Sinkron : jurnal dan penelitian teknik informatika
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Enhancing Feature-Efficient Network Intrusion Detection Using Gradient Boosting and Chi-Square Selection on NSL-KDD
No full textThis study examines the growing complexity of cyber threats that increasingly challenge the effectiveness of traditional Network Intrusion Detection Systems (NIDS). Modern attacks, particularly zero-day intrusions, require detection approaches capable of handling high-dimensional network traffic data. However, existing studies rarely examine the trade-off between feature efficiency and generalization performance in boosting-based NIDS under controlled feature-reduction strategies. Moreover, the role of statistical feature selection in mitigating overfitting in classical boosting models remains underexplored. This study evaluates the performance of NIDS by combining boosting ensemble algorithms, namely AdaBoost, Gradient Boosting, and XGBoost, with filter-based feature selection methods, including Information Gain, Chi-Square, and ReliefF. The NSL-KDD dataset is used as the primary benchmark, with Min–Max normalization applied during preprocessing to ensure numerical feature consistency. Model development is conducted using Orange Data Mining, and performance is assessed through 10-fold cross-validation. Experimental results show that Gradient Boosting achieves the highest baseline accuracy among the evaluated models. Further performance improvements are obtained through feature selection, with the Chi-Square method yielding the best result at 81.2% accuracy using 19 selected features. Information Gain also enhances performance, achieving 80.8% accuracy with 13 features, while ReliefF provides comparatively lower gains. These findings demonstrate that effective feature reduction improves generalization performance, reduces computational complexity, and mitigates overfitting. Overall, the proposed combination of Gradient Boosting and statistical feature selection provides a feature-efficient, generalizable intrusion detection strategy for modern network environments
An Integrated K-Means and Composite Risk Scoring Framework for Urban Dengue Vulnerability Mapping
No full textThe rising incidence of dengue hemorrhagic fever (DHF) in Indonesian urban areas highlights the urgent need for analytical frameworks capable of capturing spatial heterogeneity in vulnerability while supporting targeted public health interventions. However, most existing dengue vulnerability studies rely on clustering or indicator-based scoring in isolation, limiting interpretability and reducing their operational relevance for policy-driven decision making. This study explicitly addresses this gap by proposing an integrated spatial clustering and epidemiologically weighted composite risk scoring framework for urban dengue vulnerability mapping. Using Semarang Municipality as a case study, K Means based spatial clustering was combined with composite risk scoring to analyze dengue vulnerability across administrative subdistricts. Seven key indicators consisting of population density, area size, total population, morbidity, mortality, incidence rate, and health facility availability were processed through systematic imputation, normalization, and attribute selection to ensure data consistency and analytical robustness. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, after which K-Means clustering was applied to generate spatially coherent vulnerability groupings. A composite risk scoring mechanism was subsequently employed to classify regions into five operational risk categories: Low-Risk, Moderate-Risk, High-Risk, Very High-Risk, and Emergency-Priority. The results reveal clear structural differentiation in dengue vulnerability patterns, where Emergency-Priority and Very High-Risk clusters are not only characterized by elevated epidemiological indicators but also by constrained health service availability, amplifying outbreak susceptibility. Specifically, 13 subdistricts (7.5%) were identified as Emergency-Priority and 22 subdistricts (12.4%) as Very High-Risk, together accounting for approximately 20% of the study area. Beyond numerical classification, the integration of spatial clustering and composite risk scoring enhances interpretability by linking cluster structure with epidemiological severity and service capacity, thereby improving policy relevance compared to conventional clustering-only approaches. Validation through heatmap visualization, risk category distribution, and cluster ranking confirms the stability and interpretive clarity of the proposed framework. By moving beyond descriptive clustering toward an integrated analytical model, this study contributes a scalable and adaptive decision-support framework for dengue risk mapping. The findings provide actionable insights for policymakers, enabling evidence-based prioritization, optimized resource allocation, and the development of responsive intervention strategies to mitigate dengue burden in complex urban environments
Academic Performance Prediction from Student–VLE Bipartite Interaction Graphs Using Centrality Features A Comparative Study with Classical Classifiers
