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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 1,006 Documents
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Mapping Compliance–Maturity Gaps in EdTech Personal Data Security: Integrating the PDP Law and KAMI Index 5.0 Ocha Oktafia; Nelmiawati Nelmiawati; Putri Hening Graha; Kessy Dealova
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13447

Abstract

The rapid post-pandemic growth of Educational Technology (EdTech) platforms in Indonesia is not always accompanied by personal data security readiness, despite the high compliance demands mandated by Law Number 27 of 2022 concerning Personal Data Protection (PDP Law). Previous studies utilizing the KAMI Index generally assessed technical maturity separately from legal frameworks, leaving a gap in understanding how regulatory compliance correlates with technical maturity within a single entity. This study aims to evaluate the information security maturity level and legal compliance of PT XYZ's EdTech platform, while simultaneously mapping the connection between the PDP Law requirements and the assessment areas of KAMI Index 5.0. This research employs a qualitative case study approach. Data were collected through questionnaires based on the KAMI Index 5.0 instrument and PDP Law articles, completed by three key respondents—the CEO, CTO, and VP of Information Security—and subsequently validated through interviews and verification of supporting documents. The results reveal a significant gap: procedural compliance with the PDP Law is relatively high (28 out of 36 articles fully implemented), yet the KAMI Index maturity level falls into the "Inadequate" (Tidak Layak) category with a final score of 222. The system is notably weak in risk management and personal data protection areas (Level I+). These findings emphasize that procedural compliance does not equate to holistic security maturity. This research contributes by providing a legal-technical gap mapping alongside recommendations based on ISO/IEC 27002:2022, which can be adopted by other EdTech organizations.
Facial Deepfake Detection System Using YOLOv11 and Xception Architecture Fachril Akbar; Nurdin Nurdin; Kurniawati Kurniawati
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13449

Abstract

The rapid development of artificial intelligence has led to the emergence of deepfakes, which pose serious threats to information security and public trust in digital media. This study develops a facial deepfake detection system that integrates YOLOv11 for face detection and the Xception architecture for classifying real and manipulated faces. YOLOv11 successfully localized all facial regions in the tested dataset with high confidence scores. The Xception model achieved a testing accuracy of 90.10%, with a Recall of 97.11% for the Fake class and an AUC of 0.98. Visual explanation using Grad-CAM showed that the model focused on critical areas such as the forehead, temples, and face boundaries to detect manipulation artifacts. The system was implemented as a desktop application named "Snap Detector" and passed black-box testing. However, the average processing speed of 6.26 FPS on an NVIDIA T4 GPU indicates that further optimization is needed for real-time performance.
Evaluation of Information Security Readiness Level Using the Integration of KAMI Index 5.0 and ISO/IEC 27001:2022 Nasya Ananda Rozi; Agus Wijayanto; Wahyu Nur Alimyaningtias
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13451

Abstract

Information security is a strategic aspect that determines the continuity of organizational operations amid an increasing dependence on digital systems. PT. QAZ, as a regionally owned business entity providing clean water services, relies on information systems to support customer management, payment systems, and distribution network management, so evaluating the readiness of its information security is crucial. This study aims to measure the level of information security readiness of PT. QAZ through the integration of the KAMI Index 5.0 with ISO/IEC 27001:2022 control using a mixed methods approach. The results of the evaluation of the Electronic Systems Category obtained a score of 19 points with a high category, while the accumulation of the sixth score in the evaluation area reached 372 out of a maximum total of 918 points, with the achievement of the supplement category of 26%. In detail, the maturity level of each domain is: Level I+ Information Security Governance, Level II Risk Management, Level I Information Security Management Framework, Level I+ Information Asset Management, Level II Information Technology and Security, and Level I Personal Data Protection.
Real-Time Weapon Detection and Suspect Face Capturing System Using YOLOv8 Chairina Ulfa; Muhammad Fikry; Hafizh Al Kautsar AIdilof
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13454

Abstract

The rise of violent crimes involving sharp weapons and firearms in public spaces, including educational campuses, demands an automated real-time surveillance system to assist security personnel. This study proposes a web-based weapon detection system using YOLOv8, specifically designed to detect seven object classes: sickle, machete, axe, sword, knife, pistol, and rifle. When a weapon is detected, the system automatically captures the suspect's facial image using Haar Cascade and triggers alarm notifications, detection logs, and statistical reports. This integrated data package serves as critical digital evidence to support post-incident identification and investigation. To train the model, we constructed a dataset of 11,445 images sourced from public datasets, video frame extraction, and smartphone camera captures, which was subsequently augmented to 27,687 images to enhance model generalization. The evaluation results demonstrate strong performance with a Precision of 94.3%, Recall of 87.8%, mAP@0.5 of 93.2%, and mAP@0.5:0.95 of 60.1%. Real-time testing at distances ranging from 50 cm to 500 cm confirmed that the system reliably detects most weapon classes, particularly achieving consistent detection for sickles, machetes, and rifles across all tested ranges, while performance for smaller objects like knives and pistols showed decreased accuracy at extreme distances, indicating directions for future work. The findings confirm that the proposed system effectively detects and classifies sharp weapons and firearms in real-time while simultaneously providing visual documentation of the perpetrator, offering a practical and comprehensive security solution for campus environments.
Evaluating Post-Training Employment Outcomes for Workforce Policy through Clustering: A Comparative Study of K-Means, Hierarchical Clustering, Gaussian Mixture Model, and Fuzzy C-Means Mayrisa Andriyani; Siti Nurwilda; Wahyu Ningtiyas Mergianti; Nurissaidah Ulinnuha
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13482

