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INDONESIA
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 1,259 Documents
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Optimization of Web-Based Printing Order Management System Using Redis Database for Efficient Data Handling Mellati, Pita; Galuh Wilujeng Saraswati; Wildan Mahmud; Lutfina, Erba; Resha Meiranadi Caturkusuma
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.v10i1.15502

Abstract

The rapid advancement of information technology has encouraged small and medium-sized enterprises to shift from manual operational procedures to structured digital systems. However, many small printing businesses continue to face delays, data inconsistencies, and limited real-time monitoring due to conventional order management practices. These challenges highlight the need for a more responsive and efficient ordering system capable of improving transaction accuracy and service delivery speed. This study addresses the issue by developing a web-based ordering system using an iterative Agile Scrum approach, followed by a comprehensive performance evaluation through simulated concurrent user testing. The results show a substantial improvement in system responsiveness, with user data retrieval time decreasing from 11,228 ms to 2,148 ms (an 80.9% improvement) and order processing time reduced from 16,954 ms to 4,697 ms (a 72.3% improvement), resulting in an overall average efficiency gain of 76.6%. The integration of Redis caching significantly enhances system performance, stability, and load distribution, addressing the current gap in Redis implementation for small-scale printing environments. This study demonstrates that adopting a hybrid data-handling architecture can provide a scalable, reliable, and high-performance solution for digital ordering processes, enabling small enterprises to improve operational efficiency and customer satisfaction.
IndoBERT-Based Pediatric Disease Classification and Symptom-Based Traditional Medicine Recommendation from Lontar Usada Rare Winata, I Putu Erick Prawira; Sudipa, I Gede Iwan; Meinarni , Ni Putu Suci; Wulandari, Dewa Ayu Putri; Yanti, Christina Purnama
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.v10i1.15507

Abstract

This study aims to develop a Balinese traditional text-based pediatric disease classification model using a fine-tuned IndoBERT model on the Lontar Usada Rare dataset. The dataset used consists of 422 entries containing disease symptoms, disease types, medicinal ingredients, and treatment procedures obtained from transliteration of lontar manuscripts and interviews with traditional medicine experts. Pre-processing was done through case folding, cleansing, and normalization, followed by label encoding on 35 disease classes. The IndoBERT model was fine-tuned using the AdamW optimizer with a learning rate of 5e-5, batch size 8, and 15 epochs. Evaluation results showed the model was able to achieve 90.59% accuracy, 94.71% precision, 90.59% recall, and 90.99% F1-score, indicating excellent performance in understanding the linguistic context of traditional medical text. The developed recommendation system integrates model prediction with TF-IDF-based cosine similarity method to provide the most relevant treatment recommendations based on user symptom input. This research makes an important contribution to the digitization and preservation of Balinese traditional medical knowledge through the development of a structured and widely accessible digital knowledge base.
SVM-Based Pediatric Disease Classification Model from the Balinese Lontar Usada Rare Manuscript Bhawanaputra, I Gusti Made Ngurah Ari; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Aristamy, I Gusti Ayu Agung Mas; Pratistha, Indra
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.v10i1.15508

Abstract

Lontar Usada Rare is a traditional Balinese manuscript containing pediatric medical knowledge based on local wisdom, yet its narrative format limits accessibility and utilization in modern contexts, while its physical fragility threatens long-term preservation. This study aims to develop a pediatric disease classification model using a Support Vector Machine (SVM) combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting to support the digitalization of Balinese traditional medicine. A total of 422 data samples were collected through expert interviews and manuscript analysis, covering symptoms, disease types, herbal ingredients, and treatment procedures. The research stages included text preprocessing (cleansing, tokenizing, stopword removal, stemming), manual labeling into 35 disease classes, and model evaluation using five train–test split ratios (80:20 to 60:40) with variations of the complexity parameter C (0.5, 1, 10, 100, 1000). The best performance was achieved using C=10 with an 80:20 ratio, resulting in 87.06% accuracy, 91.55% precision, 87.06% recall, and an F1-score of 87.96%. Confusion matrix analysis showed strong classification performance for most classes, although minority classes with overlapping symptoms exhibited misclassification. Overall, the TF-IDF and linear SVM combination effectively classifies pediatric disease symptoms from Lontar Usada Rare and contributes to the preservation and digital transformation of Balinese traditional medical knowledge for potential modern healthcare applications.  
Comparison of XGBoost and Naive Bayes Models in Type 2 Diabetes Prediction with RFE Feature Selection Barus, Hanisa putri; Robet; Feriani Astuti Tarigan
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.v10i1.15509

Abstract

Type 2 diabetes mellitus is a chronic disease with an increasing prevalence rate that can cause serious complications if not detected early. The application of machine learning algorithms can aid prediction, but selecting the right model and features greatly determines the accuracy of the results. This study aims to compare the performance of the Extreme Gradient Boosting (XGBoost) and Naive Bayes algorithms in predicting type 2 diabetes with and without Recursive Feature Elimination (RFE) feature selection. The data used were from the UCI Machine Learning Repository, comprising 768 samples and eight clinical features. The research process included data preprocessing, dividing the data into 614 training data and 154 testing data, applying RFE to select the most influential features, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that Naive Bayes without RFE achieves 70.77% accuracy, 0.57377 precision, 0.648148 recall, F1-score 0.608696, and 0.772778 AUC, while Naive Bayes with RFE increases the accuracy to 74.02% and the AUC to 0.793333. Meanwhile, XGBoost with RFE provided the best results with an accuracy of 74.67%, precision of 0.653061, recall of 0.592593, F1-score of 0.621359, and the highest AUC of 0.804259. Besides, applying RFE also improves the computational efficiency. These findings indicate that applying RFE significantly improves classification and computation time performance. The practical implication is that this model could aid early detection of diabetes in clinical settings. Further research can be conducted by optimizing parameters and using more diverse datasets.
Blockchain Disaster-Relief DApps with SVM and Data Anchors for Fraud-Prevention Ardhi, Agil Zaky; Sari, Ratih Titi Komala; Nathasia, Novi Dian; Ningsih, Sari
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.v10i1.15522

Abstract

VoucherAid and DataAnchor are prototype DApps for disaster-relief voucher processing that integrate on-chain rule enforcement, cryptographic data anchoring through fixed-size hash commitments, and an off-chain SVM-based analytics gateway. VoucherAid issues non-transferable vouchers, restricts redemption to certified merchants, and emits auditable events, while DataAnchor records time-stamped digests to support provenance verification without exposing sensitive content. A 200-record dataset was generated from on-chain logs and enriched with behavioral–temporal features derived from redemption activity. Experiments conducted in a single-node Ganache environment using a 70:30 split show that the SVM achieves 0.75 accuracy with perfect precision but limited recall for fraud (1.00 precision, 0.32 recall, 0.48 F1), indicating that the model cannot serve as a reliable stand-alone detector and is more appropriate as a conservative decision-support tool under human oversight. The prototype demonstrates that separating on-chain enforcement from off-chain analytics can enhance auditability and support model evolution without contract redeployment. However, the findings remain constrained by the small, partially synthetic dataset, the single-node evaluation environment, and programmatic labeling. Future work will expand datasets, incorporate richer temporal and graph-based features, adjust thresholds and class weights, and evaluate the system on multi-node networks to improve fraud recall while maintaining usability and inclusion.
Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset Saputra, Kurniawan Aji; Alzami, Farrikh; Kurniawan, Defri; Naufal, Muhammad; Muslih, Muslih; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
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.v10i1.15530

Abstract

Retinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity.
Causal Analysis of Stunting Determinants Using the Peter-Clark and Greedy Equivalence Search Algorithms Tou, Nurhaeka; Endraswari, Putri Mentari; Iftizam, Syafiranur
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.v10i1.15532

Abstract

Child stunting remains a major public health challenge, reflecting the long-term effects of inadequate nutrition, limited maternal education, and restricted access to health services. However, most existing studies rely on correlational analysis, leaving the underlying causal mechanisms insufficiently understood. This gap limits the development of effective interventions, as policymakers require evidence on how determinants interact causally. To address this issue, this study applies two causal discovery algorithms Greedy Equivalence Search (GES) and Peter Clark (PC) to identify causal relationships among eight key determinants of stunting using secondary data from the West Bangka District Health Office (2024). The variables include anthropometric indicators, maternal characteristics, and environmental conditions. Causal assumptions such as causal sufficiency, acyclicity, and faithfulness were imposed to ensure identifiability of the resulting graphs. Model performance was evaluated using Directed Density (DD) and Causal Density (CD) metrics. GES generated a parsimonious causal structure highlighting maternal education, posyandu visits, and exclusive breastfeeding as dominant causal candidates affecting height-for-age (TB/U) and weight-for-age (BB/U). In contrast, the PC algorithm produced a more complete and dense structure, achieving DD = 1.0 and CD = 0.12, compared with GES (DD = 0.80; CD = 0.10). These results indicate that PC is more exploratory in mapping complex causal interactions, while GES offers a simpler and more conservative model. Collectively, the findings demonstrate that combining score-based and constraint-based discovery approaches yields complementary insights into the mechanisms driving stunting.
Machine Learning Analysis of Jakarta Bay Water Quality: Comparing Models Savira, Aura; Andrianingsih, Andrianingsih
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.v10i1.15540

