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ComTech: Computer, Mathematics and Engineering Applications
Published by Universitas Bina Nusantara
ISSN : 20871244     EISSN : 2476907X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The journal invites professionals in the world of education, research, and entrepreneurship to participate in disseminating ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : -
Articles 1,585 Documents
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Implementation of Clustering and Association for Early Warning of Disasters in Bojonegoro Regency Nurdiansyah, Denny; Hayati, Erna; Purnamasari, Ika; Hidayanti, Anna Apriana; Rahayu, Yuliana Fuji
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 2 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i2.11933

Abstract

The research aimed to analyze the relationships between different types of disasters, assess the likelihood of disaster occurrences, and enhance knowledge and understanding of disaster patterns in Bojonegoro Regency. The goal was to enable better disaster prediction and preparedness in the future. The methods applied included mapping, clustering using the K-means algorithm, and association rule mining with the Apriori algorithm. Secondary data were obtained from the National Disaster Management Agency and the Bojonegoro Regency Regional Disaster Management Agency Office, covering eight types of disasters. The results reveal that the K-means model groups the data into 5 clusters from 28 sub-districts in Bojonegoro. There are 13 sub-districts in Cluster 0, 1 sub-district in Cluster 1, 4 sub-districts in Cluster 2, 6 sub-districts in Cluster 3, and 4 sub-districts in Cluster 4. The association rule analysis produces four association rules using a minimum support of 10% and a minimum confidence of 50%. The findings highlight that the Ngasem and Bojonegoro sub-districts require more focused disaster management. The fourth association rule has the highest confidence level at 78.79%, indicating that forest and land fires are likely to follow when drought occurs. The research implies that it can support more targeted disaster management focusing on high-risk sub-districts such as Ngasem and Bojonegoro. The originality of the research lies in its novel application of clustering and association rules to analyze disaster patterns in the region, with implications for more targeted disaster mitigation strategies.
Association Analysis Using Apriori Algorithm of GANs-Expanded Student Performance Dataset Sumacot, Rannie M.
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 2 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i2.11948

Abstract

Traditional datasets are often limited, which can affect the accuracy of analyses. Additionally, the use of students’ real data raises privacy concerns. Generative Adversarial Networks (GANs) offer a solution by generating synthetic data that closely mirrors real-world data without compromising sensitive information. The research explored the application of GANs to enhance student performance datasets by addressing challenges related to data scarcity and privacy in educational research. In the research, GANs were utilized to generate synthetic student performance data. The accuracy of the data was assessed using Mean Absolute Percentage Error (MAPE), with values ranging from 0.004% to 19.92% across various statistical measures and means. These results demonstrated the reliability of the synthetic data, making it suitable for further analysis. The synthetic datasets were then analyzed using the Apriori algorithm, a well-known method in data mining for discovering significant patterns and relationships. A lower bound minimum support of 0.1 (10%) and a minimum confidence threshold of 0.6 (60%) were applied, ensuring the identification of meaningful associations. The analysis reveals important patterns and relationships among student attributes and behaviors. The research highlights the potential of GANs to advance data-driven educational research. By generating high-quality synthetic data, GANs allow researchers to conduct comprehensive analyses while addressing privacy concerns. The research contributes to the methodological approach to data augmentation in education, offering new opportunities for ethical and robust research.
A Novel Machine Learning for Ethanol and Methanol Classification with Capacitive Soil Moisture (CSM) Sensors Sari, Devina Intan; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 2 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i2.12051