No full textThe rapid growth of digital learning platforms has increased the availability of student academic records and fine-grained interaction logs, creating opportunities for Educational Data Mining (EDM) to support early academic monitoring. However, many predictive models still rely mainly on individual tabular attributes and underutilize relational signals embedded in learning interactions. This study proposes a graph-mining feature approach for predicting student academic performance using a bipartite Student–VLE interaction graph. Centrality measures—degree, weighted degree, HITS hub, PageRank, and eigenvector centrality—are extracted to form a centrality feature set and combined with standard student information features. Using the public OULAD dataset, we compare three supervised classifiers: Random Forest, Support Vector Machine, and XGBoost. Experiments show that adding the centrality feature set consistently and substantially improves performance across all models compared to baseline tabular features. On the test set, XGBoost achieves the strongest results with accuracy 0.842, ROC-AUC 0.922, PR-AUC 0.902, and MCC 0.684, while Random Forest is close behind (accuracy 0.834, ROC-AUC 0.916, PR-AUC 0.894, MCC 0.672). The SVM model also benefits (accuracy 0.800, ROC-AUC 0.869, PR-AUC 0.811, MCC 0.599), confirming the robustness of the graph-derived signal. Scientifically, this study provides empirical evidence that a multi-centrality representation offers more systematic and transferable predictive value than relying on a single graph metric, across multiple classical model families under the same evaluation protocol. These findings indicate that graph-mining centrality features capture complementary structural information about learning engagement that is not represented by tabular attributes alone, and they offer a practical, interpretable enhancement to classic EDM pipelines for academic performance prediction
Median-Average Round Robin (MARR) Algorithm for Optimal CPU Task Scheduling
No full textAbstract: In operating systems, multitasking or multiprocessing terms are used. If more than one task operating consecutively, but the users feel that they are running simultaneously, than it is called multitasking. Round robin algorithm is a noted algorithm in multitasking. Several modifications of classical round robin algorithm have been proposed by experts. The idea behind these modifications are to get lower turnaround time and lower waiting time. The main topic’s discussion is about median-average round robin (MARR) algorithm. In this algorithm, the processes are arranged in ascending order. Then we get the median of the burst time. Afterwards, calculation of the average burst time is done. The summation of average and median, divide by two is the time quantum. So, the time quantum will be dynamic, based on each iteration of round robin. First iteration can have different time quantum compared to the second and so on. Each iteration will have one time quantum. Three analysis’s are given. Each with five processes. In the first analysis, time quantum for 1st iteration is 11 and the 2nd iteration is 4. The average turnaround time is 29. The average waiting time is 19. For the second analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 8. The average turnaround time is 24.2. The average waiting time is 13.6. For the third analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 9. The average turnaround time is 23.2. The average waiting time is 12.8
Parameter Testing on Random Forest Algorithm for Stunting Prediction
No full textStunting is a significant public health problem, especially in developing countries like Indonesia. It is often caused by chronic malnutrition in the first 1,000 days of life, which can impact a child's physical growth and cognitive development. To find risk factors and find effective solutions, data analysis was conducted by utilising machine learning to predict stunting. This research uses the Random Forest algorithm with a focus on setting parameters such as n_estimators, max_depth, and the number of features to optimise model efficiency and accuracy. Using the 2023 Indonesian Health Survey data consisting of 25,800 data, this study managed to get the highest accuracy of 91.65% by a combination of Random Forest with parameter settings n_estimators 200, max_depth 30, and Synthetic Minority Oversampling Technique (SMOTE). Despite the high accuracy results, there are limitations such as potential noise coming from synthetic data from SMOTE and the limited number of features analysed. It is hoped that this research can contribute to the field of machine learning model development that is practically used to predict stunting, and support the government's efforts to reduce the stunting prevalence rate to 14% as targeted. This model also provides strategic insights for policy makers to design more effective data-driven interventions, which can help in decision making
Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency
No full textStunting, characterized by impaired growth and development in children, is one of the most serious public health problems often caused by chronic malnutrition. This study aims to identify patterns among stunting cases through clustering analysis of child health data. The algorithm used in this research uses K-Means. The dataset used in this study uses health data from 599 children in the Sambas Regency area of East Kalimantan Province. This dataset has several features that are quite diverse such as height, weight, age, nutritional intake, socioeconomic status, and others. This research process begins with cleaning the data, as well as looking at the correlation between features. One of the methods used is to conduct a data analysis process using Principal Component Analysis (PCA) which aims to reduce the dimensions of the data. After that, the process of finding the number of clusters using the Elbow method is carried out to determine the optimal number of clusters. This research uses 4 clusters in the process. The clustering results revealed that family structure (main family vs extended family) and parental income levels significantly influence stunting prevalence in the region
