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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 1,196 Documents
 112   113   114   115   116 
A Comparative Study between Logistic Regression and SVM for Resource Management in Network Slicing Younus, Ahmed; Al-Allawee, Ali
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15222

Abstract

Network slicing is an essential component of 5G and subsequent networks. It enables administrators to partition shared physical infrastructure into several virtual segments, each with distinct Quality of Service (QoS) requirements. Effective and adaptable real-time resource management is essential for optimal performance in dynamic situations, characterized by low latency and high throughput. Despite the increasing body of literature on machine learning in communication networks, there is a paucity of direct comparisons between Logistic Regression (LR) and Support Vector Machines (SVM) concerning network slicing resource management. Prior comparisons have predominantly concentrated on sectors such as education, healthcare, and the Internet of Things (IoT), resulting in minimal exploration of slicing prospects. This study rectifies this gap by doing a comparative analysis of Logistic Regression and Support Vector Machine models utilizing the CICIDS2017 dataset in a network slicing simulation environment. Both models were utilized independently, employing class balancing and feature selection to forecast overload. We evaluated their performance for accuracy, ROC AUC, latency, jitter, and throughput across network slices. Results indicate that SVM exhibited somewhat superior classification accuracy; however, LR consistently surpassed SVM in critical network-level parameters, including reduced delay, enhanced throughput, and improved jitter stability. These results indicate that LR is an effective option for the real-time management of network slicing resources due to its practicality and comprehensibility. In conclusion, LR is a dependable primary option for scholars and professionals pursuing effective, low-latency solutions, improving the superior classification accuracy of SVM with enhanced overall network performance.
Association Rule Mining across Multiple Domains: Systematic Literature Review Syahirah, Dayini; Priati, Priati; Martadireja, Okky Pratama
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15227

Abstract

This Systematic Literature Review (SLR) synthesizes 50 studies published between 2020 and 2025 that applied Association Rule Mining (ARM) across multiple domains, using the PRISMA 2020 framework. The review examines application areas, algorithm choices, implementation tools, parameter settings, and emerging trends. Results indicate that transportation and market analysis are the most prominent domains, followed by healthcare, manufacturing, and governance, with smaller contributions from tourism, agriculture, energy, and the environment. Apriori remains the most widely used algorithm due to its simplicity, FP-Growth is preferred for efficiency, and hybrid or modified approaches are adopted to address scalability issues. Python dominates as the primary implementation tool, alongside RapidMiner and R-Studio, with parameter thresholds generally adapted to dataset size and domain-specific needs. The novelty of this review lies in providing a cross-domain synthesis of ARM, filling the gap left by prior reviews that were limited to specific fields or algorithms. This broader perspective reveals temporal trends and recurring challenges, particularly scalability and interpretability, while identifying opportunities such as integration with deep learning, real-time ARM, and cross-domain adaptation. By offering a structured overview of developments in ARM, this study contributes both conceptual insights and practical guidance, serving as a reference for optimizing applications and informing future research directions.
Mobile Banking Service Quality and User Loyalty Using MSQUAL: A Systematic Literature Review Nashikha, Ainun; Huda, Muhammad Qomarul; Fitroh, Fitroh; Durachman, Yusuf; Waspodo, Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15231

Abstract

Digital transformation has made mobile banking a core service in the banking industry, emphasizing service quality as a critical factor for user satisfaction and loyalty. This study presents a systematic literature review (SLR) of mobile banking research from 2021 to 2025, guided by PRISMA and structured using the PICOC framework (Population, Intervention, Comparison, Outcome, Context) to systematically select and evaluate relevant studies. The MS-QUAL model, comprising nine dimensions: efficiency, system availability, responsiveness, privacy, content, contact, billing, fulfillment, and compensation, was used as the evaluation framework. Out of 924 initially identified articles, 20 met the inclusion criteria for in-depth analysis. Findings show that efficiency, system availability, privacy, responsiveness, content, and fulfillment consistently drive user satisfaction, while compensation, contact, and billing have limited influence. Satisfaction serves as the primary mediator connecting service quality to loyalty, indicating that improvements in MS-QUAL dimensions must translate into positive user experiences to foster long-term loyalty. The study further highlights challenges in maintaining security standards, adapting traditional dimensions to evolving user expectations, and ensuring consistent service quality. Opportunities lie in leveraging technologies such as AI, blockchain, and big data to create personalized, secure, and interactive experiences, enhancing both functional and emotional engagement. Overall, MS-QUAL remains a relevant and flexible framework for evaluating mobile banking service quality when aligned with contemporary technological advances and user-centered strategies.
Blockchain Model for Tracking Plastic Waste Using Smart Contracts to Reduce Emissions Utomo, Andri Dwi; Jeffry; Ahmad Irfandi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15245

