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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 Documents
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Search results for , issue "Vol. 12 No. 2: May 2025" : 16 Documents clear
Safety Stock and Reorder Point System for RF Media Stock Optimization Afrizal, Nova; Minardi, Joko; Mahendra, Danang
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.22232

Abstract

Purpose: This study develops a web-based inventory management system by applying the Safety Stock and Reorder Point (ROP) methods to address inventory issues at RF Media small and medium enterprises (SMEs) in the printing sector. The system aims to improve operational efficiency and reduce the risk of stockouts, which frequently occur in SMEs due to their reliance on manual inventory processes. Methods: This study develops a web-based inventory management system by applying the Safety Stock and Reorder Point (ROP) methods to address inventory issues at RF Media small and medium enterprises (SMEs) in the printing sector. The system aims to improve operational efficiency and reduce the risk of stockouts, which frequently occur in SMEs due to their reliance on manual inventory processes. Result: The simulation showed a 21.38% increase in operational efficiency and a 16.10% reduction in the risk of stockouts. The system ensures complete inventory visibility, facilitates faster decision-making, and minimizes manual errors. Usability testing revealed high user satisfaction regarding interface clarity, ease of use, and quick access to inventory information. Novelty: This study introduces an innovative integration of the Safety Stock and ROP methods into a lightweight, cost-effective web-based system specifically designed for SMEs. Inventory digitization plays a critical role in enhancing competitiveness. This system offers a practical and scalable solution for efficient inventory management in SMEs environments with limited resources.
Integrating UX Five Elements and Design Thinking to Design a Learning Management System Sari, Rika Perdana; Henim, Silvana Rasio
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24272

Abstract

Purpose: This study aims to enhance the user experience of Learning Management Systems (LMS) by integrating two established design frameworks: the UX Five Elements and Design Thinking. The research addresses the need for a more structured yet human-centered design process to improve the usability and engagement of LMS platforms in higher education. Methods: The research adopts a design and development approach by combining the UX Five Elements, which offer a systematic structure across five user experience layers, with Design Thinking, which emphasizes empathy and iterative user involvement. This integration forms an Extended Model Design (EMD) used to guide the development of a new LMS interface. The final system was evaluated using usability testing involving students as target users. Result: Evaluation of the LMS prototype using the User Experience Questionnaire (UEQ) showed positive perceptions on all six dimensions, with the highest scores on the Efficiency (1.644) and Attractiveness (1.634) aspects, reflecting a practical and attractive system design. Although the Novelty (1.203) aspect had the lowest score, its value was still above the positive threshold, indicating that the system was functionally good but could still be improved in terms of innovation to strengthen user engagement. Novelty: This study introduces a novel design framework by integrating UX Five Elements with Design Thinking in the context of LMS development. Extended Model Design (EMD) offers a replicable model that balances structure and user empathy, contributing to user-centered e-learning system design.
From Interaction to Intention: Investigating Key Drivers of User Retention in Digital Marketplaces Nugraheni, Dinar Mutiara; Noranita, Beta; Isy, Auliya Daffa
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24463

Abstract

Purpose: The rapid evolution of digital marketplaces has heightened the importance of understanding the drivers of user engagement. While prior research has focused on perceived ease of use and perceived usefulness, limited attention has been given to the impact of emerging interactive features such as affordances (metavoicing, guidance shopping, visibility, and trading). This study aims to bridge that gap by examining the combined effects of metavoicing, guidance shopping, visibility, trading, perceived ease of use, and perceived usefulness on users' perceptions and their continued intention to use a digital marketplace platform. These factors interact synergistically to create a cohesive and engaging user experience. Methods: A quantitative research design was adopted, utilizing structured questionnaires distributed to 200 active users of a leading Indonesian marketplace platform. Structural Equation Modeling (SEM) was employed to test the hypothesized relationships among the study variables. Result: The results reveal that affordances (metavoicing, guidance shopping, visibility, and trading) significantly influence user behavioral intention through their impact on perceived usefulness and ease of use, with trading affordance showing the strongest effect (β = 0.469) and perceived usefulness being the primary driver of behavioral intention (β = 0.753). Novelty: This study provides valuable insights for Digital marketplace developers to enhance user engagement by a comprehensive Digital Marketplace Requirements Framework that structures implementation around four core affordance categories: Trading Requirements (payment systems, checkout processes), Visibility Requirements (product display, visual search), Metavoicing Requirements (communication systems, review mechanisms), and Guidance Shopping Requirements (personalization, search functionality).
Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Abstract

