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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,259 Documents
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User Satisfaction in Moo Opinion App: Machine Learning for Cooperative Segmentation Megawati, Citra Dewi; Palevi, Bima Romadhon Parada Dian; Teo Pei Kian; Ramanda, Pramadika
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15589

Abstract

This study addresses the critical need to understand digital application user satisfaction within the agricultural cooperative sector, specifically for the Moo Opinion application at the Village Unit Dairy Cooperative (KUD). The study's primary novelty lies in the implementation of an integrated, sequential Machine Learning framework—combining Random Forest (RF), Principal Component Analysis (PCA), and K-Means Clustering—to provide a granular analysis of user behavior in a specialized dairy ecosystem. The methodology first utilized RF for key feature selection, followed by PCA for dimensionality reduction, and K-Means for precise user segmentation. Primary data was collected from 40 respondents (20 farmers, 20 customers). Key findings reveal that Service Quality (0.42) and Milk Quality (0.36) are the most significant drivers of satisfaction, considerably outweighing economic factors like Milk Price (0.08). PCA identified two core satisfaction dimensions: Quality-Service Synergy (explaining 56.7% variance) and Structural-Economic Factors (explaining 25.7% variance), confirming the dominance of non-economic aspects. K-Means Clustering successfully identified three segments: Highly Satisfied (45%), Moderately Satisfied (38%), and Low Satisfaction (17%), with high cluster validity (Silhouette Coefficient 0.71). A recognized limitation of this study is the small sample size (N=40), which may affect the generalizability of the findings to larger cooperative populations. However, the results offer significant practical implications, highlighting the need for KUD to prioritize digital service quality and product value over pricing strategies to enhance loyalty and prevent churn.
Adaptive Learning System Based on Human-in-the-Loop for PDF Template Data Extraction Rahman, Moh Syaiful; Andrianingsih , Andrianingsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15598

Abstract

PDF template data extraction remains a substantial challenge due to semi-structured document formats and variations. While large pre-trained models achieve high accuracy, they require extensive computational resources and labeled datasets, making them impractical for resource-constrained environments. Conversely, rule-based approaches are efficient but rigid. This research addresses this gap by developing an adaptive learning system that integrates rule-based approaches with Conditional Random Fields (CRF) in a hybrid framework, designed for data-scarce scenarios. The system implements parallel extraction strategies with confidence-based selection and Human-in-the-Loop (HITL) feedback for incremental learning. Pattern learning updates rule-based strategies, while CRF models are retrained incrementally. Evaluated on synthetically generated documents across diverse template types, the system achieves 98.61% accuracy with minimal training data and 7% user correction rate, demonstrating high learning efficiency (1.88 corrections per percentage point). The improvement is statistically significant (paired t-test, p < 0.001, Cohen’s d = 8.95). The system operates on CPU-only hardware with 50-100 MB footprint and 0.1-0.5 seconds processing time. This work fills a practical gap in document extraction, providing a middle-ground solution balancing high accuracy, minimal data requirements, low resource consumption, and real-time adaptability—suitable for small organizations and rapid deployment where large models are impractical. The evaluation uses synthetic data to ensure reproducibility and controlled assessment, though real-world validation would strengthen practical applicability.
Implementing a Payment Gateway in the Mount Slamet Hiking Ticketing System Bahy, Faishal; Arif, Dani; Amin, M. Syaiful
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15600

