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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
 116   117   118   119   120 
Mathematical Modeling of the Vehicle Routing Problem with Relaxed Time Windows and Delay Penalties Fitrie, Rosa; Suwilo, Saib; Mawengkang, Herman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14977

Abstract

The Vehicle Routing Problem with Relaxed Time Windows (VRP-RTW) is an extension of the classic Vehicle Routing Problem (VRP) that incorporates flexibility in service time windows. In VRP-RTW, vehicles are allowed to arrive later than the specified time window. However, a violation will be imposed for exceeding the specified time limit. in the form of fines or similar penalties. This research aims to design a mathematical model for VRP-RTW to minimize total travel costs and delay penalties, while ensuring that all customers are served within the capacity limits of the available vehicles. This research uses literature review methods and mathematical formulation approaches to describe the logistics distribution problem. The developed model considers several constraints, such as vehicle capacity, route balance, and service time limitations. The results of this research are expected to contribute to more efficient and flexible logistics distribution decision-making and serve as a basis for the development of vehicle route optimization models that can be applied in real-world scenarios.
Comparative Performance of Yolov8 and Ssd-mobilenet Algorithms for Road Damage Detection in Mobile Applications Dharma, Arie Satia; Pardosi, Chantika Nadya Serebella; Silaen, Zan Peter
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15008

Abstract

Road damage is a serious issue that can impede traffic and increase the risk of accidents in any area. Fast and accurate detection and classification of road damage are crucial for efficient maintenance and repair. Considering the ease of access, the implementation of this detection can be done using a mobile application. This study aims to compare the performance of two object detection algorithms, YOLOv8 and SSD-MobileNet, in detecting and classifying road damage in mobile application. Evaluation is conducted using accuracy, speed, and memory utilization, and classification of road damage into six categories namely block cracks, alligator cracks, transverse cracks, edge cracks, patches, and potholes using a confusion matrix. The results show that YOLOv8 has an overall accuracy of 86.4%, a speed of 0.5 ms, and consumes 0.41 GB of RAM. SSD-MobileNet shows an overall accuracy of 91.1%, speed 0.7 ms, and consumes 0.14 GB of RAM. The comparison indicates that YOLOv8 excels in detection speed, while SSD-MobileNet is more higher accuracy and efficient in memory. This study is limited to a performance measurement approach for YOLOv8 and SSD-MobileNet algorithms in a mobile-based road defect detection context. Its contribution lies in the trade-off between accuracy, speed, and the memory required to implement the models in limited devices. In future research is recommended to explore model with pruning to reduce memory usage.
Smart Contract Architecture for a Blockchain-Driven Multi Criteria DSS in Forest Fire Monitoring and Response Cahyo, Fajar Yusuf Nur; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15009

Abstract

The current centralized system is vulnerable to data manipulation due to the absence of independent verification mechanisms, thereby compromising the reliability of information. In addition, the inconsistency of formats and data silos across agencies exacerbates information fragmentation. Delays in data distribution hamper rapid response in emergency situations, while uneven communication infrastructure—especially in remote areas—reduces real-time monitoring capabilities. Lack of coordination among stakeholders—such as BNPB, forestry agencies, local communities, and the private sector—adds to the complexity of disaster management and often leads to overlapping tasks. The decision-making process is further complicated by competing criteria, such as priority areas, resource availability, dynamic weather conditions, and limited IoT sensor coverage. Additionally, high operational costs for system maintenance and limited audit trails make it difficult to track data history and ensure accountability. Therefore, the Multi-Criteria Decision Making (MCDM) method is necessary to handle uncertainty, combine different geospatial factors in an organized way, and make sure the decision-making process is reliable and clear. This research fills the technological gap by introducing a decentralized audit trail while facilitating cross-sector collaboration in fire mitigation decision-making and minimizing the risk of evidence-based data errors.
MLP Model Optimization for Heart Attack Risk Prediction: A Systematic Literature Review Supriyanto, Heru; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15027

Abstract

Heart disease remains a leading cause of global mortality, making the development of accurate predictive models a clinical priority. While Multilayer Perceptron (MLP) models offer significant potential, their application is hindered by challenges in optimization, data imbalance, and interpretability. This systematic literature review aims to address these issues by synthesizing current research on MLP model optimization for heart disease prediction, focusing on strategies for handling class imbalance and achieving model transparency with SHapley Additive exPlanations (SHAP). Following PRISMA guidelines, a structured search of major scientific databases resulted in the in-depth analysis of 30 peer-reviewed studies. The findings indicate that MLP optimization is increasingly sophisticated, employing automated hyperparameter tuning and novel architectures. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is the predominant data-level solution, though a trend towards advanced algorithm-level techniques is emerging. The application of SHAP has successfully validated models by confirming the importance of known clinical risk factors like age and chest pain type, while also demonstrating potential for new discovery. This review concludes by providing a comprehensive roadmap for researchers, highlighting a critical need for comparative studies on imbalance techniques, deeper applications of explainable AI for local-level analysis, and a stronger focus on validation using large-scale, real-world clinical data to develop truly robust and trustworthy predictive systems.
Creditworthiness Classification Utilizing AHP-SVM Based on 5C Criteria Amalia, Junita; Manalu, Agnes Judika Margaretha; Ambarita, Jeremia Nico Pratama; Sihombing, Dwita
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15049

