interactive Application Security Testing (IAST) with AppSweep. The results show that the combination of SAST and IAST is superior to single testing methods due to its wider detection coverage. SAST detects vulnerabilities in static code, while IAST identifies exploits in runtime. The testing showed significant improvements, with high-severity vulnerabilities decreasing from 3 cases in OTP-1 to zero in OTP-5, and the security score increasing from 43 (B) to 78 (A) in MobSF. Meanwhile, the number of vulnerabilities in AppSweep decreased from 14 to 9, with all high-severity vulnerabilities resolved. However, this study still has limitations, such as limited dataset coverage and potential bias from the testing tool. For further improvement, additional research can integrate artificial intelligence to automate vulnerability detection, as well as explore biometric-based authentication to enhance system security even further.

Komparasi Distance Measure pada K-Means dalam Klasterisasi Peserta KB Aktif Anshori, Mochammad; Ningrum, Afifah Vera Ferencia Fitria; Pradini, Risqy Siwi
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.5006

The rapid population growth in Indonesia poses significant challenges to public welfare, economic stability, and sustainable development. The Family Planning program aims to regulate population growth through various contraceptive methods; however, participation rates often differ across regions. Understanding these variations is crucial for designing targeted interventions. This study investigates how different distance measures in the K-Means clustering algorithm affect the segmentation quality of KB participants in Kalirejo Village, Lawang District. Eight distance metrics—Euclidean, Manhattan, Minkowski, Chebyshev, Mahalanobis, Bray-Curtis, Canberra, and Cosine—were compared using standardized data from the local BKKBN office (January–September). Cluster validity was evaluated using the Silhouette Coefficient across k=2–10. Results show that the Manhattan distance with k=2 achieved the best clustering quality (SC = 0.7191), effectively distinguishing participant groups by contraceptive method preference. The study highlights the importance of selecting suitable distance measures to improve data-driven policy and decision-making in family planning management.

Sistem Deep-Learning Yolov8 untuk Deteksi Penggunaan APD Secara Real-Time Langi, Nelson Mandela Rande; Fadllullah, Arif
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.5051

Although workplace safety regulations in construction are clear, many workers are still reluctant to use Personal Protective Equipment (PPE) due to a lack of awareness, work pressure, and limited facilities. As a result, the risk of serious accidents increases. Conventional approaches such as verbal warnings or CCTV monitoring are considered less effective for early detection and prevention of violations. This study proposes an automatic detection system for PPE usage in construction areas using YOLOv8. The model was trained on a secondary dataset of 3,569 images for 100 epochs, with a 60% training, 20% validation, and 20% test split. Testing on 90 real-time frames showed good performance in detecting 8 PPE classes, with an average precision of 0.935, recall of 0.806, and F1-measure of 0.862. The results indicate that the system can classify PPE usage with high accuracy. However, a recall below 1 suggests that some objects, particularly "not wearing glasses" and "not wearing shoes," failed to be detected. The F1-measure of 0.862 reflects a good balance between precision and recall.

Uji Efektifitas Kompresi Golomb-Rice dan Huffman untuk Metadata EXIF dalam File JPEG Hasan, Yasir
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.5060

Compression algorithms are now called modern compression algorithms. This improvement is characterized by the combination of various classical techniques and is even based on machine learning and AI. However, the important part of compression is not only the algorithm, but also knowledge of the internal structure and metadata of the file is required. Like JPEG has a file structure that can be changed, cannot be changed, every marker (header), and EXIF metadata. Lack of knowledge of the file structure can cause data damage and file corruption. This study evaluates the compression of EXIF metadata of JPEG files using the Golomb-Rice and Huffman algorithms. Golomb-Rice can produce compression that affects the k parameter, while Huffman is optimal based on symbol frequency, but requires a code table. This study measures the effectiveness of both algorithms based on the compression ratio (CR). The test results of Golomb-Rice are more effective than those of Huffman. So, it can be concluded that the Golomb-Rice algorithm is superior in the context of compressing EXIF JPEG metadata, while Huffman shows lower efficiency in the tested scenarios.

Perbandingan Kinerja MobileNetV2 dan VGG16 dalam Klasifikasi Penyakit pada Citra Daun Tanaman Cabai Khoirunnisa, Itsnaini Irvina; Fadlil, Abdul; Yuliansyah, Herman
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.5075

Chili peppers play a crucial role in the Indonesian economy, serving as a significant source of income for many farmers. Price fluctuations influenced by weather conditions make this crop vulnerable to diseases that can impact productivity. However, leaves are key indicators of plant health, revealing early disease symptoms before they spread. This research focuses on detecting diseases in chili plants using neural network architectures via transfer learning, specifically MobileNetV2 and VGG16, to classify chili leaf images. The study aims to identify three disease classes: begomovirus, leaf spots, and healthy leaves. The dataset comprises 3,150 leaf images, split into 70% for training and 30% for testing. Results show that MobileNetV2 achieved an accuracy of 99.47% and VGG16 98.62%, with evaluation using a confusion matrix indicating good performance in disease identification, where MobileNetV2 offers better computational efficiency. Thus, transfer learning can effectively identify leaf diseases in chili plants.

