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Engineering, Mathematics and Computer Science Journal (EMACS)
Published by Universitas Bina Nusantara
ISSN : -     EISSN : 26862573     DOI : https://doi.org/10.21512/emacs
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics
Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their 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 : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 14 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): EMACS" : 14 Documents clear
Analysis and Design Pet Healthcare, Pet Adoption, and Pet Community iOS Mobile Application for Animal Lovers Darmawan, Dion; Khresna, Roberto
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.12205

Abstract

This research aims to create an iOS based mobile applications that is expected to assist pet owners who want to search for reliable caretaker for their pets, ensuring a safe and trustworthy environment for pet entrustment. The application will also serve as an extensive informational hub, providing users with valuable resouces, including articles about pet and directory of veterinary clinics around the user’s area. The development of this mobile application begins with data collection through, literature study, observation and questionnaire. Literature study aims to find supporting theories which can assist the development of this application, observation are made to compare the application to be developed with similar applications, and questionnaire is used to identify the requirement from potential users. After analyzing all the obtained data, the process continues with designing application systems with UML diagrams such as, use case diagram, use case descripton, class diagram, sequence diagram, and activity diagram. Then, after completing the system design phase, the development of the application will be carried out with kanban methodology. After the application has been developed, a survey will be distributed to potential users to asses their level of satisfaction with the developed application. This survey will aim to gather feedback on various aspects, including user experience, functionality, ease of navigation, and overall effectiveness in meeting user needs.
“BAKSO BOSS”: Evaluating the Impact of Serious Games Using GDLC and FFFL-HS for Small and Medium Enterprises (SMEs) Ramdhan, Dimas; Bramanta Pandya Wisesa, Gede
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13146

Abstract

Small and Medium Enterprises (SMEs) are fundamental to economic growth, substantially aiding employment and income generation. Many SMEs face sustainability issues from deficient management and limited financial literacy, particularly in Indonesia (where they form ~99% of businesses and 95% of employment). Financial literacy—making informed financial decisions —is crucial for owners to manage resources and improve outcomes effectively. This study addresses this critical gap by evaluating the impact of "Bakso Boss," a serious game developed to enhance financial knowledge and business management capabilities among beginner entrepreneurs. The game, which simulates food business management scenarios to replicate typical challenges faced by small enterprises, was created using the structured Game Development Life Cycle (GDLC) method. Its effectiveness was rigorously assessed through pre- and post-tests administered to 30 respondents aged 16-30 in Jakarta who were either new or aspiring SME owners, utilizing the Financial Fitness for Life: High School (FFFL-HS) questionnaire. Data analysis using the Wilcoxon Signed Rank Test revealed statistically significant improvements (p<0.01) across all assessed variables: General Financial Attitudes, Financial Self-Efficacy, Perceived Financial Behavioral Control, Financial Autonomy, and Financial Satisfaction. These findings strongly suggest that "Bakso Boss" effectively improves these essential skills in aspiring entrepreneurs. By linking game mechanics directly to real-world financial behaviors, "Bakso Boss" improved test scores and equipped participants with practical, applicable skills. Future iterations could expand scenarios by including digital payment systems to reflect evolving SME challenges, thereby further enhancing the game's relevance and applicability in fostering SME development.
Combining Academia and Industry Approach for Secure Coding and Requirements Checklist in S-SDLC: Systematic Literature Review Anderies, Anderies; Rachmawati, Ika Dyah Agustia; Jingga, Kenny; Candra, Calvin Linardy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13429

Abstract

Rapid progress of digital transformation has occurred governments, organization and vendors around the world. where this rapid digital transformation is not linearly followed by the security protection of digital infrastructure and its application. For example, in Indonesia One of the largest banks was unable to operate its online and physical services for three consecutive days due to a cyber-attack. And many international organizations also experienced the same thing or even worse like bankruptcy. Because of this phenomenon the authors have performed a systematic literature review and identified there are two important phases namely requirement and coding in secure software development lifecycle (S-SDLC). In this study the authors compose 18 Secure Requirement practices (SREC) and 72 Secure Coding Checklist (SCOC) checklist based on Combining previous academia research study and international standard of open secure coding practices (OSCP) in which we target the security vulnerable most occurred to governments, organization and vendors around the world according to Open Web Application Security Project Foundation.  This checklist can be embedded in the Quality Assurance process to check in sequence whether the Requirements and Coding that are produced are safe or not from the cyber-attack. Additionally, the checklist approach is simple to understand and can be implemented to a popular public consumer automation testing tools enabling faster software development while maintaining software security.
Cost-Sensitive Learning with LightGBM for Class Imbalance in Intrusion Detection Systems Novika, Andien Dwi; Mujhid, Almuzhidul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13435

Abstract

Imbalanced data is a common challenge in classification problems, where standard models tend to be biased toward majority classes, leading to poor detection of minority instances. This paper presents a comparative study of Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost) models, enhanced with cost-sensitive learning to address class imbalance at the algorithmic level. The objective is to evaluate the impact of cost-sensitive loss adjustments on model performance using various evaluation metrics. Experimental results show that both models achieved high cross-validation and test accuracies, with LightGBM and XGBoost recording over 99.9% accuracy. However, only cost-sensitive LightGBM achieved perfect scores in precision, recall, and F1-score, indicating its ability to handle minority class identification effectively. In contrast, XGBoost exhibited lower recall and F1-score despite similar accuracy, reflecting limitations in sensitivity to minority instances. Models without cost-sensitive learning demonstrated further drops in performance across minority-related metrics. The findings suggest that cost-sensitive LightGBM is a more robust solution for imbalanced classification tasks, outperforming both its baseline and the cost-sensitive XGBoost variant. This approach is particularly beneficial for critical applications such as fraud detection, cybersecurity, and medical diagnostics, where class imbalance is prevalent and misclassification costs are high
'DREAMS D': New Matrix Evaluation for Software Architecture Rasjid, Zulfany Erlisa; Aldora, Ivana Yoshe; Piyono, Welly; Yulistiani, Risma; Pranoto, Hady
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13003

Abstract

The microservices software architecture is highly popular and commonly used in developing large-scale systems. Does this mean that microservices are superior, or could older architectures like monolithic be more adaptable to modern developments? The selection of software architecture is crucial to support overall system performance, quality, and user experience. Effective evaluation also plays a significant role in assessing system performance. In this paper, an evaluation matrix model is proposed, called 'DREAMS D,' comprising of seven vital components to test the quality of systems built using specific architectures. The focus is on microservices and monolithic architecture as our sample Software Architectures. The evaluation is conducted through a systematic review, and each architecture is scored based on factors such as Development, Response time, Error handling, Availability, Maintenance, Scalability, and Deployment. The result shows that microservices architecture scores higher in most evaluation criteria, suggesting better suitability for complex and adaptive systems. However, monolithic architecture may still be appropriate for simpler systems due to its lower cost and straightforward integration. This study provides a structured and measurable framework for assisting developers and organizations in making strategic decisions when choosing or transitioning between software architectures. The DREAMS D matrix can be used as a reference model for future evaluations or as a foundation for extending the framework to other architectural paradigms such as serverless or event-driven systems.
Breast Cancer Diagnosis Based on a Hybrid Genetic Algorithm and Neural Network Architecture Charisma, Rifqi Alfinnur; Maulina, Ayu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13409

Abstract

Breast cancer is one of the diseases with a high prevalence and is a leading cause of death among women. Early detection is crucial in improving patient survival rates. However, a major challenge in diagnosis using machine learning methods is the high dimensionality of the data, which can lead to overfitting and reduced interpretability of the model. This study proposes a new approach to improve breast cancer prediction accuracy by using a combination of Genetic Algorithm + Neural Network (GA + NN). The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, consisting of 569 samples with 32 numerical features that describe the characteristics of tumor cells. The experimental results show that the GA + MLP method achieved the highest accuracy of 99.42%, outperforming the benchmark model using PCA and logistic regression with an accuracy of 97.37%. This approach demonstrates that GA-based feature selection can improve prediction accuracy while reducing model complexity, making it more efficient for medical applications.
Advancing Indonesian Audio Emotion Classification: A Comparative Study Using IndoWaveSentiment Majiid, Muhammad Rizki Nur; Setiawan, Karli Eka; Pamungkas, Prayoga Yudha; Annas, Taufiq; Setiawan, Nicholas Lorenzo
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13415

Abstract

This study addresses the critical gap in Indonesian Speech Emotion Recognition (SER) by evaluating machine learning models on the IndoWaveSentiment dataset, a novel corpus of 300 high-fidelity recordings capturing five emotions (neutral, happy, surprised, disgusted, disappointed) from native speakers. The research aims to identify optimal classification techniques and acoustic features for Indonesian SER, given the language’s unique linguistic characteristics and the scarcity of annotated resources. Six models, Logistic Regression, KNN, Gradient Boosting, Random Forest, Naive Bayes, and SVC, were trained on 45 acoustic features, including spectral contrast, MFCCs, and zero crossing rate, extracted using Librosa. Results demonstrated Random Forest as the top performer (90% accuracy), followed by Gradient Boosting (85%) and Logistic Regression (75%), with spectral contrast (contrast2, contrast7) and MFCC1 emerging as the most discriminative features. The findings highlight the efficacy of ensemble methods in capturing nuanced emotional cues in Indonesian speech, outperforming prior studies on locally sourced datasets. Practical implications include applications in customer service analytics and mental health tools, though limitations such as the dataset’s-controlled conditions and fixed sentence structure necessitate caution in real-world deployment. Future work should expand the dataset to include regional dialects, spontaneous speech, and hybrid architectures like CNN-LSTMs. This study establishes foundational benchmarks for Indonesian SER, advocating for culturally informed models to enhance human-computer interaction in underrepresented linguistic contexts.
Adaptive Fuel Subsidy Optimization Using Deep Q-Learning and Bandit-Based Policy Selection: A Simulation Study Pambudi, Pandu Dwi Luhur
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13419