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions Hafsah, Siti; Indahwati, Indahwati; Wijayanto, Hari
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.26413

Abstract

Purpose: This study aims to address methodological challenges in evaluating biclustering algorithms under simultaneous collinearity and overlap, which often co-occur in real world multivariate data but are rarely analyzed simultaneously. This research highlights the importance of understanding how these structural challenges affect local pattern detection in data mining applications. Methods: A simulation study was conducted using synthetic matrices embedded with two constant biclusters under 15 combinations of collinearity levels (ρ = 0.3,0.6,0.9) and overlap degrees (none, small, large). Each scenario was replicated 100 times. Performance was assessed using the Liu and Wang Index (ILW), while a three-way ANOVA tested the effects of algorithm type, collinearity, and overlap. Result: Spectral Biclustering maintained stable ILW scores despite increasing collinearity, while CC performed better in low-overlap scenarios but was more sensitive to collinearity. Under high collinearity and large overlap, both algorithms experienced notable degradation. The ANOVA confirmed all main effects and interactions were significant (p < 0.001). Novelty: This study contributes empirical evidence regarding the influence of interacting structural characteristics on biclustering performance. The results deliver practical insights for selecting suitable algorithms and emphasize the potential advantages of hybrid approaches that integrate the stability of spectral methods with the adaptability of residual-based techniques.
A Hybrid Sampling Approach for Handling Data Imbalance in Ensemble Learning Algorithms Astari, Reka Agustia; Sumertajaya, I Made; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.19163

Abstract

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection. Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP). Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean. Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.
In Vivo Diagnostic Automation: Identification of Malaria Parasites from Red Blood Cells Using Image Segmentation and Convolutional Neural Network Methods Huda, Nurul; Prihandoko, Prihandoko; Dewi, Alfa Yuliana
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.23502

Abstract

Purpose: This study aims to address the limitations of conventional malaria diagnosis—namely, its reliance on manual microscopy, which is time-consuming, labor-intensive, and prone to human error—by developing an automated diagnostic system using the Inception V3 convolutional neural network. The focus is on accurately identifying the four main Plasmodium species responsible for malaria (P. falciparum, P. malariae, P. ovale, and P. vivax) through image-based analysis of red blood cells. The study’s significance lies in its contribution to scalable, AI-assisted diagnostic solutions that support national and global malaria elimination goals, particularly in high-burden countries such as Indonesia. Methods: This study utilized an experimental approach based on a dataset of 194 microscopic images of red blood cells, each labeled according to one of four Plasmodium species. The process involved image enhancement through pre-processing techniques—illumination correction, contrast adjustment, and noise filtering—followed by segmentation using the Otsu thresholding method to isolate parasite-infected cells. Two classification models were applied: Inception V3, a deep learning convolutional neural network, and a traditional Support Vector Machine (SVM), with both evaluated for their accuracy in species identification. Result: The findings revealed that the Inception V3 model significantly outperformed the Support Vector Machine (SVM), achieving highest accuracy of 100%, at select epochs and an average accuracy of 97.93%, with 98.32% validation accuracy compared to 82% for SVM. The high performance of Inception V3 is attributed to its deep architecture, consisting of over 23 million parameters, which enables superior feature extraction and classification of Plasmodium species. These results confirm that CNN-based models, particularly Inception V3, are more effective than traditional machine learning approaches for automated malaria diagnosis. Novelty: In identifying four species of Plasmodium, this study presents a very simple yet highly accurate technique using an Inception V3 model. The method represents 100% accuracy in its multi-class detection as opposed to earlier works concentrating on binary classifications. It therefore adds real usefulness in high-burden, low-resource settings such as Indonesia through working on the improvement of diagnosis and on speedier detection of malaria.
Information System Evaluation Framework to Improve Teacher and Education Personnel Competency (GTK Room): Extended Hot-Fit Framework Approach Waluyo, Retno; Hariguna, Taqwa; Setiawan, Ito
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.23841

