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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 1,259 Documents
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Comparative Analysis of Four Machine Learning Algorithms for Smoke Detection Using SMOTE-Rebalanced Sensor Data Liecero, Marcus; Robet, Robet; Hendrik, Jackri
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.v10i1.15546

Abstract

Smoke detection plays a critical role in preventing fire-related hazards, particularly in intelligent monitoring and early warning systems. Conventional smoke sensors often exhibit limited responsiveness in dynamic environmental conditions, prompting the adoption of IoT-based sensor data combined with machine learning techniques. This study presents a comparative evaluation of four supervised classification algorithms, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Gradient Boosting, using the Smoke Detection Dataset from Kaggle. The methodology integrates SMOTE to address class imbalance and Z-score normalization for feature standardization. Hyperparameter tuning was performed using GridSearchCV with 5-fold cross-validation, and model performance was assessed based on accuracy and execution time. Experimental results show that KNN achieved the highest accuracy (98.33%) with the lowest execution time (0.0327 s), whereas Decision Tree recorded the lowest accuracy (84.17%) but remained computationally fast (0.0406 s). Random Forest and Gradient Boosting demonstrated strong predictive capability (97.22% and 96.94%, respectively), but at higher computational costs (1.4338 s and 8.3819 s, respectively). Almost all models achieved perfect scores (1.00) for precision, recall, and F1-score following SMOTE-based balancing, except KNN which obtained slightly lower values (0.99). The findings indicate a trade-off between predictive performance and computational efficiency, suggesting that lightweight models such as KNN are better suited for real-time IoT-based smoke detection. In contrast, ensemble models may be more appropriate for backend analysis. This research contributes an integrated evaluation framework that combines data rebalancing, multi-model benchmarking, and time-based performance analysis, providing practical insights for the development of responsive and scalable early smoke detection systems.
Implementation of YOLOv11 for Food Detection to Support Nutritional Information in Stunting Prevention Adji, Dian Restu; Lutfina, Erba; Caturkusuma, Resha Meiranadi; Galuh Wilujeng Saraswati; Mahmud, Wildan
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.v10i1.15553

Abstract

Stunting remains a persistent public health challenge in Indonesia, mainly due to chronic malnutrition and limited parental literacy regarding balanced diets. To address this issue, this study developed an integrated nutrition education system using YOLOv11 and Generative AI, structured based on the ADDIE framework. This system aims to bridge the literacy gap by automating food identification and transforming technical nutritional data into easy-to-understand insights for stunting prevention. The study used a dataset of 2,413 images, which was expanded to 4,687 through augmentation. Technical evaluation showed strong performance with a Mean Average Precision (mAP@0.5) of 97%, ensuring reliable detection of important protein sources such as eggs. In addition to accuracy, the system applies a heuristic nutritional assessment algorithm visualized through a ‘Traffic Light’ system to reduce the cognitive load on users. Qualitative evaluation with posyandu cadres showed a significant increase in nutritional understanding, with 90% of users able to explain appropriate dietary interventions based on AI recommendations. These results conclude that the integration of computer vision with structured educational design effectively transforms mobile devices into real-time decision support systems for stunting prevention initiatives at the community level.
Integrating Agile Development and Content-Based Filtering for Personalized Digital Cultural Heritage Applications: A Case Study of Sri Ranggah Rajasa Sang Amurwabhumi Megawati, Citra Dewi; Asriningtias, salnan Ratih; Teo Pei Kian; Sutawijaya, Bayu
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.v10i1.15555

Abstract

The preservation of Indonesia’s cultural heritage increasingly requires digital innovation that not only archives historical material but also engages users through adaptive interaction. However, existing digital cultural platforms seldom provide personalized learning experiences and often lack iterative user-centered development, creating a clear gap in adaptive digital cultural heritage applications. This study aims to design and develop a cultural application titled Sri Ranggah Rajasa Sang Amurwabhumi using a hybrid framework that integrates the Agile Development Method with a Content-Based Filtering (CBF) approach. Agile was applied through iterative cycles of design, development, implementation, integration, and testing, enabling continuous enhancement based on user feedback. Meanwhile, the CBF algorithm was used to generate personalized cultural content recommendations by analyzing semantic similarities among historical items. The novelty of this research lies in the unified hybridization of Agile and CBF to support adaptive, personalized digital cultural learning centered on a specific Indonesian cultural figure. Data were gathered from 30 respondents, including students and cultural practitioners, through usability testing and structured questionnaires. Results indicate high performance across key aspects: functionality (91%), usability (90%), recommendation accuracy (88%), and user satisfaction (93%). These findings demonstrate that combining Agile and CBF strengthens technical reliability while improving engagement through adaptive content delivery. Agile supports iterative refinement of user interfaces and system responsiveness, whereas CBF enables intelligent personalization in cultural learning environments. Nevertheless, this study is limited by its modest sample size and its focus on a single cultural topic, which may reduce generalizability. Future work will expand the dataset, incorporate multimodal cultural content, and validate the hybrid framework across broader Indonesian cultural domains..
Comparative Study of Baseline and CBAM-Enhanced ResNet50 and MobileNetV2 for Indonesian Rupiah Banknote Classification Alvin, Alvin; Robet, Robet; Feriani, 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.v10i1.15558