Abstract

Although Gas Chromatography (GC) is highly accurate, it is costly, highlighting the need for a more affordable method for alcohol detection. Ethanol and methanol have different evaporation rates and dielectric constants, suggesting the potential for classification as an alternative initial step to GC based on differences in dielectric due to evaporation using Capacitive Soil Moisture (CSM) sensors, although it has not been previously attempted. The research aimed to present a novel machine learning for ethanol and methanol classification with CSM sensors. The method involved placing evaporated samples on CSM plates and measuring the change in evaporative dielectric properties over time. The data were then processed using Python, preprocessing data, splitting data, and training various classifiers with key differentiators based on standard deviation, mean, difference, and cumulative summary. Then, model accuracy was evaluated. The research results show that the approach can distinguish between pure ethanol and methanol based on the dielectric differences in each substance's evaporation rate using machine learning training methods with classifiers such as Random Forest, Extra Trees, Gaussian Naive Bayes, AdaBoost, and Logistic Regression with seven folds in cross-validation, L2 regularization, and Newton-Cholesky solver, with accuracies of 96.67%, 96.67%, 96.67%, 93.33%, and 93.33%, respectively. Although the research is limited to the classification of two types of alcohol, the novel approach can classify methanol and ethanol, leading to a potential initial step in determining alcohol content in the future. It can be an alternative to GC with a simpler and more affordable setup using CSM sensors.
A Data Mining Approach to Understanding Financial Literacy Knowledge and Behavioral Patterns among Tertiary Students Usita, Maricris M.; Luriban, Virgie Liza
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12221

Abstract

The research sought to data mine the financial literacy of tertiary students to evaluate and pinpoint deficiencies in their financial knowledge, measure the degree of financial goal-setting and budgeting practices, and determine their primary sources of financial advice. The data were gathered through validated questionnaires and disseminated through surveys. The research focused on tertiary students with 316 valid responses for analysis. The research used data mining techniques under the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to identify students’ financial literacy. Furthermore, a comprehensive analysis using Ms Excel, Statistical Package for Social Sciences (SPSS), and Waikato Environment for Knowledge Analysis (WEKA) reveals significant disparities in financial literacy among various fields of study and course levels. The investigation highlights essential financial behaviors, such as credit card utilization, saving patterns, and budgeting strategies, while revealing deficiencies in formal financial education. The analysis highlights the necessity for specialized financial literacy initiatives in educational programs to bridge knowledge deficiencies and encourage proficient budgeting and goal-setting techniques. The results offer practical guidance for educators, policymakers, and higher education institutions to improve students’ financial well-being, in line with Sustainable Development Goals (SDGs) focused on poverty alleviation and economic development. The research advocates for financial literacy programs in the school curriculum and emphasizes enhancing student participation in workshops. Higher education institutions must provide well-structured financial advice and support services. Lastly, Future studies should delve deeper into socioeconomic factors to improve predictive models and intervention strategies.
Optimizing Quality Attributes of Piper Retrofractum Vahl. Through Partial Least Squares Regression: Insights from Pretreatment and Drying Experiments with Fruit Peel Infusions Lumintu, Ida
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12246

Abstract

The research aimed to optimize the quality attributes of Piper retrofractum Vahl.—piperine content, color brightness, and water content—using Partial Least Squares Regression (PLSR) to evaluate the pretreatment effects with fruit peel infusions and drying conditions. The research urgency lied in addressing the challenges of achieving consistent product quality while promoting sustainable food processing practices. Around 30 samples of Piper retrofractum Vahl. were subjected to varying pretreatment concentrations, soaking durations, drying durations, and peel types (orange and pineapple). The PLSR model was employed to identify key factors influencing the quality attributes and assess predictive performance based on Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) values. As a result, the PLSR model explains 43.22% of the variance in piperine content, highlighting the importance of shorter soaking durations and higher pretreatment concentrations in preserving piperine levels. For water content, the model captures 75.08% of the variance, emphasizing the critical role of drying duration in reducing moisture. However, the color brightness model explains only 18.5% of the variance, indicating the need to explore contributing factors further. The research introduces the innovative use of fruit peel-infused water as a sustainable pretreatment method, contributing to eco-friendly food processing practices and offering practical insights into optimizing production for improved product quality. The findings underscore the importance of balancing pretreatment and drying parameters to address inconsistencies in quality while promoting sustainability. Future research should expand experimental conditions, integrate additional variables, and explore advanced modeling techniques to enhance predictive accuracy and product quality.
Implementation of Decision Tree with Best Subset Approach to Identify Suicide Cases in Central and East Java Choiri, Moh. Miftahul; Nurdiansyah, Denny; Rokhim, Auliyaur; Patmawati, Pebriana Putri; Pebriani, Putri Vatria
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12265

