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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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Hyperparameter-Tuned Artificial Neural Networks for Early Stunting Prediction in Toddlers Asriningtias, Salnan Ratih; Megawati, Citra Dewi; Kusumaningtyas, Dian; Surya, Dwi Utari
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.v9i2.14695

Abstract

The growing accessibility of varied health data requires the creation of efficient and practical techniques for deriving actionable insights, particularly for the early identification of severe health issues. This study tackles the issue of recognizing stunting—a disorder with enduring health consequences—among children under five by employing Artificial Neural Networks (ANN) with hyperparameter optimization by GridSearchCV. The dataset, sourced from Kaggle, comprises 121,000 records detailing age, gender, height, and nutritional status according to WHO standards. Critical hyperparameters, including dropout rate, batch size, and epochs, were optimized using a five-fold cross-validation approach within GridSearchCV, ensuring a robust model that reduces overfitting and generalizes well to new data. The findings demonstrate a notable performance improvement, as the optimized ANN model attained an accuracy of 99%, exceeding the baseline model's 98%. Enhancements in accuracy, recall, and F1-score across the four stunting classifications—normal, stunted, severely stunted, and tall—underscore the improved specificity and sensitivity of the optimized model. This research demonstrates the efficacy of hyperparameter tuning in ANN for stunting prediction, offering a reliable tool for early intervention in malnutrition management.
Loan Repayment Prediction Using XGBoost and Neural Network in Japan's Technical Internship Training Suhendry, Mohammad Roffi; Gerry Firmansyah; Nenden Siti Fatonah; Agung Mulyo Widodo
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.v9i2.14709

Abstract

Delayed repayment of financial aid among participants in Japan’s Technical Internship Training Program presents challenges for training institutions in managing funds efficiently. To address this issue, this study aims to compare the performance of two machine learning models: Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) in predicting the likelihood of delayed loan repayments. The research begins with data preprocessing, including handling missing values, normalization, and feature selection based on a correlation threshold of 0.06, where features with absolute correlation values below this threshold are excluded. Three models are tested: XGBoost Default, XGBoost optimized using GridSearchCV, and MLP. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The XGBoost Default model achieves the highest accuracy at 95% and precision of 95%, although its recall is slightly lower at 83%. Tuning XGBoost improves recall to 84%, albeit with a marginal reduction in accuracy to 94%. In contrast, the MLP model demonstrates the lowest performance, with an accuracy of 92% and recall of 74%, indicating limitations in identifying delayed repayments. XGBoost also outperforms MLP in terms of ROC-AUC, scoring 91% compared to MLP’s 86%. These findings suggest that XGBoost is the more effective model for this predictive task. The results have practical implications for training institutions, enabling better participant selection, reducing repayment delays, and supporting more effective financial aid management.
Metaheuristic-Optimized SVM for Stunting Risk Detection in Pregnancy Wibowo, Yudha; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
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.v9i2.14710

Abstract

Stunting is a chronic growth disorder that originates during pregnancy, making early risk detection crucial for effective prevention and long-term child development. This study introduces a stunting risk prediction model based on urine testing, employing a Support Vector Machine (SVM) algorithm enhanced through metaheuristic optimization. Three metaheuristic algorithms—Grey Wolf Optimizer (GWO), Simulated Annealing (SA), and Firefly Algorithm (FA)—were utilized to fine-tune the SVM hyperparameters (C and gamma). Clinical urine samples collected from pregnant women served as the dataset for model training and validation. The results indicate that the SVM model optimized using GWO achieved the highest prediction accuracy at 94.15%, outperforming both the default SVM (88.46%) and the models optimized using SA (94.12%) and FA (85.71%). Additionally, significant improvements were observed in precision, recall, and F1-score metrics, affirming the effectiveness of metaheuristic tuning in enhancing classification performance. These findings highlight the potential of integrating metaheuristic algorithms with SVM for robust medical prediction tasks, especially in the early detection of stunting risks. The proposed model offers a promising and non-invasive diagnostic approach that can be implemented in prenatal care settings, enabling timely interventions to mitigate stunting and improve maternal and child health outcomes.
LDA Topic Modeling: Twitter-Based Public Opinion on Indonesian Ministry of Finance Choirinnisa, Dina; Alzami, Farrikh; Indrayani, Heni; Rohmani, Asih; Nugraini, Siti Hadiati; Zulfiningrumi, Rahmawati; Susanti, Fitri
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.v9i2.14719

Abstract

People in the modern era use social media daily to exchange opinions regarding government policies, such as discussions related to the Indonesian Ministry of Finance (Kemenkeu). This study aims to analyze the topics of discussion about the Ministry of Finance on the Twitter platform, now known as 'X', and to determine the results of more effective preprocessing. The data in this study was taken from Twitter using the Tweet Harvest Tool with the keyword 'Ministry of Finance' from January 2024 to July 2024. The data is then processed through cleaning, preprocessing, calculation of coherence values, LDA modeling, and visualization. The preprocessing process includes several scenarios to compare the best results that are easy for the reader to understand. The highest coherence value obtained is 0.572250 by using stemming from NLTK library. The most effective preprocessing results are normalization, tokenization, stopwords, and stemming using Sastrawi. Modeling is done to find latent topics through LDA topic modeling techniques. Visualizing the intertopic distance map provides information on the distance between each topic. The results show that the distance between one topic and another has a variety of distance variations. This study shows that social media platforms can serve as a source of evaluation for the Indonesian government. The findings of these topics are helpful as insights for readers and the Kemenkeu. Finally, the analysis identified several key topics in public discussion, including fiscal policy, budget transparency, and the Ministry of Finance's performance in addressing current economic issues.  
Comparative Analysis of SVM and BERT for Sentiment and Sarcasm Detection in the Boycott of Israeli Products on Platform X Sabrina, Siti Sarah; Shiddieq , Diqy Fakhrun; Roji, Fikri Fahru
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.v9i2.14723

Abstract

The Israel-Palestine conflict has triggered a global consumer movement, including a widespread boycott of Israeli-affiliated products in Indonesia. As this campaign gains momentum on digital platforms like X (formerly Twitter), understanding public sentiment becomes crucial—not only for gauging public opinion but also for anticipating potential socio-economic impacts. This study evaluates the effectiveness of two sentiment analysis models—Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT)—in classifying sentiment and detecting sarcasm related to the boycott campaign. A total of 5,637 Indonesian-language tweets were manually labeled into positive, neutral, and negative categories, with sarcasm detection performed using a fine-tuned IndoBERT, model which classified tweets into two categories: sarcastic and non-sarcastic. The models were assessed using accuracy, precision, recall, F1-score, and computational efficiency. Results show that BERT outperforms SVM in both sentiment classification (accuracy: 69.26% vs. 64.58%; F1-score: 69.47% vs. 62.40%) and sarcasm detection (accuracy: 92.20% vs. 86.15%; F1-score: 92.38% vs. 85.27%). However, BERT requires significantly longer processing times 194.76 seconds for sentiment classification and 191.92 seconds for sarcasm detection, while SVM required only 18.81 seconds and 10.99 seconds. These findings highlight a trade-off between contextual comprehension and real-time efficiency. Future research may explore ensemble methods or threshold-tuning to optimize this balance. The practical implications of this research lie in its application for real-time public discourse monitoring and data-driven policy development. By improving the detection of nuanced expressions such as sarcasm, this study contributes to more accurate sentiment interpretation in polarized digital environments.
Strategic Clustering of Poverty Areas in Central Java Using K-Means and Silhouette Evaluation Tacharri, Chusnuut; Rohmani, Asih; Fahmi, Amiq
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.v9i2.14734

Abstract

Indonesia is one of several developing nations that struggle with poverty. Central Java is one of Indonesia's provinces with the third-highest percentage of the country's inadequate. This study aims to explore and improve the application of the K-Means Algorithm in investigating socioeconomic disparities. In this study, the Elbow method is used to determine the optimal number of clusters to overcome the weaknesses in determining the number of clusters in conventional K-Means. Model evaluation using the silhouette coefficient shows the effectiveness of this method approach with a value of 0.504 and several clusters (K = 3), which meets the medium structure category. The Human Development Index (HDI) and Uninhabitable Households (RTLH) were two criteria used to categorize poverty areas using the K-Means Algorithm optimization successfully. According to the clustering results, there were 12 regions in Cluster 0, 2 in Cluster 1, and 21 in Cluster 2. These findings are anticipated to offer the Central Java Provincial Government critical insights, facilitating the development of precise and well-targeted initiatives to address deprivation issues effectively. Furthermore, a more systematic and structured optimization of the K-Means algorithm has the potential to significantly improve both the accuracy and practical relevance of studies on socioeconomic inequality in Central Java Province. This enhanced methodological approach can provide more in-depth results on data-driven regional disparities to reduce these disparities comprehensively.
Data Mining Framework for EDC Terminal Repair Protocol: Combining Apriori and PrefixSpan Praharto, Suwandhy; Santoso, Handri
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.v9i2.14759

Abstract

Electronic Data Capture (EDC) terminals are vital for financial transactions, but their repair processes often lack standardization, causing inefficiencies. Data mining techniques like Association Rule Mining (ARM) and Sequential Pattern Mining (SPM) can extract hidden patterns from service logs to inform maintenance strategies. This research addresses the limited use of these techniques within Electronic Data Capture (EDC) repair centers. Specifically, it applies Association Rule Mining (ARM) using the Apriori algorithm, and Sequential Pattern Mining (SPM) using the PrefixSpan algorithm, to optimize repair protocols based on historical repair data from PT. XYZ Indonesia. The study aimed to discover frequent fault-action-component associations and repair sequences to formulate standardized procedures. A quantitative case study analyzed 56,629 repair transactions. After data cleaning and transformation, Apriori (evaluated by support, confidence, lift) mined association rules, while PrefixSpan found frequent sequential patterns (evaluated by minimum support). Several high-confidence rules emerged: "Battery Not Charging" almost always led to "Replace Battery Pack" (≈95% confidence, lift ≈6.0), and error "2000000" (tamper indication) strongly correlated with detampering procedures and internal battery replacement (≈96% confidence, lift ≈4.9). PrefixSpan uncovered consistent repair sequences, including length-3 patterns for complex issues, with "Replace CMOS → Reinstall OS" for error "7FFFFF" being a prominent shorter sequence. Integrating these data-driven patterns into protocols and aligning inventory can improve service efficiency, reduce repair time, and enhance EDC reliability.
Hybrid Deep Learning and USE Algorithm for Essay Scoring: Accuracy and Performance Analysis Sriyanto, Agus; Kusrini , Kusrini
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.v9i2.14784

Abstract

The main challenge in digital education, particularly in the automatic assessment of essay answers in online learning systems, lies in the complexity of natural language understanding and semantic evaluation required to achieve the level of precision equivalent to human judgment. This study aims to develop and analyze the performance of a hybrid model that combines deep learning with a semantic similarity-based approach to essay auto-grading. The methods used include the collection of essay answer data from various disciplines, text processing to extract semantic representations, and the calculation of the degree of similarity between the participant's answers and the answer key using the similarity measure. The evaluation was carried out by comparing the results of automatic assessments with manual assessments by teachers. The results showed that the developed model achieved the highest accuracy level of 90.22% at 0.8 treshold, with a precision of 84.63%, a recall of 100%, and an F1 score of 91.68%. To strengthen the reliability of the findings, statistical validation was carried out using error evaluation metrics. RMSE value is 0.32 and RMAE value is 0.19. These findings show that the model is able to mimic human judgment reliably and consistently, and can distinguish linguistic variations that arise in different types of essay questions. This system offers an effective solution for the automation of assessments in an online learning environment, while maintaining the integrity and objectivity of the evaluation.
ECG-Based Arrhythmia Classification in Students Using Random Forest: A Case Study with Class Imbalance Analysis Adya Zizwan Putra; Sitorus, Ariyanto; Simanjuntak, Paulus Anggiat Ruben; Mega Cristin Angelina. H.; Situmorang, Kevin Agustino
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.v9i2.14793

Abstract

Arrhythmia is a heart rhythm disorder that can indicate a student’s heart health status. This research aims to develop a Random Forest model to classify arrhythmia in students based on ECG signals. ECG data was collected from 100 students at SMK Swasta Teladan Sumatera Utara 2 after learning activities. The extracted signal features include RR interval, PR interval, QRS duration, QT interval, ST segment, beats per minute (BPM) and R/S ratio. Data labeling was carried out manually by the researchers based on the range of ECG feature values that had been determined by the doctor for each class: Normal, Abnormal, Potential Arrhythmia and Very Potential Arrhythmia. The dataset is divided into 70% for training and 30% for testing. SMOTE is applied to address class imbalance. The model achieved 80% accuracy with the best performance in normal class with precision, recall and f1-score  of  94%. However, no samples were identified for Potential Arrhythmia class, as there were no extracted feature values that met the criteria set by the doctor, so model could neither learn nor make predictions for this category, even after applying balancing methods such as SMOTE. For further research, based on these findings, it highlights the need for balanced class representation and expert-guided labeling to improve the performance of ECG -based arrhythmia classification.
Comparison of Xgboost, Random Forest and Logistic Regression Algorithms in Stroke Disease Classification Sitompul, Lia Relita; Nababan, Adli Abdillah; Manihuruk, Mey Lestari; Ponsen, Wildan Andika; Supriyandi
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.v9i2.14794

Abstract

Stroke remains a critical global health concern, ranking as the second leading cause of mortality and third cause of disability worldwide. Early detection and accurate classification of stroke risk could significantly improve patient outcomes through timely interventions. This research evaluates and compares the performance of three machine learning algorithms—XGBoost, Random Forest, and Logistic Regression—for stroke disease classification using a dataset of 5,110 patient records with 12 attributes including demographic, lifestyle, and health factors. Due to significant data imbalance between stroke and non-stroke cases, Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance model performance. Comprehensive evaluation metrics including accuracy, precision, recall, and F1-score were utilized to assess each algorithm's effectiveness. Results demonstrate that XGBoost achieved superior performance with 95% accuracy, followed by Random Forest at 94% and Logistic Regression at 82%. Feature importance analysis identified age, average blood glucose level, and history of heart disease as the most significant predictors for stroke diagnosis. This study contributes to the advancement of clinical decision support systems by highlighting the effectiveness of ensemble learning approaches for stroke prediction, potentially enabling earlier interventions and improved patient management. These findings suggest that integration of machine learning tools in clinical settings could enhance stroke risk assessment, though further validation with diverse patient populations is recommended for broader implementation.

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