Edu Komputika Journal starting in 2024 migrates from OJS 2 to OJS 3 to better secure from various unwanted things, including journal hacking and so on. To submit, the author please visit the new website page of our journal at the link https://journal.unnes.ac.id/journals/edukom MIGRATION OFFICIAL STATEMENT HERE Edu Komputika Journal is an open-access, peer-reviewed journal published by the Study Program of Informatics and Computer Engineering Education, Universitas Negeri Semarang, Indonesia. It was first published in 2014 and published two issues per year, in July and December. Edu Komputika has a printed and online International Standard Serial Number: p-ISSN 2252-6811 (printed version) and e-ISSN 2599-297X (electronic version). Articles can be the results of research, scientific studies, literature reviews, and the analysis and solving of issues relevant to the fields of Education, Computer Science & IT, and Computer Engineering. Edu Komputika Journal uses Open Journal Systems (OJS) for online journal management in submission, review, copyediting, and publication. Submitted manuscripts are written in English and should follow the style of the Edu Komputika Journal. Manuscripts are original research results, or theoretical/literature study results that have never been published in other journals or are not considered for publication elsewhere. The author should follow all the provisions and processes. Accepted papers will be available online and will be charged a publication fee. This journal has been covered by following indexing and abstracting services: DIMENSION | SINTA | GARUDA
Artificial Intelligence Based Educational Game Design for Literacy and Numeracy Skills in Elementary School Mathematics Learning
Low literacy and numeracy achievement among elementary school students, particularly in mathematics learning, indicates the need for innovative learning media capable of providing adaptive and engaging learning experiences. Therefore, this study aims to design and evaluate an Artificial Intelligence (AI) based educational game for plane geometry learning to improve elementary school students' literacy and numeracy skills. The study employed the Research and Development (R&D) method using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation. The developed product is an AI based Monopoly educational game that integrates adaptive question generation, automatic feedback, and learning analytics to adjust the difficulty level according to students' performance. The game was validated by two media experts and two mathematics education experts before being implemented in learning. The trial involved 25 fifth-grade students at Muhammadiyah 1 Labschool Umsida Elementary School. Data were collected through expert validation, classroom observation, and literacy and numeracy pretests and posttests. The validation results indicated that the developed game was highly feasible for classroom use. During implementation, students demonstrated high enthusiasm and active engagement in learning activities. The average pretest score increased from 65.64 to 80.16 in the posttest. The data met the normality assumption (pretest = 0.253; posttest = 0.567; p > 0.05). Furthermore, the Paired Sample t-test revealed a significant improvement in students' literacy and numeracy skills (p = 0.000), while the average N-Gain score of 40.07% indicated a moderate level of effectiveness. These findings demonstrate that the proposed AI based educational game is a feasible and effective learning medium for enhancing elementary students' literacy and numeracy skills while supporting Sustainable Development Goal 4 on Quality Education.
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Customer Review Sentiment Analysis on Google Maps as a Basis for CRM Strategy Optimization: Evidence from Coffeeshop Z Purwokerto
The rapid growth of digital platforms has encouraged customers to share their experiences and evaluations through online reviews, particularly on Google Maps. These reviews provide valuable insights that can support Customer Relationship Management (CRM) strategies when analyzed systematically. This study aims to analyze customer review sentiment toward Coffee Shop Z in Purwokerto using the Multinomial Naïve Bayes algorithm and to utilize the findings as a basis for CRM strategy formulation through the get, keep, and grow framework. A mixed-method approach was employed, combining quantitative sentiment analysis of 242 Google Maps reviews and qualitative interviews with customers for methodological triangulation. The research process included data collection through web scraping, text preprocessing, TF-IDF feature extraction, sentiment labeling, classification modeling, and performance evaluation. The results indicate that negative sentiment dominated customer reviews (57.02%), followed by neutral sentiment (30.16%) and positive sentiment (12.80%). The classification model achieved an accuracy of 63.27%, with stronger performance in identifying negative sentiment than neutral and positive sentiments. The findings reveal that customer dissatisfaction is primarily associated with service quality and waiting time, while positive perceptions are related to food quality, affordability, and cleanliness. Based on these findings, CRM strategies were developed through customer acquisition (get), retention (keep), and loyalty enhancement (grow) initiatives. This study demonstrates that sentiment analysis can serve not only as a customer perception measurement tool but also as a valuable source of evidence-based recommendations for customer relationship management and digital marketing strategy development.
