Sinkron : jurnal dan penelitian teknik informatika
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Comparative Study on Machine Learning Algorithms for Code Smell Detection
No full textDetecting 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
Optimization of Web-Based Printing Order Management System Using Redis Database for Efficient Data Handling
No full textThe 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
Classification of Instagram and TikTok Addiction Levels among University Students Using the Naive Bayes Classifier
No full textThe 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
No full textIndonesia'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
Towards Adaptive Learning: A Bayesian Knowledge Tracing Approach to Student Skill Prediction Bayesian Knowledge Tracing for Modeling Daily Living Skills in Children with ASD
No full textAutism Spectrum Disorder (ASD) presents challenges in mastering Activities of Daily Living (ADLs), which are essential for independence. This study applies Bayesian Knowledge Tracing (BKT) to model the mastery of five ADL skills—eating, dressing, toothbrushing, combing, and bathing—using data from 27 learners (1,350 responses). BKT parameters, including initial mastery, learning transition, guessing, and slipping, were used to estimate individual learning trajectories. Results showed that eating was the easiest skill (predicted mastery = 0.78), while bathing and combing were the most difficult (<0.55). The model achieved an overall accuracy of 0.62, with strong detection of actual mastery (TP = 722) but a high false-positive rate (FP = 429), indicating sensitivity to the guessing parameter. Learning curves and heatmaps revealed substantial inter-student variability. A comparative evaluation with the Performance Factors Analysis (PFA) model showed that BKT achieved higher overall predictive accuracy (BKT = 0.6356; PFA = 0.5917), while PFA demonstrated a higher AUC (0.6747) but exhibited strong positive-class bias in classification. These findings demonstrate the usefulness of BKT in modeling ADL development and highlight its potential for adaptive learning systems that support personalized interventions for ASD learners
Sarcasm Detection in Indonesian YouTube Comments using Fine-Tuned IndoBERT with Class Imbalance Handling
No full textSarcasm detection in Indonesian social media faces challenges in natural language processing due to implicit meanings and limited labeled datasets. YouTube, with 143 million users in Indonesia, represents a largely unexplored source of sarcastic expressions. This study aims to develop an automatic sarcasm detection system for Indonesian YouTube comments using fine-tuned IndoBERT and evaluate the performance of two IndoBERT variants. A dataset of 5,291 YouTube comments was collected and automatically labeled using GPT-4o with structured prompts based on linguistic indicators of sarcasm. Two IndoBERT variants (IndoNLU and IndoLEM) were fine-tuned with three class imbalance mitigation strategies: imbalanced, under-sampling, and class weighting. Zero-shot evaluation was conducted as a baseline to measure fine-tuning effectiveness. Models were evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained models without fine-tuning showed very limited sarcasm detection capability with F1-scores of 0.1613 for IndoNLU and 0.3519 for IndoLEM. Fine-tuning with under-sampling dramatically improved F1-scores to 0.6499 for IndoNLU and 0.6568 for IndoLEM, showing improvements up to 303%. IndoBERT-IndoNLU provided more balanced performance with 0.6424 accuracy, while IndoLEM showed higher sarcasm recall of 0.7639. Fine-tuning IndoBERT is effective for detecting sarcasm in Indonesian YouTube comments. This study contributes by providing a new labeled dataset, demonstrating the effectiveness of automatic labeling using large language models, and providing empirical evidence of the significant value of fine-tuning for Indonesian sarcasm detection
Emotion-Based Multi-Class Sentiment Analysis Of FirstMedia Customers Reviews Using SVM With Kernel Comparison
No full textThe advancement of digital technology has made users increasingly reliant on online services, with user reviews serving as an essential resource for evaluating the quality of service provided by companies such as FirstMedia. However, these valuable data have not undergone comprehensive analysis to assess users’ emotional responses. This study aims to classify FirstMedia customers’ emotions into four categories (joy, sadness, anger, and neutral) and to evaluate the Support Vector Machine (SVM) method using four different kernel functions. Most existing studies primarily focus on polarity-based sentiment analysis and do not explicitly examine multi-emotion classification or kernel comparison in machine learning models. A total of 4,001 reviews were collected through web scraping from the Google Play Store and the X app and processed through several preprocessing steps. Emotion classification was conducted using the NRC Indonesian Emotion Lexicon, while word significance was determined using TF-IDF weighting. After preprocessing, 3,069 labeled reviews were retained and distributed as 1,065 neutral, 748 anger, 692 joy, and 564 sadness reviews, which were used for emotion classification. Model performance was evaluated using a hold-out validation scheme with an 80:20 train-test split and assessed through a confusion matrix. To address class imbalance, undersampling was applied, resulting in a balanced dataset for model training. The evaluation results show that the Linear kernel achieved the highest performance, with an accuracy of 82.63%, precision of 82.86%, recall of 82.63%, and an F1-score of 82.60%, outperforming the Gaussian, Polynomial, and Sigmoid kernels. This study demonstrates that multi-emotion sentiment analysis provides a more comprehensive understanding of user perceptions beyond conventional sentiment polarity, thereby supporting more informed evaluations of digital service quality.
