Sinkron : jurnal dan penelitian teknik informatika
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Vehicle Type Classification and Detection System using YOLOv7-tiny Model on Single-Board Computer
No full textTransportation is playing an important role for human civilization, for example transportations is being used as distributing goods and products. Therefore, the total numbers of vehicles as a part of transportation will continue to increase every year. But in Indonesia, the majority of its people is still using their personal transport rather than public transportation. This is supported by the data of total number of vehicles in Indonesia from 2018 – 2020, which is shows that personal transport is still dominant than public transportation. The causes of traffic jams is a result of various factors, such as the roads are not designed to accommodate the increasing number of vehicles, insufficient traffic signs, and poor traffic management. The road traffic data is one of the aspects that could reduce traffic jams. The process of collecting road traffic data which is still done manually has several shortcomings, such as it takes a long time and there may be errors due to human error. This research has a goal to create a vehicle type detection and classification system that have a good detection accuracy and detection speed that can be run on single-board computer devices. YOLOv7-tiny model that performs detection and classification using input from video on the NVIDIA Jetson Nano device gets a True Positive (TP) score of 96.58%, a False Positive (FP) score of 0.98%, and a False Negative (FN) score of 2.44%. YOLOv7-tiny on the NVIDIA Jetson Nano device can run with an average Frame per Second (FPS) of 6 FPS
Machine Learning to Predict Food Prices in Aceh Province Using the Fuzzy Time Series Method Based on Average
No full textThis study aims to develop a food commodity price prediction system based on Fuzzy Time Series (FTS) using average-based methods, with a case study of price data from 2018 to 2023. The system is designed to predict the prices of five main commodities: Super Quality Rice, Fresh Chicken Meat, Fresh Chicken Eggs, Bulk Cooking Oil, and Premium Quality Sugar. The prediction process involves constructing the Universe of Discourse, intervals, and fuzzy logic relations (FLR and FLRG) to model historical price patterns. The results show that this model provides accurate predictions, with the best Mean Absolute Percentage Error (MAPE) value of 0.49% for Super Quality Rice, while MAPE for other commodities ranges from 0.69% to 1.44%. The comparison graph between actual data and prediction results demonstrates consistent pattern alignment, suitable for commodities with both high price fluctuations and stable trends. This system proves effective in projecting future food prices with low error rates, making it a reliable tool to support strategic decision-making in managing food commodity prices during the five-year analysis period
Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector
No full textLoan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processe
Research and Analysis of Exchange Sort Algorithm in Data Structure
No full textExchange sort is different from bubble sort. Exchange sort compares an element with other elements in the array, and swaps elements if necessary. So there is an element that is always the center element (pivot). Here is its theoretical description: Comparison, the algorithm compares each element with its adjacent element. Then continue until all elements are compared. Swap: If the elements are in the wrong order (for example, in ascending order, if the left element is greater than the right), they are swapped. This swapping continues until all match numbers are swapped. Iteration, this process of comparing and swapping, is repeated for each pair of adjacent elements in the array. Looping, this process is repeated a number of times (traversing) the array until no more swapping is required, indicating that the array is sorted. It is concluded that for the six numbers in these three case studies, the iterations needed are 5 iterations each. The swaps counts needed are 7, for case study 1. The swap counts needed are 12 for case study 2 and the swap counts are 8 for case study 3. In this research and analysis, the order, all of them is descending, although it can be made ascending. In modern days, exchange sort plays a very important role in terms of sorting algorithms. This paper is only research and analysis. For novelty, the analysis is given with a clear step-by-step procedure of the algorithm
Integrating Bayesian Optimization into Ensemble Logistic Regression for Explainable AI-Based Customer Behavior Analysis
No full textUnderstanding customer behavior is a strategic factor in business decision-making, particularly within the automotive sector, where competition is intense and product variety is diverse. While previous studies often rely on limited demographic variables, such as age and gender, this research advances the field by integrating ensemble logistic regression with Bayesian Optimization for hyperparameter tuning and SHAP-based interpretability. The proposed model incorporates additional features beyond demographics, including vehicle category, product type, vehicle year, dealer branch, and transaction source, to enhance predictive accuracy. The methodology involves data preprocessing through encoding and cleaning, class balancing using SMOTE combined with undersampling, and stratified train-test splitting (80:20). Baseline Logistic Regression achieved an accuracy of 80%, ROC AUC of 0.89, precision of 0.47/0.96, recall of 0.84/0.79, and F1-scores of 0.59/0.89. By applying ensemble logistic regression with Bayesian Optimization, performance improved to 84% accuracy, ROC AUC of 0.92, precision of 0.51/0.98, recall of 0.83/0.84, and F1-scores of 0.63/0.92. SHAP analysis confirmed that the additional features significantly contribute to prediction outcomes. The novelty of this study lies in combining Ensemble Logistic Regression with Bayesian Optimization and SHAP explainability in the automotive domain, offering not only improved accuracy but also interpretability and fairness for business decision-making, providing actionable insights for targeted marketing strategies and product management. Future studies may incorporate broader behavioral and transactional variables to capture more nuanced customer decision patterns.
