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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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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.
Sentiment Analysis of Public Opinion on BAWASLU Using Random Forest and Particle Swarm Optimization Untoro, Meida Cahyo; Farhan, Muhammad
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.v12i1.22234

Abstract

Purpose: Sentiment analysis, commonly referred to as opinion mining, involves the study of people's opinions, emotions, and attitudes toward various subjects. While the Random Forest algorithm is frequently employed in sentiment classification tasks, its integration with Particle Swarm Optimization (PSO) for feature selection remains relatively underexplored. This study investigates whether PSO-based feature selection can enhance the predictive performance of Random Forest by optimizing the selection of relevant textual features, ultimately leading to more accurate sentiment classification. Methods: The research adopts a structured text preprocessing approach that includes data cleansing, case folding, normalization, stop-word removal, and stemming to refine the input text. Term Frequency-Inverse Document Frequency (TF-IDF) is applied to extract features, followed by PSO-driven feature selection to refine the input set for the Random Forest classifier. The proposed model is evaluated using a Twitter sentiment dataset related to “Bawaslu”, with performance measured based on Out-of-Bag (OOB) error and accuracy metrics. Result: Empirical results demonstrate that incorporating PSO-based feature selection into the Random Forest model substantially lowers the OOB error to 20.42%, compared to 28.72% in the baseline Random Forest model. Furthermore, the optimized model achieves an accuracy of 78.35%, outperforming the standard approach. However, the introduction of PSO-based feature selection increases computational demands, indicating a trade-off between classification accuracy and processing efficiency. Novelty: This study introduces the novel integration of PSO-driven feature selection with Random Forest classification for sentiment analysis, addressing challenges in imbalanced text data. By optimizing feature selection through a metaheuristic approach, it enhances model accuracy and efficiency. The novelty lies in applying PSO to refine feature selection in text classification, offering new insights into improving machine learning models for imbalanced datasets. Future research could explore reducing computational overhead and investigating hybrid selection techniques to further enhance scalability and performance.
Evaluating User Acceptance of Virtual Class on Bali Melajah Portal Using Technology Acceptance Model and Importance Performance Analysis Purnawan, I Putu Abdi; Sudarma, Made; Indra ER, Ngurah
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.v12i1.22833

Abstract

Purpose: The purpose of this study was to evaluate the acceptance and performance of users of the Virtual Class application in the Bali Melajah Portal using the Technology Acceptance Model (TAM) and Importance-Performance Analysis (IPA). This study found the main components of adoption, evaluated performance disparities, and suggested ways to improve LMS adoption in secondary schools. Methods: This study used a quantitative approach through an online survey conducted to collect data from teachers and students. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the relationship between the variables in the research model. IPA was used to evaluate system performance and determine areas for improvement. Results: The study showed that perceived ease of use significantly influenced perceived usefulness, which in turn affected attitudes and behavioral intentions. Meanwhile, behavioral intentions positively affected actual system use. Performance mismatch was found, with the lowest percentage of conformity of 97.76% for actual system use. Quadrant analysis highlighted that, although the system was perceived as useful and easy to use, involvement, accessibility, and institutional support were necessary for effective system use. Novelty: This study offers a two-pronged assessment of LMS adoption in secondary schools by combining TAM and IPA. Unlike earlier studies that only evaluated user perceptions or system performance separately, this paper provides a complete approach to maximizing government-supported digital learning platforms. These findings give policymakers and application developers knowledge to improve the adoption of Virtual Classroom LMS.
Information System Governance Evaluation at Diskominfo Central Java Using COBIT 2019 Framework Zaini, Ahmad; Widodo, Aris Puji; Nugraheni, Dinar Mutiara Kusumo
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.v12i1.22883

Abstract

Purpose: To evaluate the governance of the LaporGub Central Java service portal information system using the COBIT 2019 framework to obtain a comprehensive overview of the implemented capability levels. Methods: This study employs a qualitative method using the COBIT 2019 framework to assess capability levels, which is further combined with CMMI to provide improvement recommendations. The method produces valid and accurate results as respondents are selected using the RACI model, and the assessment is conducted at the subdomain level. Consequently, each domain yields valid outcomes, enabling precise and well-targeted recommendations. Results: This study successfully determined the level of LaporGub service capability using COBIT 2019 and showed that most domains have achieved, and in some cases even exceeded, the set targets. However, several areas still require improvement, especially in services security and business process control, as well as problem management. These findings indicate that the Central Java Provincial Communication and Information Office has a strong foundation in operational management and service sustainability. With proper improvements in IT governance, LaporGub is expected to serve as a model for adaptive and accountable digital public services that can be replicated in other regions across Indonesia. Novelty: This study introduces innovation by integrating COBIT 2019 with CMMI, an uncommon combination in capability level assessments within government institutions. Innovative techniques such as text development, respondent questionnaires based on the RACI model, and interviews are used to avoid redundancy in the research findings. By utilizing questionnaires, interviews, and document reviews, this approach enhances the validity of the study results.
Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction Ilmiyah, Miftakhul; Barata, Mula Agung; Yuwita, Pelangi Eka
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.v12i1.22886

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination. Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate. Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%. Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.

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