Sinkron : jurnal dan penelitian teknik informatika

Optimizing Supplier Selection Through Hybrid BWM and AHP Integration

2025-08-22T17:32:13+00:00

This study proposes a hybrid decision-making model that integrates the Best-Worst Method (BWM) with the Analytic Hierarchy Process (AHP) to optimize supplier selection. The primary objective is to address limitations in traditional Multi-Criteria Decision-Making (MCDM) methods, such as inconsistency, subjectivity, and cognitive overload when handling complex criteria. The proposed model leverages AHP's hierarchical structuring and BWM’s efficiency in reducing comparison load, aiming for a more accurate and consistent evaluation framework. The research design involves developing a hybrid AHP-BWM model and applying it to a dataset from the Vietnamese Textile and Apparel (T&A) sector. The methodology includes two stages: determining the weight of each criterion using a Hesitant-AHP approach, followed by evaluating supplier alternatives with BWM. The performance of the model is assessed using classification metrics, namely accuracy, precision, recall, and F1-score. The results show that the proposed model outperforms conventional methods such as TOPSIS, ELECTRE, VIKOR, and SWARA. It achieves an accuracy of 92%, precision of 87%, recall of 86%, and an F1-score of 86%. These outcomes confirm the model’s superior ability to consistently classify supplier suitability. Furthermore, the model identifies Quality Assurance as the most critical criterion, followed by Assistance, Capacity, Charge, and Shipment. In conclusion, the hybrid AHP-BWM model offers a robust, scalable, and data-driven approach for supplier selection. Its strength lies in balancing systematic evaluation with reduced cognitive effort, making it suitable for complex real-world decision-making environments. Future research may explore its application in other domains and enhance its scalability for larger datasets.

Implementation of a Hybrid Cryptosystem Using ChaCha20 and ECC for Image Encryption in an Android Application

2025-08-25T14:50:23+00:00

This study aims to develop an Android application capable of securely encrypting and decrypting images using a hybrid cryptographic method. The system combines the ChaCha20 algorithm as symmetric cryptography to encrypt image files, and Elliptic Curve Cryptography (ECC) as asymmetric cryptography to encrypt the ChaCha20 key. The key used is temporary (ephemeral), ensuring that only the intended recipient who possesses the appropriate ECC private key can decrypt the file. The application was developed using the Kotlin programming language in Android Studio, with a PHP-based backend and MySQL database. Testing was conducted using the black-box method and involved 15 beta testers to evaluate functionality, security, and usability aspects. The results show that all features of the application run properly, and the encryption and decryption processes can be performed efficiently and securely. Beta testers gave an average rating of 4.6 out of 5 and stated that the application is easy to use and provides sufficient protection for personal data. Therefore, the developed application successfully meets the objectives of the study and offers an alternative solution for securing image file transfers between users via Android devices.

Multiple Linier Regression Analysis Effects of Education Media on Student Experience and Satisfaction

2025-09-03T04:19:56+00:00

The purpose of this study is to explore the effect of quiz- and game-based learning media on student learning experiences and satisfaction. This study models the relationship between media, motivation, experience, and learning satisfaction differently from previous studies with single variables. The research method uses a multiple linear regression approach with data collected through a Likert scale questionnaire. The research subjects consisted of 107 tenth-grade students majoring in Network and Computer Engineering at SMK Bhinneka Karya Simo. Data processing was carried out using Python and Microsoft Excel software for data analysis. The results showed that the variables of media, motivation, and response had a significant effect on learning experience (p < 0.05). The regression equations obtained were: experience (Y2) = 0.5918 + 0.2594X1 + 0.2840Y1, while satisfaction (Y3) = 0.5918 + 0.2594X1 + 0.2840Y1 + 0.3239Y2. In conclusion, learning experience is mainly influenced by media and motivation, while learning satisfaction is influenced by media, motivation, and the experience itself. These findings confirm that game-based learning strategies can create more meaningful learning experiences and encourage increased student satisfaction, which can be used as a basis for improving the quality of learning in the classroom.