Sistem Deteksi Gerakan Kecurangan UTBK Real-Time dengan YOLOv8 dan Optical Flow Ardiansyah, Muhammad Naufal; Suryahadi, Farrel Zikri; Pratama, Hendrico Edhent Surya; Sari, Anggraini Puspita
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.5365

Integrity and honesty are fundamental aspects of education, including the implementation of the Computer-Based Written Examination (UTBK). Conventional exam supervision is considered less effective in monitoring participants’ behavior due to the limitations in human observation capabilities and consistency. This study develops a real-time cheating-detection system based on camera input by integrating the YOLOv8 algorithm with Farnebäck optical flow. The YOLOv8 algorithm identifies participants’ body poses and activities directly from video footage, while Optical Flow analyzes the direction and motion patterns between frames over time. The system is designed to recognize various suspicious poses such as head-turning, bowing, and cheating-related gestures that indicate potential dishonesty. All detection results are automatically recorded in an SQLite database, complete with timestamps and visual evidence. Experimental results show that the system achieves 94.3% accuracy in detecting suspicious movements. The combination of both methods also helps maintain detection stability when keypoints are not consistently captured in some frames. Additionally, the system is equipped with a graphical user interface (GUI) to facilitate easier monitoring and analysis. These results demonstrate that a pose-and-motion analysis-based approach offers an intelligent and efficient solution for enhancing digital supervision of UTBK examinations.

Comparative Analysis of Camellia and AES Across File Sizes and Types Tju, Teja Endra Eng; Alfadhillah, Fauzi
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.5418

Data security is a critical aspect of modern information systems that requires processing cryptographic efficiency and resilience. This study compares two widely used symmetric encryption algorithms, named Camellia and AES, based on their performance and resistance to standard attack methods. An experimental approach was applied using 72 files across eight commonly used formats (*.mp3, *.jpg, *.png, *.pdf, *.docx, *.xls, *.pptx, and .txt) in three predefined sizes: 100 KB, 1 MB, and 10 MB. Each file underwent encryption and decryption in a controlled environment, with metrics such as processing time, CPU usage, and RAM consumption recorded. Simulated Dictionary, Birthday, and Brute-Force attacks were conducted to assess algorithm robustness. Results show that AES performs faster, especially on large files, but with higher memory usage. Camellia demonstrated more consistent RAM usage and stronger resistance, successfully withstanding all attacks except one brute-force case on a small plaintext file. AES suffered multiple breaches on structured files of smaller sizes. The findings suggest that algorithm selection should consider workload characteristics and system constraints. The main contribution of this research lies in its comprehensive dataset and empirical comparison, providing practical insights to support encryption algorithm choices in real-world applications.

Model Prediksi Risiko Kanker Serviks dengan Pendekatan Support Vector Machine Hutapea, Juwita Stefany; Harani, Nisa Hanum; Prianto, Cahyo
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.5445

Cervical cancer is one of the leading causes of death in women, especially in developing countries due to delays in early diagnosis. Developing a risk prediction model based on the Support Vector Machine (SVM) algorithm is one way to support a more accurate and efficient early detection process. The research object is medical records of female patients obtained from hospitals in Medan City, with a total of 164 patient data. The development process was carried out through the CRISP-DM stages, which include data cleaning, feature transformation, class balancing with SMOTE, and dimensionality reduction using PCA. The evaluation results showed that the best model was obtained with a PCA configuration with 9 principal components (90% variance) and a test size of 80:20, resulting in an accuracy of 88%, a precision of 88%, a recall of 84%, and an F1-score of 86%. Cross-validation evaluation with 5 folds provided the best average performance and the smallest standard deviation, indicating model stability. The final model was implemented in a web-based system to facilitate digital early detection. This study shows that SVM with the SMOTE and PCA approaches is effective in predicting cervical cancer risk accurately and efficiently.

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Muhammad Taufiq Nuruzzaman

Contact Email
m.taufiq@uin-suka.ac.id

Phone
+6287708181179

Journal Mail Official
jiska@uin-suka.ac.id

Editorial Address
Teknik Informatika, Fak. Sains dan Teknologi, UIN Sunan Kalijaga Jln. Marsda Adisucipto No 1 55281 Yogyakarta

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Muhammad Taufiq Nuruzzaman

Contact Email m.taufiq@uin-suka.ac.id

Phone +6287708181179

Journal Mail Official jiska@uin-suka.ac.id

Editorial Address Teknik Informatika, Fak. Sains dan Teknologi, UIN Sunan Kalijaga Jln. Marsda Adisucipto No 1 55281 Yogyakarta

Location Kab. sleman, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Muhammad Taufiq Nuruzzaman

Contact Email
m.taufiq@uin-suka.ac.id

Phone
+6287708181179

Journal Mail Official
jiska@uin-suka.ac.id

Editorial Address
Teknik Informatika, Fak. Sains dan Teknologi, UIN Sunan Kalijaga Jln. Marsda Adisucipto No 1 55281 Yogyakarta

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA