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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 25 Documents
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Search results for , issue "Vol. 11 No. 4: November 2024" : 25 Documents clear
Enhancing Medical Image Security Using Hyperchaotic Lorenz and Josephus Traversing Encryption Rachmawanto, Eko Hari; Pramudya, Elkaf Rahmawan; Pratama, Zudha
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.v11i4.9815

Abstract

Purpose: The present work focuses on developing a methodology to encrypt medical images using combined Hyperchaotic Lorenz systems with Josephus Traversing. This, therefore, forms the basis of the present paper to establish the efficacy of the proposed method against glioma, meningioma, and pituitary kinds of brain tumor images at 256 × 256 and 512 × 512 pixels image sizes. Methods: In this regard, a state-of-the-art encryption technique based on the Hyperchaotic Lorenz systems for Josephus Traversing has been proposed against the medical images of glioma, meningioma, and pituitary tumor datasets obtained from the repository via medical imaging. Result: The different distortion of test outcomes has the MSE value lying between 69.01 and 172.1, while fidelity preservation-PSNR lies between 12.971 and 18.321 dB for different tumor types and sizes of images. The UACI is between 3.625 and 11.34, while the NPCR is always greater than 99% to show very high tamper resistance. This approach is very new in integrating chaos and traversal algorithms for encrypting medical images. Hence, it has a great promising enhancement of security and protection of patient privacy. Novelty: This research contributes a comprehensive investigation based on different metrics that allows exploring not only the efficiency but also strength against decryption techniques for a proposed encryption method. More investigations could be done for further research work in order to enhance the encryption speed, which would improve robustness against advanced decryption techniques in medical image security for digital health applications.
Applying User Centered Design and System Usability Scale to Design Knowledge Management System for Exam Proctors in Higher Education Shabrina Salsabila Kurniawan; Nandhita Zefania Maharani; Dana Indra Sensuse; Erisva Hakiki Purwaningsih; Deden Sumirat Hidayat
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.v11i4.9919

Abstract

Purpose: This research aims to develop a system interface design solution to address the university’s knowledge management problem with exam proctors. Developing a knowledge management system is expected to maintain the integrity of examinees and reduce the risk of plagiarism. This research identifies user needs in business processes and maps them to relevant features based on previous research. Methods: This research adopted a user-centered design methodology in developing interface design solutions, which consisted of four stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating against requirements. Semi-structured interviews were used for data collection, and a system usability scale (SUS) questionnaire was employed for design solution evaluation. Result: This research identified the needs of business processes in higher education in the context of exam proctors and mapped them to a suitable feature solution. Recommendations for information architecture and knowledge management system design implementation in higher education were also provided. This research achieved a SUS score of 74.8, indicating that the developed system met users’ needs. Novelty: This research provides a practical implementation of developing a knowledge management system in higher education with user-centered design.
Brain Tumor Detection Using Improved Fuzzy Logic Classifier Model Based on K-folds Validation Tresnawati, Shandy; Alfianti, Henny
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.v11i4.13964

Abstract

Purpose: This study aims to improve brain tumor detection by integrating Fuzzy Logic with K-folds validation to enhance classification accuracy and robustness. The research addresses the challenge of distinguishing between normal and abnormal brain MRI images. Methods: This study utilized a public dataset from Kaggle comprising 2,660 MRI images, initially categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor. For the study, Glioma, Meningioma, and Pituitary were combined into one abnormal label, resulting in two classes: Normal and Abnormal. The methodology involved pre-processing the images, applying Fuzzy Logic with K-folds validation (K=3), and evaluating the model’s performance using single prediction tests. Result: The proposed approach achieved an exceptional accuracy of 99.88% during the K-folds validation process. The model demonstrated strong performance across all test samples, accurately classifying both normal and abnormal cases, with true positive results in single prediction tests. Novelty: This study introduces a novel combination of Fuzzy Logic with K-folds validation, demonstrating a significant improvement in classification accuracy compared to existing methods. The integration of these techniques offers a robust framework for brain tumor detection, enhancing diagnostic precision and addressing the challenge of distinguishing between various tumor types in MRI images.
Aesthetic Photography Analysis on Instagram: A Visual Study of Social Media using ATLAS.ti Wibowo, Mars Caroline; Purnomo, Hindriyanto Dwi; Hartomo, Kristoko Dwi; Sembiring, Irwan
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.v11i4.13985