Abstract

Evaluating the effectiveness of government training programs requires more than a binary employed/unemployed indicator, since participants who find work still differ substantially in salary level, waiting time to employment, job position, gender, and employment sector. Prior studies applying clustering to workforce or socio-economic data have generally compared only two or three algorithms at a time, on datasets from other domains such as banking or health, leaving it unclear which method best segments multidimensional post-training outcome data with mixed numerical and categorical attributes and no natural hard boundaries between groups. This study addresses that gap by comparing four clustering algorithms, K-Means, Hierarchical Clustering, Gaussian Mixture Model, and Fuzzy C-Means, to segment post-training participant data from the Surabaya City Government, with the aim of providing local policymakers with an evidence-based grouping of participants that can inform which sectors and job levels most need follow-up support. The dataset consists of 509 observations with attributes for job position, monthly salary, gender, employment sector, and waiting time. Prior to clustering, the data were preprocessed through cleaning of inconsistent categorical entries, median imputation of missing numerical values, One-Hot Encoding of categorical attributes, and Min-Max normalization of numerical attributes to a common 0-1 scale. Clustering performance was evaluated using Silhouette Score and Davies-Bouldin Index (DBI) across cluster counts k = 2-5. Fuzzy C-Means with five clusters achieved the best overall performance, with a Silhouette Score of 0.649 and a DBI of 0.640; its closest competitor, Ward-linkage Hierarchical Clustering, achieved a comparable Silhouette Score of 0.647 but a markedly higher DBI of 0.872, indicating that FCM produced more compact, well-separated clusters overall even though the two methods separated participants almost equally well. The resulting five clusters show clear policy-relevant differences: the largest cluster, dominated by the industrial sector, has the highest average salary but also the longest waiting time to employment, suggesting that industrial-sector training would benefit from faster competency certification and job-matching support, while smaller, female-dominated clusters in the social sector show shorter waiting times but lower salaries, pointing to a need for upskilling pathways into higher-paying roles. These findings illustrate how clustering can move post-training evaluation beyond simple placement rates toward data-driven, sector-specific recommendations for workforce training policy.
Efficiency Without Security Trade-off: Statistically Validated Cryptographic Hash Selection for Ethereum Fraud Detection Ahmad Dani; Wildanil Ghozi; Ramadhan Rakhmat Sani; Fauzi Adi Rafrastara
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13484

Abstract

Cryptographic hash functions form the preprocessing layer of Ethereum fraud detection pipelines, yet prior research evaluated hash performance and fraud detection as separate concerns, leaving practitioners without evidence-based guidance for selecting an algorithm that balances speed, energy efficiency, and cryptographic security. This study benchmarks SHA-256, SHA3-256, BLAKE2s-256, and BLAKE3 on a stratified sample of 6,396 fraud-labeled transactions from a public 9,841-record Ethereum dataset, measuring execution time, throughput, energy consumption, and avalanche effect. Its novelty is, to the best of our knowledge, the first inferential validation, on real fraud-labeled data, that the four algorithms are cryptographically equivalent in diffusion strength—combining non-parametric testing (Shapiro-Wilk, Mann-Whitney U, Kruskal-Wallis) with formal two one-sided equivalence tests (TOST) and replicating the result on an independent 454,289-transaction dataset—establishing that selection can be decided on efficiency grounds alone without sacrificing security. BLAKE2s-256 achieved the lowest execution time (0.65 µs), highest throughput (≈1.54 million ops/s), and lowest energy (0.029 mJ/op), while all algorithms converged near the 50% avalanche ideal with no significant security difference (H = 3.03, p = 0.387). BLAKE2s-256 attained the highest composite score (99.98/100) and, in an end-to-end pipeline test, improved batch screening throughput by up to 11.5%, confirming it as optimal for off-chain Ethereum fraud-detection preprocessing on processors without SHA hardware acceleration.
Classification of Depression Indication Based on Facial Expression Using MobileNetV2 and Support Vector Muhammad Eswin Bakkar; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13495