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Performance Evaluation of Machine Learning Methods for Real-Time Rainfall Classification
Reliable real-time rainfall intensity classification is essential for supporting early warning systems and disaster mitigation, particularly in regions vulnerable to hydrometeorological hazards. This study evaluates three machine learning algorithms SVM, Neural Network, and AdaBoost for multiclass rainfall intensity classification using real-time data collected from Internet of Things (IoT)-based sensors. Rainfall intensity is categorized into four classes: no rain, light rain, moderate rain, and heavy rain, based on threshold values defined by BMKG standards. The dataset is imbalanced and dominated by the no rain class, therefore, model performance is evaluated using imbalance aware metrics, including per-class precision and recall, macro F1-score, balanced accuracy, and overall accuracy. Experimental results show that SVM and Neural Network achieve very high overall accuracy of up to 99.46%, however, this performance is mainly influenced by accurate classification of the majority class, leading to low recall for minority rainfall classes. In contrast, AdaBoost provides a more balanced baseline performance, achieving an accuracy of 92.4% and a macro F1-score of 0.714 on the original dataset. To enhance minority class detection, the SMOTE is applied to the training data using an 80:20 train test split. After data balancing, AdaBoost demonstrates improved recall and macro F1-score for light and moderate rain classes, although overall accuracy decreases to 77.1%. These results are acceptable for early warning applications, where sensitivity to rainfall onset is prioritized over majority class dominance. Consequently, balanced AdaBoost, evaluated using time-based data partitioning and imbalance aware metrics, is considered an effective approach for real-time IoT-based rainfall classification.
Hybrid CNN-Fuzzy Logic System for Type 2 Diabetes Mellitus Prediction: A Clinical Decision Support Tool with Interpretability Enhancement
Type 2 Diabetes Mellitus (T2DM) is a critical global health challenge, with 589 million adults currently diagnosed and projected to reach 853 million by 2050. Early detection is crucial, as it can reduce complication incidence by 30-40%, yet approximately 50% of cases remain undiagnosed. While machine learning approaches demonstrate promise for T2DM risk prediction, current systems face a fundamental accuracy-interpretability paradox: deep learning models achieve high accuracy (88-93%) but lack clinical transparency, while interpretable models sacrifice predictive performance. This study develops and validates a hybrid CNN-Fuzzy Logic system that directly addresses this paradox by combining high predictive accuracy with clinical interpretability. The system employs a Convolutional Neural Network component for non-linear feature abstraction combined with Mamdani Fuzzy Logic incorporating clinically derived weights aligned with ADA 2024 diagnostic criteria. Tested on the Pima Indian Diabetes dataset (n=154 test cases), the hybrid model achieved 92.5% accuracy (95% CI: 88.2-96.1%), 91.2% sensitivity, 93.1% specificity, and AUC-ROC 0.925, statistically superior to standalone CNN (88.9%, p=0.0037) and Fuzzy Logic (88.3%, p=0.0015) approaches. Interpretability scores reached 0.78-0.86, exceeding pure neural network baselines (0.32-0.42) and supporting clinician-understandable risk stratification. The system is operationalized as a web-based Clinical Decision Support System supporting both individual patient assessment and batch population screening. This hybrid architecture directly bridges the accuracy-interpretability paradox that has historically constrained ML adoption in clinical diabetes management.
Integrating SERVQUAL and TAM to Evaluate Service Quality and User Acceptance of a Government Website
This study aims to evaluate the service quality and user acceptance of a government website by integrating the SERVQUAL model and the Technology Acceptance Model (TAM). The study addresses the limited empirical evaluation of regional government websites, particularly in capturing both service-oriented and technology-based user perceptions. A quantitative approach was employed using a structured questionnaire distributed to 569 respondents who had used the Satpol PP website of Central Lombok Regency. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) combined with Importance–Performance Map Analysis (IPMA). The results indicate that assurance and reliability significantly influence perceived usefulness and perceived ease of use, which in turn affect user satisfaction and behavioral intention. The dimensions of tangibles and empathy showed weaker influence, highlighting the importance of system functionality over aesthetic or emotional design elements. The IPMA findings identify perceived ease of use and assurance as strategic priorities for improvement due to their high importance and moderate performance. This study provides contextual and methodological insights by applying an integrated SERVQUAL, TAM, IPMA framework to evaluate a local government law-enforcement website in Indonesia, a setting that has been rarely examined in prior digital service quality studies. Practically, it offers actionable recommendations for enhancing user trust, system usability, and service responsiveness in digital public platforms. These insights are expected to support the broader agenda of inclusive and sustainable digital governance at the local government level.