Abstract

Background—Many hiking basecamps in Indonesia still process bookings manually, causing queues, quota uncertainty, and errors in payment verification that hinder operations. Objective— Design and implement a web-based information system (e-ticketing) for Mount Slamet hiking, integrated with the Midtrans payment gateway; validate transactions in near-real-time and issue ticket IDs for gate inspection. Methods—Development followed Agile/Scrum. Requirements were gathered through observation and interviews; the design employed use-case, activity, ERD, and payment-flow models. Implementation used React (UI), Express and Prisma ORM (API), MySQL, and Midtrans Snap, with signature-verified, idempotent webhooks. Trials covered end-to-end black-box testing (booking; transitions among pending, paid, expired, and canceled; ticket-ID issuance; and check-in), cross-browser compatibility (Chrome, Edge, Firefox, Safari on desktop and mobile), and the System Usability Scale (SUS; n = 13). We also monitored propagation time from settlement to order update and behavior in the admin panel (route, quota, and date-closure management). Results—All functional scenarios passed; behavior was consistent across major browsers; mean SUS = 75.0 (> 68) indicates acceptable usability. Webhooks ensured automatic, duplicate-free status updates, with propagation on the order of seconds, so the reservation–payment–e-ticket chain operated end-to-end and was traceable via ticket-ID logs. Conclusion—The proposed e-ticketing system is technically feasible for basecamp operations and provides an architectural blueprint, core data schema, and a replicable Midtrans integration pattern. Future work will refine the public interface, add refund/void features, and conduct production-grade performance and security testing.
Towards Adaptive Learning: A Bayesian Knowledge Tracing Approach to Student Skill Prediction Bayesian Knowledge Tracing for Modeling Daily Living Skills in Children with ASD Dharsika, I Gde Eka; Setiawan, I Made Dedy; Sarasvananda, Ida Bagus Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15605

Abstract

Autism Spectrum Disorder (ASD) presents challenges in mastering Activities of Daily Living (ADLs), which are essential for independence. This study applies Bayesian Knowledge Tracing (BKT) to model the mastery of five ADL skills—eating, dressing, toothbrushing, combing, and bathing—using data from 27 learners (1,350 responses). BKT parameters, including initial mastery, learning transition, guessing, and slipping, were used to estimate individual learning trajectories. Results showed that eating was the easiest skill (predicted mastery = 0.78), while bathing and combing were the most difficult (<0.55). The model achieved an overall accuracy of 0.62, with strong detection of actual mastery (TP = 722) but a high false-positive rate (FP = 429), indicating sensitivity to the guessing parameter. Learning curves and heatmaps revealed substantial inter-student variability. A comparative evaluation with the Performance Factors Analysis (PFA) model showed that BKT achieved higher overall predictive accuracy (BKT = 0.6356; PFA = 0.5917), while PFA demonstrated a higher AUC (0.6747) but exhibited strong positive-class bias in classification. These findings demonstrate the usefulness of BKT in modeling ADL development and highlight its potential for adaptive learning systems that support personalized interventions for ASD learners.
Sarcasm Detection in Indonesian YouTube Comments using Fine-Tuned IndoBERT with Class Imbalance Handling Fanani, Ahmad Muhlis; Wahyuddin, Moh. Iwan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15607

Abstract

Sarcasm detection in Indonesian social media faces challenges in natural language processing due to implicit meanings and limited labeled datasets. YouTube, with 143 million users in Indonesia, represents a largely unexplored source of sarcastic expressions. This study aims to develop an automatic sarcasm detection system for Indonesian YouTube comments using fine-tuned IndoBERT and evaluate the performance of two IndoBERT variants. A dataset of 5,291 YouTube comments was collected and automatically labeled using GPT-4o with structured prompts based on linguistic indicators of sarcasm. Two IndoBERT variants (IndoNLU and IndoLEM) were fine-tuned with three class imbalance mitigation strategies: imbalanced, under-sampling, and class weighting. Zero-shot evaluation was conducted as a baseline to measure fine-tuning effectiveness. Models were evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained models without fine-tuning showed very limited sarcasm detection capability with F1-scores of 0.1613 for IndoNLU and 0.3519 for IndoLEM. Fine-tuning with under-sampling dramatically improved F1-scores to 0.6499 for IndoNLU and 0.6568 for IndoLEM, showing improvements up to 303%. IndoBERT-IndoNLU provided more balanced performance with 0.6424 accuracy, while IndoLEM showed higher sarcasm recall of 0.7639. Fine-tuning IndoBERT is effective for detecting sarcasm in Indonesian YouTube comments. This study contributes by providing a new labeled dataset, demonstrating the effectiveness of automatic labeling using large language models, and providing empirical evidence of the significant value of fine-tuning for Indonesian sarcasm detection.
Hybrid Multilayer Architecture Integrating Suricata, Wazuh, and Cyber Threat Intelligence for Drive-by-Download Malvertising Detection Adrian, Aurell Zulfa Angger; Megantara, Rama Aria; Al Zami, Farrikh
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15616