Abstract

The research was conducted to determine the descriptive statistics of suicide cases and classify suicide cases based on the attributes of victims who made suicide attempts. The research design used was a quantitative method in the form of exploratory research using the Decision Tree method. The research novelty was applying the Decision Tree method with the Best Subset approach. The research data sources were obtained from online mass media news such as DetikJatim and DetikJateng for suicide attempt cases from January 2022 to July 2024. The research finds significant differences in the number of suicide attempts in East Java and Central Java, with Surabaya, Malang, Blitar, Semarang, and Klaten recording higher numbers. The findings show that males more often attempt suicide, while females more often experience failed attempts. Young adults (20−39 years) record the highest rate, and hanging is the most common method. Unknown mental disorders and depression are the main risk factors, with many attempts occurring without rescue. The implication is that improving emergency response systems and mental health services is essential. The research recommends strengthening mental health and social support for older adults and those under stress. Then, enhancing rapid rescue efforts with comprehensive psychological interventions is essential for suicide prevention. The originality of the research lies in the use of a Decision Tree with the Best Subset approach to identify suicide patterns based on risk factors and methods used.
The Implementation of the Fuzzy C-Means Method in Handling Outlier Data in the 2021 Village Potential Data of Bengkulu Province Panjaitan, Intan Juliana; Indahwati, Indahwati; Afendi, Farit Mochamad
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12274

Abstract

Clustering groups aims to ensure similarity within clusters and disparity between them. The research evaluated the Fuzzy C-Means method’s effectiveness in clustering large datasets containing outliers, focusing on the 2021 Village Potential data from Bengkulu Province. The dataset, comprising 1,514 observations from villages and urban villages, provided a comprehensive resource for understanding regional development. Outliers, a common challenge in cluster analysis, were detected using univariate and multivariate methods, revealing substantial variability. PCA was applied, improving clustering quality to address multicollinearity among variables. In the results, the fuzzifier (w) parameter in the FCM method plays a crucial role in controlling the degree of membership for data points in clusters, which can potentially reduce the impact of outliers, enhancing clustering robustness and accuracy. The FCM method effectively produces clusters with high intra-cluster homogeneity and inter-cluster heterogeneity. Using the Elbow method, three optimal clusters are identified. Cluster 1, dominated by villages in Bengkulu City, is the most advanced, with superior infrastructure and services, but the fewest villages business units, necessitating economic empowerment. Cluster 2, comprising villages in North Bengkulu Regency, demonstrates moderate development but suffers from poor transportation access, requiring improvements to support socio-economic activities. Cluster 3, dominated by villages in Kaur Regency, is the least developed, with limited basic services and infrastructure, highlighting the need for substantial investments in governance and essential services. These findings provide actionable insights for village development in Bengkulu Province, supporting targeted policies tailored to each cluster’s unique characteristics.
Comparative Analysis of Reconciliation Techniques: Bottom-Up, Top-Down, and MinT for Product Forecasting in Retail SMEs Rambing, Danni; Kusumaningrum, Retno; Sugiharto, Aris
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12293

