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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. 1: February 2025" : 16 Documents clear
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.
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.
Audit of Governance and Service Management of Unisnu Jepara's Library Information System (SIPERPUS) Using ITIL V4 Based on Website Nuradira, Afrida Hilda; Azizah, Noor; Minardi, Joko
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.v12i1.19168

Abstract

Purpose: This research aims to assess the maturity level of governance and service management in the Library Information System (SIPERPUS) at UNISNU Jepara. The objective is to identify strengths and gaps, ensuring better alignment with ITIL V4 best practices. Methods: The research was conducted using a qualitative methodology that incorporated a literature review, user questionnaires, and the creation of a web-based audit application designed specifically for this study. Service practices such as Service Request Management, Service Desk, Change Control, and Service Catalog Management were evaluated using the ITIL V4 framework. Result: The findings indicate that SIPERPUS has independently attained a managed maturity level with well-defined Service Request Management and Service Desk practices. Additional work is required to improve Change Control and Service Catalog Management to increase process integration and make information clearer. The new web-based audit application described in this study demonstrates its capability to systematically evaluate, produce data-based recommendations, and continuously improve digital library services. Novelty: This study introduces a new web-based audit application that conforms to ITIL V4, providing an efficient and reliable alternative to manual audit methodologies. The tool significantly reduces audit time, enhances operational efficiency, and optimizes user satisfaction while improving overall service quality. The study adds to the literature on IT governance audits, specifically in higher education, and presents a scalable approach for institutions striving to meet the standards of Society 5.0.
Neural Style Transfer and Clothes Segmentation for Creating New Batik Patterns on Clothing Design Adali, Farhan; Agus Subhan Akbar; Danang Mahendra
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.v12i1.19554

Abstract

Purpose: Applying the original batik image style to other object images and generating new batik patterns that applied to clothing. Methods: This research uses the Neural Style Transfer method to apply object images to batik to produce new batik patterns, and Clothes Segmentation is used to select areas of clothing in the image so the new batik patterns can be applied to clothing images. And Testing using SSIM, LPIPS and PSNR metrics. This research uses Google Colab, batik image data, and clothing mockup images taken from the internet. Result: This study shows high average results on SSIM, LPIPS and fair results on PSNR. The results show that the similarity is relatively high with high detected noise. Novelty: This research develops a new approach in the field of batik pattern innovation and its application to clothing design images. The novelty of this research lies in the implementation of Neural Style Transfer and Clothes Segmentation, which results in a method of exploring new batik patterns and applying them to clothing design images.
Development of Double-Tail Generative Adversarial Network with Adaptive Style Transfer for Anime Background Production with Makoto Shinkai's Stylization Purwanto, Agus; Kusrini, Kusrini; Utami, Ema; Agustriawan, David
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.v12i1.20850

Abstract

Purpose: Traditionally, 2D anime production involves the expertise of experienced animators and is labor-intensive and time-consuming. Generative adversarial networks (GANs) have been developed to create high-quality anime over the years. However, the developed GANs still have caveats, such as the presence of artifacts, high-frequency noise, color and semantic structure mismatches, blurring, and texture issues. Additionally, research on AI-generated anime images with a particular style is still lacking. Thus, this study aimed to develop double-tail generative adversarial network (DTGAN) with adaptive style transfer to generate quality anime background images aligning with Makoto Shinkai's anime style. Methods: A dataset of real world and anime images was collected and preprocessed. The training was run, and an inference process was done to generate background images with the anime style of Makoto Shinkai using DTGAN with adaptive style transfer. Evaluations of the images produced were performed using visual comparison and quantitative analysis using Fréchet Inception Distance (FID) and peak signal-to-noise ratio (PSNR). Result: Compared to other methods, the images generated by DTGAN with adaptive style transfer had the lowest FID and highest PSNR values of.38.7 and 19.4 dB, respectively. Visual comparison of the images against other methods and real anime image of Makoto Shinkai demonstrated that images from DTGAN had the best quality that matched Makoto's style, as observed from color, background preservation, photorealistic style, and light contrast. Novelty: These findings suggest that DTGAN with adaptive style transfer using adaptive instance normalization (AdaIN) and linearly adaptive denormalization (LADE) outperforms other methods, highlighting its practical use for 2D anime production.
Development of a Mental Health Classifier Using LSTM and Text Preprocessing Techniques Haryoko, Priyo; Syukur, Abdul; Rijati, Nova
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.v12i1.21216

Abstract

Purpose: This study aims to address undiagnosed mental health conditions using social media for early detection. By applying advanced preprocessing techniques and LSTM models, the research improves classification accuracy for depression and PTSD. It highlights deep learning’s potential to process unstructured data and provides a scalable solution for real-world mental health monitoring. Methods: Data was collected from Twitter using keywords like "depression" and "anxiety." Preprocessing included normalization, tokenization, stemming, and stopword removal. An LSTM-based model with GloVe embeddings, LSTM layers, and dropout was developed. The model’s performance was evaluated using metrics like accuracy, precision, recall, and F1-score to ensure robust and applicable results. Result: The LSTM model achieved 90% accuracy, outperforming Random Forest (89%) and SVM (89%). Preprocessing steps like tokenization and stemming boosted performance by 15%. The model effectively captured temporal dependencies in text, showcasing its ability to analyze unstructured social media content for mental health detection. Novelty: This study integrates advanced text preprocessing with LSTM to enhance mental health detection. Unlike traditional methods, it captures temporal nuances using GloVe embeddings. The scalable framework provides a reliable solution for real-world applications, paving the way for multilingual and cross-platform research in mental health analytics.
Evaluating ISO Standards for Indonesian PDP Law Compliance: A Regulatory Mapping and Literature Review Aristianto, Egriano; Hafizhuddin Hilman, Muhammad; Yazid, Setiadi
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.v12i1.21538