Frequent Pattern Mining for Cyberattack Detection Using FP-Growth on Network Traffic Logs
No full textCybersecurity threats have become increasingly complex, coordinated, and adaptive, creating significant challenges for traditional intrusion detection systems (IDS) that rely on static, signature-based mechanisms. These systems often fail to recognize novel, evolving, or multi-vector attacks that do not match predefined patterns. To overcome these limitations, this study proposes a data-driven framework that applies the Frequent Pattern Growth (FP-Growth) algorithm to analyze co-occurring events within network traffic logs. Using the CIC-IDS2017 benchmark dataset, which includes a wide range of real-world attack scenarios, network events were preprocessed and transformed into transactional data. This transformation enabled the efficient extraction of frequent itemsets and association rules without the computational burden of candidate generation. The experimental results show that the proposed method effectively uncovers meaningful attack correlations, such as brute force attempts preceding privilege escalation or malware infections leading to large-scale DDoS attacks. The model achieved a precision of 77.27%, recall of 70.83%, and F1-score of 73.91%, confirming its reliability in detecting sophisticated attack chains. A heatmap visualization was also generated to improve interpretability, allowing security analysts to quickly identify critical attack relationships. In conclusion, this research demonstrates that FP-Growth provides a scalable, interpretable, and computationally efficient approach to cyberattack detection, with potential integration into real-time IDS environments. Future work will focus on temporal sequence mining and hybrid models combining FP-Growth with machine learning to enhance adaptive, context-aware threat detection
Naïve Bayes–Based Chatbot with Sentiment Analysis for Culinary Preferences in Bali
No full textThe rapid growth of digital technology has increased the adoption of chatbots across industries, including the culinary and tourism sectors. However, existing systems often lack integration of customer sentiment and user preferences, limiting recommendation relevance. This study develops a personalized chatbot by combining sentiment analysis of Google Maps reviews with user taste preferences for traditional Balinese cuisine. A dataset of 5,000 reviews was analyzed using the Naïve Bayes classifier, achieving 88% accuracy. User evaluation with 100 respondents showed positive perceptions of usability and engagement, though recommendation suitability scored lower. The findings highlight the potential of sentiment-driven personalization and suggest future improvements through advanced models, larger datasets, and multilingual features for tourism
Comparative Analysis of SDLC and R&D Methods in System Development: A Case Study of Integrity Zone Management System
No full textThis paper presents a comprehensive comparative analysis of Software Development Life Cycle (SDLC) and Research and Development (R&D) methodologies in system development, with a specific focus on their application to the Integrity Zone Management Information System. Through a systematic literature review and an in-depth case study analysis, this research examines the fundamental differences, strengths, and limitations of each methodology. The study identifies key dimensions for comparison including flexibility, risk management, innovation potential, documentation requirements, and stakeholder engagement. Findings reveal that while SDLC methodologies provide structure and predictability for well-defined requirements, R&D approaches offer greater innovation capacity for exploratory projects. The Integrity Zone Management Information System case demonstrates how hybrid approaches can leverage the strengths of both methodologies and improved stakeholder satisfaction by 94%. This research contributes to the theoretical understanding of system development methodologies and provides practical guidance for selecting appropriate approaches based on project context, objectives, and constraints. The paper concludes with recommendations for practitioners and suggestions for future research in methodological integration and adaptation
Leveraging Label Preprocessing for Effective End-to-End Indonesian Automatic Speech Recognition
No full textThis research explores the potential of improving low-resource Automatic Speech Recognition (ASR) performance by leveraging label preprocessing techniques in conjunction with the wav2vec2-large Self-Supervised Learning (SSL) model. ASR technology plays a critical role in enhancing educational accessibility for children with disabilities in Indonesia, yet its development faces challenges due to limited labeled datasets. SSL models like wav2vec 2.0 have shown promise by learning rich speech representations from raw audio with minimal labeled data. Still, their dependence on large datasets and significant computational resources limits their application in low-resource settings. This study introduces a label preprocessing technique to address these limitations, comparing three scenarios: training without preprocessing, with the proposed preprocessing method, and with an alternative method. Using only 16 hours of labeled data, the proposed preprocessing approach achieves a Word Error Rate (WER) of 15.83%, significantly outperforming the baseline scenario (33.45% WER) and the alternative preprocessing method (19.62% WER). Further training using the proposed preprocessing technique with increased epochs reduces the WER to 14.00%. These results highlight the effectiveness of label preprocessing in reducing data dependency while enhancing model performance. The findings demonstrate the feasibility of developing robust ASR models for low-resource languages, offering a scalable solution for advancing ASR technology and improving educational accessibility, particularly for underrepresented languages
Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear
No full textAs the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics