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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 131 Documents
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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.
Optimization of Random Forest Algorithm with SMOTE Method to Improve the Accuracy of Early Diabetes Prediction Nisa, Siti Khoirun; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.22986

Abstract

Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.
ATM Network Infrastructure Migration Strategy from Bank to PT XYZ Dharmawan, Arief; Hilman, Muhammad Hafizhuddin Hilman
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.24713

Abstract

Purpose: This study addresses PT XYZ’s technical challenges in optimizing the Automatic Teller Machine (ATM) network infrastructure, which caused service disruptions and customer complaints during the initial pilot migration. The research aims to solve the network availability problem by designing a reliable migration strategy, thereby improving service quality and strengthening partnerships with banks. Methods: A qualitative case study analysis was conducted, combining stakeholder interviews with technical evaluations. The Data Center WAN zone deployment followed the waterfall methodology for structured network and security appliance implementation, while remote ATM migrations used an incremental approach. Service Level Agreement (SLA) metrics were monitored to measure performance. Result: The solution improved the ATM network average SLA from 98.31% to 99.53%. Stakeholder feedback confirmed the effectiveness of the phased migration and enhanced operational reliability. This demonstrates the benefit of direct ATM WAN network re-engineering in eliminating legacy infrastructure complexity. Novelty: The study provides actionable benchmarks for ATM network implementation, including phased migration steps combined with high availability WAN topology. The implementation and migration method presents a replicable model for similar financial institutions in emerging markets.
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates Setiawan, Very Dwi; Iswavigra, Dwi Utari; Anggiratih, Endang
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26320

Abstract

Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
Development of Employee Management Information System UI/UX Using a User Centered Design Approach Saputra, Deva Dimastawan; Sukadarmika, Gede; Purnama, Fajar
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.27979

Abstract

Purpose: This study aims to develop a user interface and user experience (UI/UX) prototype for an Employee Management Information System (EMIS) at PT. Galsoft. The research focuses on addressing the manual administrative processes that are still widely used in the company's daily operations. The primary objective is to produce an interface design that aligns with user needs and improves the efficiency of work processes. Methods: This research adopts a User Centered Design (UCD) approach, consisting of four main stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the design. The prototype developed focuses solely on the UI/UX aspects without involving full system implementation. Usability evaluation was conducted using the System Usability Scale (SUS), involving 38 respondents from various divisions within the company. Result: The evaluation results show that the developed UI/UX prototype achieved an average SUS score of 89.4. This score indicates that the design has a high level of usability, is easy for users to operate, and supports the administrative workflows required by the organization. These findings demonstrate that the UCD approach effectively contributed to creating a design that is both functional and responsive to user needs. Novelty: This study contributes to the field of administrative information system prototyping in workplace environments that have yet to adopt digital solutions. The novelty of this research lies in the comprehensive application of the UCD approach, combined with SUS based usability evaluation, to produce a relevant and functional design that is ready to be further developed into a fully implemented system.
Biclustering-Based Analysis to Identify Fruit Production Potential in Indonesia Using Plaid Model Algorithm Alwani, Nadira Nisa; Sumertajaya, I Made; Wigena, Aji Hamim
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.25054

Abstract

Purpose: The application of biclustering using the plaid model aims to simultaneously identify mapping or grouping patterns of provinces and fruit type in Indonesia. The performance evaluation of the plaid model algorithm is used to assess its capability to discover and generate optimal biclusters, thereby representing the relationship between regions and fruit types with similar production characteristics. Methods: The plaid model algorithm produces optimal biclusters by configuring parameter scenarios such as model selection, managing the number of layers, and determining threshold values for rows and columns. The Average Mean Square Residue (MSR) value and the number of biclusters that can provide the most relevant data are used to determine the optimal parameter selection. Result: The plaid model algorithm effectively grouped provinces and fruit varieties into multiple biclusters. The row-constant model was choosen based on the average MSR value of 2.0537, which formed five overlapping biclusters across provinces and fruit types. Several provinces, such as Central Java and West Java, demonstrated a high potential for rose apples, breadfruit, and salak. Other provinces showed comparatively moderate levels of production. Novelty: This study presents a novel way to apply the plaid model biclustering algorithm to data on fruit varieties in various Indonesian provinces. Rarely used in horticulture, this method offers an alternative perspective on structured commodity mapping, especially when identifying specific patterns between fruit varieties and geographic distribution.
Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models Paramita, Cinantya; Rakasiwi, Sindhu; Andono, Pulung Nurtantio; Shidik, Guruh Fajar; Shier Nee Saw; Rafsanjani, Muhammad Ivan
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26257

Abstract

Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.

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