A Disaster-Aware Traffic Assignment Model: Comparative Evaluation of Frank-Wolfe and Simulated Annealing Algorithms

2025-09-23T02:42:26+00:00

Traffic assignment under disaster-induced disruptions poses unique challenges, as traditional models often overlook sudden capacity loss and unpredictable demand. This study introduces a disaster-aware Traffic Assignment Problem (TAP) model that integrates a modified Bureau of Public Roads (BPR) cost function, explicitly accounting for effective capacity changes during disasters. The Frank-Wolfe (FW) algorithm is applied to solve the model, chosen for its scalability and convergence properties. A comparative analysis with Simulated Annealing (SA) is also performed across various network sizes and disruption scenarios. Results show that FW consistently delivers near-optimal flow distributions with lower travel costs and faster convergence. While SA exhibits higher variability under tight capacity constraints, FW demonstrates robust stability, particularly in medium to large networks under moderate to severe disruptions. Flow patterns from FW highlight adaptive traffic redistribution, effectively bypassing congested or blocked links. This study is the first to systematically compare Frank-Wolfe and Simulated Annealing under disaster-induced TAP conditions with capacity degradation. Contributions include (1) formulating a disaster-aware TAP model, (2) applying FW to disrupted networks, and (3) validating through structured simulations. Findings suggest that FW offers a reliable optimization tool for real-time traffic reallocation, supporting resilient urban mobility in emergencies.

Research and Analysis of Exchange Sort Algorithm in Data Structure

2025-07-30T07:28:33+00:00

Exchange 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.

Analyzing User Acceptance of NFJuara Mobile Application Using TAM and D&M IS Success Model

2025-08-17T05:56:06+00:00

This study purposes to know how NFJuara application is accepted by the users in Nurul Fikri Lampung using the Technology Acceptance Model (TAM) Integrated with D&M IS Success Model. Data was collected by a validated questionnaire with inner model and outer model testing using PLS-SEM software SmartPLS. The type of data in this study is a quantitative approach. The number of samples collected was 143 respondents. Results of this research show that one of the hypotheses is rejected, that is, Service Quality (SEQ) does not affect Perceived Usefulness (PU) significantly. Besides that, this study shows that Perceived Usefulness (PU) and Perceived Ease of Use (PEU) affect as significant Acceptance of IT (AI) with R2=0.59 (Moderate) and β=0,36 (PUàAI), β=0,46 (PEUàAI). These findings imply that developers of NFJuara applications need to improve the service quality to increase acceptance, although overall NFJuara application is accepted by the user because they still feel the benefits and usefulness of the application. The contribution of this study lies in testing the technology acceptance model in the context of mobile learning, which enriches the literature on the adoption of application-based e-learning, as well as providing practical recommendations for application developers to enhance user experience.

Frequent Pattern Mining for Cyberattack Detection Using FP-Growth on Network Traffic Logs

2025-08-13T06:59:46+00:00

Cybersecurity 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.

Mobile Banking Service Quality and User Loyalty Using MSQUAL: A Systematic Literature Review

2025-08-17T06:01:12+00:00

Digital transformation has made mobile banking a core service in the banking industry, emphasizing service quality as a critical factor for user satisfaction and loyalty. This study presents a systematic literature review (SLR) of mobile banking research from 2021 to 2025, guided by PRISMA and structured using the PICOC framework (Population, Intervention, Comparison, Outcome, Context) to systematically select and evaluate relevant studies. The MS-QUAL model, comprising nine dimensions: efficiency, system availability, responsiveness, privacy, content, contact, billing, fulfillment, and compensation, was used as the evaluation framework. Out of 924 initially identified articles, 20 met the inclusion criteria for in-depth analysis. Findings show that efficiency, system availability, privacy, responsiveness, content, and fulfillment consistently drive user satisfaction, while compensation, contact, and billing have limited influence. Satisfaction serves as the primary mediator connecting service quality to loyalty, indicating that improvements in MS-QUAL dimensions must translate into positive user experiences to foster long-term loyalty. The study further highlights challenges in maintaining security standards, adapting traditional dimensions to evolving user expectations, and ensuring consistent service quality. Opportunities lie in leveraging technologies such as AI, blockchain, and big data to create personalized, secure, and interactive experiences, enhancing both functional and emotional engagement. Overall, MS-QUAL remains a relevant and flexible framework for evaluating mobile banking service quality when aligned with contemporary technological advances and user-centered strategies.

