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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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Integration of YOLOv8 and FastAPI for Early Detection of Nail Diseases Pakpahan, Ferdinand Linggo; Sembiring, Joni Satrio; Abellista, Tivanez Ballerina; Indra, Evta
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.v9i2.14796

Abstract

Nails are important indicators of various health conditions, including fungal infections (onychomycosis), autoimmune disorders (psoriasis), and subungual melanoma (black line). However, early detection of these diseases remains limited due to low accessibility and public awareness. This study aims to develop an end-to-end, web-based early detection system for nail diseases by integrating the YOLOv8 object detection algorithm with the FastAPI framework. A total of 600 annotated nail images obtained from Kaggle were categorized into four classes: healthy nail, psoriasis, black line, and onychomycosis. The model was trained using PyTorch on Google Colab with GPU acceleration and evaluated using precision, recall, and mean Average Precision (mAP@0.5). The model achieved a precision of 93%, recall of 88%, and mAP@0.5 of 89%. Manual testing on 100 images via the deployed web application showed an overall accuracy of 97%. Class-wise accuracy reached 100% for healthy nail and psoriasis, 92% for black line, and 96% for onychomycosis. These results demonstrate that the system performs reliably across various conditions. The main contribution of this study is the implementation of a real-time, web-integrated nail disease detection system that is accessible to both medical professionals and the general public. Future research may focus on expanding the dataset, optimizing model robustness under varied lighting and background conditions, and conducting clinical validation.
Comparative Study of Forecasting Models for Smart Campus Air Pane, Dodi Dores; Ardito, Ardito; Sitompul, Enjeliana; Khairani, Nalla; Nababan, Marlince NK
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.v9i2.14802

Abstract

Air quality monitoring has become increasingly critical in urban environments, especially in densely populated smart campuses situated in tropical regions. This study presents a comparative evaluation of three predictive models CNN-GRU, LSTM, and Random Forest, for forecasting air pollution levels, specifically particulate matter concentration (PM), using real-time sensor data. The data were collected from an IoT-based monitoring system built with NodeMCU ESP8266 devices deployed on campus. Each model was trained and evaluated using performance metrics including the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results indicate that the Random Forest model achieved the highest predictive accuracy with R² = 0.9073, MAE = 123.31, and RMSE = 274.45, outperforming both LSTM (R² = 0.8341) and CNN-GRU (R² = 0.8714). The hybrid CNN-GRU model, although capable of capturing both spatial and temporal dependencies, required larger data volumes and longer training times. The LSTM model, while effective in modeling time-series data, demonstrated a tendency to overfit when data was limited. This study highlights the practical advantages of Random Forest in modeling complex environmental data under limited resource constraints, while also demonstrating the potential of hybrid deep learning architectures. These findings contribute to the development of efficient air quality prediction systems that support health-conscious decision-making and environmental management strategies in tropical innovative campus environments.
A Multi-Objective Decomposition Model for Integrated Urban Transit Line Planning and Passenger Routing Hasibuan, Shubuhan Syukri; Suwilo, Saib; Mardiningsih, Mardiningsih
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.v9i2.14803

Abstract

: Urban public transport networks must balance traveler convenience with tight budgetary and capacity constraints. This study develops a comprehensive multi-objective integer programming framework that unifies line selection, frequency setting, and passenger routing to minimize door-to-door travel time and operating cost while respecting vehicle capacities and limiting transfers. The model is solved using a Dantzig–Wolfe decomposition approach with linear-programming relaxation, which enables tractable solutions on realistically scaled networks. To reflect real-world commuting behavior, three increasingly sophisticated formulations are proposed: a Basic Line Planning Model, a Direct Connection Capacity Model, and a Change-and-Go Model that embeds walking and waiting penalties. On a six-edge, four-node network with 6,000 passenger trips, the Change-and-Go Model emerges as the most effective, reducing average travel time by 47%, halving transfers, and increasing cost by only 11% compared to the incumbent plan. Sensitivity analysis reveals that the model remains robust under varying demand levels and cost–time priorities. The proposed framework thus offers a scalable and passenger-friendly decision-support tool that significantly improves public transport efficiency with moderate investment, making it especially valuable for urban transit agencies seeking to modernize their services.
Enhanching Prophet Time Series Forecasting on Sparse Data via Hyperparameter Optimizattion: A Case Study in Retail Atamimi, Fadel Muhamad Hafid; Witanti, Wina; Abdillah, Gunawan
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.v9i2.14804