Development of Gamification-Based Mobile Learning to Improve Learning Outcomes for Automotive Measurement Tools
Inclusive education, a key priority of SDG 4, requires learning innovations that are accessible and engaging for all learners, including students with disabilities. In Indonesian inclusive schools, limited availability of adaptive learning technology remains a major barrier, particularly for students with mild intellectual disabilities who often struggle with practical competencies such as reading precision measuring tools. This study developed and evaluated a gamification-based mobile learning (m-learning) medium on automotive measurement tools (vernier calipers) tailored to the needs of students with mild intellectual disabilities through accessible design elements (preferred colors, readable fonts, simplified sentences, and adapted assessments). Using a Research and Development (R&D) approach with the ADDIE model, the product was validated by one media expert and one material expert, and its effectiveness was examined through a one-group pretest–posttest design with 35 Grade 10 Automotive Engineering students at SMK Palapa Semarang. Expert validation results indicated that the media was very feasible, with scores of 85% (Display Design), 83% (Technical Quality), 88% (Visual Communication), and 90% (Media Attractiveness), while content feasibility was also very feasible, with 82% (Content Completeness), 87% (Content Accuracy), 84% (Presentation Cohesion), and 86% (Language Use). Student learning outcomes improved substantially, with mean scores increasing from 68.29 (pretest) to 88.86 (posttest), yielding a gain of 20.6 points and reduced score variability (SD from 10.43 to 6.31). These findings suggest that gamification-based m-learning designed using assistive technology principles is feasible and can enhance learning outcomes in inclusive vocational education contexts.
Development of Interactive Mobile Learning Based on Problem-Based Learning for Computational Thinking Material in High School
This research was motivated by students’ low interest and motivation in learning computational thinking. Computational thinking material is still presented in the form of theory and is not based on case studies so that students find it difficult to understand its implementation in everyday life. This study aimed to develop KompuThink, a Problem-Based Learning (PBL)-based mobile learning application for computational thinking instruction. Research and Development using the ADDIE model, through the stages of analysis, design, development, implementation, and evaluation. The research subjects were 35 class X students of SMA Negeri 6 Malang who were taking informatics subjects. Data were collected using expert validation questionnaires and user testing instruments and analyzed using descriptive quantitative methods. The results showed that media expert validation yielded an average feasibility score of 88% and material expert validation yielded 86%, both categorized as very feasible. Classroom trials resulted in an average student acceptance score of 92.27%, also categorized as very feasible. The novelty of this study lies in the development and feasibility evaluation of a PBL-based mobile learning application that explicitly targets core computational thinking topics (searching, sorting, stack, and queue) within the Indonesian high school context, a combination that has been rarely addressed in previous studies. These findings indicate that KompuThink is a highly feasible and well-accepted interactive mobile learning medium for computational thinking and can serve as a reference for future research and development in informatics education technology.
Comparative Evaluation of Statistical and Deep Learning Models for Multi-Product Sales Forecasting
Sales forecasting plays a crucial role in retail decision-making, particularly in multi-product environments with heterogeneous demand characteristics. Unlike most previous studies that focus on finding a single best model globally, this study conducts a standardized comparative evaluation between statistical (TES, SARIMA) and deep learning (LSTM, CNN–LSTM) models using six years of real retail sales data covering eight products with stable seasonal patterns to volatile and nonlinear demand, thus representing the complexity of retail scenarios. To ensure the validity and reliability of the evaluation, data leakage was prevented by splitting the data seasonally (80% training, 20% testing), applying all transformations (scaling, log transform, sequence generation) estimated only on the training data, and using a consistent multi-step autoregressive scheme across all models. Model performance was evaluated using MAPE, MAE, MSE, and RMSE. The adaptive ensemble strategy was implemented by combining SARIMA and CNN–LSTM through optimization of product-specific weights (α ∈ [0,1], interval 0.01) using grid search to minimize MAPE over the testing horizon. The results show that the deep learning model outperforms products with fluctuating and nonlinear demand, while the statistical model remains competitive on products with stable seasonal patterns. Aggregated across products, the adaptive ensemble yielded the lowest average MAPE (0.2184), lower than all individual models, indicating better stability in the face of demand heterogeneity. This study confirms the product-dependent nature of forecasting performance and offers a replicable adaptive framework for multi-product forecasting. By enhancing data-driven supply chain efficiency and supporting skill development in advanced forecasting methodologies, this research aligns with Sustainable Development Goals (SDG) 8 and SDG 4.
A Modified TAM-ECT Model for Evaluating User Satisfaction and Behavioral Intention in Community-Based Internet Services
This study develops and validates a modified Technology Acceptance Model–Expectation Confirmation Theory (TAM-ECT) framework to evaluate user satisfaction and behavioral intention in the context of community-based internet services (RT/RW Net). Unlike prior TAM-ECT studies predominantly conducted in commercial ISP or e-service environments, this research explicitly focuses on decentralized, community-managed internet services characterized by informal governance structures, low switching barriers, and non-contractual user relationships. Addressing the lack of research on decentralized internet service models, this study integrates external factors service quality, cost-effectiveness, system quality, and customer support and moderating factors, namely digital literacy and switching cost. A quantitative survey approach was employed, collecting valid responses from 803 active users between January and March 2025. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The results demonstrate that perceived ease of use strongly influences perceived usefulness and behavioral intention, while perceived usefulness significantly impacts both user satisfaction and behavioral intention. Notably, and contrary to the core assumption of Expectation Confirmation Theory, user satisfaction does not significantly predict behavioral intention, indicating a context-specific deviation in community-based digital services where pragmatic usability considerations outweigh affective satisfaction. External factors such as customer support and system quality significantly affect user perceptions, highlighting the importance of technical performance and user experience in decentralized service settings. Digital literacy positively moderates the relationship between perceived ease of use and behavioral intention. The IPMA findings reveal that ease of use, service usefulness, and customer support are the most critical areas for improvement. Theoretically, this study extends TAM-ECT by demonstrating that continuance intention in community-based internet services is driven more by usability and functional value than by satisfaction-driven confirmation mechanisms commonly observed in commercial platforms. This study offers practical insights for optimizing technical quality, service functionality, and user digital competencies to foster sustainable adoption in community-managed internet infrastructures.
Energy Supply Chain Optimization: Design of a Transportation Vendor Assessment System Using the Simple Additive Weighting Method
In the energy logistics sector, which demands high speed and efficiency, fuel transportation vendor selection is a strategic decision that significantly impacts operational smoothness. To transform the cumbersome manual selection process into digital precision, a study developed a Vendor Management Information System based on the Simple Additive Weighting (SAW) method. This system is designed to provide objective decision-making support by analyzing 2024 performance data through eight key evaluation criteria, including service quality, price, and fleet availability. After going through a normalization and weighting process in the decision matrix, the system determined Vendor A1 (PT. X) as the best provider with the highest score. The data is descriptive quantitative in nature, where the data collection process involved respondents from three departments within the company who are experts in the field of procurement, with proof of ownership of procurement certification for goods and services. A total of 23 respondents served as the basis for SAW data processing, and 5 people served as references for creating criteria for weighting in the method. This automation logic was then technically mapped through Data Flow Diagrams (DFDs) and Entity-Relationship Diagrams (ERDs) to ensure an integrated workflow. The implementation of this system marks a significant shift towards digital efficiency, which not only minimizes human error and increases transparency but also lays a strong foundation for the adoption of more sophisticated decision-making technologies in the future.
The Impact of Implementing the VAK Model in Practical Learning with Case Method to Improving Students' 21st Century Skills
The advancement of the Industrial Revolution 4.0 era requires university graduates to master 21st-century skills in order to actively contribute and play a role in the workforce. This necessity has become a key factor influencing graduates’ employability. However, challenges arise during the learning process, such as low student participation, limited conceptual understanding, and difficulties in developing critical thinking and problem-solving skills, particularly in practicum activities. A solution to address these issues is to implement a combination of student-centered learning models, namely the VAK (Visualization, Auditory, Kinesthetic) model, in practical learning based on the Case Method. The purpose of this study is to analyze the effect of applying the VAK model in case method–based practical learning on the improvement of students’ 21st-century skills. This research employed a Pre-Experimental Design with a One-Shot Case Study approach. The sample consisted of 94 regular class students of the PTIK Study Program at UNNES in the even semester of 2024/2025, enrolled in the Computer Networks course. The data collection instruments were based on 21st-century skill indicators, including communication, collaboration, critical thinking and problem solving, as well as creativity and innovation. Data analysis was conducted using descriptive and inferential statistics. The findings revealed that the application of the VAK model in case method–based practical learning positively influenced the enhancement of students’ 21st-century skills with a significance value of 0.001. The most significant improvements were observed in Collaboration at 87%, Critical Thinking and Problem Solving at 86.2% with a very strong category. Further research is needed to explore the ideal combination of learning models and approaches to achieve greater improvements in 21st-century skills.
Public Sentiment Classification of Danantara in Social Media X Using Support Vector Machine and Random Forest
The increasing use of social media as a platform for public discourse provides valuable data for understanding societal responses to national strategic policies. One prominent example is the establishment of Danantara (Daya Anagata Nusantara), a sovereign wealth fund launched by the Indonesian government in February 2025. This study aims to analyze public sentiment toward Danantara using Indonesian-language posts collected from social media platform X and to comparatively evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A dataset of 1,434 public tweets was collected through web scraping and processed using text preprocessing techniques, including cleaning, tokenization, stopword removal, stemming, and TF-IDF feature extraction. Sentiment labels were generated using an Indonesian RoBERTa model and validated by a linguistic expert. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE). Model performance was evaluated using 5-fold stratified cross-validation with accuracy, precision, recall, and F1-score metrics. Experimental results show that Random Forest achieved slightly superior performance, reaching an average accuracy of 91.47%, compared to 91.06% obtained by SVM. Confusion matrix analysis indicates that RF better distinguishes neutral sentiment, while SVM performs competitively in identifying strong sentiment polarity. This study contributes by providing the first empirical comparison of classical machine learning approaches for analyzing public sentiment toward Indonesia’s sovereign wealth fund discourse, offering methodological insights and practical implications for data-driven policy evaluation using social media analytics.
Evaluating Learning Management System Adoption Effectiveness for Educators and Students in Batam Higher Education
The rapid expansion of digital technology and online platforms is reshaping higher education, driving widespread adoption of Learning Management Systems (LMS) and synchronous ICT tools for teaching and learning. As institutions increasingly rely on these technologies, it is essential to understand their effectiveness in supporting educational transformation rather than simply digitizing traditional practices. This study investigates the perceived integration and impact of LMS and ICT tools among students and educators across six universities, using the Technological Pedagogical Content Knowledge (TPACK) framework. A quantitative survey of 579 respondents assessed seven TPACK domains with reliable measurement instruments. Descriptive statistics, t-tests, correlation, and regression analyses were employed to compare perspectives and identify key predictors of successful technology integration. Results reveal that both groups express positive views of LMS and ICT, with all TPACK constructs rated above 3.8. Educators report higher self-assessed competencies, particularly in technological and pedagogical domains, although the differences are statistically significant but practically minor. Strong correlations among TPACK factors and regression modeling highlight Technological Pedagogical Knowledge and Technological Content Knowledge as the most important predictors of effective integration, underscoring the importance of system supported pedagogical design and content delivery rather than simple content digitization. Despite robust infrastructure and readiness, the findings suggest that LMS and ICT are primarily used to support rather than truly transform learning experiences. The study contributes to educational information systems literature by providing empirical evidence on user perceptions and the need for a system design strategies to achieve more innovative, student centric, technology‑enhanced education in Batam.
Weakly Supervised Sentiment Analysis of Indonesian Rural Tourism Reviews: A TF-IDF Baseline for Melung Tourism Village
This study investigates sentiment classification of Indonesian-language tourist reviews from the rural destination of Melung Tourism Village. A total of 724 user-generated reviews from 546 unique users are preprocessed using Indonesian-specific text cleaning, stopword filtering, and stemming, then weakly labeled through a stemmed positive–negative lexicon. TF-IDF unigram–bigram features are extracted from the preprocessed texts and used to train three classical classifiers: Naive Bayes, linear Support Vector Machine (SVM), and Logistic Regression. To address class imbalance, RandomOverSampler is applied only to the training data, and model evaluation combines stratified 5-fold cross-validation with a held-out test set, using weighted F1-score as the primary metric. Logistic Regression achieves the best performance on the test set (weighted F1 = 0.8799, accuracy = 0.8828), closely followed by SVM, while Naive Bayes lags behind. The results show that, even with a modest, weakly supervised dataset, a carefully designed classical pipeline can yield reliable sentiment indicators to support data-driven management of rural tourism destinations.
Interactive Multimedia as a More Effective and Engaging Solution for Visual Programming Learning
This study developed and evaluated an interactive multimedia learning product for the Visual Programming course at Sekolah Tinggi Teknologi STIKMA Internasional. Using the ADDIE development model Analysis, Design, Development, Implementation, and Evaluation the research aimed to improve learning effectiveness and student engagement through multimedia-based instruction. The product, designed as an executable (EXE) application, includes six chapters integrating text, animation, video, quizzes, and interactive navigation. Validation involved four experts in content, media, language, and user interface, as well as student trials in both small-group and field settings. Expert validation results averaged 89.26%, categorized as highly valid, while student trials showed an average of 84.8%. Field testing indicated that all students achieved the course passing standard, with scores ranging from 60 to 100. These findings demonstrate that the interactive multimedia effectively enhances students’ comprehension, motivation, and learning outcomes. The results are consistent with the Cognitive Theory of Multimedia Learning, which emphasizes the integration of visual and verbal channels to optimize cognitive processing. Practically, the product benefits students, lecturers, and institutions by promoting independent learning and supporting digital transformation in education. Overall, the developed multimedia is considered valid, effective, and feasible as an innovative instructional medium for Visual Programming.
