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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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Comparative Analysis of LSTM, GRU, and Bi-LSTM Deep Learning Models for Time Series Cryptocurrency Price Forecasting Priadinata, I Putu Bramasta; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Radhitya, I Made Leo; Supartha, I Kadek Dwi Gandika
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.v9i3.14795

Abstract

Cryptocurrency is a highly volatile digital asset that requires accurate predictive methods. This study compares the performance of three deep learning architectures LSTM, GRU, and Bi-LSTM in forecasting the prices of Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) using univariate historical data. Evaluation was conducted through regression metrics (RMSE and MAPE) and classification of price movement into five categories, ranging from very bearish to very bullish, assessed using a confusion matrix. The results show that GRU performed best for BTC (RMSE 974.72, MAPE 1.18%), while Bi-LSTM outperformed others for ETH and BNB (RMSE 43.19 and 6.83; MAPE 1.16% and 1.08%) and achieved the highest classification accuracy (55% and 52%). However, overall classification accuracy remains low, reflecting the complexity of cryptocurrency price patterns. The study is limited by its univariate approach without incorporating external variables. Its contribution lies in combining regression and classification evaluation, and it recommends exploring multivariate and ensemble models in future research.
Design of Real-Time Project Monitoring Dashboard Using Kimball’s Data Warehouse Approach and Google Data Studio Savitri, Ni Kadek Wiliya; Sandhiyasa, I Made Subrata; Fittryani, Yuri Prima; Sudipa, I Gede Iwan; Putra, Desak Made Dwi Utami
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.v9i3.14801

Abstract

The growth of the construction industry in Indonesia triggers an increasing need for an efficient project management system, especially in presenting project data accurately and in real-time. PT Dream Island Development (PT DID), a specialist MEP contractor company, faces challenges in presenting project reports to executives because the data is still presented in the form of Excel tabulations which require up to three days of processing time and are difficult to interpret quickly. This research aims to design an interactive dashboard-based project data visualization system using Google Data Studio (Looker Studio) to present project information intuitively and responsively. The method used includes a software engineering approach with five main stages: requirements analysis, data warehouse design, ETL process using Pentaho Data Integration, visualization using Google Data Studio, and testing using User Acceptance Test (UAT). Project data from 2022-2024 was modeled using a star schema and displayed in four main dashboards: project cost, project value, project progress, and details per project. The test results showed a high level of user satisfaction with a functionality score of 93.5%, reliability 91.33%, usability 96%, and efficiency 94.66%. These findings indicate that the developed system effectively supports PT DID's needs in project monitoring and data-based decision-making. The system also has the potential to be replicated in other construction companies as an efficient and scalable business intelligence solution. This research contributes to the growing body of construction informatics by integrating Kimball’s nine-step methodology with modern data visualization tools to enhance project transparency and decision-making.
Convolutional Neural Network Algorithm Implementation for Classifying Traditional Wood Carving Motifs of Patra Bali Widyatama, I Dewa Gede Surya; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Wulandari, Dewa Ayu Putri; Jayanegara, I Nyoman
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.v9i3.14841

Abstract

This research develops an automatic classification system to recognize Balinese Patra carving motifs using deep learning method based on Convolutional Neural Network (CNN). The data used are images of Cina Patra, Mesir Patra, Punggel Patra, and Sari Patra motifs, which have gone through preprocessing stages such as cropping, resizing, and augmentation in the form of flip and rotation to increase data variation. Three pre-trained CNN models were used in testing, namely DenseNet169, InceptionResNetV2, and MobileNetV2. The training process was performed with Adam optimization, batch size 32, and 100 epochs. Model performance evaluation was performed using accuracy and confusion matrix metrics. The results show that all three models were able to achieve 100% accuracy on the test data, with MobileNetV2 recording the lowest loss of 0.75%, followed by DenseNet169 (1.14%) and InceptionResNetV2 (1.18%). Based on the confusion matrix, all motifs were recognized very well, although there was a slight misclassification of the Patra Sari motif by the InceptionResNetV2 model. These findings prove that CNN is effectively used in the recognition of traditional carving motifs and has the potential to support cultural preservation through interactive visual technology.
Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry Syahra, Yohanni; Fadlil, Abdul; Yuliansyah, 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.v9i3.14907

