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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 161 Documents
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Development of VR-Based Virtual Tours for South Sorong Regency Using the Analysis, Design, Development, Implementation, and Evaluation Model Miswar, Nur; Fitriyani Tella; Adrians Marthinus Demi Isir
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38496

Abstract

Purpose: The limited availability of digital documentation and interactive promotional media has reduced public awareness of tourism destinations in South Sorong Regency. This study aims to develop and evaluate a Virtual Reality (VR)-based virtual tour as a solution to enhance user understanding, engagement, and visitation intention. Methods: This study employed a Research and Development approach using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. Three tourism destinations were captured using 360° panoramic video and integrated into an interactive VR-based virtual tour. The virtual tour was evaluated with 97 respondents. Respondents completed a pre-test before using the virtual tour and a post-test after the experience to measure changes in understanding and interest, analyzed using the N-Gain method. Usability and acceptance were assessed through Likert-scale user questionnaires and percentage-based feasibility analysis. Media and material experts evaluated technical quality and content accuracy; their feedback was used for virtual tour media revision before final validation. Results: The results show improved user understanding and engagement, indicated by an N-Gain score of 0.64, categorized as moderate to high effectiveness. User evaluation yielded a feasibility score of 93%. Expert validation confirmed readiness, with feasibility scores of 89% from media experts and 94% from material experts. Novelty: This study introduces a systematic ADDIE-based engineering framework for multi-destination VR virtual tour development, integrating effectiveness analysis, usability evaluation, and expert validation as a scalable informatics solution for digital tourism promotion in underdeveloped regions.
Combination Of VADER Sentiment Analysis and SEQ Scale For Evaluating the Usability of The Gojek Application Zakiyah Apriliya Budiarti; Wahyuningrum, Tenia; Adnan Purwanto; Muhammad Akbar Setiawan; Singgih Setia Andiko; Abdul Karim
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.35956

Abstract

Purpose: This study aims to analyze user sentiment toward the Gojek mobile application using the Valence Aware Dictionary for Sentiment Reasoning (VADER) method and to evaluate the perceived ease of use of the application using the Single Ease Question (SEQ) instrument. Methods: The research data were obtained by scraping 30,000 user reviews of the Gojek application from the Google Play Store. The reviews were processed through text preprocessing, sentiment classification using the VADER method, and subsequent mapping of sentiment polarity scores to a 1–7 SEQ usability scale. A Spearman rank correlation analysis was conducted to examine the relationship between sentiment scores and derived SEQ values. Result: The results indicate that user sentiment toward the Gojek application is predominantly positive, followed by neutral and negative sentiments. The overall average SEQ score is 4.11, suggesting that the application is generally perceived as fairly easy to use. Furthermore, a strong and statistically significant positive association was found between VADER sentiment scores and SEQ usability scores, indicating that more positive sentiment tends to be associated with higher perceived ease of use. Novelty: This study contributes to the literature by empirically integrating sentiment analysis and usability evaluation using VADER and SEQ within the context of an Indonesian super-app. The findings provide practical insights for digital application developers to identify usability strengths and areas for improvement based on large-scale user feedback.
IoT-Integrated Computerized Maintenance Management System (CMMS) for Optimizing Maintenance Efficiency in Smart Manufacturing Robby Maududy; Alam, Alam; Wuslah Raia Maulidiyah; R Reza El Akbar
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.36306