Usability Scale to Measure the Acceptability of Interactive Learning Media for Ratio Material in Grade VI
Mathematics learning, particularly for abstract topics such as ratios, requires a transition from additive to multiplicative thinking, which is often challenging to visualize. To support teachers, an Interactive Learning Media (ILM-Ratio) was developed using Canva to ensure accessibility and ease of content modification. Before measuring student effectiveness, it is crucial to assess the media's technical acceptability from the teachers' perspective, as they are the primary gatekeepers of classroom technology. This study aims to evaluate the usability of ILM-Ratio using the System Usability Scale (SUS). A total of 47 sixth-grade teachers participated by simulating a teaching session using the media. The SUS calculation yielded an average score of 90 (Grade A), categorized as "Excellent." This score indicates that ILM-Ratio possesses high usability and imposes minimal technical burden on teachers. These findings suggest that the media is technically robust and ready for classroom implementation to support the visualization of ratio concepts.
Ensemble RNN–Random Forest Model for Earthquake Prediction Based onSpatio-Temporal Seismic Data
This study proposes a hybrid Ensemble RNN–Random Forest (RNN–RF) model for short-term earthquake prediction based on spatio-temporal seismic data from the Sulawesi–Maluku region. The purpose of this research is to develop a lightweight and interpretable machine learning framework that integrates temporal and spatial features using local datasets provided by the Manado Geophysical Station of BMKG. The methodology includes six stages: data acquisition, preprocessing, feature engineering, model development, ensemble integration, and evaluation. The RNN captures sequential dependencies in seismic activity, while the Random Forest learns spatial and contextual relationships such as fault proximity and event clustering. The ensemble fuses probabilistic outputs (0.75 RNN and 0.25 RF) followed by domain-based calibration using mean magnitude, event frequency, and fault distance. Experimental results show that the proposed ensemble achieved F1 = 0.89 and AUC = 0.975, outperforming individual RNN and RF models in predictive stability and accuracy. The model demonstrates that integrating domain-specific adjustments enhances both recall and precision, while maintaining interpretability for operational deployment. This study contributes to explainable AI in seismology by bridging deep temporal modeling with geophysical reasoning, offering a scalable approach for early-warning applications in Indonesia.
Assessing Generative AI with Context-Augmented Zero-Shot Prompting for HOTS Question Generation Aligned with Bloom’s Taxonomy
This study investigates the use of HOTS-GenAI, a generative AI system employing context-augmented zero-shot prompting, to automatically generate multiple-choice questions aligned with higher-order thinking skills (HOTS) in Bloom’s taxonomy. A dataset of 200 items for vocational high schools was validated by three experts. The ground truth data demonstrated good quality with an inter-rater reliability of 0.75 (Gregory’s Index). System performance across analysis (C4), evaluation (C5), and creation (C6) levels was evaluated using Content Validity Index (CVI), gap analysis, and confusion-matrix-based metrics. The findings revealed that HOTS-GenAI performed relatively well at the analytical level, where 70% of items met the HOTS threshold, supported by higher expert consensus. However, only 10% of items achieved the threshold for evaluation, and none for creation. CVI results indicated moderate validity overall, with stronger agreement for C4 than for C5 or C6. Confusion matrix analysis further confirmed this imbalance: accuracy and F1-scores were highest for analysis items but dropped sharply for evaluation and creation, where recall and precision were near zero. These results suggest that while HOTS-GenAI has potential in generating analytical questions, its capacity to model evaluative and creative tasks remains underdeveloped. Future research should involve larger datasets, refined prompt design, and more operational rubrics to enhance both validity and reliability in AI-generated HOTS assessments.
Generative AI Higher Order Thinking Skills (HOTS)
Comparison of Machine Learning and Deep Learning Algorithms for Daily Retail Sales Forecasting
This study presents a comparative analysis of four machine learning (ML) and deep learning (DL) algorithms: Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for predicting daily retail sales time series. The models were evaluated using key metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). Results show that RF and SVM outperformed both CNN and LSTM in terms of MAE (3500.28 and 3325.11, respectively) and RMSE (4660.60 and 4293.42, respectively). However, all models had negative R² values, indicating none could explain the variation in the data. LSTM, in particular, was the least efficient model, with an MAE of 54087.25, RMSE of 54257.51, and R² of -158.59. The poor performance of LSTM can be attributed to overfitting, improper model configuration, and misalignment with the nature of the data. The dataset used includes over 1,000 daily retail sales transaction records collected over one year, with key attributes like CustomerID, ProductID, Quantity, Price, TransactionDate, PaymentMethod, StoreLocation, ProductCategory, DiscountApplied, and TotalAmount. While the dataset is representative, its size and complexity may not have been sufficient for deep learning models like LSTM and CNN, which generally require larger datasets for optimal performance. This study highlights the challenges of using deep learning for retail forecasting and suggests future research should focus on refining models and incorporating external datasets to improve prediction accuracy.
