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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 65 Documents
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Search results for , issue "Vol. 10 No. 1 (2026): Article Research January 2026" : 65 Documents clear
Digital Transformation of Toddler Posyandu Services via an Android-Based Application Harsono, Harsono; Mulyono, Mulyono; Rinayati, Rinayati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15360

Abstract

Abstract: Posyandu Balita as a community-based health service holds an essential role in improving maternal and child health in Indonesia. Nevertheless, the dependency on manual documentation frequently causes delays in reporting immunization, incomplete records, and limited access for parents to monitor child growth. This study sought to design and assess an Android-based Posyandu Balita application by applying a Research and Development (R&D) model combined with the System Development Life Cycle (SDLC) approach. The development process covered several phases: needs analysis, system design, application construction, pilot implementation, and evaluation through the Technology Acceptance Model (TAM). The pilot, which involved 10 health cadres and 10 parents, revealed that the application reduced data loss, facilitated more accurate immunization tracking, and encouraged stronger parental involvement. Functional testing indicated that the main features—digital medical records, reminder notifications, and growth chart visualization—worked consistently as intended. Based on TAM analysis, perceived usefulness (PU) and perceived ease of use (PEOU) significantly shaped users’ behavioral intention to utilize the system (PU = 62%, PEOU = 58%). Moreover, the level of parental compliance in child health monitoring increased, where 85% of parents actively accessed the digital platform compared to only 40% before the trial. Overall, the results demonstrate that mobile health applications developed with user-centered approaches can improve the effectiveness and efficiency of community-based services. The Posyandu Balita application is a promising innovation to support Indonesia’s digital health transformation. Further research is required to examine large-scale implementation, integration with national health information systems, and strategies for long-term sustainability. Keywords: Community Health, Toddler Posyandu, Android-based Application, Mobile Health, Technology Acceptance Model, Digital Innovation
Decision Model for Best Contraceptive Technique Recommendation Based on Patient's Ideal Profile Hugo, Veronika Novia; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Pratistha, Indra; Atmaja, Ketut Jaya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15377

Abstract

Choosing the right contraceptive method is essential to support the success of family planning programs. Many patients still choose methods without considering their medical conditions, which can lead to failure or side effects. This study designed a decision-making model based on Profile Matching to recommend contraceptive methods according to the patient’s ideal profile. The dataset was obtained from Faskes Level 1 Udayana Denpasar. Validation was conducted through discussions with midwives as experts, referring to the KLOP KB Wheel as the standard issued by the WHO. The evaluation results show a high level of agreement between the model’s recommendations and expert judgments, indicating that the model provides more objective and easily understood recommendations compared to manual approaches.
Analysis of Factors Causing Toddler’s Malnutrition in Medan City Using the Random Forest Method Simamora, Windi Saputri; Harahap, Siti Sarah; Pratama, Andre
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15380

Abstract

Malnutrition and severe malnutrition in toddlers remain critical public health concerns that impair physical growth, cognitive development, and long-term productivity. Deficiencies in essential nutrients increase the risks of stunting, weakened immunity, and developmental delays. Although interventions such as supplementation and routine anthropometric monitoring are implemented, comprehensive identification of multidimensional causal factors is still limited, reducing the effectiveness of targeted policies. This study aims to predict toddler nutritional status using a quantitative data mining approach. A dataset consisting of 328 samples and 17 features was collected from health facilities in Medan City, including Puskesmas, the Health Office, and Posyandu. A Random Forest Classifier was developed with missing-value handling, feature engineering, and feature importance analysis to identify dominant predictors of nutritional outcomes. The model achieved an overall accuracy of 92.42 percent and showed strong performance in identifying the “Normal” class, although predictive sensitivity for minority classes such as “Gizi Kurang” and “Gizi Buruk” remained comparatively lower. Feature importance analysis indicated that complete immunization and health insurance ownership were the most influential determinants of nutritional status. This research provides a machine learning–based tool for early nutritional risk prediction and offers data-driven insights to support more precise malnutrition interventions. Future enhancement may include expanding feature diversity and applying advanced interpretability techniques to strengthen model reliability. The findings reinforce the importance of evidence-based nutrition policy strategies that prioritize early prevention and improved child health outcomes.
Implementation of YOLOv12 and PaddleOCR for Indonesian Bank Statement Table Extraction Kristanto, Samuel Miracle; Tanuwijaya, Evan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15383