Abstract

Purpose: The manufacturing industry continues to face persistent challenges in maintaining equipment reliability and maintenance efficiency, particularly in scheduling, spare parts control, and real-time monitoring of machine conditions. This study aims to develop an Internet of Things (IoT)-based Computerized Maintenance Management System (CMMS) to improve maintenance effectiveness, minimize equipment downtime, and support the realization of smart manufacturing in the automotive sector. Methods: The system was developed using the ADDIE model, consisting of analysis, design, development, Implementation, and Evaluation. and was integrated with IoT sensors to acquire real-time machine temperature and operational status data. and integrated with IoT sensors to collect real-time data on machine temperature and operational status. The collected data were processed in a centralized database and presented through a web-based CMMS application comprising work order management, preventive and corrective maintenance, inventory control, and analytical reporting modules. System functionality was validated using black-box testing, while performance evaluation was conducted by comparing Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and maintenance efficiency before and after system implementation. Result: The results indicate that all evaluated equipment experienced performance improvements following CMMS implementation, characterized by increased MTBF and reduced MTTR. On average, overall maintenance efficiency increased by approximately 368%, demonstrating significant reductions in downtime and improvements in maintenance responsiveness supported by real-time condition data. Novelty: The novelty of this study lies in the integration of IoT technology into CMMS that emphasizes the utilization of real-time machine condition data not only for monitoring purposes but also to support maintenance planning, work order management, and data-driven decision-making within a single application. The findings provide empirical evidence that effective data utilization strategies within CMMS implementations can significantly enhance maintenance efficiency and support smart maintenance practices aligned with Industry 4.0 principles.
Improving the Custom Color Ordering Process with Artificial Intelligence for a Manufacturing Firm Utami, Muhamad Reggi Tresna; Putra, Panca O. Hadi; Handri, Eko Yon
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.36414

Abstract

Purpose: Practitioners who try to apply AI in Business Process Management (BPM) often face a gap between theory and real-world implementation, because most prior work stays at a high level and offers limited replicable implementation blueprints or measurable evidence of value. This study addresses that gap by reengineering a custom color ordering workflow, following the BPM lifecycle and integrating two AI components: machine learning for palette extraction and generative AI for reference image previews. Methods: The as-is process was mapped in BPMN 2.0 and analyzed using root-cause and value-added analysis. A to-be process was then designed using BPM redesign levers (task elimination, resequencing, integration, and enabling technology). An integrated web platform prototype was built and tested to measure four Process Performance Indicators (PPI): Cycle Time, Response Time, administrative staff hours per order, and Automation Ratio. Result: Prototype tests showed significant improvement. Response Time reduced from 8 days to 8 minutes (-99.4%). The overall Cycle Time reduced from 92 to 74 days (-19.5%). The administrative workload per order was reduced from 88 to 0.4 staff hours (-99.5%). The redesigned workflow automated 8 of 15 activities, increasing the Automation Ratio from 0% to 53.3%. Novelty: This study delivers a practical, replicable roadmap for embedding AI capabilities into an operational workflow while staying aligned with BPM principles and tracking measurable PPI. The blueprint can help organizations respond faster to customers, improve throughput, and reduce administrative burden, so staff can focus on higher-value work.
Designing A Mobile-Based Attendance Application Using Location-Based Service for Activities Mosque Zulaikha, Atika Dewi; Anggara, Afwan
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.36512

Abstract

Purpose: The purpose of this study is to design a mobile-based attendance application that uses Location-Based Services to support community-based religious institutions, namely mosques, which are simple to understand for all age groups, make it easier for participants to take attendance, and make it easier for administrators to manage participant data, schedules, and attendance. Methods: Interviews and direct observations were conducted to identify problems and determine the new system required. This system utilizes the concept of geolocation by utilizing GPS and geofencing for location validation, which is described using UML and implemented in programming. The completed system was tested through blackbox testing for its features and GPS accuracy testing to determine the stability of the location tracking system. Result: Blackbox testing results show that the functions integrated into the attendance application can run 100% as expected. The GPS accuracy system performance test results obtained are sufficient for semi-outdoor and outdoor spaces with 100% valid results. Novelty: The uniqueness of this research lies in its contribution to community-based religious institutions, namely mosques, in digital attendance recording, as well as providing GPS accuracy evaluation results provided by the system to determine the most appropriate radius range. Further developments that can be made to the system include improving security procedures, as this application directly accesses the user's location, making improvements to the security system and fake location detection very important for attendance applications.
Hybrid Feature Selection for Effective Heart Disease Detection: A Multi-Algorithm Machine Learning Approach Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Syuhada, Fahmi; Firdaus, Asno Azzawagama; Masitha, Alya
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38815

Abstract

Purpose: This research aims to develop an effective early detection model for heart disease with data balancing and hybrid feature selection. The study seeks to enhance predictive accuracy and minimize errors, providing a robust model for clinical decision support systems. Methods: The study used the Heart Failure Prediction dataset derived from Kaggle. A novel hybrid framework was implemented, integrating SMOTEENN (Synthetic Minority Over-sampling Technique + Edited Nearest Neighbors) for data balancing and a Hybrid Feature Selection (HFS) method combining Chi-square and Backward Elimination. Eight machine learning algorithms, including Logistic Regression, Naïve Bayes, Decision Tree, K Nearest Neighbor, Random Forest, Gradient Boosting, Support Vector Machine, and XGBoost. Performance was assessed based on accuracy, precision, recall, f1-score, specificity, AUC Score, fallout and miss rate. Result: The proposed framework significantly improved classification performance across all algorithms. The Random Forest model emerged as the optimal classifier, achieving an accuracy of 99.44%, AUC Score of 99.98%, and a specific reduction in miss rate to 0.92% (from 10.03% baseline). The HFS method successfully reduced the feature space by 54%, identifying 'ExerciseAngina', 'FastingBS', 'ST_Slope', 'ChestPainType', and 'Sex' as the most critical predictors. The model outperformed standard approaches and recent state-of-the-art benchmarks by over 10% in accuracy. Novelty: This study introduces a synergistic integration of SMOTEENN with hybrid feature selection. The combination significantly improves model performance in early heart disease detection.
Rainfall Prediction at Ahmad Yani Meteorological StationUsing Integration ARIMA and LSTM Pramudya, Naufal Daffa; Rahmat Gernowo; Indra Waspada
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.39297

Abstract

Purpose: Predicting rainfall using ARIMA, LSTM, and Hybrid ARIMA-LSTM models to obtain accuracy values ​​on data at the Ahmad Yani Semarang station. Methods: This study implements the ARIMA, LSTM, and hybrid ARIMA-LSTM models to determine which of these models produces the most significant predictions using rainfall data at the Ahmad Yani Meteorological Station in Semarang. This method proves whether using the hybrid ARIMA-LSTM, which is a combination of the two models, is able to provide greater accuracy compared to the ARIMA/LSTM model. The results of these predictions can certainly help relevant stakeholders to improve rainfall accuracy, especially at the Ahmad Yani Meteorological Station. Result: By utilizing the power of statistical models (ARIMA) with deep learning (LSTM), the results of these two models provide higher accuracy compared to each model, as seen from the accuracy of the best ARIMA model using RMSE 15.8 and MAE 8.7, the best LSTM model RMSE 14.65 and MAE 9.06, while in the HYBRID ARIMA-LSTM model the best RMSE is 14.1 and MAE 9.06. Novelty: This research adds to the knowledge regarding the accuracy or combination of ARIMA and LSTM models which are rarely used, especially in the world of meteorology or rainfall. By utilizing the ARIMA model which is able to read linear patterns and the LSTM model which reads non-linear patterns, the accuracy of rainfall increases and can help related stakeholders.
Automated Essay-Answer Grading using Transformer and GenAI-Based Error Analysis on Answer Sentence Qusyairi, Mohammad Mirza; Yusep Rosmansyah
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.39859

Abstract

Purpose: This study aims to address the limitations of manual essay assessment, which is time-consuming and prone to subjectivity, by developing an automated essay grading system that is faster, more efficient, and more objective. In addition to producing scores, the system is intended to support learning by providing meaningful feedback on students’ writing errors. Methods: The research applies a transformer-based approach to automated essay scoring in order to capture deeper semantic relationships between student answers and reference answers. Unlike conventional word embedding methods, transformers are used to model contextual meaning at the word, phrase, and sentence levels. The system is further enhanced with an AI-based error analysis module that identifies categories of student errors, including content omissions, conceptual misunderstandings, and structural inaccuracies, enabling the generation of corrective feedback alongside numerical scores. Result: The experimental results show that the proposed model achieves strong agreement with human scoring, indicated by a Pearson correlation coefficient (r) of 0.8432 and a Mean Absolute Error (MAE) of 0.6013. These results demonstrate an improvement in prediction accuracy compared to word embedding-based approaches. Furthermore, the system successfully generates relevant and actionable error feedback that supports students in understanding their mistakes and improving their essay quality. Novelty: The novelty of this research lies in the integration of transformer-based automated essay scoring with an AI-driven error analysis module. Rather than focusing solely on score prediction accuracy, the proposed approach combines quantitative scoring with qualitative error feedback, enabling the system to function not only as an assessment tool but also as a learning support mechanism that helps students understand and improve their writing.
Performance Evaluation of Otsu and Sauvola Thresholding for Structured Document Binarization Darpito, Muhammad Noko; Kartika Firdausy; Abdul Fadlil
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40245

Abstract

Purpose: Digitizing public administration records, particularly structured forms such as the Transport of Plants and Wildlife Abroad (Surat Angkut Tumbuhan dan Satwa Liar Luar Negeri / SATS-LN), necessitates meticulous preparation for precise subsequent analysis. Most of the photos in the SATS-LN archives are scanned, and they have inconsistent lighting, varying resolution, and background noise, which makes it difficult to separate the text from the backdrop and read it clearly. This work identifies the optimal SATS-LN binarization approach for preserving textual structure and suppressing background artifacts. Methods: A four-stage pipeline is used. First, Detectron2 localizes seven important SATS-LN fields. Second, binarization is investigated with global Otsu and adaptive Sauvola thresholding under three parameter configurations. Third, following binarization, Contrast-Limited Adaptive Histogram Equalization (CLAHE) boosts local contrast. Finally, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Difference from Reference for Distortion (DRD), Precision, Recall, F1-score, and Foreground Ratio are assessed on 200 annotated SATS-LN documents (150 scanner-based/DOC and 50 camera captured/CAM). Result: The acquisition domain and assessment model affect binarization performance on 200 SATS-LN documents (150 DOC scans and 50 CAM images). Global Otsu_T10 has the highest median PSNR (21.19 dB) and the lowest median MSE (494.69), indicating a visually cleaner background. However, segmentation-based metrics show better stroke preservation with Sauvola, as Sauvola_k05 has the strongest DOC text–background separation (F1 = 0.938). In the CAM domain, where illumination variability dominates, Sauvola performs better across structural and segmentation indicators, with Sauvola_k04 performing best overall (F1 = 0.980) and mitigating the over-segmentation tendency of strict global thresholds. The Sauvola window (25x25) and CLAHE clip limit (1.0) results suggest using Sauvola_k05 for DOC and Sauvola_k04 for CAM to preserve text integrity and reduce background artifacts. Novelty: This study presents a novel field-level binarization assessment that combines automated cropping and ground-truth evaluation, providing practical guidance for robust preprocessing that supports scalable, reliable, and cross-device public document digitization.
Development of Segmentation Method to Localize Epileptic Symptoms in EEG Signal Praja, Reval Bima; Nugroho, Hertog; Ginanjar, Teguh
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40414

Abstract

Purpose: Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide, where early detection through EEG signal analysis is crucial for proper management. However, the quality of EEG signals is often affected by noise and artifacts, which can lead to diagnostic errors of up to 30% in the early stages. This study aims to develop an EEG signal preprocessing method to improve the classification performance of epileptic symptoms through preprocessing, segmentation, and seizure interval analysis approaches. Methods: The preprocessing stage involved applying a 50 Hz notch filter and a 0.5–60 Hz bandpass filter. The contribution of this work is in the development of  hybrid segmentation based on frequency and amplitude analysis, while seizure intervals were identified using distances criteria between consecutive spikes detected on signals. The method was tested using the CHB-MIT dataset consisting of 23 EEG channels. Result: The results showed that the system successfully identified seizure segments with an average accuracy of 62.09%, and 9 out of 23 channels achieved accuracies above 70%. Channels Ch08 (86.60%), Ch09 (86.36%), and Ch19 (80.51%) achieved the highest accuracies. The results also showed high specificity(99.85%) and low False Positive rate(0.15%) indicating the system’s effectiveness to reduce falase positive. Novelty: This method proved effective in detecting epileptiform activity and shows potential as an EEG-based early detection tool for epilepsy, although further optimization is needed to improve accuracy on channels with low signal-to-noise ratio (SNR).

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