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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
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Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases Jauhary, Wilson; Yaphentus, Albert Julius; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14654

Abstract

Rice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer.
Implementing TOGAF Enterprise Architec-ture in Indonesia’s Merchant Acquiring In-dustry: A Framework for Digital Trans-formation Praharto, Suwandhy; Yohanis, Alfa Ryano
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14668

Abstract

The digital transformation of Indonesia's merchant-acquiring industry, accelerated by regulatory initiatives, fintech innovations, and changing consumer behavior, has created significant technological and organizational challenges. Fragmented legacy systems and complex regulatory requirements hinder seamless digital payment adoption. This study investigates the strategic implementation of The Open Group Architecture Framework (TOGAF) to systematically manage these challenges. Through an extensive literature review and case studies of major industry players—including BRI, BCA, Mandiri, BNI, and GoPay—this research uniquely explores TOGAF's specific applicability to merchant acquiring in Indonesia. The proposed TOGAF-based framework aligns closely with Bank Indonesia's Payment System Blueprint 2025, emphasizing enhanced interoperability, regulatory compliance, and sustainable growth. Findings suggest that enterprise architecture can unify fragmented technologies, bridge gaps in merchant activation, and strengthen cybersecurity, ultimately driving innovation in digital payment services. By providing a structured implementation roadmap tailored to Indonesia's regulatory environment, this research not only addresses current industry needs but also sets a foundation for future technological advancement and financial inclusion in Indonesia's merchant acquiring landscape.
A Business Intelligence: Enhancing Apache Superset Capabilities in PBB-P2 Receivables Monitoring: - Pranoto, Sugeng; Wahyuni, Sri; Novelan , Muhammad Syahputra
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14611

Abstract

PBB-P2 Tax Revenue plays an essential role in regional finance, but managing receivables and analyzing taxpayer compliance levels still face many challenges. Business Intelligence (BI) technologies such as Apache Superset are often used for interactive data visualization. Still, they have limitations in advanced analysis, especially the application of machine learning algorithms such as K-Means for data clustering. This research aims to overcome the limitations of Apache Superset by developing an external application-based solution using the Java programming language and the SMILE library. This application is designed to cluster the level of taxpayer compliance in a batch process, with the results stored in the MySQL database. The clustered data is then visualized using Apache Superset. The results show that integrating these external applications can improve the efficiency of data analysis by utilizing more complex clustering algorithms. Visualization of clustering results also allows for more effective management of PBB-P2. This approach not only expands the capabilities of Apache Superset but also contributes to supporting data-driven tax revenue optimization strategies. This research opens up further opportunities for the integration of BI tools with machine learning algorithms in monitoring and managing complex data in the tax sector
A Comparative Study of Data Mining Algorithms for Fraud Detection in Financial Transactions Syahbani, Arif Marzuq; Firdaus, Wildan; Musodo, Krisna Adiyarta
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14645

Abstract

Deteksi penipuan dalam transaksi keuangan merupakan tantangan penting bagi industri perbankan dan e-commerce. Seiring dengan semakin canggihnya aktivitas penipuan, kebutuhan akan metode deteksi tingkat lanjut menggunakan teknik penambangan data pun meningkat. Studi ini melakukan analisis komparatif terhadap berbagai algoritma machine learning, termasuk Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, dan model Deep Learning, untuk mendeteksi transaksi keuangan yang curang. Penelitian ini menggunakan kumpulan data yang terdiri dari transaksi yang curang dan sah serta menerapkan beberapa metrik evaluasi seperti akurasi, presisi, recall, F1-score, dan AUC-ROC untuk mengukur kinerja algoritma. Hasilnya menunjukkan bahwa model pembelajaran ensemble, khususnya Random Forest dan XGBoost, mengungguli metode klasifikasi tradisional dalam hal akurasi, efisiensi, dan ketahanan. Model Deep Learning juga menunjukkan hasil yang menjanjikan tetapi memerlukan sumber daya komputasi yang besar, kumpulan data yang besar, dan penyempurnaan untuk mencapai kinerja yang optimal. Selain itu, teknik praproses data seperti pemilihan fitur, pengurangan dimensionalitas, dan penyeimbangan kelas berdampak signifikan terhadap efektivitas deteksi. Temuan studi ini memberikan wawasan berharga bagi lembaga keuangan dalam memilih algoritma deteksi penipuan yang paling efisien, yang pada akhirnya meningkatkan keamanan transaksi dan mengurangi kerugian finansial. Penelitian di masa mendatang dapat mengeksplorasi pendekatan hibrida yang memadukan berbagai teknik, serta metode pemrosesan waktu nyata, untuk lebih meningkatkan akurasi deteksi penipuan dan meminimalkan kesalahan positif dalam sistem keuangan berskala besar.
Machine Learning to Predict Food Prices in Aceh Province Using the Fuzzy Time Series Method Based on Average Fadillah, Rizky; Ula, Munirul; Suwanda, Rizki
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14649

Abstract

This study aims to develop a food commodity price prediction system based on Fuzzy Time Series (FTS) using average-based methods, with a case study of price data from 2018 to 2023. The system is designed to predict the prices of five main commodities: Super Quality Rice, Fresh Chicken Meat, Fresh Chicken Eggs, Bulk Cooking Oil, and Premium Quality Sugar. The prediction process involves constructing the Universe of Discourse, intervals, and fuzzy logic relations (FLR and FLRG) to model historical price patterns. The results show that this model provides accurate predictions, with the best Mean Absolute Percentage Error (MAPE) value of 0.49% for Super Quality Rice, while MAPE for other commodities ranges from 0.69% to 1.44%. The comparison graph between actual data and prediction results demonstrates consistent pattern alignment, suitable for commodities with both high price fluctuations and stable trends. This system proves effective in projecting future food prices with low error rates, making it a reliable tool to support strategic decision-making in managing food commodity prices during the five-year analysis period.
Beyond Traditional QoS Management- Harnessing Machine Learning for Predictive Network Service Optimization Mareta, Arvin; Sakti, Irwin; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14664