Abstract

Small and Medium Enterprises (SMEs) have experienced rapid growth, contributing approximately 95% to the global economy, 60% to global employment, and 50% to global GDP. This growth is accompanied by significant challenges, with approximately 70% of SMEs failing within the first three years, primarily due to poor inventory management. It emphasizes the crucial role of accurate demand forecasting for SMEs, particularly in the retail sector, where time series at various levels of hierarchical structure exhibit different scales and display diverse patterns. However, most existing research on demand forecasting for SMEs focuses on a single hierarchical level—either bottom, middle, or top—without addressing the entire hierarchy. The research sought to address this gap by forecasting across all hierarchical levels and evaluating different reconciliation techniques to generate coherent and accurate forecasts for multiple products in retail SMEs. The ETS state space model was used as the base forecasting model. This model was widely recognized as a benchmark in forecasting competitions. The reconciliation methods assessed were Bottom-Up, Top-Down based on historical proportions (average proportions), Top-Down based on forecast proportions, and Minimum Trace (MinT) (Ordinary Least Squares (OLS), OLS Non-Negative (OLS Non-Neg), Weighted Least Squares (WLS), and WLS Non-Negative (WLS Non-Neg)). The evaluation results show that the OLS Non-Negative method, with an average SMAPE value of 35.335%, produces more accurate reconciliation than other methods. In addition, this method also outperforms the base model with an increase in accuracy of 13%.
Designing School Building Maintenance Priorities Using the Cost-User Effectiveness Ratio Purwosri, Visaretri Pramuktia; Prasetyo, Hari; Solikin, Mochamad; Harnaeni, Senja Rum; Sunarjono, Sri
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12385

Abstract

Prioritizing school building maintenance solely based on structural damage often leads to inefficient budget allocation and fewer beneficiaries. The research introduced an integrated Cost-User Effectiveness Ratio (CUER) to establish maintenance priorities by combining three critical factors: damage severity, maintenance costs, and the number of affected students. The CUER formulation employed the Geometric Mean or the root mean multiplication of the cost effectiveness and user effectiveness ratio to balance these factors systematically. The methodology encompassed several steps, including damage assessment and calculation of component importance weights using the Analytical Hierarchy Process (AHP), to determine integrated damage levels, costs, and student weights. These inputs were subsequently used to generate priority rankings of schools requiring maintenance. As a result, the case study in Wonogiri Regency illustrates the superiority of the proposed method over the conventional method. While the conventional approach prioritizes 27 schools benefiting 2,442 students, the CUER approach prioritizes 33 schools benefiting 2,957 students, demonstrating increased efficiency and broader impact. The CUER-based model presents a systematic and equitable solution to prioritize school building maintenance, ensuring the optimal allocation of resources and maximizing benefits within existing budgetary constraints. This innovative approach addresses current challenges in maintenance planning and offers significant implications for improving the management of educational infrastructure.
Clustering Analysis of MAMA 2024 Song of the Year Nominees Based on Musical Elements and Popularity Indicators Harahap, Libelda Aldinaduma; Sofro, A'yunin
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12860

Abstract

As K-pop continues to dominate global music charts, understanding the factors behind the success of songs has become increasingly essential. This study explores how musical elements and popularity indicators reveal patterns among topperforming songs. A total of 57 songs nominated for the 2024 Song of the Year category were grouped using hierarchical cluster analysis. The genre variable was consolidated into six broader categories and converted into numerical labels. All variables are normalized using the Min-Max normalization method before clustering. The data includes musical elements such as genre, tempo, danceability, energy, and happiness, as well as popularity indicators like YouTube views and Spotify streams. The analysis employs single, complete, and average linkage methods. Among these, the average linkage method yields the best results, with an agglomerative coefficient value of 0.8167. Seven distinct clusters are identified: Cluster 1 features R&B and hip-hop styles with varied energy and rhythms; Cluster 2, the largest group, includes high-energy pop, hip-hop, and dance-pop tracks that are popular on streaming platforms; Cluster 3 contains indie and experimental tracks; Cluster 4 emphasizes high-energy stage performances; Cluster 5 is an outlier with experimental traits; Cluster 6 highlights R&B and funk with global appeal; and Cluster 7 includes emotional OSTs and ballads with slower tempos. By combining musical elements and popularity indicators, this research uncovers patterns of success in K-pop songs. These findings offer actionable insights for artists, producers, and marketers, providing a datadriven reference for creating music that resonates with modern audience preferences.

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