A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island
Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.
Enhancing Waste Classification with MobileNetV2: Adding a Plastic Sachets Class for Sustainable Management
The issue of waste management remains a critical concern due to its adverse impact on the environment. This research enhances a deep learning-based waste classification model by introducing a new class, namely plastic sachets, to broaden the classification scope and increase the model's relevance to waste types commonly found in the community. The dataset used is an extended version of a previous open-source dataset, comprising 2,968 images divided into seven classes. Data preprocessing steps include stratified data splitting, data augmentation to increase image diversity, and pixel normalization. The model adopts the MobileNetV2 architecture through a transfer learning approach, utilizing 2D Global Average Pooling and Dense layers with softmax activation for multi-class classification. Evaluation using precision, recall, and F1-score demonstrated strong performance, with an overall accuracy of 97%. While the model performs well across most classes, further improvement is needed for minority classes such as plastic sachets. This study highlights the promising potential of deep learning in supporting automated waste sorting to promote sustainable waste management practices in Indonesia.
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection
The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
Ontology Engineering for Modeling National Student Achievements in Higher Education
The need for structured and semantically rich data in higher education underscores the role of ontology-based knowledge modeling. This study develops an ontology to represent national-level student achievements, covering key aspects such as institution, achievement field, category, year, level, and student status. Using a formal ontology engineering approach, the ontology was developed in Protégé and encoded in OWL. Evaluation involved technical validation and reasoning tests including class subsumption, consistency checking, instance classification, and rule-based inference to assess logical soundness and semantic correctness. Description Logic (DL) queries were also executed based on competency questions to evaluate the ontology’s ability to support semantic querying. The results demonstrate that the ontology effectively supports knowledge inference and structured data retrieval, offering strong potential for integration within semantic web environments. This provides a foundation for data interoperability and knowledge sharing across educational systems at the national level. Future work includes expanding the ontology to incorporate dynamic achievement updates and linking with external educational data sources.
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Benchmarking YOLOv3 and SSD: A Performance Comparison for Multi-Object Detection
Multiple object detection remains a significant challenge in the field of computer vision. One of the key factors affecting detection performance is the feature extraction process, especially when objects are relatively small or positioned closely together. This study aims to compare the effectiveness of two popular object detection models, YOLO (You Only Look Once) and Single Shot MultiBox Detector (SSD), in detecting multiple objects within images. These models were selected due to their reported high accuracy and real-time processing capabilities, outperforming traditional methods such as the Hough Transform, Deformable Part-based Models (DPM), and conventional CNN architectures. The models were evaluated using a subset of the PASCAL VOC dataset, which includes object categories such as aircraft, faces, cars, and others, with a total of 1,447 annotated images used in training and testing. The evaluation metric used was mean Average Precision (mAP) to assess detection accuracy. Experimental results indicate that YOLO achieves a mAP of 82.01%, while SSD achieves 70.47%. These findings demonstrate that YOLO provides better performance in detecting multiple objects under the same conditions. Overall, this study confirms the advantages of YOLO in scenarios requiring fast and accurate multi-object detection, highlighting its potential for deployment in real-time applications such as autonomous vehicles, surveillance systems, and robotics. The main contribution of this study lies in providing a comparative performance benchmark between YOLO and SSD on a standard multi-object dataset to guide practical model selection in real-time computer vision tasks.
Multisensor Integration in Early Fire and Gas Detection Based on Artificial Neural Network
Fire detection systems commonly rely on a single variable, such as smoke or flame, to detect fires. However, using a single sensor or threshold-based method for early detection is prone to false alarms. To address this issue, this study proposes an early fire detection system using an artificial neural network based on the Radial Basis Function Network (RBFN) architecture. The aim of this research is to minimize false alarms by implementing an early fire detection system that not only detects flames but also hazardous gases, temperature, and humidity as potential sources of fire. A multisensor system comprising an IR flame sensor, gas sensors MQ-9, MQ-2, and MQ-4, as well as a DHT-11 temperature and humidity sensor, was integrated and processed using an RBFN-based ANN on a Raspberry Pi 3. The ANN processes a series of datasets trained to generate a model that determines fire conditions. Testing results showed that the proposed method did not produce any false alarms, with a response time of 2.1 seconds from the ignition of a fire source to the issuance of a warning.