Abstract

Purpose: This study aims to analyze the dominant trends in color and composition within aesthetic photography on Instagram and explore their influence on user interaction, specifically likes and comments. Given the growing role of visual aesthetics in digital marketing, understanding these elements is crucial for content creators, brands, and businesses aiming to maximize engagement. Unlike previous studies that focus on general social media engagement, this research integrates technology-driven qualitative analysis using ATLAS.ti, enabling structured coding and thematic identification of visual elements. Methods: A qualitative content analysis was conducted on 591 Instagram posts tagged with #AestheticPhotography and #VisualAesthetic. Data was collected using Instagram scraping (PhantomBuster), extracting both visual (color palettes, composition techniques) and textual (captions, metadata) elements. The ATLAS.ti software was used to analyze recurring visual patterns and color extraction was performed via Google Colab and Python for accuracy. Result: The results show that natural colors (48.18%) and pastel tones (30.90%) are dominant in aesthetic photography, contributing to higher engagement due to their harmonious and calming effect. Composition techniques such as center alignment (40.51%) and the Rule of Thirds (23.27%) significantly correlate with user interaction, as they align with cognitive load theory and visual perception principles. Additionally, short captions (≤10 words) were more effective in enhancing engagement, receiving 8,876 likes and 4,432 comments on average, compared to longer captions. Novelty: This study bridges the gap between visual aesthetics and computational analysis, using ATLAS.ti to systematically examine social media trends. Unlike previous studies that focus solely on quantitative metrics, this research provides qualitative insights into how color and composition influence engagement. The findings offer practical guidance for content creators, designers, and marketers, suggesting that strong visual composition and color harmony can enhance audience engagement.
Determining Lecturers’ Research Linearity Using Simple Additive Weighting and Technique for Order Preference by Similarity to Ideal Solution Narulita, Siska; Nugroho, Ahmad; Abdillah, M. Zakki
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.v11i4.14024

Abstract

Purpose: This study aims to conduct a comparative analysis of the decision-making methods of Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and build a decision support system that can be used to determine the linearity of lecturers’ research in their scientific fields. The analysis aims to assist relevant institutions in determining research linearity and guiding lecturers in their research policies and directions. Methods: This study compares the performance of the SAW and TOPSIS methods, against the alternative ranking of lecturers’ research linearity. The performance of both methods was measured using accuracy and MSE, where a high accuracy value indicates that the method performs better and the value of MSE is small or close to zero indicating a better relevance. Result: The results showed that the SAW method has a better accuracy value of 100% and an average MSE value of 0.13. At the same time, the TOPSIS method has an accuracy value of 33% and an average MSE value of 0.58. Novelty: This study provides knowledge about the effectiveness and efficiency of decision-making methods to determine the lecturers’ research linearity, as a guideline for related institutions regarding research policy making. In addition, the results of this study also provide a framework for evaluating the recent decision-supporting method and improving the understanding of the implementation and performance of the methods.
Mackerel Tuna Freshness Identification Based on Eye Color Using K-Nearest Neighbor Enhanced by Contrast Stretching and Histogram Equalization Dahlan, Dahlan; Iskandar, Rachmat; Ekawati, Nia; Sugianto, Castaka Agus
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.v11i4.14494

Abstract

Purpose: The present study focuses on the development of a robust fish freshness classification system based on the application of different digital image processing techniques from mackerel tuna eye images toward better classification. Methods: Contrast stretching and histogram equalization were done to improve image quality before the classification. The system contained 250 training images in a dataset, for five freshness classes which are 3, 6, 9, 12, and 15 hours post-catch, with 50 test images. For classification, the K-Nearest Neighbor (KNN) algorithm was employed with a parameter setting of K = 5, ensuring effective differentiation between the various freshness levels based on the enhanced image features. Result: The results depicted very low MSE values after enhancement at 6 hours, as low as MSE = 0.0012606 and PSNR = 28.9944 dB for contrast stretching, and for 12 hours, histogram equalization gave the best results, MSE = 0.0030712 and PSNR = 25.127 dB. Further, classification done through the KNN classifier with K=5 gave results with accuracy as high as 100% was achieved on the testing data, proving that the model was successfully able to identify the classes of freshness. Novelty: The novelty in the present research work is the integration of advanced image-processing techniques, which allow the achievement of an improved level of detection of fish freshness and a very useful solution to the seafood industry in view of product quality and safety assurance. Generally, the paper epitomizes an important milestone in the application of machine learning and image processing for the assessment of the quality of foods.
Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures Auni, Ahmad Ramadhan; Sugiharti, Endang
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.v11i4.15169

Abstract

Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.
Classification Performance of Stacking Ensemble with Meta-Model of Categorical Principal Component Logistic Regression on Food Insecurity Data Pangestika, Dhita Elsha; Fitrianto, Anwar; Sadik, Kusman
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.v11i4.15315

Abstract

Purpose: Stacking is one type of ensemble whose base-models use different algorithms. The classification results from its base-models are categorical and tend to be associated with each other. They then become input for the stacking meta-model. However, there are no currently definite rules for determining the classifier that becomes the meta-model in stacking. On the other hand, recent research has found that CATPCA-LR can work well on categorical predictor variables associated with each other. Therefore, this study focuses on the classification performance of the stacking algorithm with the CATPCA-LR meta-model. Methods: The study compared the classification performance stacking with CATPCA-LR meta-model to stacking with other meta-models (random forest, gradient boost, and logistic regression) and its base-models (random forest, gradient boost, extreme gradient boost, extra trees, light gradient boost). This research used food insecurity data from March 2022. Result: The stacking algorithm with the CATPCA-LR meta-model performs better insecurity data regarding sensitivity, balanced accuracy, F1-Score, and G-Means values. This model offers a sensitivity of 46.28%, a balanced accuracy of 59.82%, an F1-Score of 37.82%, and a G-Means of 58.26%. Meanwhile, regarding specificity values, the light gradient boost (LGB) algorithm gives the highest value compared to other algorithms. This model provides a specificity value of 88.40%. Generally, the stacking with the CATPCA-LR meta-model algorithm provides the best performance compared with other algorithms on food insecurity data. Novelty: This research has explored a stacking classification performance with CATPCA-LR as meta-model.
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.

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