Abstract

Depression is a mental health disorder that can affect an individual's emotional condition, behavior, and quality of life. Facial expressions can represent a person's emotional state and therefore have the potential to be utilized as a visual source of information in the development of artificial intelligence-based classification systems. This study aims to develop a depression indication classification model based on facial expressions using MobileNetV2 as a feature extractor and Support Vector Machine (SVM) as a classifier. The FER2013 dataset was used and grouped into two classes, namely depression indication and non-depression indication based on predefined facial expression categories used in this study. After the labeling process, a total of 19,275 facial images were obtained, with 3,855 images used as testing data. The proposed method consists of image preprocessing, feature extraction using MobileNetV2, classification using SVM, threshold optimization, and model evaluation. Experimental results show that the proposed model achieved an accuracy of 79.69% with an AUC value of 88.62%. Threshold optimization produced an optimal threshold value of 0.44 and improved the accuracy to 80.34%. The precision, recall, and F1-score values indicate relatively balanced performance across both classes. The results demonstrate that the combination of MobileNetV2 and SVM can provide good classification performance on the FER2013 dataset grouped into depression indication and non-depression indication classes.
Real-Time Facial Emotion Recognition Using Mini-Xception and EfficientNetB4 Gede Pradistya Evan Aryaputra; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13496

Abstract

Facial Emotion Recognition (FER) is an important field within computer vision and human–computer interaction that focuses on the automatic recognition of human emotional expressions through facial images. This study presents a comparative analysis of two Convolutional Neural Network (CNN) architectures, namely Mini-Xception and EfficientNetB4, for real-time facial emotion classification using the RAF-DB (Real-world Affective Faces Database) dataset. Mini-Xception was employed as a lightweight model with lower computational requirements, whereas EfficientNetB4 utilized a transfer learning approach to achieve superior classification performance. The RAF-DB dataset consists of seven primary emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise. The preprocessing stage included facial image resizing, grayscale conversion for Mini-Xception, RGB normalization for EfficientNetB4, and the application of data augmentation techniques to improve model generalization capability. Experimental results demonstrated that Mini-Xception achieved a validation accuracy of 52.12%, while EfficientNetB4 attained a validation accuracy of 86.02%. In real-time implementation using a webcam and OpenCV, Mini-Xception exhibited advantages in inference speed, whereas EfficientNetB4 produced more stable and accurate emotion predictions. The findings indicate a trade-off between computational efficiency and classification performance. Therefore, EfficientNetB4 is more suitable for systems requiring high classification accuracy, while Mini-Xception is more appropriate for real-time applications operating under limited computational resources.
Banana Ripeness Classification Based on Color and Texture Using a CNN with ResNet50 Architecture Raafiandy Wirawan Avicenna; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13508

Abstract

Banana ripeness is one of the main factors affecting fruit quality before distribution and consumption. Conventional ripeness assessment is generally performed through visual observation, which may lead to subjective and inconsistent results. Previous studies have reported promising results in banana ripeness classification. However, distinguishing adjacent ripeness stages remains challenging because of their similar visual characteristics. This study proposes a banana ripeness classification model using a Convolutional Neural Network (CNN) with the ResNet50 architecture. The dataset consisted of four ripeness categories, namely unripe, ripe, overripe, and rotten. Data balancing, preprocessing, and augmentation were applied before model training. A total of 1,120 images were used to train and evaluate the model. Transfer learning with full fine-tuning was employed to adapt pretrained visual features to different banana ripeness levels. The experimental results showed that the proposed model achieved an accuracy of 92.86%, while precision, recall, and F1-score reached 93%. Several misclassifications were observed between adjacent ripeness categories due to similarities in visual characteristics. These results indicate that the proposed ResNet50 model can effectively classify banana ripeness levels on the testing dataset based on color and texture information learned automatically from digital images.
Waste Image Classification Using EfficientNet B4 with MD5 and pHash Data Duplication Analysis on Two Datasets Abdul Qohhar; Christy Atika Sari; Hidayah Rahmalan
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13510

Abstract

Waste sorting automation through deep learning is an important approach to support sustainable waste management systems. However, many studies still overlook dataset quality issues, especially exact and near duplicate images that can cause information leakage and overly optimistic evaluation metrics. This study proposes a waste image classification pipeline that integrates an explicit data quality analysis stage using MD5 hashing for exact duplicate detection and perceptual hashing (pHash) for near duplicate detection, followed by fine tuned EfficientNet B4 as the classification backbone. Experiments are conducted on two public datasets with distinct characteristics: Garbage Classification V2 (6 classes, 9,421 images) and RealWaste (9 classes, 4,749 images). With a Hamming distance threshold τ≤2, the pHash cleansing identifies zero duplicates in Dataset 1 and only three near duplicates (0.06%) out of 1,404,077 compared pairs in Dataset 2, confirming no evidence of image-duplication-based information leakage in either dataset. EfficientNet B4 achieves 97.77% test accuracy with a macro F1 Score of 0.9768 on Dataset 1 and 93.26% accuracy with a macro F1 Score of 0.9379 on Dataset 2, demonstrating consistent performance across these two datasets with different numbers of classes, data volumes, and visual heterogeneity. These findings should be interpreted as evidence of robustness within the scope of the two evaluated datasets, rather than as a claim of generalization to unseen, external, or cross-domain waste image datasets.

Page 97 of 101 | Total Record : 1006

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