Abstract

Purpose: This paper aims to demonstrate how ISO standards such as ISO/IEC 27001:2022, ISO/IEC 27002:2022, and ISO/IEC 27701:2019 can assist Indonesian organizations in facilitating compliance with the Personal Data Protection (PDP) Law. It highlights the challenge organizations face due to the lack of clear guidance in the law, then shows how these ISO standards can guide them to achieve the compliance. The study also maps the regulation’s requirements and how that requirements can be fulfilled by certain approaches provided by the standards and offers a clearer path toward full compliance. Methods: This research employs a qualitative approach, combining a literature review, document analysis, and comparative assessment. It provides systematic Indonesian PDP Law-ISO/IEC 27001, ISO/IEC 27002, and ISO/IEC 27701 mapping, an analysis of their alignment, a gap analysis, and how these standards able to demonstrate compliance to Indonesian PDP Law. Result: This study shows that from 14 mandatory requirement topics of Indonesian PDP Law that have been mapped, The ISO/IEC 27001:2022 only able to cover 1 topic, while ISO/IEC 27002:2022 able to provide controls to accommodating 8 topics and ISO/IEC 27701:2019 able to provide controls to accommodating 13 topics. But by combining these standards, then all of mandatory requirements of Indonesian PDP Law can be satisfied. Novelty: This study shows how international standards like ISO/IEC 27001, ISO/IEC 27002, and ISO/IEC 27701 would help organize compliance to the Indonesian PDP Law while also strengthening data protection practices in Indonesia.
Integrating C4.5 and K-Nearest Neighbor Imputation with Relief Feature Selection for Enhancing Breast Cancer Diagnosis Purwinarko, Aji; Budiman, Kholiq; Widiyatmoko, Arif; Sasi, Fitri Arum; Hardyanto, Wahyu
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.v12i1.21673

Abstract

Purpose: Breast cancer remains a significant cause of mortality among women, requiring accurate diagnostic methods. Traditional classification models often face accuracy challenges due to missing values and irrelevant features. This investigation advances the classification of breast cancer through the amalgamation of the C4.5 algorithm with K-Nearest Neighbor (KNN) imputation and Relief feature selection methodologies, thereby augmenting data integrity and enhancing classification efficacy. Methods: The Wisconsin Breast Cancer Database (WBCD) was the core reference for evaluating the proposed methodology. KNN imputation addressed missing values, while Relief selected the most relevant features. The C4.5 algorithm executed training by utilizing data segregations in the corresponding proportions of 70:30, 80:20, and 90:10, with its efficiency gauged through a range of metrics, particularly accuracy, precision, recall, and F1-score. Result: This innovative methodology achieved the highest classification accuracy of 98.57%, surpassing several existing models. Particularly noteworthy, the strategy being analyzed exhibited remarkable success relative to PSO-C4.5 (96.49%), EBL-RBFNN (98.40%), Gaussian Naïve Bayes (97.50%), and t-SNE (98.20%), demonstrating associated advancements of 2.08%, 0.17%, 1.07%, and 0.37%. These results confirm its effectiveness in handling missing values and selecting relevant features. Novelty: Unlike prior studies that addressed missing values and feature selection separately, this research integrates both techniques, enhancing classification accuracy and computational efficiency. The findings suggest that this approach provides a reliable breast cancer diagnosis method. Future work could explore deep learning integration and validation on larger datasets to improve generalizability.
Optimized Non-Overlapping Multi-Object Segmentation for Palm Oil Images Using FCN with Squeeze-and-Excitation and Attention Mechanisms Pratama, Yovi; Rasywir, Errissya; Siswanto, Agus
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.v12i1.22212

Abstract

Purpose: Palm oil plantation monitoring using UAV imagery presents significant challenges in multi-object segmentation due to homogeneous texture, low resolution, and difficulty in distinguishing disease symptoms. Traditional segmentation methods struggle to accurately separate overlapping and visually similar objects, reducing the effectiveness of automated analysis. This study aims to address these issues by proposing an optimized Fully Convolutional Network (FCN) incorporating Squeeze-and-Excitation (SE-Block) and Attention Mechanisms to enhance segmentation accuracy for multi-object, non-overlapping palm oil images. Methods: The proposed model utilizes ResNet50 as a backbone, integrating SE-Block to enhance the feature representation of important regions while suppressing less relevant features. Additionally, Attention Mechanisms are incorporated to improve the model's spatial understanding and feature discrimination, which is crucial for segmenting visually similar objects in UAV imagery. A dataset of UAV-captured palm oil images was used to train and evaluate the model, applying deep learning techniques for feature extraction and classification. Result: Experimental results demonstrate that the proposed method achieves an average Intersection over Union (IoU) of 0.7928, accuracy of 0.9424, precision of 0.9126, recall of 0.8622, F1-score of 0.8693, and mAP of 0.7673. The highest-performing model attained a maximum IoU of 0.8499 and an accuracy of 0.9490, significantly outperforming conventional FCN models. These findings confirm that incorporating SE-Block and Attention Mechanisms enhances segmentation accuracy, making the model more robust in handling UAV imagery complexities. Novelty: The novelty of this research lies in the integration of SE-Block and Attention Mechanisms within FCN for palm oil segmentation, specifically targeting multi-object, non-overlapping segmentation in challenging UAV imagery conditions. By improving feature extraction and spatial attention, this approach advances deep learning-based agricultural monitoring and can be extended to other remote sensing applications requiring high-precision segmentation.

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