Abstract

In today’s highly competitive retail landscape, businesses face increasing challenges in retaining customer loyalty and achieving sustainable growth. A common issue, particularly among small and medium-sized enterprises (SMEs), is the absence of a structured method for identifying and categorizing customers based on their value and behavior. This study addresses the challenge by implementing a data-driven customer segmentation approach using Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means clustering algorithm. The research utilized 2,353 transaction records from 369 unique customers collected over three years from a local retail business. After preprocessing and normalizing the RFM values using Min-Max scaling, the Elbow Method was applied to determine the optimal number of clusters, resulting in four distinct customer segments. Cluster 3, labeled “Loyal Customers,” consisted of customers with high purchase frequency and very high spending; Cluster 1 (“Potential Loyalists”) included those with moderate activity; Cluster 0 represented “At-Risk Customers,” and Cluster 2 comprised “One-Time Buyers.” This segmentation framework supports the development of targeted Customer Relationship Management (CRM) strategies, such as loyalty programs and re-engagement campaigns. However, the approach also has limitations, including potential data bias due to the use of static transaction records and the challenge of interpreting clusters without qualitative customer feedback. Despite these constraints, the study demonstrates the practical utility of combining RFM analysis with clustering techniques to extract actionable insights in environments with limited technical infrastructure.
Meeting Room Booking System with WhatsApp Notification Feature Using Extreme Programming Methods in RS Muhammadiyah Lamongan Firdonsyah, Arizona; Putri, Nafisyah Alyana
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.v9i3.14927

Abstract

Meeting room management in hospitals plays an important role in supporting operational efficiency and coordination between departments. At RS Muhammadiyah Lamongan, common issues such as overlapping schedules, delays in booking information, and lack of transparency in the scheduling process are still frequently encountered. This study aims to develop a web-based meeting room booking system using the Extreme Programming (XP) method, integrated with a WhatsApp notification feature. The system is designed to improve transparency, minimize scheduling conflicts, and enhance communication between administrators and users. Requirements gathering was conducted through interviews with the hospital's secretariat, and the system was developed using the Laravel Framework and WhatsApp API. The system testing was carried out using Blackbox Testing and User Acceptance Testing (UAT) with a Likert scale. The test results showed that the system achieved a perfect score of 100 out of 100 points, indicating that all core features functioned as expected without significant technical issues. This system is expected to serve as an effective solution to support a more efficient, real-time, and structured meeting room scheduling process at RS Muhammadiyah Lamongan.
Bibliometric Mapping and Trend Analysis of Beta Regression Modeling: A Decade of Development (2015–2024) Sihombing, Pardomuan Robinson; Erfiani, Erfiani; Notodiputro, Khairil Anwar; Kurnia, Anang
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.v9i3.14949

Abstract

Beta regression is a statistical model designed to handle dependent variables that assume values within the open interval (0, 1), such as rates, proportions, or percentages. The study aimed to determine the development of beta regression over the last 10 years with a bibliometric approach. The source of the article database used comes from the Scopus website. The tool used for analysis is R software with a bibliometrix package. The results of this study show that there are 293 articles published in the Scopus Journal. Research develops in various research fields. The author with the most articles is Cribari-Neto, F., with the most significant number of documents, i.e., 12. According to the author's country of origin related to the beta regression method, Brazil has the most countries, while Indonesia is in 12th place. Therefore, research on beta regression still has excellent potential to continue to be developed.
Hyperparameter Optimization with MobileNet Architecture and VGG Architecture for Urban Traffic Density Classification Using Bali Camera Image Data Suputra, I Putu Arsana; I Gede Aris Gunadi; 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.v9i3.14971

Abstract

Traffic congestion in urban areas is a critical issue, particularly in densely populated regions such as Bali. This study addresses the challenge by implementing a Convolutional Neural Network (CNN) method to classify traffic density levels based on images captured by road surveillance cameras. The primary focus of this research is hyperparameter optimization to enhance the model's performance in classifying traffic conditions. Various combinations of hyperparameters—such as the number of neurons in the dense layer, dropout rate, learning rate, batch size, and number of epochs—were tested on two popular CNN architectures: MobileNet and VGG16. MobileNet offers lightweight computing, while VGG16 provides strong feature extraction capabilities, albeit with higher computational resource demands. Quantitative results show that after hyperparameter tuning, the MobileNet architecture achieved an accuracy of 96.94% and an F1 score of 0.969, while the VGG16 architecture achieved an accuracy of 97.22% and an F1 score of 0.972 in traffic density classification. These findings confirm that hyperparameter optimization can significantly improve classification accuracy. The scientific contribution of this research lies in the structured approach to CNN hyperparameter optimization and the demonstration that this process directly impacts the enhancement of model performance in traffic image classification tasks. This study offers valuable insights for the development of intelligent traffic management systems, especially in urban areas with limited resources.
Smart Diagnosis of Coffee Diseases via Web-Based Expert System Ginting, Deo Ekel Pindonta; Sitorus Pane, Siti Anzani; Nababan, Marlince Novita Karoseri; Christnatalis
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.v9i3.14974