Decision-Making Framework Using MARCOS for Evaluating Sealing Machines in Small and Medium Enterprises
No full textIn the era of globalization, Micro, Small, and Medium Enterprises (MSMEs) hold a vital position in Indonesia's economy, contributing significantly to GDP and employment. Despite their importance, MSMEs need help in selecting appropriate sealer machines, which affects production efficiency and product quality. There are six different kinds of sealer machines that are looked at in this study. They are manual, vertical, continuous, horizontal semi-automatic, impulse, and vacuum. The MARCOS method is used to find the best option. Results indicate that the Impulse Sealer Machine (A5) is the most suitable, with a Ki value of 1.7, followed by the Continuous Sealer Machine (A3), with a Ki of 1.63. Machines such as Manual (A1), Vertical (A2), and Vacuum (A6) scored 1.6, while the Horizontal Semi-Automatic Sealer Machine (A4) ranked lowest at 1.36. These findings provide MSMEs with practical guidance for selecting sealer machines that enhance production efficiency and competitiveness in the global market while also contributing to the development of packaging technology research
ISO 27001 As Information Security Solution In Society 5.0 Era: Systematic Literature Review
No full textIn the era of Society 5.0, information security is an important issue along with the increasing use of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data. ISO 27001 acts as a globally recognized standard framework for managing information security. The ISO 27001 standard provides a systematic framework for identifying, assessing, and managing information security risks so as to ensure the integrity, confidentiality, and availability of data in an organization. This research aims to evaluate the implementation of ISO 27001 as an information security solution in the Society 5.0 era through a systematic literature review. Using the Systematic Literature Review (SLR) method, this research collects and analyzes relevant literature to identify benefits, challenges, and recommendations related to the application of ISO 27001 in an era of increasingly integrated technology. The results showed that the implementation of ISO 27001 in the Society 5.0 era proved to make a significant contribution in improving organizational information security. This is done through a PDCA (Plan-Do-Check-Act) approach that integrates information security policies into business processes, strengthens risk management, technology infrastructure, and human resource competencies. In conclusion, the implementation of ISO 27001 in the Society 5.0 era not only improves information security, but also supports the achievement of operational efficiency and organizational sustainability amid rapid technological developments
Sentiment Analysis On Indonesian Tweets about the 2024 Election
No full textThis study investigates public sentiment on Indonesian Twitter regarding the 2024 General Election, employing machine learning and deep learning techniques, including Naïve Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset was collected using a Tweet Harvest method with the keyword "Pemilu" and underwent preprocessing steps such as case folding, removal of symbols and URLs, stopword elimination, and tokenization to ensure data quality. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were applied to assess the models' effectiveness. Naïve Bayes achieved the highest accuracy of 64%, followed by SVM at 63%, LSTM at 60%, and GRU at 57%. The findings indicate that traditional models like Naïve Bayes and SVM perform effectively on smaller datasets with structured features, while deep learning models excel in capturing complex sequential dependencies. However, deep learning methods exhibited overfitting tendencies, indicating the need for better regularization and optimization techniques. Furthermore, it emphasizes the potential of integrating traditional algorithms with advanced methods to enhance sentiment classification accuracy and generalizability across diverse datasets
Implementation of Cloud Computing for SOS Application Back-End using Google Cloud Platform
No full textThis research discusses the implementation of cloud computing on the Backend of the SOS application using the Google Cloud Platform. The background of the research is based on the high crime rate in Indonesia, especially theft cases which reached 3396 cases in the period August-September 2024. The purpose of the research is to develop an application that can help users in emergency situations by providing information on the location of the nearest police station within a maximum radius of 5 KM. The method used is Agile Kanban, which was chosen because of its flexible nature and emphasizes rapid response to change. The Backend implementation uses Google Cloud Platform services including Maps API (Places API, Geocoding API, and Distance Matrix API) for location features, and Google Firestore for data storage. The results of the research show that the implementation of cloud computing for the Backend of the ResQHub application successfully displays the location of the nearest police station from the user, but there are still obstacles in the integration of Firestore for storing user data and signup/login authentication. Further research will focus on frontend development for mobile implementation and completion of Firestore integration