Implementation of Equivalence Partitioning Techniques and ISO/IEC 25010 Functional Suitability Standards for Testing SCM Applications in Small-Scale Automotive Industries
A Supply Chain Management (SCM) application prototype for PT XYZ has been developed. Before its release, the testing phase is crucial to minimize functional failures. This study aims to conduct comprehensive testing to verify that all functions operate as intended, deliver the expected results, and identify potential errors in the application. Unlike previous research, this study not only uses Equivalence Partitioning as a functional testing technique but also combines it with the ISO/IEC 25010 standard, which guides Functional Suitability and software quality more comprehensively. This approach addresses the limitations of Equivalence Partitioning, which typically focuses only on input class division without ensuring coverage of specific quality standards. Furthermore, integrating testing techniques with the internationally recognized ISO/IEC 25010 standard enhances the relevance and applicability of this research in software development for industries, particularly in supply chain information processing automation. By combining both methods, the testing process becomes more thorough and structured, as evidenced by the 254 test cases generated. Additionally, this study provides quantitative data in the form of valid (83%) and invalid (17%) test case percentages, which can serve as indicators of testing effectiveness and the prototype's quality.
Development of Service Satisfaction Instrument Application Based on 5 Stars Rating Using PHP, SQL, and Javascript on FT UNNES Website
Service satisfaction instruments are tools used to measure the level of user satisfaction with services provided by an institution, company, or organization. Service satisfaction instruments can be in the form of forms, applications, interviews, and many more. Service satisfaction instruments in the form of website applications have more advantages than others. Applications can be designed to suit various user needs. The service satisfaction instrument application at the Faculty of Engineering, UNNES, was designed as a form of renewal of the instrument that previously used a form. The instrument application has many advantages over the previously used form. The service satisfaction instrument application can present data in real-time after the user inputs an assessment. The application design is designed to be as attractive as possible so that users can use and receive information from the application easily. The application was developed using the agile software development method. This method is used because it has many advantages such as good flexibility and adaptability.In addition, the program is designed with a good structure to facilitate the development and maintenance process. With an organized structure, the code becomes more modular, clearly separated between components, and follows the principles of clean code.
Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports
Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.
Decision Support System for Service Development Priority Selection at Pitcar Service Using the ELECTRE Method
The increasing competition in the automotive industry is pushing companies like Pitcar Service, a car workshop in South Purwokerto, to develop efficient and quality services. However, this workshop faces limitations in assets, materials, and capabilities. Therefore, Pitcar Service requires a Decision Support System (DSS) to assist in determining the priority for service development. The DSS helps identify and prioritize the most important services or those that have the greatest impact on business growth. By focusing on strategic development that provides significant added value, the company can achieve its strategic goals in line with the company's vision. The method used in this study is ELECTRE (Elimination and Choice Translation Reality), a suitable algorithm for designing a decision support system. This method allows pairwise comparisons between alternatives based on relevant criteria. The assessment is done by comparing each criterion individually to determine whether an alternative dominates other alternatives. With this method, the assessment of workshop services can be done objectively. The service alternatives considered include Workshop Service, Pick Up Service, Emergency Service, Home Service, Variations, and B2B Services. Meanwhile, the criteria used for the assessment include the number of requests, average income per job, tool usage, the number of mechanics, and processing time. The calculation results show that B2B Service is the main priority for service development with a dominant aggregation matrix result is 2.
Ecofy App UX Design using User-Centered Design Approach for Waste Management
Indonesia faces a significant waste management challenge, particularly with plastic waste, which poses severe environmental risks and has low recycling rates. This study aims to address these challenges by designing and developing the Ecofy application using the User-Centered Design (UCD) approach. The focus is on creating a solution that not only meets user needs but also effectively facilitates the waste management process, making it more accessible and efficient for the general population. The UCD methodology was employed to ensure that the application’s features are intuitive, user-friendly, and tailored to the local waste bank model, which encourages recycling at the community level. The research involved multiple stages, including user needs assessment, iterative design, and usability testing, to refine the application. The findings demonstrate that the Ecofy application significantly enhances the effectiveness of waste management by offering practical tools for users to sort, manage, and recycle waste. Furthermore, the implementation of this application is projected to reduce the burden on Final Processing Sites (TPA), lower environmental pollution, and increase recycling rates, especially for non-biodegradable materials like plastic. The success of Ecofy could serve as a model for other regions facing similar waste management issues, contributing to broader sustainable development goals in Indonesia.