Abstract

In today’s competitive business landscape, accurate sales forecasting is crucial for retailers to optimize inventory, prevent overstock, and support strategic decision-making. However, many small to medium enterprises operate with sparse and irregular sales data, making conventional forecasting methods less effective. This study aims to evaluate the performance of the Prophet time series model in such non-ideal conditions and to investigate how hyperparameter tuning affects its forecasting accuracy. The research adopts the Prophet algorithm, an additive time series forecasting model developed by Facebook, which incorporates trend, seasonality, and holiday components. The model was implemented in two configurations: one using default parameters, and another with manually tuned hyperparameters, including changepoint prior scale (CP), seasonality prior scale (SP), and seasonality mode. A total of 32 experiments were conducted using historical transaction data from PT Eko Hejo. Results show that the default Prophet model achieved a MAPE of 9.50%, while the best-performing configuration (CP = 0.5, SP = 0.01, additive mode) reduced the MAPE to 6.80%. This indicates that hyperparameter tuning significantly improves forecast accuracy, even in sparse data environments. The study contributes both practically and scientifically by demonstrating that Prophet, when properly configured, is a robust and adaptable tool for business forecasting with limited data. It also highlights the value of manual tuning in enhancing model responsiveness and generalization, offering insights for further research in model comparison, automated optimization, and hybrid forecasting approaches.
Earthquake Detection IoT Prototype with Early Warning System Based on Vibration Sensor Harahap, Nur Salimah; Purnama, Iwan; Rohani, Rohani
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.v9i2.14807

Abstract

This research developed a prototype of an earthquake detector using a vibration sensor, integrated with a real-time early warning system through a buzzer and LCD display. The system is designed to detect vibrations that potentially indicate an earthquake in real time and to promptly provide alerts to the surrounding environment. The research followed a Research and Development (R&D) method using the waterfall model, which includes requirements analysis, system design, implementation, testing, and maintenance. The hardware components include Arduino Uno, a SW-420 vibration sensor, a buzzer, and an LCD, while the software utilizes Arduino IDE for coding. The test results show that the system successfully detects vibrations and displays real-time notifications, proving its effectiveness in early earthquake warning scenarios. This system is expected to support disaster mitigation efforts by offering a simple and affordable solution that can be implemented in vulnerable areas.
Comparison of WSM and Weight Product Methods with WSM-Score and Vector Approaches Nasyuha, Asyahri Hadi; Tujantri , Harkam; Veza, Okta; Nurarif, Saiful; Chung, Meng-Yun
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.v9i2.14817

Abstract

Fertilizers are essential in modern agriculture as they supply vital nutrients to plants, enhancing growth and yield. However, selecting the most appropriate fertilizer involves multiple criteria and a diverse range of available options. This study conducts a comparative analysis of two Multi-Criteria Decision-Making (MCDM) methods: the Weighted Sum Model (WSM) and the Weight Product (WP) method, supplemented by WSM-Score and vector-based approaches. The evaluation is based on four criteria price, quality, ease of availability, and fertilizer form across seven alternatives: Urea, Compost, TSP, KCL, Gandasil, NPK, and ZA. Using normalized weights from expert judgment, both methods were used to rank the alternatives. A key contribution of this study is the integration of WSM-Score and vector approaches, which enhance traditional MCDM by improving score comparability (WSM-Score) and enabling geometric interpretation of alternative positioning (vector). Results show that Compost (A2) ranks highest across all methods, indicating convergence despite differences in computational logic. WSM offers ease of interpretation, while WP better accounts for proportional differences but is more sensitive to low-performing criteria. The findings suggest that method selection should be context-dependent. Although the ranking results are consistent, the absence of empirical validation through expert comparison or field data limits the generalizability of the conclusions. Further research should include such validation to strengthen the reliability of MCDM-based decision support systems in agricultural applications.
Clustering IT Incidents Using K-Means: Improving Incident Response Time in Service Management Anggraeni, Rini; Alzami, Farrikh; Nurhindarto, Aris; Budi, Setyo; Megantara, Rama Aria; Rizqa, Ifan; Muslih, Muslih
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.v9i2.14822