Smart CRM Application Development Using Artificial Intelligence and Extreme Programming Method

2025-08-22T16:50:05+00:00

Customer Relationship Management (CRM) is an important strategy for companies to understand customer behavior, increase loyalty, and reduce churn rates. However, the challenge that is often faced is how to manage increasingly complex customer transaction data and turn it into useful information for decision-making. This research aims to develop an artificial intelligence-based smart CRM application by integrating the K-Means algorithm for customer segmentation and XGBoost for retention prediction, as well as using the Extreme Programming (XP) methodology in the development process. The XP methodology was chosen because it is able to provide a fast, adaptive, and user-oriented iterative cycle, so that applications can be developed according to user needs. The results showed that K-Means can group customers into segments that are relevant to marketing strategies, while XGBoost provides retention prediction results with good accuracy. In addition, the application was tested using Blackbox Testing to ensure that the functionality runs according to specifications, as well as the System Usability Scale (SUS) which resulted in an average score of 89 and was included in the excellent usability category. This confirms that the system built is not only technically feasible, but also well received by users. This research contributes to presenting a smart CRM application that combines AI with modern software development methodologies, as well as opening up opportunities for advanced research at a larger data scale and integration with digital marketing systems.

Comparative Performance Benchmarking of WebSocket Libraries on Node.js and Golang

2025-08-25T15:05:43+00:00

The demand for responsive real-time web applications continues to grow, making the selection of backend technology and WebSocket libraries a crucial factor in determining performance. Node.js and Golang are popular platforms for real-time applications. However, the WebSocket library within them offers a trade-off between features and efficiency, the impact of which has not been comprehensively measured. This research aims to fill this gap by conducting a quantitative performance analysis to compare the efficiency and scalability of four WebSocket libraries: ws and socket.io on Node.js, and gorilla/websocket and coder/websocket on Golang. This research uses a benchmarking experimental method with client load simulations that gradually increase from 100 to 1000 concurrent clients. The experiment was conducted through two scenarios, namely the Echo Test and Broadcast Test. In the Echo Test, the performance metrics measured were Connection Time, Round Trip Time (RTT), and Throughput. Meanwhile, in the Broadcast Test, the performance metric measured was Broadcast Latency. The results from the Echo Test show a significant performance disparity. At a peak load of 1000 clients, socket.io achieved a throughput of only 27,152 messages/second, whereas the lightweight libraries (ws, gorilla/websocket, and coder/websocket) all achieved over 44,000 messages/second. In the Broadcast Test with a high load, the latency difference between the four libraries became insignificant. Therefore, for applications prioritizing raw performance in point-to-point communication, certain WebSocket libraries such as ws, gorilla/websocket, and coder/websocket are more suitable for future development.

Naïve Bayes–Based Chatbot with Sentiment Analysis for Culinary Preferences in Bali

2025-09-03T04:57:32+00:00

The 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.

Designing a Stunting Prediction Model Using Machine Learning to Support SDGs Achievement in Indonesia