A Blockchain-Assisted Neural Network Model for Flood Detection and Data Integrity Assurance
No full textFlooding is one of the most frequent natural disasters and has substantial impacts on social, economic, and environmental conditions. Therefore, early detection plays a critical role in minimizing potential damage and supporting effective disaster response. This study proposes a Flood Detection System Using an Artificial Neural Network (ANN) with Blockchain-Based Data Integrity, which integrates predictive analytics and secure data management in a unified framework. The ANN model processes multisource environmental data such as satellite imagery, rainfall intensity, water level fluctuations, and soil moisture obtained from Google Earth Engine (GEE). Training is conducted using a sigmoid activation function and backpropagation algorithm to identify spatial and temporal patterns associated with flood-prone areas. The resulting classification outputs are stored in a blockchain ledger to ensure immutability, transparency, and protection against unauthorized data modification. Experimental evaluations demonstrate that the proposed hybrid approach achieves an accuracy of 95.82%, supported by precision, recall, and F1-score values that indicate consistent model performance across varying environmental conditions. The integration of blockchain provides verifiable and tamper-proof documentation of ANN predictions and related metadata. Overall, this research contributes a reliable, secure, and technically robust method for early flood detection, offering valuable support for data-driven decision-making in disaster mitigation and environmental risk management
Heart Disease Classification Using Optimised XGBoost and Random Forest with SHAP Explanations
No full textHeart 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
Adaptive Learning System Based on Human-in-the-Loop for PDF Template Data Extraction
No full textPDF template data extraction remains a substantial challenge due to semi-structured document formats and variations. While large pre-trained models achieve high accuracy, they require extensive computational resources and labeled datasets, making them impractical for resource-constrained environments. Conversely, rule-based approaches are efficient but rigid. This research addresses this gap by developing an adaptive learning system that integrates rule-based approaches with Conditional Random Fields (CRF) in a hybrid framework, designed for data-scarce scenarios. The system implements parallel extraction strategies with confidence-based selection and Human-in-the-Loop (HITL) feedback for incremental learning. Pattern learning updates rule-based strategies, while CRF models are retrained incrementally. Evaluated on synthetically generated documents across diverse template types, the system achieves 98.61% accuracy with minimal training data and 7% user correction rate, demonstrating high learning efficiency (1.88 corrections per percentage point). The improvement is statistically significant (paired t-test, p < 0.001, Cohen’s d = 8.95). The system operates on CPU-only hardware with 50-100 MB footprint and 0.1-0.5 seconds processing time. This work fills a practical gap in document extraction, providing a middle-ground solution balancing high accuracy, minimal data requirements, low resource consumption, and real-time adaptability—suitable for small organizations and rapid deployment where large models are impractical. The evaluation uses synthetic data to ensure reproducibility and controlled assessment, though real-world validation would strengthen practical applicability