Abstract

The increasing reliance on digital financial documents has highlighted the need for automated methods to extract structured information from bank statements. Traditional optical character recognition (OCR) systems often fail to capture complex tabular structures, leading to incomplete or error-prone transaction records. To address this challenge, this research proposes a two-stage detection and recognition pipeline that combines YOLOv12 for table and structural element detection with PaddleOCR for text extraction, followed by automated Excel conversion. The objective of this study is to improve accuracy in localizing tables, detecting rows and columns, and generating structured financial data that can be directly utilized for downstream applications. The methods involve training a YOLOv12-n model in two stages: Stage 1 focuses on detecting entire table regions, while Stage 2 focuses on identifying row and column structures within the detected tables. A lightweight AdamW optimizer with conservative augmentation strategies was applied to preserve the geometric integrity of document layouts. Results show that Stage 1 achieved precision of 0.998, recall of 1.0, and mAP50-95 of 0.989, while Stage 2 achieved precision of 0.992, recall of 0.964, and mAP50-95 of 0.899, demonstrating strong localization and structural recognition. The conclusions confirm that the proposed two-stage pipeline is effective for financial document processing, with potential applications in digital banking, auditing, and automated record management. Future research may focus on expanding datasets and addressing domain-specific variability.
Comparative Study on Machine Learning Algorithms for Code Smell Detection U, Hayya; Saputri, Theresia Ratih Dewi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15439

Abstract

Detecting code smells is crucial for maintaining software quality, but rule-based methods are often not very adaptive. On the other side, existing machine learning studies often lack large-scale comparisons on modern datasets. The goal of this research is to comprehensively compare the performance of various machine learning algorithms for multi-label code smells classification in terms of effectiveness and efficiency. The dataset used in this research is SmellyCode++, containing more than 100,000 samples. Seven models: Logistic Regression, Linear SVM, Naive Bayes, Random Forest, Extra Trees, XGBoost, and LightGBM combined with Binary Relevance were trained on data balanced using random undersampling and multi-label synthetic minority over-sampling. The performance of each model was evaluated using the F1-Macro, Hamming Loss, and Jaccard Score metrics. A non-parametric statistical analysis was also conducted to validate the findings. The experiment found that ensemble-based models statically significantly outperformed the linear and probabilistic models. The performance among the top ensemble models was found to be statistically equivalent. With this statistical equivalence in accuracy, computational efficiency measured with training time became the critical tiebreaker. BR_RandomForest, BR_XGBoost, and BR_ExtraTrees proved highly efficient, while BR_LightGBM was significantly slower. This study concludes that BR_RandomForest offers the best overall trade-off in providing top tier accuracy combined with excellent computational efficiency, making it a robust choice for practical applications.
Comparative Analysis of XGBoost, KNN, and SVM Algorithms for Heart Disease Prediction Using SMOTE-Tomek Balancing Yuliana, Yuliana; Robet, Robet; Hoki, Leony
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15469

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection crucial for improving patient outcomes. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting heart disease using the 2015 BRFSS dataset, which includes responses from 253,680 individuals. The three algorithms examined are Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data preprocessing steps involved feature encoding, class imbalance handling using the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE-Tomek), and hyperparameter tuning through RandomizedSearchCV. The models were assessed on a hold-out validation set using several metrics, including accuracy, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), F1-score, precision, and recall. The results demonstrated that XGBoost achieved the highest performance, with an accuracy of 94%, a ROC-AUC score of 0.98, and an F1-score of 0.94. In comparison, KNN achieved an accuracy of 87% (ROC-AUC 0.95), while SVM attained an accuracy of 79% (ROC-AUC 0.86). These findings suggest that XGBoost is a robust model for large-scale heart disease classification and holds potential for implementation in clinical decision support systems.
Comparison of IndoBERT and SVM Performance in Sentiment Analysis of Digital Education Platforms Br Sembiring, Aldina Bonaria Siva; Robet M.Kom; S.Kom., S.A.B., M.M, Leony Hoki,
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15472