2025-09-03T05:03:03+00:00

Stunting remains a major public health challenge in Indonesia, with national prevalence among children under five reaching 21.6% in 2022, according to the Ministry of Health. This condition, defined by the World Health Organization as a height-for-age less than -2 SD, is associated with long-term consequences including impaired cognitive development, reduced educational attainment, and diminished economic productivity. Addressing stunting is therefore critical to achieving Sustainable Development Goals (SDGs) related to hunger, health, and education. Despite multiple national initiatives, early identification of stunting risk is still limited by reliance on conventional, reactive surveillance methods. Recent advances in machine learning (ML) provide promising alternatives for proactive stunting prediction, with several studies reporting high predictive accuracy using ensemble methods, hybrid frameworks, and geographically weighted models. Building upon this evidence, the present study develops and evaluates ML models for stunting risk prediction using a large dataset of 10,000 records from North Sumatra, Indonesia. The dataset included three predictor variables—age, height, and weight—and a target variable, nutritional status (Normal, Stunted, Severely Stunted, Tall). Four algorithms were compared: K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest. Performance was assessed using accuracy, precision, recall, F1-score, and ROC area, with 10-fold cross-validation ensuring robust estimation. Results demonstrated that Decision Tree (88.6% accuracy) and Random Forest (88.3% accuracy) outperformed KNN (84.7%) and Naïve Bayes (72%). ROC areas further confirmed the superiority of ensemble-based approaches, particularly Random Forest (0.979). Statistical significance was tested using McNemar’s test, revealing that Decision Tree and Random Forest achieved comparable performance (p = 0.651), both significantly outperforming KNN and Naïve Bayes (p < 0.05). This study contributes a context-specific evaluation of ML methods for stunting prediction in North Sumatra, emphasizing not only predictive accuracy but also interpretability to support health policy and program implementation. By bridging data-driven insights with actionable decision support, the proposed framework advances progress toward SDG-aligned strategies and provides a foundation for more targeted and preventive interventions in child nutrition and growth monitoring.

Cervical Cancer Classification Using Multi-Directional GLCM Shape-Texture Features in LBC

2025-09-07T11:27:11+00:00

Alsalatie, M., Alquran, H., Mustafa, W. A., Zyout, A., Alqudah, A. M., Kaifi, R., & Qudsieh, S. (2023). A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images. Diagnostics, 13(17), 2762. https://doi.org/10.3390/diagnostics13172762

Arbyn, M., Weiderpass, E., Bruni, L., Sanjosé, S. de, Saraiya, M., Ferlay, J., & Bray, F. (2020). Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. The Lancet Global Health, 8(2), e191–e203. https://doi.org/10.1016/S2214-109X(19)30482-6

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Collective Intelligence for Cybersecurity: Federated Learning under Non-IID Conditions for Intrusion Detection

2025-07-10T13:24:20+00:00

Cyber threats are becoming increasingly complex in cyberspace, which highlights the necessity for strong Intrusion Detection Systems (IDS). However, traditional centralized IDS methods have large problems with data privacy and scalability. Federated Learning (FL) is an intriguing new idea that lets multiple clients train a model together without sharing data directly, which keeps privacy intact. The proposed federated intrusion detection model develops and assesses FL models for detecting network intrusions, focusing on the important issue of non-independent and non-identically distributed (non-IID) data among clients. This work implements and compares two widely recognized FL algorithms, Federated Averaging (FedAvg) and Federated Proximal (FedProx), using a 1D Convolutional Neural Network (CNN) architecture specifically designed for tabular network traffic data. The authors utilize a Dirichlet distribution (α=0.1) to distribute the data among 10, 20, and 30 clients, thereby simulating non-IID conditions in the experiment. The authors thoroughly compare the performance of algorithms using two benchmark datasets: NSL-KDD and NF-Bot-Net-V2. The comparison reveals that while both FedAvg and FedProx achieve high detection rates on NSL-KDD, FedProx is more capable of maintaining stability and converging on the more complex NF-Bot-Net-V2 dataset, achieving an accuracy of 0.9953. The results highlight that FedProx is a more appropriate algorithm for implementing robust and privacy-preserving federated intrusion detection systems in statistically heterogeneous network environments found in the real world.

Enterprise Architecture for the Cruise Industry: A TOGAF-ADM and ArchiMate-Based Approach

2025-08-22T16:53:08+00:00

Despite its growth and resilience over the last decades, the cruise industry faces significant challenges in its strategic, operational, and technology domains. The unique complexity of the industry requires cruise companies to adopt a structured approach to enterprise transformation. To address this problem, this aims to provide an Enterprise Architecture (EA) blueprint for the cruise industry. Using a case study of a leading cruise line, CruiseX, this study analyzes the operational model of the cruise line and apply two industry-leading standards: The Open Group Architecture Framework (TOGAF) and the ArchiMate modelling language. This study applies the four core phases of TOGAF Architecture Development Method (ADM) from the initial phase of Architecture Vision (Phase A), through the definition of Business Architecture, Information System Architecture, and Technology Architecture (Phase B to D). The ArchiMate language is utilized to visualize the core business processes, information systems, and technology architecture. By using TOGAF ADM as the technical guidelines and ArchiMate as the modeling language, the result of this study is a blueprint of core business processes, application and data that support each business processes, and the underlying technology infrastructure, that provides a structured framework and serves as an actionable tool for implementing enterprise architecture in cruise industry. This research also extends the application of TOGAF and ArchiMate to the under-research cruise industry domain. The study’s limitations include the reliance on publicly available data, the limited scope of business processes, and the lacks of practitioner validation, suggesting clear directions for future research.