Abstract

Jakarta Bay experiences persistent anthropogenic pressures that produce spatially heterogeneous water-quality conditions. This study develops a regulation-aligned, explainable classification framework using a 2024 in-situ dataset collected at 53 stations across two sampling periods (March and August). After preprocessing—including unit harmonization, outlier screening, missing-value imputation, and treatment of below-detection-limit measurements—the dataset yielded 104 complete samples classified into Good (n=46), Lightly Polluted (n=28), and Moderately Polluted (n=34) categories based on KEPMEN LH No. 51/2004. Three ensemble algorithms (LightGBM, CatBoost, and Random Forest) were evaluated using stratified cross-validation to maintain class balance and prevent spatial leakage. CatBoost achieved the best overall performance (Accuracy = 0.8338; F1 = 0.8257), followed by Random Forest, while LightGBM showed the highest variability across folds. Class-level metrics indicate that CatBoost produced the most balanced predictions, particularly for the borderline Lightly Polluted class. SHAP analysis identified turbidity/TSS, nutrients, dissolved oxygen, salinity, and spatial gradients as dominant predictors, enabling transparent interpretation of model decisions. The resulting framework provides a reproducible and operationally deployable approach for rapid screening, hotspot detection, and decision support in Jakarta Bay’s water-quality management.
Heart Disease Classification Using Optimised XGBoost and Random Forest with SHAP Explanations Hutagalung, Pancar Hizkia; Andrianingsih, Andrianingsih
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.v10i1.15544

Abstract

Heart disease remains one of the leading causes of global morbidity, creating a need for accurate and interpretable computational tools to support early diagnosis. However, many existing studies on the Cleveland Heart Disease dataset rely on limited validation protocols, apply only a single hyperparameter optimisation strategy, or provide narrow explainability analyses, which can lead to optimistic performance estimates and inconsistent clinical insight. This study addresses these gaps by proposing a classification-based prediction framework that evaluates Random Forest and XGBoost for binary heart-disease classification under three hyperparameter optimisation strategies random search, Bayesian optimisation, and particle swarm optimisation (PSO) within a nested, anti-leakage cross-validation design, while SHAP is employed to analyse model interpretability across the best-performing configurations. The experimental results show that the ensemble classifiers achieve strong and consistent performance, with ROC–AUC values ranging from 0.8908 to 0.9089 across all scenarios; Random Forest optimised with PSO obtained the highest ROC–AUC (0.9089 ± 0.0146) and F1-score (0.8188 ± 0.0206), whereas XGBoost with Bayesian optimisation reached comparable performance without statistically significant differences. SHAP analyses identified oldpeak, ca, thal, cp, thalach, and exang as the most influential features, in line with established clinical indicators of myocardial ischemia and perfusion abnormalities. These findings indicate that combining tree-based ensemble classifiers with systematic hyperparameter optimisation and SHAP-based interpretability can enhance the reliability and transparency of heart-disease classification on the Cleveland dataset, while highlighting the need for further validation on contemporary, multi-centre clinical data.
Comparing XGBoost and LightGBM for Optimizing Health Content Categories Oktaviana, Nanda; Andrianingsih, Andrianingsih
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.v10i1.15545

Abstract

Indonesia’s social media platforms contain large amounts of unverified health information. Research on Indonesian health-text mining still rarely focuses on disease-based classification, leaving a gap compared with studies that only address sentiment or general topic categorization. This study proposes a multi-class classification approach that uses IndoBERT embeddings combined with gradient-boosting classifiers (XGBoost and LightGBM) to categorize tweets into diabetes, hypertension, and heart disease. The dataset comprises 4,075 tweets collected from platform X (Twitter). Preprocessing involves text cleaning, anonymization, normalization, and the extraction of 768-dimensional IndoBERT embeddings. Experiments are conducted in Google Colab (Intel Xeon CPU, 13 GB RAM, optional NVIDIA T4 GPU) using stratified five-fold cross-validation.The best results are obtained by the IndoBERT × LightGBM pipeline, which achieves an accuracy of 0.8526 and a macro-averaged F1-score of 0.8527, outperforming the IndoBERT × XGBoost model (accuracy 0.8325 and macro F1-score 0.8326). Feature-importance analysis shows that contextual terms related to blood sugar, the heart, and blood pressure strongly influence the predictions. Overall, the proposed method provides an effective baseline for monitoring health-related text and supporting disease-oriented analytics in Indonesian-language social media.

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