Abstract

Sentiment analysis on user-generated reviews is essential for understanding the quality and effectiveness of digital education platforms. This study compares the performance of Support Vector Machine (SVM) and IndoBERT in classifying sentiments from Ruangguru user reviews. The original dataset contains 111,838 reviews, from which a stratified sample of 10,000 entries was selected for experimentation to maintain class proportion. Text preprocessing applied standard/light normalization (case folding and light cleaning, handling URLs/users/hashtags and repetition) without stopword removal to preserve polarity cues. Auto labels are validated on 139 manually annotated samples (accuracy 0.763, Cohen’s κ 0.644), indicating reliable yet imperfect alignment. To ensure a fair, leakage-safe comparison, we use a fixed 20% standard test split for all models; within the remaining data, 10% is used for validation, and IndoBERT checkpoints are selected based on validation macro-F1 (early stopping). The SVM baseline combines word- and character-level TF-IDF with class-balanced LinearSVC and grid search, achieving accuracy 0.888 and macro-F1 0.543, strong on positives but limited for the neutral class. IndoBERT yields more balanced performance: the class-weighted variant attains the best macro-F1 0.601 (accuracy 0.857), while the baseline reaches the highest IndoBERT accuracy (0.867) with macro-F1 0.596. These results show that Transformer models provide a more balanced trade-off under severe imbalance, whereas SVM remains a competitive accuracy-oriented baseline. In practice, platforms should prioritize macro-F1, use optimized IndoBERT when minority opinions matter, and invest in expanded manual labeling and advanced imbalance handling to improve neutral detection further.
IoT Sensor Data Analysis for Early Fire Detection Using Dynamic Threshold Br Tarigan, Widia; Robet, Robet; Tarigan, Feriani Astuti
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15478

Abstract

Early fire detection using Internet of Things (IoT) technology plays a vital role in minimizing potential material losses and casualties. Conventional systems generally still rely on static thresholds that are less adaptive to environmental dynamics, leading to high false alarm rates and delayed detection. This study proposes a dynamic threshold approach based on a hybrid method of Fuzzy Logic–Random Forest–Adaptive Z-Score and compares it with the static threshold method. Testing was conducted using publicly available secondary datasets, and the algorithms were implemented and tested in Jupyter Notebook. Evaluation was performed using accuracy, false alarm rate (FAR), detection time, F1-score, precision, and recall metrics. The test results show that the dynamic threshold method provides better performance with an increase in accuracy from 59.5% to 74.8%, a decrease in FAR from 31.1% to 14.3%, and a reduction in detection time from 21 seconds to 0 seconds. In addition, the F1-score increased from 0.459 to 0.638, precision from 0.473 to 0.716, and recall from 0.446 to 0.575. These results show that the dynamic threshold approach is more adaptive and reliable in IoT-based fire detection systems than conventional static threshold methods.
Tourism Destination Recommendation Using Blockchain Technology and MCDM Approach Sanjaya, Irfan; Azimah, Ariana; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15482

Abstract

The rapid advancement of digital tourism services has revolutionized how travelers search and select destinations, yet privacy and trust issues remain major challenges in centralized recommendation systems. User data such as preferences, location history, and feedback are often stored on centralized servers, making them vulnerable to data breaches and manipulation. This research proposes a Blockchain-Driven Multi-Criteria Decision Making (MCDM) Approach to develop a privacy-preserving and trustworthy tourist recommendation system. The proposed framework integrates blockchain technology to ensure secure, transparent, and immutable data management, while MCDM techniques such as the Analytic Hierarchy Process (AHP) and TOPSIS are employed to evaluate and rank tourist destinations based on multiple criteria, including popularity, cost, safety, accessibility, and sustainability. The blockchain layer enforces decentralized data verification through smart contracts and cryptographic consensus, ensuring that user privacy is protected without sacrificing system transparency. The experimental results indicate improved recommendation accuracy, reduced privacy risks, and enhanced user trust compared to conventional systems. The proposed model achieved 12.5% higher recommendation accuracy and 30% lower privacy risk compared to centralized models. This study demonstrates that combining blockchain and MCDM can effectively support transparent and fair decision-making in digital tourism, offering a scalable and secure foundation for next-generation recommendation systems.
Blockchain and SVM Integration for Distributed DDoS Attack Detection Hia, Septua Ginta Putra; Hayati, Nur; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15483

Abstract

Rapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results.

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