A Comparative Study between Logistic Regression and SVM for Resource Management in Network Slicing

2025-08-15T17:19:04+00:00

Network slicing is an essential component of 5G and subsequent networks. It enables administrators to partition shared physical infrastructure into several virtual segments, each with distinct Quality of Service (QoS) requirements. Effective and adaptable real-time resource management is essential for optimal performance in dynamic situations, characterized by low latency and high throughput. Despite the increasing body of literature on machine learning in communication networks, there is a paucity of direct comparisons between Logistic Regression (LR) and Support Vector Machines (SVM) concerning network slicing resource management. Prior comparisons have predominantly concentrated on sectors such as education, healthcare, and the Internet of Things (IoT), resulting in minimal exploration of slicing prospects. This study rectifies this gap by doing a comparative analysis of Logistic Regression and Support Vector Machine models utilizing the CICIDS2017 dataset in a network slicing simulation environment. Both models were utilized independently, employing class balancing and feature selection to forecast overload. We evaluated their performance for accuracy, ROC AUC, latency, jitter, and throughput across network slices. Results indicate that SVM exhibited somewhat superior classification accuracy; however, LR consistently surpassed SVM in critical network-level parameters, including reduced delay, enhanced throughput, and improved jitter stability. These results indicate that LR is an effective option for the real-time management of network slicing resources due to its practicality and comprehensibility. In conclusion, LR is a dependable primary option for scholars and professionals pursuing effective, low-latency solutions, improving the superior classification accuracy of SVM with enhanced overall network performance.

Blockchain Model for Tracking Plastic Waste Using Smart Contracts to Reduce Emissions

2025-09-02T16:14:15+00:00

This research focuses on the design and development of a blockchain-based plastic waste tracking system aimed at enhancing transparency, efficiency, and accountability in plastic waste management. The system utilizes Hyperledger Fabric as a permissioned blockchain platform and integrates smart contracts to manage transactions between organizations, including waste generators, collectors, sorting warehouses, and final processing warehouses. This system records each stage of the plastic waste journey, from creation to final processing, in a permanent, transparent, and immutable manner. The testing results demonstrate that the system can accurately record the status and history of waste, manage transfers between organizations, and process plastic waste into recycled products. Moreover, the system shows a significant potential for carbon emission reduction, with an estimated reduction of up to 50% compared to traditional plastic waste management methods, such as incineration or landfilling. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. The blockchain-based system also provides real-time monitoring, ensuring that each transaction is verified and recorded immediately, contributing to more effective management. The implementation of smart contracts further guarantees that waste-related activities are executed automatically when predefined conditions are met, reducing administrative overhead. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. Ultimately, this system presents a scalable solution that could be adopted in various regions to improve global waste management strategies.

Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data

2025-08-27T08:38:24+00:00

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.