Abstract

Indonesia’s coffee industry faces persistent threats from plant diseases and pests, which significantly impact crop yield and farmer livelihoods. Many smallholder farmers lack access to timely expert guidance, leading to delays in diagnosis and ineffective treatments. This study proposes a web-based expert system designed to assist farmers in diagnosing coffee plant diseases and pests based on observed symptoms. The system integrates a Bayesian Network (BN) to model the probabilistic relationships between symptoms and diseases. It employs a Breadth-First Search (BFS) algorithm to optimize the exploration of symptom-disease associations. Developed using Node.js, Next.js, and MySQL, the system enables users to input their symptoms and receive probabilistic diagnoses along with treatment suggestions. Validation results show over 85% accuracy compared to expert assessments, highlighting the system's reliability and scalability. This research demonstrates that combining probabilistic reasoning and structured graph traversal provides an effective diagnostic tool, especially for underserved rural communities. Furthermore, the system serves as a foundation for future development of intelligent agricultural support tools, with potential integration of real-time environmental data, mobile platforms, and adaptive learning models to enhance decision-making in precision farming.
Integrating SMOTE with XGBoost for Robust Classification on Imbalanced Datasets: A Dual-Domain Evaluation Siagian, Novriadi Antonius; Sipayung, Sardo P; Alex Rikki; Marbun, Nasib
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.v9i3.15029

Abstract

Class imbalance is one of the main challenges in classification problems, as it can reduce the model's ability to accurately identify minority classes and negatively impact the overall reliability of predictions. In response to this problem, this study proposes an integrated approach combining SMOTE and XGBoost to improve classification performance on imbalanced data. This approach aims to evaluate the impact of oversampling techniques on prediction accuracy and model sensitivity to class distribution. The evaluation was conducted using two public datasets representing different domains and different amounts of data, namely Spambase and Diabetes, to assess the effectiveness and generalization of the applied approach. The experimental results show that this integrated model consistently outperforms traditional comparison algorithms, with an F1 score of 0.94 and ROC-AUC of 0.98 on the Spambase dataset and ROC-AUC of 0.83 on the Diabetes dataset, with a good balance between precision and recall. The 10-fold cross-validation technique was applied to ensure objective performance estimates free from random data splitting bias. Additionally, this study highlights the importance of selecting appropriate evaluation metrics in the context of imbalanced data, as single accuracy often provides a misleading performance picture. This study makes a significant contribution by providing a benchmark for comparing the effectiveness of SMOTE-XGBoost integration using two different datasets, accompanied by rigorous cross-validation. These findings reinforce the position of integrating data preprocessing strategies and ensemble learning as a competitive and adaptive solution for addressing class imbalance challenges in data-driven classification systems.
Comparative Performance Analysis of Decision Tree And SVM Algorithms in Detecting Multiple System Atrophy Based on Clinical Features Simatupang, Silvina Enjelia Br; Andreas Nababan; Ruth Agnes E. Tarihoran; Jepri Banjarnahor
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.v9i3.15073

Abstract

Multiple System Atrophy (MSA) is a progressive neurodegenerative disorder that presents significant challenges in early and accurate diagnosis. Advances in machine learning algorithms offer promising solutions for improving diagnostic support in medical fields, particularly in complex disorders such as MSA. This study compares the performance of two widely used classification algorithms Decision Tree (DT) and Support Vector Machine (SVM) in detecting MSA using clinical datasets consisting of 300 patient records. Supervised learning techniques with cross-validation were employed, and key performance metrics including accuracy, precision, recall, and F1-score were evaluated. SVM achieved an accuracy of 88.1% and F1-score of 87.1%, outperforming Decision Tree, which recorded 85.4% accuracy and an F1-score of 83.9%. The novelty of this study lies in its direct comparative benchmark using standardized clinical features for MSA detection, offering practical insights into model selection for neurodegenerative disease screening. The SVM model’s superior performance indicates its suitability for reliable early detection of MSA from clinical data. This research contributes to the development of machine learning-based decision support tools in neurology.

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