Abstract

This study investigates the performance of Convolutional Neural Network (CNN) architectures enhanced with Convolutional Block Attention Module (CBAM) for Indonesian banknote classification. Although attention mechanisms have shown strong potential in improving fine-grained visual recognition, their effectiveness for the classification of banknotes with fine textures and similar color patterns remains underexplored, forming a key research gap addressed in this work. Four architectures, ResNet50, ResNet50+CBAM, MobileNetV2, and MobileNetV2+CBAM, were evaluated using K-Fold cross-validation on a dataset of 1,281 images representing seven banknote denominations. Experimental results show that ResNet50 achieves strong baseline performance with a weighted Train accuracy of 99.14% and a Val accuracy of 96.72%, while the integration of CBAM further improves feature discrimination, with ResNet50+CBAM obtaining the highest average accuracy across all folds with a weighted Train accuracy of 100% and a Val accuracy of 99.45%. MobileNetV2 showed lower performance due to its lightweight capacity with a Train accuracy of 91.88% and a decrease in Val accuracy of 85.71%. However, the addition of CBAM provided measurable improvements and greater stability with a Train accuracy of 99.61% and Val accuracy of 92.82%. Overall, CBAM improved CNN’s ability to focus on spatial information and salient channels, resulting in more reliable classification. ResNet50+CBAM emerged as the best-performing model, offering the best balance between accuracy and consistency. These findings support the development of reliable computer vision systems for financial technology applications, including automatic banknote recognition, counterfeit detection, and secure transaction verification.
Facial Expression Recognition for Monitoring Learning Satisfaction in Smart Learning Environments Using MobileNetV2 Radytia, Sandy; Darusalam, Ucuk
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.v10i1.15565

Abstract

This study develops a lightweight, privacy-aware Facial Expression Recognition (FER) framework to monitor learning satisfaction in Smart Learning Environments (SLEs). Using MobileNetV2 with a two-stage training scheme on the FER2013 dataset and evaluated on 35,000 test samples, the system addresses two main questions: (1) how effectively a customized MobileNetV2 recognizes core student expressions under authentic classroom conditions, and (2) how temporal aggregation and confidence calibration improve the stability of a Learning Satisfaction Index (LSI). The model achieves 0.39 accuracy and 0.34 macro-F1, with strong performance for happy, neutral, and surprise, while challenges remain for fear–surprise and neutral–sad. Temporal smoothing reduces prediction noise and enhances the reliability of LSI signals for instructional decision-making. The findings highlight practical implications for education, particularly in supporting real-time formative assessment and improving teachers’ awareness of student engagement through privacy-preserving, on-device affect monitoring.
A Hybrid YOLOv11 and LightFM Model for Emotion-Driven Anime Recommendation Ramadityo, Kafka; Nurhaida, Ida
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.v10i1.15579

Abstract

Existing anime recommendation systems focus on genre preferences and viewing history without considering users' emotional states, leading to context-blind recommendations that may exacerbate negative moods and reduce satisfaction. Most existing systems employ outdated architectures with limited accuracy and lack diversification mechanisms to prevent filter bubbles. This study develops an emotion-based anime recommendation system integrating YOLOv11 for facial emotion recognition with hybrid collaborative filtering using LightFM and Maximum Marginal Relevance diversification. The primary novelty lies in seamlessly combining YOLOv11's superior emotion recognition, LightFM's hybrid matrix factorization for cold-start mitigation, and MMR diversification for preventing filter bubbles while maintaining emotional congruence. The methodology employed the KDEF dataset (3,597 images, five emotion classes) for training YOLOv11 with data augmentation, and the MyAnimeList dataset (744,330 interactions) for recommendation modeling. Emotion-to-genre mappings informed by survey data from 51 participants were implemented with MMR diversification to balance relevance and variety. The YOLOv11 model achieved 93.70% validation accuracy, outperforming CNN-LSTM approaches by 37.55 percentage points. The hybrid recommendation model demonstrated test AUC of 0.8567 and Precision@10 of 0.1457, representing 417% improvement over pure collaborative filtering, while diversification increased genre representation by 20.9% with minimal precision loss. This system demonstrates real-time applicability for streaming platforms through camera-based emotion capture and immediate recommendation generation, enhancing user engagement and emotional well-being. The integration represents a significant advancement toward affective computing in entertainment media.
Enhanced Performance Evaluation of VGG16 and ResNet50 for Deepfake Detection Using Local Ternary Pattern Rizqullah, Ghifari Ferdian; Eosina, Puspa; Pramuko, Andik Eko Kristus
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.v10i1.15582