Sentiment Analysis of Roblox Game Reviews Using Support Vector Machine Method

2025-08-25T15:26:08+00:00

The development of digital technology has driven changes in entertainment consumption patterns, especially among the younger generation. Roblox has become one of the most popular online gaming platforms, with a wide range of user opinions recorded on Google Play Store. This study aims to classify the sentiment of Roblox user reviews (positive, negative, neutral) and evaluate the performance of the Support Vector Machine (SVM) algorithm with TF-IDF weighting and automatic labeling using Lexicon InSet. Data was obtained by crawling 10,000 reviews during the period of April 2–May 23, 2025, and after the preprocessing stage, 8,950 data remained for analysis. The classification results show that the sentiment distribution consists of 41.3% positive (3,703 reviews), 41.8% neutral (3,739 reviews), and 16.8% negative (1,507 reviews). Model evaluation using a confusion matrix produced high performance with 87.03% accuracy, 87.29% precision, 87.03% recall, and an F1-score of 86.67%. WordCloud visualization shows that positive reviews emphasize creativity and interactive features, while negative reviews are dominated by technical complaints such as lag and errors. These findings prove that the combination of SVM, TF-IDF, and Lexicon InSet is effective in sentiment analysis and provides valuable input for developers to improve application quality and user protection. Further research is recommended to adopt a hybrid approach based on deep learning and aspect-based sentiment analysis to generate more insights.

Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER

2025-09-03T05:04:30+00:00

With the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications.

Explainable Machine Learning for Poverty Prediction in Central Java Regencies and Cities

2025-09-07T11:00:21+00:00

Poverty remains a multidimensional challenge in Central Java, necessitating robust data-driven approaches to identify its socioeconomic determinants. This study applied six machine learning models, specifically Extreme Gradient Boosting (XGBoost), Random Forest, CatBoost, LightGBM, Elastic Net Regression, and a Stacking ensemble using district-level data from Statistics Indonesia covering demographics, education, labor, infrastructure, and household welfare. Model evaluation combined an 80:20 hold-out split, 10-fold cross-validation, and noise perturbation tests. Results show that XGBoost achieved the best individual performance (MAE = 2,180.01; RMSE = 3,512.07; R² = 0.931), while the Stacking ensemble surpassed all single learners (MAE = 2,640.99; RMSE = 3,202.79; R² = 0.942). Interpretability was ensured through SHAP (Shapley Additive Explanations), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE), consistently identifying Number of Households, Per Capita Expenditure, and Uninhabitable Houses as the most influential predictors. Counterfactual simulations indicated that increasing per capita expenditure by 10% could reduce the poverty index by 9.9%, while reducing household size by 10% lowered it by 11.3%. Robustness checks revealed Brebes as an influential district shaping model stability. Overall, the findings demonstrate that boosting and stacking ensembles, when combined with explainable AI tools, not only enhance predictive accuracy but also provide transparent, policy-relevant evidence to strengthen poverty alleviation programs in Central Java. This study contributes both methodological advances in explainable machine learning and practical insights for targeted poverty reduction strategies.

Addressing Class Imbalance in Stunting Classification Using SMOTE Enhanced Random Forest

2025-09-19T17:37:26+00:00

Stunting is a chronic nutritional problem that poses serious long-term effects on children’s health, including impaired physical growth, delayed cognitive development, and reduced productivity in adulthood. Early and accurate detection of stunting is therefore essential to support effective public health interventions and targeted policy implementation. However, one of the central challenges in developing machine learning models for this purpose is the presence of class imbalance in health-related datasets. Such imbalance frequently leads to biased classifiers that perform well on majority classes but fail to identify minority categories, reducing the overall reliability of the system. To overcome this issue, the present study utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the distribution of classes in a dataset containing 110,000 records. A Random Forest algorithm was then employed as the base classifier, with hyperparameter optimization carried out using the Optuna framework to ensure robustness and generalizability. The experimental results demonstrate that the combined application of SMOTE and Optuna significantly improved classification performance, producing the highest Macro Area Under the Curve (AUC) of 0.9972. This outstanding score indicates the model’s superior ability to distinguish nutritional status categories across both majority and minority classes. The study concludes that addressing data imbalance through oversampling is a fundamental methodological step in constructing fair and effective machine learning systems for stunting detection, ultimately contributing to improved health outcomes and evidence-based policy design.