Abstract

Malvertising has emerged as a serious cybersecurity threat, leveraging legitimate advertising networks to deliver malware through drive-by-download techniques without requiring user interaction. Existing standalone network- or host-based detection solutions provide limited protection because they lack integrated visibility and contextual validation across detection layers. However, no existing research has specifically evaluated the integration of Suricata, Wazuh, and VirusTotal for endpoint-focused malvertising detection, creating a critical gap in multi-layer defense strategies. This study proposes a hybrid multilayer architecture combining Suricata as a Network Intrusion Detection System, Wazuh as a Host-based Intrusion Detection and Prevention System, and VirusTotal as an external Cyber Threat Intelligence source to provide correlated threat detection and automated mitigation. The system was evaluated in a controlled virtual laboratory consisting of attacker, victim, and SIEM environments replicating real malvertising scenarios. The results show that the proposed architecture successfully detected malicious payloads and completed an end-to-end detection-to-mitigation cycle in approximately 5-7 seconds while maintaining zero false positives under non-malicious conditions. This research contributes a practical and reproducible architecture for endpoint-based malvertising detection, demonstrating effective multi-layer correlation and rapid autonomous response. The limitation of this study lies in its reliance on signature-based detection and external API communication, which may reduce effectiveness against zero-day threats or offline deployments.
Line-of-Sight Dominance Over Vegetation: Simulation-Based LoRa Performance in Tropical Forest Terrain Atmoko, Rachmad; Rahmat Hidayatullah, Rifqi; Nur Na’im, Septian Ghuslal; Izzun Ni`am, Muhammad; Bagus Setiawan, Akas
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15627

Abstract

Low-Power Wide-Area Network (LPWAN) technologies, especially LoRa, are receiving considerable interest for applications involving environmental monitoring in difficult terrain conditions. However, existing research predominantly examines vegetation attenuation or terrain elevation effects separately, leaving a critical research gap in understanding their combined and interactive impacts on LoRa connectivity in tropical forest environments. Furthermore, most studies rely on simplified propagation models that inadequately represent the complex radio environment of tropical forests, and few investigations systematically compare the relative importance of vegetation density, elevation, and line-of-sight conditions. This work addresses these gaps through an in-depth simulation-based investigation of LoRa network behavior in the University of Brawijaya (UB) Forest, which serves as a typical tropical forest setting in Indonesia. We performed detailed simulations using Python and LoRaSim, employing fine-resolution elevation datasets and precise vegetation classification to examine how dense vegetation, medium vegetation, and elevation parameters influence LoRa communication performance. Our findings indicate that, in contrast to traditional propagation models, nodes located in dense vegetation zones reached a 90.0% success rate, as opposed to 65.0% in zones without vegetation. Additional investigation shows that line-of-sight presence (28.6% versus 0.0% success rate) and relative elevation relative to the gateway (11.1% versus 27.3% success rate for nodes positioned above and below the gateway, respectively) represent more crucial factors for connectivity compared to vegetation attenuation by itself. These outcomes offer important guidance for enhancing LoRa-based environmental monitoring systems in tropical forest settings through strategic node positioning that considers elevation characteristics and line-of-sight availability.
Improving Machine-Learning Malware Detection Through IQR-Based Feature Reduction Setyanto, Nurcahyo Fajar; Pramitasari, Rina; Kuswanto, Jeki
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15634