Abstract

Deepfake video generation has become increasingly sophisticated, posing challenges for detection methods that rely solely on convolutional neural networks (CNNs without explicit texture enhancement). Many existing approaches have limited robustness in capturing subtle texture inconsistencies caused by manipulation, compression, and noise. This study investigates the integration of Local Ternary Pattern (LTP)–based texture enhancement with transfer learning models for deepfake video detection. Specifically, VGG16 and ResNet50 architectures are evaluated using the Celeb-DF (v2) dataset. LTP is employed to extract fine-grained texture features due to its higher robustness to illumination variations and noise compared to conventional descriptors such as Local Binary Pattern (LBP). Video frames are processed individually and used to train CNN classifiers, followed by evaluation at both frame and video levels. Experimental results show that ResNet50 outperforms VGG16, achieving a test accuracy of 93% with a validation loss of 0.2228, while VGG16 reaches an accuracy of 88% with a validation loss of 0.2636. Further testing on 20 withheld videos demonstrates that ResNet50 correctly classifies all samples, whereas VGG16 misclassifies two real videos, indicating lower robustness to real-video misclassification. These results demonstrate that LTP-based texture enhancement effectively supports CNN-based deepfake detection and that deeper architectures benefit more from enriched texture representations. This study provides empirical insights into improving robustness and reliability in deepfake video classification.
Classification of Instagram and TikTok Addiction Levels among University Students Using the Naive Bayes Classifier Silalahi, Indri Monica Cristiani; Athiyah, Ummi; Fransisca, Diandra Chika
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.v10i1.15583

Abstract

The widespread use of gadgets and internet connectivity has become an essential aspect of daily life, especially through intensive interaction with social media platforms. Excessive usage can lead to addictive behaviors that disrupt students’ academic productivity and concentration. Although research on social media addiction continues to grow, few studies specifically examine platform-level addiction (Instagram vs. TikTok) using multi-class classification approaches. Therefore, this study aims to assess the level of social media addiction among university students, focusing on users of Instagram and TikTok at Telkom University Purwokerto. The analysis employs the Naive Bayes Classifier algorithm using data collected from 100 respondents. Model performance is evaluated through a multi-class confusion matrix to compute accuracy, precision, recall, and F1-score. Separate datasets for Instagram and TikTok are used to enable platform-specific behavioral assessment. The results show that the Naive Bayes Classifier achieves strong performance, with 93% accuracy for the Instagram dataset and 90% for the TikTok dataset. Precision scores reach 95% and 91%, recall values 93% and 90%, and F1-scores 93% and 90%, respectively. These findings confirm that Naive Bayes is effective for classifying students’ levels of social media addiction. Overall, this research contributes a reliable machine-learning–based approach for evaluating digital behavior and provides insights for early detection, enabling universities to design targeted interventions for students at risk of problematic usage. The methodology may also be extended to analyze engagement patterns on emerging social media platforms in future studies.
Fuzzy Time Series Chen Model for Dual-Commodity Agricultural Forecasting: Evidence from Indonesia’s Rice and Corn Production Wiguna, I Kadek Artha; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Atmaja, Ketut Jaya; Ekayana, Anak Agung Gede
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.v10i1.15584

Abstract

Indonesia's strategic food commodities, particularly rice and corn, exhibit strong seasonal fluctuations and irregular production shocks driven by climate anomalies and policy changes, generating nonlinear time-series patterns that conventional statistical models often fail to capture. This study evaluates the forecasting capability of the standard Chen Fuzzy Time Series (FTS) model for dual-commodity agricultural data under varying seasonal and anomaly conditions. Monthly production data from January 2021 to March 2025 from the Indonesian Central Bureau of Statistics (BPS) were processed through a complete FTS pipeline: universe-of-discourse construction, triangular membership function design, fuzzification, FLR and FLRG formation, and midpoint-based defuzzification. Forecast accuracy was assessed using MAE, MSE, RMSE, MAPE, and R², with residual distribution analysis, Shapiro-Wilk tests, and scatter plots conducted to validate model stability. The model achieved high precision with overall MAPE of 4.37% for rice and 8.12% for corn, both classified as Highly Accurate. Monthly accuracy revealed consistent stability during May-December, while transitional months (January-March) showed greater variability due to extreme anomalies such as the January 2024 production collapse. Residual analysis confirmed near-normal error distribution for rice (p = 0.062) and mild deviation for corn (p = 0.031), while scatter plots demonstrated strong linear relationships (Rice R² = 0.9876; Corn R² = 0.9654). The findings establish Chen's FTS as a transparent and operationally reliable baseline method for national food production forecasting, although its sensitivity to structural breaks highlights the need for future hybridization with climate and policy indicators.
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk Sofiani, Hilda Ayu; Maulana, Isa Iant; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Rizqa, Ifan; Santoso, Dewi Agustini; Nugraini, Siti Hadiati
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.v10i1.15585

Abstract

Hypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs.

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