ECG-Based Heart Rate Variability and KNN Classification for Early Detection of Baby Blues Syndrome in Postpartum Mothers

2025-07-05T17:20:13+00:00

Early detection of baby blues syndrome plays an important role in preventing postpartum emotional disturbances from developing into more serious mental health conditions. This study proposes a simple and non-invasive approach to identify early signs of baby blues in postpartum mothers by analyzing electrocardiogram (ECG) signals using the K-Nearest Neighbor (KNN) algorithm. The ECG data were gathered through wearable sensors and processed to extract heart rate variability (HRV) features such as RMSSD, SDNN, entropy, and energy. These features were then used to train and test a KNN classification model through a five-fold cross-validation process. KNN was chosen because it is easy to implement, does not assume any specific data pattern, and works well with small datasets like those commonly found in clinical settings. Its ability to group data based on similarity makes it suitable for recognizing subtle physiological changes linked to emotional stress. The model reached an accuracy of 87.5%, with strong precision and recall scores, showing its reliability in distinguishing mothers who show early symptoms of baby blues from those who do not. Among all features, RMSSD and SDNN had the highest impact, pointing to reduced parasympathetic activity in affected individuals. These findings suggest that combining HRV analysis with a straightforward machine learning approach like KNN offers a promising, low-cost solution for early emotional screening in maternal care, especially where resources are limited.

Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI)

2025-07-28T03:05:42+00:00

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.

Integrating Bayesian Optimization into Ensemble Logistic Regression for Explainable AI-Based Customer Behavior Analysis

2025-08-15T17:40:35+00:00

Understanding 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..

Association Rule Mining across Multiple Domains: Systematic Literature Review

2025-08-15T07:16:55+00:00

This Systematic Literature Review (SLR) synthesizes 50 studies published between 2020 and 2025 that applied Association Rule Mining (ARM) across multiple domains, using the PRISMA 2020 framework. The review examines application areas, algorithm choices, implementation tools, parameter settings, and emerging trends. Results indicate that transportation and market analysis are the most prominent domains, followed by healthcare, manufacturing, and governance, with smaller contributions from tourism, agriculture, energy, and the environment. Apriori remains the most widely used algorithm due to its simplicity, FP-Growth is preferred for efficiency, and hybrid or modified approaches are adopted to address scalability issues. Python dominates as the primary implementation tool, alongside RapidMiner and R-Studio, with parameter thresholds generally adapted to dataset size and domain-specific needs. The novelty of this review lies in providing a cross-domain synthesis of ARM, filling the gap left by prior reviews that were limited to specific fields or algorithms. This broader perspective reveals temporal trends and recurring challenges, particularly scalability and interpretability, while identifying opportunities such as integration with deep learning, real-time ARM, and cross-domain adaptation. By offering a structured overview of developments in ARM, this study contributes both conceptual insights and practical guidance, serving as a reference for optimizing applications and informing future research directions.

Attention Augmented Deep Learning Model for Enhanced Feature Extraction in Cacao Disease Recognition

2025-08-25T15:48:07+00:00

Accurate cacao disease recognition is critical for safeguarding yields and reducing losses. Prior cacao studies primarily rely on handcrafted descriptors (eg, Color Histogram, LBP, GLCM) or standard CNN/transfer-learning pipelines, often limited to ≤ 3 classes and a single plant organ; explicit channel-spatial attention and comprehensive multiclass evaluation remain uncommon. To the best of our knowledge, no prior work integrates Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) on a ResNeXt50 backbone for six-class cacao disease classification, accompanied by a standardized ablation study and t-SNE-based interpretability. We propose a six-class classifier (five diseases + healthy) built on ResNeXt-50 enhanced with SE (channel recalibration) and CBAM (channel-spatial emphasis) to highlight lesion-relevant cues. The dataset comprises labeled leaf and pod images from public sources collected under field-like conditions; preprocessing includes resizing to 224x224, normalization, and augmentation (flips, small rotations, color jitter, random resized crops). Trained with Adam and early stopping, ResNeXt50+SE+CBAM attains 97% test accuracy and 0.97 macro-F1, surpassing a ResNeXt50 baseline of 94% and 0.95 and SE-only/CBAM-only variants. Confusion matrix and t-SNE analyses show fewer mix-ups among visual classes and clearer separability, while the ablation validates complementary benefits of SE and CBAM. On a desktop-hosted, web-based setup, batch-1 inference at 224x224 is 7.46 ms/image (134 FPS), demonstrating real-time capability. The findings support deployment as browser-based decision-support tools for farmers and integration into continuous field-monitoring systems.