Abstract

Credit risk occurs when borrowers fail to meet loan repayment obligations, posing significant challenges to the financial stability of lending institutions. Accurate classification of creditworthiness is essential to mitigate such risks. This study proposes a hybrid approach that integrates the Analytical Hierarchy Process (AHP) and Support Vector Machine (SVM) to evaluate borrower eligibility based on the 5C model: Character, Capacity, Capital, Collateral, and Condition. The AHP method is used to assign weights to credit attributes based on expert judgment, while SVM performs the classification. Three experiments were conducted to compare the effectiveness of different feature selection strategies: (1) expert-defined 5C attributes, (2) AHP weighting conducted by experts, and (3) AHP weighting conducted by non-experts. Experimental results show that the 5C-SVM model achieved the highest performance with 96% accuracy, followed by AHP-SVM (expert) with 95% and AHP-SVM (non-expert) with 93%. The findings indicate that expert involvement in the feature selection process significantly improves model performance. This study demonstrates the effectiveness of combining domain knowledge with machine learning in building intelligent decision support systems for credit risk analysis. The proposed approach offers practical value for financial institutions seeking more objective, accurate, and consistent credit evaluation processes. Furthermore, it opens new opportunities for integrating expert-based reasoning with automated analytics in financial decision-making.  
Lightweight YOLO Models for Real-Time Multi-Vehicle Detection Ashari, Imam; Negara, Iis Setiawan Mangku; A, Arif Setia Sandi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15071

Abstract

This study presents a comparative evaluation of three lightweight YOLO architectures: YOLOv5n, YOLOv8n, and YOLOv11n, for multi-class vehicle detection using CCTV imagery captured under dense traffic conditions in Semarang, Indonesia. The models were tested on their ability to detect four types of vehicles, including motorcycle, car, bus, and truck. To enhance generalization across different lighting conditions, image qualities, and environmental noise, six data augmentation techniques were applied during training. These included Blur, Brightness Adjustment, Color Jitter, Noise Injection, Scaling, and Zoom In. Among these, the Blur technique yielded the most significant improvement in detection accuracy. YOLOv8n with Blur augmentation achieved the best performance with a precision of 0.875, recall of 0.655, mAP@0.5 of 0.756, and mAP@0.5:0.95 of 0.467. Class-wise analysis showed that buses and trucks were easier to detect due to their larger size and distinct features, while motorcycles were the most difficult due to their smaller dimensions and visual similarity to other objects. Training curves demonstrated consistent decreases in loss values and progressive improvements in evaluation metrics across 60 epochs. These findings emphasize the importance of selecting appropriate model architecture and augmentation strategies to improve object detection performance, particularly in real-time and resource-limited applications. YOLOv8n with Blur augmentation proved to be the most effective configuration in this study.
Implementation of an Integrated Cloud-Based Electronic Medical Record System at Community Health Center Fu’ad, Raja Nasrul; Sugeng Riyadi; Deny Pratama; Ramadhan Wicaksono
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.v9i4.15206

Abstract

Klambir Lima is a village located in Hamparan Perak District, Deli Serdang Regency, North Sumatra Province. It is a small village with a dense population but has only one government community health center (puskesmas). This results in suboptimal patient services. Furthermore, there is no existing application for recording patient data or medical records that could assist in patient data management. Patient medical records are a crucial feature in healthcare services. They are useful for recording or storing a patient's health or illness history, which enables accurate treatment or medication tailored to the patient's needs. Therefore, the Klambir Lima Health Center requires an electronic medical records application based on Cloud Computing. Data will be stored in cloud storage, aiming to minimize damage or loss of data, which is a vital asset. In this research, the author developed the application using the R&D (Research and Development) method. The role and utility of the research were well established to ensure better implementation of the application. The research objective is to create a system capable of recording electronic-based medical records via cloud computing media. This will enable the Klambir Lima Health Center to improve its healthcare services for both BPJS (national health insurance) and non-BPJS patients
Smart CRM Application Development Using Artificial Intelligence and Extreme Programming Method Syofiawan, Doni; Miftahul Ilmi
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.v9i4.15254