Abstract

This research focuses on the design and development of a blockchain-based plastic waste tracking system aimed at enhancing transparency, efficiency, and accountability in plastic waste management. The system utilizes Hyperledger Fabric as a permissioned blockchain platform and integrates smart contracts to manage transactions between organizations, including waste generators, collectors, sorting warehouses, and final processing warehouses. This system records each stage of the plastic waste journey, from creation to final processing, in a permanent, transparent, and immutable manner. The testing results demonstrate that the system can accurately record the status and history of waste, manage transfers between organizations, and process plastic waste into recycled products. Moreover, the system shows a significant potential for carbon emission reduction, with an estimated reduction of up to 50% compared to traditional plastic waste management methods, such as incineration or landfilling. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. The blockchain-based system also provides real-time monitoring, ensuring that each transaction is verified and recorded immediately, contributing to more effective management. The implementation of smart contracts further guarantees that waste-related activities are executed automatically when predefined conditions are met, reducing administrative overhead. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. Ultimately, this system presents a scalable solution that could be adopted in various regions to improve global waste management strategies.
Attention Augmented Deep Learning Model for Enhanced Feature Extraction in Cacao Disease Recognition Robet, Robet; Perangin Angin, Johanes Terang Kita; Siregar, Tarq Hilmar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15249

Abstract

Accurate cacao disease recognition is critical for safeguarding yields and reducing losses. Prior cacao studies primarily rely on handcrafted descriptors (eg, Color Histogram, LBP, GLCM) or standard CNN/transfer-learning pipelines, often limited to ≤ 3 classes and a single plant organ; explicit channel-spatial attention and comprehensive multiclass evaluation remain uncommon. To the best of our knowledge, no prior work integrates Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) on a ResNeXt50 backbone for six-class cacao disease classification, accompanied by a standardized ablation study and t-SNE-based interpretability. We propose a six-class classifier (five diseases + healthy) built on ResNeXt-50 enhanced with SE (channel recalibration) and CBAM (channel-spatial emphasis) to highlight lesion-relevant cues. The dataset comprises labeled leaf and pod images from public sources collected under field-like conditions; preprocessing includes resizing to 224x224, normalization, and augmentation (flips, small rotations, color jitter, random resized crops). Trained with Adam and early stopping, ResNeXt50+SE+CBAM attains 97% test accuracy and 0.97 macro-F1, surpassing a ResNeXt50 baseline of 94% and 0.95 and SE-only/CBAM-only variants. Confusion matrix and t-SNE analyses show fewer mix-ups among visual classes and clearer separability, while the ablation validates complementary benefits of SE and CBAM. On a desktop-hosted, web-based setup, batch-1 inference at 224x224 is 7.46 ms/image (134 FPS), demonstrating real-time capability. The findings support deployment as browser-based decision-support tools for farmers and integration into continuous field-monitoring systems.
Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data Zahro, Azzula Cerliana; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Fahmi, Amiq; Megantara, Rama Aria; Naufal, Muhammad; Azies, Harun Al; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15260

Abstract

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.
Optimizing Supplier Selection Through Hybrid BWM and AHP Integration Siregar, Afrizal Rhamadan; Hendry, Hendry
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15261

Abstract

This study proposes a hybrid decision-making model that integrates the Best-Worst Method (BWM) with the Analytic Hierarchy Process (AHP) to optimize supplier selection. The primary objective is to address limitations in traditional Multi-Criteria Decision-Making (MCDM) methods, such as inconsistency, subjectivity, and cognitive overload when handling complex criteria. The proposed model leverages AHP's hierarchical structuring and BWM’s efficiency in reducing comparison load, aiming for a more accurate and consistent evaluation framework. The research design involves developing a hybrid AHP-BWM model and applying it to a dataset from the Vietnamese Textile and Apparel (T&A) sector. The methodology includes two stages: determining the weight of each criterion using a Hesitant-AHP approach, followed by evaluating supplier alternatives with BWM. The performance of the model is assessed using classification metrics, namely accuracy, precision, recall, and F1-score. The results show that the proposed model outperforms conventional methods such as TOPSIS, ELECTRE, VIKOR, and SWARA. It achieves an accuracy of 92%, precision of 87%, recall of 86%, and an F1-score of 86%. These outcomes confirm the model’s superior ability to consistently classify supplier suitability. Furthermore, the model identifies Quality Assurance as the most critical criterion, followed by Assistance, Capacity, Charge, and Shipment. In conclusion, the hybrid AHP-BWM model offers a robust, scalable, and data-driven approach for supplier selection. Its strength lies in balancing systematic evaluation with reduced cognitive effort, making it suitable for complex real-world decision-making environments. Future research may explore its application in other domains and enhance its scalability for larger datasets.
Comparative Performance Benchmarking of WebSocket Libraries on Node.js and Golang Fernando, Louis; Engel, Mychael Maoeretz
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15266