Abstract

Malware detection is a significant challenge in cybersecurity due to the complex and evolving nature of threats. This study evaluates the effectiveness of machine learning algorithms, specifically XGBoost and LightGBM, in detecting malware. The approach includes data cleaning, normalization, feature selection, and the use of the Interquartile Range (IQR) technique to select relevant features. The initial dataset contained 21,752 files, evenly split between malicious and benign files. After data cleaning, the number of samples decreased to 19,256 files, with numerous features that were reduced after applying IQR. Results show that XGBoost outperforms other algorithms, achieving 99.20% accuracy, an improvement over the 98.99% accuracy without IQR. The IQR technique enhances data quality by filtering out features with significant differences between malware and benign files, improving model performance. Additionally, reducing the feature set helps prevent overfitting and strengthens the model's generalization ability. The study concludes that machine learning, particularly with algorithms like XGBoost and LightGBM, can effectively improve malware detection. By using IQR in feature selection, model performance is enhanced, leading to reduced false positives and increased detection efficiency. The research highlights the importance of feature selection techniques like IQR in boosting the predictive power of machine learning models, making them more efficient in identifying malware. Future work will explore additional feature selection methods to further improve malware detection accuracy.
CataractAsist: Convolutional Neural Network-Based Early Detection System for Cataracts Ranov, Nazhif Teggar; Priambodo, Rinto
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15642

Abstract

Cataract disease is one of the leading causes of blindness worldwide, especially in developing countries with limited access to healthcare facilities. To address this challenge, this study aims to develop an automated cataract detection system using the Convolutional Neural Network (CNN) method. This system is designed to classify eye images into three classes, namely Normal, Mature, and Immature, by utilizing the "Senile Cataract" dataset from the Kaggle platform. The research methods include image pre-processing, feature extraction using the VGG16 model through transfer learning, model training with augmentation techniques, and performance evaluation using accuracy, precision, recall, f1-score, and confusion matrix metrics. The test results show that the model is capable of achieving 95% accuracy, with the highest f1-score in the Normal class at 0.96. Confusion matrix analysis shows excellent prediction rates for all classes, although there are slight classification errors between the Immature and Mature classes. In conclusion, this CNN-based cataract detection system is proven to be effective and accurate, and has great potential to be applied in web-based healthcare services as an automatic early diagnosis tool for eye diseases.
Emotion-Based Multi-Class Sentiment Analysis Of FirstMedia Customers Reviews Using SVM With Kernel Comparison Ongko, Bagus Kustiono; Vitianingsih, Anik Vega; Cahyono, Dwi; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15644

Abstract

The advancement of digital technology has made users increasingly reliant on online services, with user reviews serving as an essential resource for evaluating the quality of service provided by companies such as FirstMedia. However, these valuable data have not undergone comprehensive analysis to assess users’ emotional responses. This study aims to classify FirstMedia customers’ emotions into four categories (joy, sadness, anger, and neutral) and to evaluate the Support Vector Machine (SVM) method using four different kernel functions. Most existing studies primarily focus on polarity-based sentiment analysis and do not explicitly examine multi-emotion classification or kernel comparison in machine learning models. A total of 4,001 reviews were collected through web scraping from the Google Play Store and the X app and processed through several preprocessing steps. Emotion classification was conducted using the NRC Indonesian Emotion Lexicon, while word significance was determined using TF-IDF weighting. After preprocessing, 3,069 labeled reviews were retained and distributed as 1,065 neutral, 748 anger, 692 joy, and 564 sadness reviews, which were used for emotion classification. Model performance was evaluated using a hold-out validation scheme with an 80:20 train-test split and assessed through a confusion matrix. To address class imbalance, undersampling was applied, resulting in a balanced dataset for model training. The evaluation results show that the Linear kernel achieved the highest performance, with an accuracy of 82.63%, precision of 82.86%, recall of 82.63%, and an F1-score of 82.60%, outperforming the Gaussian, Polynomial, and Sigmoid kernels. This study demonstrates that multi-emotion sentiment analysis provides a more comprehensive understanding of user perceptions beyond conventional sentiment polarity, thereby supporting more informed evaluations of digital service quality.  

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