Abstract

Purpose: This study aims to identify the factors that influence users in the implementation of the GTK (Teachers and Educational Staff) Room System among elementary school teachers in Banyumas Regency, Central Java, Indonesia. Methods: This study employed the HOT-Fit (Human, Organization, and Technology Fit) Framework approach, with the addition of the 'Behavioral Intention to Use' variable on the Human dimension and the 'Organizational Culture' variable on the Organizational dimension. The sample consisted of 147 elementary school teachers from Banyumas Regency, Central Java, Indonesia. Data were analyzed using SmartPLS to identify the variables that influence user behavior. Result: The results of this study indicate that certain relationships between variables do not have a significant influence on others. Specifically, User Satisfaction and Behavioral Intention to Use do not significantly affect Net Benefit. Additionally, Information Quality does not have a significant effect on System Use. Furthermore, System Quality does not significantly influence User Satisfaction or Behavioral Intention to Use. Meanwhile, other variable relationships were found to significantly impact the successful implementation of the GTK (Teachers and Educational Staff) Room system. The model’s goodness-of-fit shows an NFI (Normed Fit Index) value of 0.632, indicating that the proposed model explains 63.2% of the variance in the data. Novelty: This research presents several significant novelties that contribute to the evaluation of the implementation of the GTK (Teachers and Education Personnel) Room System in primary education. The traditional HOT-Fit (Human, Organization, Technology-Fit) model was enhanced by adding two new variables, Behavioral Intention to Use and Organizational Culture, resulting in a more comprehensive and contextually relevant evaluation framework. The study was conducted within a specific local context, focusing on primary school teachers in Banyumas Regency, Central Java, Indonesia, thereby providing empirical insights into the implementation dynamics at the local level, which have been rarely explored in previous research. The findings reveal that system success is influenced not only by technical factors but also by behavioral dynamics and social contexts, such as organizational culture.
User Experience Improvement (MSMEs and Buyers) Mobile AR Using Design Thinking Methods Dwiyanasari, Desty; Nurhayati, Oky Dwi; Surarso, Bayu; Nugraheni, Dinar
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24088

Abstract

Purpose: This research aims to improve the User Experience (UX) of Augmented Reality (AR) mobile applications for MSMEs and buyers through the Design Thinking method. This research solves the problem of suboptimal UX in AR-based mobile applications. This study hypothesizes that the application of Design Thinking can result in significant improvements in the UX of AR mobile applications, which is evidenced by an increase in heuristic evaluation scores. Methods: The Design Thinking approach (Empathize, Define, Ideate, Prototype, Test) is implemented. Data were collected through interviews, observations, and heuristic evaluation questionnaires. Result: Initial heuristic testing showed several usability problems in the developed AR mobile applications, such as Help and Documentation (H10), Recognition Rather than Recall (H6), and Error Prevention (H5). After the application of the Design Thinking method and design iteration, the heuristic testing showed that the results of the evaluation comparison before and after the improvement showed a high effectiveness of the corrective actions taken, with an average decrease in severity score of 37% based on the Nielsen scale (0–4), indicating that the most critical and major issues were successfully reduced to cosmetic or minor levels. Novelty: This research contributes in the form of a practical framework to improve the UX of AR mobile applications for MSMEs and buyers by utilizing the Design Thinking method. The results of this research can be a reference for developers in designing user-friendly AR mobile applications.
Comparison of Ensemble Forest-Based Methods Performance for Imbalanced Data Classification Hasnataeni, Yunia; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24269

Abstract

Purpose: Classification of imbalanced data presents a major challenge in meteorological studies, particularly in rainfall classification where extreme events occur infrequently. This research addresses the issue by evaluating ensemble learning models in handling imbalanced rainfall data in Bogor Regency, aiming to improve classification performance and model reliability for hydrometeorological risk mitigation. Methods: Four ensemble methods: RF, RoF, DRF, and RoDRF were applied to rainfall classification using three resampling techniques: SMOTE, RUS, and SMOTE-RUS-NC. The data underwent preprocessing, stratified splitting, resampling, and 5-fold cross-validation. Performance was evaluated over 100 iterations using accuracy, precision, recall, and F1-score. Result: The combination of DRF with SMOTE-RUS-NC yielded the most balanced results between accuracy (0.989) and computation time (107.28 seconds), while RoDRF with SMOTE achieved the highest overall performance with an accuracy of 0.991 but required a longer computation time (149.30 seconds). Feature importance analysis identified average humidity, maximum temperature, and minimum temperature as the most influential predictors of extreme rainfall. Novelty: This research contributes a comprehensive comparison of ensemble forest-based methods for imbalanced rainfall data, revealing DRF-SMOTE as an optimal trade-off between performance and efficiency. The findings contribute to improved rainfall classification models and offer practical insight for disaster mitigation planning and resource management in tropical regions.

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