Abstract

The demand for responsive real-time web applications continues to grow, making the selection of backend technology and WebSocket libraries a crucial factor in determining performance. Node.js and Golang are popular platforms for real-time applications. However, the WebSocket library within them offers a trade-off between features and efficiency, the impact of which has not been comprehensively measured. This research aims to fill this gap by conducting a quantitative performance analysis to compare the efficiency and scalability of four WebSocket libraries: ws and socket.io on Node.js, and gorilla/websocket and coder/websocket on Golang. This research uses a benchmarking experimental method with client load simulations that gradually increase from 100 to 1000 concurrent clients. The experiment was conducted through two scenarios, namely the Echo Test and Broadcast Test. In the Echo Test, the performance metrics measured were Connection Time, Round Trip Time (RTT), and Throughput. Meanwhile, in the Broadcast Test, the performance metric measured was Broadcast Latency. The results from the Echo Test show a significant performance disparity. At a peak load of 1000 clients, socket.io achieved a throughput of only 27,152 messages/second, whereas the lightweight libraries (ws, gorilla/websocket, and coder/websocket) all achieved over 44,000 messages/second. In the Broadcast Test with a high load, the latency difference between the four libraries became insignificant. Therefore, for applications prioritizing raw performance in point-to-point communication, certain WebSocket libraries such as ws, gorilla/websocket, and coder/websocket are more suitable for future development.
Sentiment Analysis of Roblox Game Reviews Using Support Vector Machine Method Dewi, Ni Kadek Feby Puspita; Sudipa, I Gede Iwan; Sunarya, I Wayan; Kusuma Dewi, Ni Wayan Jeri; Kusuma, Aniek Suryanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15272

Abstract

The development of digital technology has driven changes in entertainment consumption patterns, especially among the younger generation. Roblox has become one of the most popular online gaming platforms, with a wide range of user opinions recorded on Google Play Store. This study aims to classify the sentiment of Roblox user reviews (positive, negative, neutral) and evaluate the performance of the Support Vector Machine (SVM) algorithm with TF-IDF weighting and automatic labeling using Lexicon InSet. Data was obtained by crawling 10,000 reviews during the period of April 2–May 23, 2025, and after the preprocessing stage, 8,950 data remained for analysis. The classification results show that the sentiment distribution consists of 41.3% positive (3,703 reviews), 41.8% neutral (3,739 reviews), and 16.8% negative (1,507 reviews). Model evaluation using a confusion matrix produced high performance with 87.03% accuracy, 87.29% precision, 87.03% recall, and an F1-score of 86.67%. WordCloud visualization shows that positive reviews emphasize creativity and interactive features, while negative reviews are dominated by technical complaints such as lag and errors. These findings prove that the combination of SVM, TF-IDF, and Lexicon InSet is effective in sentiment analysis and provides valuable input for developers to improve application quality and user protection. Further research is recommended to adopt a hybrid approach based on deep learning and aspect-based sentiment analysis to generate more insights.
Implementation of a Hybrid Cryptosystem Using ChaCha20 and ECC for Image Encryption in an Android Application Zai, Samuel Anaya Putra; Debi Yandra Niska; Zulfahmi Indra; Kana Saputra; Adidtya Perdana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15274

Abstract

This study aims to develop an Android application capable of securely encrypting and decrypting images using a hybrid cryptographic method. The system combines the ChaCha20 algorithm as symmetric cryptography to encrypt image files, and Elliptic Curve Cryptography (ECC) as asymmetric cryptography to encrypt the ChaCha20 key. The key used is temporary (ephemeral), ensuring that only the intended recipient who possesses the appropriate ECC private key can decrypt the file. The application was developed using the Kotlin programming language in Android Studio, with a PHP-based backend and MySQL database. Testing was conducted using the black-box method and involved 15 beta testers to evaluate functionality, security, and usability aspects. The results show that all features of the application run properly, and the encryption and decryption processes can be performed efficiently and securely. Beta testers gave an average rating of 4.6 out of 5 and stated that the application is easy to use and provides sufficient protection for personal data. Therefore, the developed application successfully meets the objectives of the study and offers an alternative solution for securing image file transfers between users via Android devices.

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