Abstract

Designing effective fuel subsidy policies is a major challenge for governments seeking to balance energy affordability, fiscal sustainability, and environmental goals. This study introduces an adaptive simulation framework combining Deep Q-Learning and a multi-armed bandit algorithm to model fuel consumption behavior and optimize subsidy distribution strategies. Moreover, this paper simulates a dual-agent system in which a DQN-based consumer interacts with a bandit driven government selecting among three subsidy policies: universal, quota-based, and targeted. By simulating consumer responses to universal, quota-based, and targeted subsidies over 1,000 episodes, the framework demonstrates how policy can adapt in real-time to maximize social welfare and reduce inefficient spending. Results show that while universal subsidies often deliver the highest consumer satisfaction, they incur significant fiscal costs, whereas quota and targeted approaches can yield more balanced trade-offs. The study highlights the potential of reinforcement learning to enhance adaptive policymaking in complex economic systems. To strengthen the analysis, the simulation tracks both consumer and government rewards across scenarios, capturing the trade-off between satisfaction and fiscal burden. Evaluation results reveal that targeted subsidies, though less popular, often provide more sustainable outcomes. The agent-based approach enables the system to dynamically adjust policy decisions based on real-time feedback, reflecting the evolving nature of economic behavior. These findings underscore the usefulness of AI-driven simulations as decision-support tools in designing responsive and cost-efficient public policies.
The Impact of Text Preprocessing in Sarcasm Detection on Indonesian Social Media Contents Jeremy, Nicholaus Hendrik
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13503

Abstract

Sarcasm is a way to convey something but delivered in the opposite way. This behavior is common on social media, where there are plenty of examples. On natural language processing, the task on its own is difficult primarily due to the lack of context. To add another layer of difficulty, communication in social media is done colloquially. One sacrasm benchmark, IdSarcasm, has alleviated one key issue in the development of sarcasm detection. However, there has not been an attempt to further preprocess the input before feeding them into the model. Pre-trained language models always use preprocessed corpus to ensure that the model is built upon quality dataset. Based on the current condition of IdSarcasm, further preprocessing step is necessary to ensure better quality. Specifically, the additional steps needed are handling HTML code, code-mixing, and colloquial writing which consists of shortened form, extended form, spelling variation, and reduplication. Several scenarios are created to observe the effect of additional preprocessing steps. Each additional preprocessing step is also tested to observe the effect of the preprocessing step independently. We prove that preprocessing step is still prevalent for data sourced from social media, and we recommend IndoNLU’s IndoBERT or large multilingual model to be used for sarcasm classification.
SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student Anshori, Mochammad; Siwi Pradini, Risqy; Teja Kusuma, Wahyu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13521

Abstract

Self-esteem plays a crucial role in students' psychological well-being, influencing their academic performance and personal development. Despite its importance, self-esteem is challenging to measure due to its abstract and subjective nature. This study aims to develop a predictive model to classify students’ self-esteem levels as high or low using machine learning and tabular data obtained through questionnaires. A dataset comprising 47 student responses, with 19 features consisting of social, emotional, demographic aspects, were analyzed. Five machine learning models were evaluated: Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM). To address the class imbalance in the dataset, the study applied SMOTE for data balancing and min-max normalization for feature standardization. Model performance was assessed using accuracy and F1-score. The results reveal that SVM, particularly with an RBF kernel, outperformed other models across all scenarios. On raw data, SVM achieved 66% accuracy and an F1-score of 57.3%. After applying SMOTE, the performance improved to 80% accuracy and a 79.9% F1-score. Further enhancement with normalization resulted in the best performance, with SVM achieving 83.33% accuracy and an F1-score of 83.3%. These results demonstrate how well preprocessing methods work to enhance machine learning models for datasets that are unbalanced. The proposed SVM-based model offers promising applications in educational and psychological settings, enabling early interventions to support students’ mental health.

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