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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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SINERGI: Human Resource Management Learning Innovation Using Website-Based Instructional Media Nugroho, Fajar Wahyu; Rr Chusnu Syarifa Diah kusuma; Wahyu Rusdiyanto
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15426

Abstract

Purpose: This research aims to determine the urgency of Human Resource Information System (HRIS) practicum and to develop the HRIS application as a human resource management learning innovation within the Vocational Faculty, Yogyakarta State University. Methods: This research is a Research and Development study designed to be a multi-year study using the ADDIE model approach. The ADDIE model is an abbreviation for the five stages of the development process, namely Analysis, Design, Develop, Implement and Evaluate. In the previous year, the researchers had completed the design of an HRIS to be used as a learning media. Design and construction focus on needs analysis and system design formulation (Analysis and Design). In the second year or the study, researchers begin to develop designs into product prototypes and trials (Development and Implementation). In the third year, the researchers focus on evaluation. Evaluation needs to be carried out to improve the system by processing data obtained from previous phases that have been carried out. So, this research uses the Research and Development method with ADDIE model over a multi-year basis. Result: The result of the study is the development and implementation of web-based HRIS for learning practices in human resource management courses for students. Novelty: The novelty of this research is to develop HRIS as an interesting and interactive learning media for all students to learn human resource management as a preparation for work later. The target output of this research is articles published in reputable international journals or proceedings as well as IPR for the HRIS products being developed.
Analysis of Sentiment Towards Educational Services in Modern Islamic Boarding Schools using the Naïve Bayes Method Minardi, Joko; Noor Azizah; Ahmad Saefudin; Alzena Dona Sabilla; Dinta Sabrina; Yulia Savika Rahmi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15861

Abstract

Purpose: This study aims to analyze public sentiment regarding educational services in modern Islamic boarding schools using the Naïve Bayes method. The findings provide recommendations for improving educational quality. Methods: The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, utilizing web scraping techniques to collect data from social media and online discussion forums. The Naïve Bayes algorithm is used for sentiment classification. Result: A dataset of 387 reviews was analyzed, showing that 82.8% of reviews were positive, while 17.2% were negative. The model achieved an accuracy of 88%. Novelty: Unlike previous studies, this research focuses specifically on modern Islamic boarding schools, employing machine learning for sentiment classification to provide actionable recommendations.
Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method Fardana, Nouvel Izza; Isnanto, R. Rizal; Nurhayati, Oky Dwi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16635

Abstract

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.
Implementation of K-Nearest Neighbor in Case-Based Reasoning for Mental Health Diagnosis Systems Pamungkas, Ardian; Isnanto , R Rizal; Nugraheni , Dinar Mutiara Kusumo
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.19912

Abstract

Purpose: Assessing a model that employs the K-Nearest Neighbor (KNN) technique within Case-Based Reasoning (CBR) for diagnosing mental health disorders, concentrating on conditions such as anxiety, depression, stress, and normalcy, while enhancing its efficacy through the utilization of historical case data for more accurate and tailored diagnostic suggestions. Methods: This study implements the KNN method in CBR to create a mental health diagnosis system that can provide accurate results without the need for complex models or intensive training. This method effectively addresses various patient needs by utilizing previous case data to provide a personalized and case-based diagnosis. This system is designed to tackle mental health issues like anxiety, depression, and academic stress, utilizing a case study of students from ITBK Bukit Pengharapan. Result: This study developed a KNN-based model for mental health diagnosis, achieving 84.62% accuracy on test data. Data processing techniques like text mining, oversampling, and cosine similarity improved performance. With an optimal K value of 2, the model achieved 88% precision, 85% recall, and an F1-score of 84%. The anxiety label performed perfectly, with 100% precision, recall, and F1-score. Novelty: This study adds innovation by integrating the rarely used CBR and KNN algorithms for mental health diagnosis systems. Innovative techniques like text mining, oversampling to get around data integration, and cosine similarity computations, which greatly enhance model performance, assist this strategy. Because this method improves accuracy and expedites the diagnosis process, both of which support clinical decision-making, it may be able to help mental health professionals.
Improve Software Defect Prediction using Particle Swarm Optimization and Synthetic Minority Over-sampling Technique Amirullah, Afif; Umi Laili Yuhana; Muhammad Alfian
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16808

Abstract

Purpose: Early detection of software defects is essential to prevent problems with software maintenance. Although much machine learning research has been used to predict software defects, most have not paid attention to the problems of data imbalance and feature correlation. This research focuses on overcoming the problems of imbalance dataset. It provides new insights into the impact of these two feature extraction techniques in improving the accuracy of software defect prediction. Methods: This research compares three algorithms: Random Forest, Logistic Regression, and XGBoost, with the application of PSO for feature selection and SMOTE to overcome the problem of imbalanced data. Comparison of algorithm performance is measured using F1-Score, Precision, Recall, and Accuracy metrics to evaluate the effectiveness of each approach. Result: This research demonstrates the potential of SMOTE and PSO techniques in enhancing the performance of software defect detection models, particularly in ensemble algorithms like Random Forest (RF) and XGBoost (XGB). The application of SMOTE and PSO resulted in a significant increase in RF accuracy to 87.63%, XGB to 85.40%, but a decrease in Logistic Regression (LR) accuracy to 72.98%. The F1-Score, Precision, and Recall metrics showed substantial improvements in RF and XGB, but not in LR due to the decrease in accuracy, highlighting the impact of the research findings. Novelty: Based on the comparison results, it is proven that the SMOTE and PSO algorithms can improve the Random Forest and XGB models for predicting software defect.
Human Digital Twin Modeling for Cardiovascular System Herman, Herman; Annafii, Moch. Nasheh; Kunta Biddinika, Muhammad; Fitriah, Fitriah
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16012