Abstract

Quality of Service (QoS) is a fundamental aspect of modern computer networks, directly influencing performance and user experience. Key parameters such as latency, throughput, packet loss, and jitter play crucial roles in determining network efficiency. Traditional QoS management approaches, often rule-based or heuristic-driven, lack adaptability to dynamic network conditions. This study explores the application of machine learning techniques to predict QoS using historical network data, enabling proactive network optimization. We employ multiple predictive models, including linear regression, random forest, and deep learning, to analyze network performance trends and forecast QoS degradation. Experimental results demonstrate that machine learning significantly enhances prediction accuracy compared to conventional methods, allowing for more effective resource allocation and congestion control. The findings highlight the potential of data-driven approaches in real-time network management, reducing latency fluctuations and improving service reliability. Moreover, deep learning models outperform traditional statistical techniques in recognizing complex patterns within network data, making them a promising solution for next-generation network optimization. The proposed methodology not only improves predictive accuracy but also offers a scalable framework for automated QoS management in cloud computing, IoT, and 5G environments. Future work will focus on refining model generalization across diverse network conditions and integrating federated learning for privacy-preserving QoS predictions. This research underscores the transformative role of machine learning in enhancing network service quality and operational efficiency.
Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector Syahra, Yohanni; Br. Tarigan, Yuni Franciska; Andriani, Karina; Nazry S, Hevlie Winda; Setik, Roziyani
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14672

Abstract

Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processes
Evaluation of Mobile Academic Information System with Notifications Using Heuristic Evaluation and WCAG-EM Nugraha, I Gede Pradipta Adi; Indrawan, Gede; Asroni, Ahmad; I Gede Aris Gunadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14675

Abstract

SIsKA-NG Mobile is a mobile information system that provides specific information related to students’ study activities in the Computer Science Study Program at Universitas Pendidikan Ganesha. This newly developed application has never been evaluated. Therefore, it is necessary to evaluate the accessibility and interface for improvement. This study focuses on the analysis related to the application accessibility of partial color blindness users, using the WCAG-EM method and the heuristic evaluation method. Based on the accessibility evaluation results on the first development, it was found that SIsKA-NG mobile did not fully meet the standards of the mobile application, while based on the results of the heuristic evaluation, it was found 24 problems, where 8 problems with the highest severity rating (Catastrophic), 7 problems with high priority (Major), and others related to low priority (Minor) problems and insignificant problems. The subsequent development to enhance the quality of the interface and accessibility of SIsKA-NG Mobile used those findings as a reference so that this application can meet the needs of all users more effectively and inclusively.
Utilizing Mobile Applications for 21st-Century Learning and Digital Preservation of Balinese Script Asroni, Ahmad; Putu Ade Pranata; Gede Indrawan; Luh Joni Erawati Dewi; Daniel Kevin Alexander
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14676

Abstract

Balinese Script and other inheritance local languages are facing extinction signalment since their use has already been replaced by their national languages, which are simpler and more practical. Bali province has already had governor regulations to preserve this local wisdom through many efforts in Bali, including conducting compulsory local content subject Balinese language (covering Balinese Script) from primary to high school, requiring institution's nameplates to be written in Balinese Script along with different languages, etc. This collaborative study, a joint effort between language and computer science, has exposed the technological side of support for preservation efforts. Through the mobile application from a smartphone, this study revealed the advantage of using this proposed Information Technology application for 21st-century learning of Balinese Script, including paperless material to conduct green-and-sustainable-oriented learning and efficiency to grab knowledge in real-time related to the transliteration and translation aspects from Latin text input. That effort was considered the main contribution to this research area.
The Influence of Service Quality Dimensions and E-Commerce Image on User Satisfaction Levels Putera, Wayan Andrika; Cipta, I Putu Agus Eka Yatna; Yudistira, I Gede Angga; Puspita, Ni Wayan Diah; Pramesti, Indra Eka Bintang
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14685

Abstract

This study investigates how service quality and brand image influence user satisfaction on Shopee, Indonesia’s leading e-commerce platform, amid persistent operational challenges like delivery delays and inefficient return processes. Using a quantitative approach, data was collected via validated questionnaires and analysed through multiple regression, with diagnostic tests ensuring model reliability. Results show both service quality and brand image significantly boost satisfaction, with brand image having nearly twice the impact. The model’s high explanatory power, with adjusted R² is 0.943, confirms their dominant role in shaping user experiences. These findings align with prior retail and hospitality studies but challenge traditional service-dominant logic by highlighting brand perception as the stronger driver in digital commerce. For practitioners, this underscores the need to prioritize brand-building strategies, such as trust signals and influencer partnerships, while maintaining service standards. However, the study’s exclusive focus on Shopee limits generalizability, calling for future cross-platform comparisons. Additional research should employ longitudinal designs, examine cultural moderators, and apply advanced analytics to uncover deeper satisfaction dynamics. By validating the interplay of service quality and brand equity in e-commerce, this study advances theoretical discourse while offering actionable insights for platforms seeking to enhance customer satisfaction in competitive digital marketplaces.

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