Abstract

Customer Relationship Management (CRM) is an important strategy for companies to understand customer behavior, increase loyalty, and reduce churn rates. However, the challenge that is often faced is how to manage increasingly complex customer transaction data and turn it into useful information for decision-making. This research aims to develop an artificial intelligence-based smart CRM application by integrating the K-Means algorithm for customer segmentation and XGBoost for retention prediction, as well as using the Extreme Programming (XP) methodology in the development process. The XP methodology was chosen because it is able to provide a fast, adaptive, and user-oriented iterative cycle, so that applications can be developed according to user needs. The results showed that K-Means can group customers into segments that are relevant to marketing strategies, while XGBoost provides retention prediction results with good accuracy. In addition, the application was tested using Blackbox Testing to ensure that the functionality runs according to specifications, as well as the System Usability Scale (SUS) which resulted in an average score of 89 and was included in the excellent usability category. This confirms that the system built is not only technically feasible, but also well received by users. This research contributes to presenting a smart CRM application that combines AI with modern software development methodologies, as well as opening up opportunities for advanced research at a larger data scale and integration with digital marketing systems.
Integrating K-Means Clustering and Apriori for Data Mining-Based Digital Marketing Strategy For Increasing UMKM: Study Case Stabat City Maulidya, Adek Maulidya; Selfira, Selfira; Sidabutar, Gomgom; Al Hafiz, Reyva Ryo
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.v9i4.15299

Abstract

Micro, Small, and Medium Enterprises (MSMEs)  or UMKM in Bahasa are play a crucial role in regional economic development, yet they often face challenges in designing effective marketing strategies due to limited access to advanced analytical tools. Digital marketing supported by data mining offers a solution to this problem by enabling more precise customer segmentation and product bundling recommendations. This study aims to integrate K-Means clustering and Apriori association rule mining to develop data-driven marketing strategies for MSMEs in Stabat City, Indonesia, with a specific focus on rice sales data. A dataset consisting of 1,000 rice sales transactions was processed through a multi-stage methodology, including data preprocessing, clustering, and association rule generation. The Elbow and Silhouette methods suggested an optimal cluster number of k = 3, resulting in three distinct customer groups: (1) loyal high-value buyers, (2) price-sensitive buyers, and (3) premium-oriented buyers. Descriptive statistics highlighted differences in average transaction values, purchase frequency, and brand preferences across clusters. Apriori analysis produced the top ten significant association rules, such as {Medium Rice} → {Pandan Wangi Rice} with support = 0.14, confidence = 0.68, and lift = 1.23. Promotional simulations showed that generic discount campaigns could increase sales by approximately 3.0%, whereas targeted bundling strategies yielded smaller short-term gains (+1.53%) but offered stronger long-term potential, particularly for premium-oriented clusters. These findings are consistent with prior international studies, where customer segmentation combined with market basket analysis has proven effective for enhancing digital marketing outcomes. The study concludes that integrating clustering and association rules can provide MSMEs with actionable insights to optimize promotional strategies and improve competitiveness. However, limitations remain, including the relatively small dataset, reliance on manual parameter selection, and simplified modeling assumptions. Future research should expand to multi-sector datasets and explore advanced algorithms to validate and extend these findings.
A Hybrid PULTS–SWARA–ELECTRE-I Model for Multi-Criteria Political Sentiment Classification on Indonesian Twitter Data Bahri, Taufik Yuandika; Aisyah, Chairini
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.v9i4.15314

Abstract

Social media platforms such as Twitter have become crucial for analyzing political sentiment, particularly in contexts where public opinion shifts rapidly. This study proposes a hybrid classification model that combines Probabilistic Uncertain Linguistic Term Set (PULTS), Stepwise Weight Assessment Ratio Analysis (SWARA), and ELimination Et Choice Translating REality (ELECTRE-I). Using a dataset of 7,800 tweets collected between January and July 2024 covering five major political parties in Indonesia, the model classifies tweets into positive, negative, and neutral sentiments. To address class imbalance, Easy Data Augmentation (EDA) was applied, while Term Frequency–Inverse Document Frequency (TF-IDF) was used for feature extraction. The results show that the proposed model achieves 90% accuracy and an F1-score of 85%, outperforming baseline methods such as SVM (86.7%), Naïve Bayes (83.3%), Decision Tree (88%), and K-Means (76.7%). These improvements demonstrate that the integration of linguistic uncertainty with expert-driven feature weighting provides measurable advantages in political sentiment classification. Beyond performance, the study contributes theoretically by extending multi-criteria decision-making methods into sentiment analysis and by offering a more interpretable alternative to opaque machine learning models. Together, these findings highlight the practical value of explainable decision frameworks for political communication while advancing methodological approaches for analyzing sentiment under uncertainty.

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