Abstract

Incident management is one of the critical processes in Information Technology service management that aims to manage disruptions and minimize the impact of unexpected incidents on business services. This study applies the K-Means algorithm to cluster IT service incidents, aiming to enhance company operational efficiency. Utilizing a dataset from the UCI Machine Learning Repository comprising 141,712 events related to 24,918 incidents, this research analyzes incident patterns and characteristics for optimized handling. The data was analyzed through a series of preprocessing stages, and the elbow and silhouette methods were used to determine the optimal number of clusters. From the results, it was successfully grouped into 4 (four) clusters with a distortion score value of 964264294.569 and 0.52 silhouette score based on incident characteristics, such as urgency, priority, and number of reassignments. From this, the clustering results show that the K-Means algorithm effectively identifies incidents that require further handling, such as those with high urgency and priority, as well as helping the company focus resources to resolve incidents that have the most impact on the business sector. This research provides a data-driven solution to improve incident management and Service Level Agreement (SLA) fulfillment, while offering a framework for more effective and efficient IT incident analysis and resource allocation.
A Systematic Review of Retrieval-Augmented Generation for Enhancing Domain-Specific Knowledge in Large Language Models Murtiyoso, Murtiyoso; Tahyudin, Imam; Berlilana, Berlilana
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.v9i2.14824

Abstract

This literature review examines the use of Retrieval-Augmented Generation (RAG) in enhancing Large Language Models (LLM) for domain-specific knowledge. RAG integrates retrieval techniques with generative models to access external knowledge sources, addressing the limitations of LLMs in handling specialized information. By leveraging external data, RAG improves the accuracy and relevance of generated content, making it particularly useful in fields that require detailed and up-to-date knowledge. This review highlights the effectiveness of RAG in overcoming challenges such as data sparsity and the dynamic nature of specialized knowledge. Furthermore, it discusses the potential of RAG to enhance LLM performance, scalability, and the ability to generate contextually accurate responses in knowledge-intensive applications. Key challenges and future research directions in the implementation of RAG for domain-specific knowledge are also identified.
A Systematic Review of Multimodal Sentiment Analysis Based on Text-Image Fusion: Trends, Models, and Research Gaps Hamidi, Mohammed Abdul Mohsen; Taqa, Alaa Yaseen; Ibrahim, Yahya Ismail
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.v9i2.14840

Abstract

Sentiment analysis has evolved from text-based approaches to multimodal sentiment analysis (MSA), which integrates textual and visual data to enhance the accuracy of emotional understanding, especially in visually rich social media contexts. This study presents a systematic literature review (SLR) focusing on recent developments in text-image-based MSA, aiming to identify prevailing methods, fusion strategies, and major research gaps. Following the PRISMA protocol, a total of 20 key articles published between 2019 and 2024 were selected and analyzed. The results indicate that deep learning models such as LXMERT, ViLBERT, and ERNIE-ViL outperform traditional architectures, achieving accuracies above 80% on datasets like MVSA and Twitter. Attention mechanisms and advanced feature fusion techniques significantly contribute to improving both accuracy and interpretability. However, challenges remain in terms of annotation quality, semantic alignment across modalities, and real-time implementation constraints. This study contributes by mapping the state-of-the-art in multimodal sentiment analysis, highlighting underexplored research gaps, and offering directions for future work toward more adaptive and context-aware sentiment systems
Enhancing EEG-Based Stress Detection Using ICA, Relative Difference, and Convolutional Neural Networks Negara, I Made Wahyu Guna; Wirawan, I Made Agus; Sunarya, I Made Gede
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.v9i3.14777

Abstract

: EEG-based stress detection is crucial for early mental health monitoring, but signal quality is often degraded by artifacts and baseline variability. This study proposes an optimized preprocessing method combining Independent Component Analysis (ICA) for artifact removal and Relative Difference for baseline reduction. Using the SAM-40 EEG dataset, features were extracted with Differential Entropy and structured into a 3D EEG cube to preserve spatial-frequency information. A Convolutional Neural Network (CNN) classified stress levels into low and high categories. The proposed approach achieved 94.44% accuracy, with 100% precision for the high stress class and 81.82% recall. These results highlight the effectiveness of combining ICA and baseline reduction to enhance deep learning-based EEG signal processing for stress detection.

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