Abstract

Purpose: The cardiovascular system is a vital system responsible for the distribution of oxygen and nutrients throughout the body. The complexity of interactions between the heart and blood vessels often presents challenges in monitoring and analyzing health conditions. The research proposes the development of a Human Digital Twin (HDT) for the cardiovascular system through application of two different modelling approaches geometric modeling and physic-based modeling. Through this model physical conditions can be represented and real time data integrated to offer insights into the dynamics of the cardiovascular system. Methods: This model development is based on two major components: a geometric modeling and a physic-based modeling. The geometric model is done in 3D to show the structure of the heart in detail, while the physical-based model is tabulated with different measurable physical parameters in the cardiovascular system, such as blood pressure and flow rate. This information is integrated into the Five Dimension Digital Twin model, including physical, virtual, data, connection, and service dimensions for the accurate simulation of cardiovascular conditions. Result: Results confirm that the Five-Dimensional Digital Twin (DT) could give further development to how the dynamics of the cardiovascular system behave, possibly in real-time updates on conditions and a supply of data that is far more detailed in view of analyzing risk and further representation of specific cardiovascular disorders while providing personalized medical support. Novelty: The Five-Dimensional Human Digital Twin Model (HDTM) developed in this research introduces novel innovations in the monitoring and simulation of the cardiovascular system through the application of geometric and physic-based modeling techniques. This approach offers a higher level of detail, compared to previous models, and added value for the advancement of health technology by integrating real time data into the simulations. This model serves not only as an advanced analytical tool but also as a reference for further research on DT technology in the medical field.
Improving Random Forest Performance for Sentiment Analysis on Unbalanced Data Using SMOTE and BoW Integration: PLN Mobile Application Case Study Rahmatullah, Muhammad Rifqi Fadhlan; Andono, Pulung Nurtantio; Affandy; Soeleman, M. Arief
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.19295

Abstract

Purpose: This research aims to improve the accuracy of sentiment analysis on PLN Mobile app reviews by overcoming the challenge of data imbalance. This goal is important to provide a better understanding of user opinions and support PT PLN (Persero) in improving mobile application services. Methods: This research uses the Random Forest algorithm combined with Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Data is collected through web scraping reviews from the Google Play Store, followed by preprocessing processes such as data cleaning, stopword removal, tokenization, and stemming. Feature extraction is performed using the Bag of Words (BoW) method, and the data is tested with four sharing schemes. Result: The results showed that the 90%-10% sharing scheme gave the best performance with an accuracy of 81% and an average precision and recall of 0.79. This finding confirms that the larger the proportion of training data, the better the model performs sentiment classification. Novelty: This research's novelty lies in combining SMOTE with BoW and Random Forest to overcome data imbalance. This approach is a significant reference for future sentiment analysis research. It provides practical insights that PT PLN (Persero) can use to improve the quality of its application services.
Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection Ningsih, Maylinna Rahayu; Unjung, Jumanto
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20582

Abstract

Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature. Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation. Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106. Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.
A Deep Learning Model Comparation for Diabetic Retinopathy Image Classification Mustaqim, Tanzilal; Safitri, Pima Hani; Muhajir, Daud
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20939

Abstract

Purpose: This study compares the performance of various deep learning models for diabetic retinopathy (DR) classification, emphasizing the impact of different optimization functions. Early detection of DR is vital for preventing blindness, and the research investigates how optimization functions influence the classification accuracy and efficiency of several convolutional neural networks (CNNs). This study fills a gap in the existing literature by examining how optimization functions affect model performance in conjunction with architectural considerations. Methods: This paper uses the APTOS 2019 dataset, which comprises 3,663 retinal fundus images classified into five classes of diabetic retinopathy severity. Four CNN-based models, including CNN, ResNet50, DenseNet121, and EfficientNet B0, were trained using five optimization techniques: Adam, SGD, RMSProp, AdamW, and NAdam. The performance of the experimental scenarios was evaluated through accuracy, precision, recall, F1-score, training duration, and model size. Result: EfficientNet B0 demonstrated superior computational efficiency with a minimal model size of 16.16 MB. Subsequently, DenseNet121 with the SGD optimizer achieved the highest test accuracy of 96.86%. The experimental results indicate that the optimizer significantly influences model performance. AdamW and NAdam yield superior outcomes for deeper architectures such as ResNet50 and DenseNet121. Novelty: This paper offers an analytical examination of deep learning models and optimization techniques for DR classification, helping to clarify the trade-offs between computational efficiency and classification performance. The findings contribute to the development of more accurate and efficient DR detection systems, which could be utilized in real-world, resource-limited settings.
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.

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