cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jumanto
Contact Email
jumanto@mail.unnes.ac.id
Phone
+628164243462
Journal Mail Official
sji@mail.unnes.ac.id
Editorial Address
Ruang 114 Gedung D2 Lamtai 1, Jurusan Ilmu Komputer Universitas Negeri Semarang, Indonesia
Location
Kota semarang,
Jawa tengah
INDONESIA
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
 1   2   3 
Search results for , issue "Vol. 11 No. 3: August 2024" : 25 Documents clear
Analysis of User Experience of the CapCut Application in Generation Z for Social Media Content Using the User Experience Questionnaire Method Wulan, Damar; Syaifullah; Saputra, Eki; Rahmawita, Medyantiwi; Marsal, Arif
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.7543

Abstract

Purpose: In the midst of these dynamics of change, research, development and application of technology continues to be the main focus in efforts to achieve progress and efficiency in various sectors of life. Generation Z has been the primary architect of cultural and behavioral change on social media. One of the most prominent characteristics of Generation Z is their desire to create "viral" content on social media. Content created using CapCut often uses interesting video and audio effects, such as progress effects, dry effects, music effects, and so on. This application has various features that make it easier for users to create video content, such as video effects, audio and animation. This shows that generation Z tends to use the CapCut application to create and edit video content that they share on social media. This research was conducted based on a phenomenon that has recently occurred, where many generations Z want to create content on social media using the CapCut application and also from several previous studies that have been conducted which are still lacking in understanding CapCup user satisfaction as a medium for creating content for Generation Z. and from several interviews the author conducted with CapCut application users. Method: This research uses the user experience questionnaire (UEQ) method with six (6) variables, namely attractiveness, efficiency, clarity, dependability, stimulation and novelty. Dr. Martin Schrepp developed a special tool that can be used to analyze UEQ questionnaire results, namely the UEQ Data Analysis Tool. The UEQ Data Analysis Tool is in the form of an Excel application which can be obtained by downloading it directly from the official https://www.ueq-online.org/ website This study employs a quantitative research strategy. This study does not know the exact number of the population to be studied. So, the sample size was calculated using the Lemeshow formula, a survey was conducted on 96 users of the Capcut application. Result: Based on the results of the discussion regarding user experience, specifically Generation Z, in the CapCut Application using the User Experience Questionnaire (UEQ) method, conclusions can be drawn from the 6 variables in the UEQ used, the 6 variables obtained positive evaluation values, namely the Attractiveness (mean 1.177), Perspicuity variables. (mean 1.109), Efficiency (mean 1.109), Dependability (mean 1.159), Stimulation (mean 1.151) and Novelty (mean 0.763) with the highest evaluation value on the Attractiveness variable. 2. Based on the benchmark results, the values obtained for the Attractiveness variable were 1.18, Perspicuity 1.11, Efficiency 1.11, Dependability 1.16, Stimulation 1.15, and Novelty 0.76. Each variable gets an Above Average value (above the average). However, there is still potential for improvement to achieve standards of perfection desired. Users provided invaluable feedback and suggestions, highlighting the need for improvement. Novelty: This research provides 2 recommendations based on the results of evaluations using UEQ, which are expected to help in improving and improving the quality of the CapCut Application in the future.
Hybrid Deep Learning with GloVe and Genetic Algorithm for Sentiment Analysis on X: 2024 Election Fitria, Mahrunissa Azmima Fitria; Setiawan, Erwin Budi
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8467

Abstract

Purpose: This research analyzes sentiment on the 2024 Indonesian Presidential Election using  data from X, employing a hybrid CNN-GRU model optimized with a Genetic Algorithm (GA) to improve accuracy and efficiency. It also explores GloVe feature expansion for enhanced sentiment classification, aiming for deeper insights into public opinion through advanced deep learning and optimization techniques. Methods: This research employs a deep learning approach that integrates Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, Term Frequency-Inverse Document Frequency (TF-IDF), Global Vectors (GloVe), and GA. The dataset comprises  62,955 Indonesian tweets focusing on the 2024 General Election using various keywords. Result: The results indicated that the Genetic Algorithm significantly improved model accuracy. The CNN-GRU + GA model achieved 84.72% accuracy for the Top 10 ranking, a 1.94% increase from the base model. In comparison, the GRU-CNN + GA model achieved 84.69% accuracy for the Top 5 ranking, a 2.76% increase from the base model, demonstrating enhanced performance with GA across configurations. Novelty: This research uses a hybrid CNN-GRU model to introduce a novel sentiment analysis approach for the 2024 Indonesian Presidential Election. The model enhances accuracy by combining CNN's spatial feature extraction with GRU's temporal context capture and GloVe's word semantics. Genetic Algorithm optimization further refines performance. Comprehensive pre-processing ensures high-quality data, and focusing on election-specific keywords adds relevance. This study advances sentiment analysis through its innovative hybrid model, feature expansion, and optimization techniques.
Sort Filter Skyline in Movie Recommendation Based on Individual Preferences: Performance and Time Complexity Analysis Fadhilah, Alvi Nur; Cahyanto, Triawan Adi; Saifudin, Ilham
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8474

Abstract

Purpose: This study seeks to deliver accurate, customized movie recommendations using the Sort Filter Skyline (SFS) algorithm. The approach considers factors like budget, box office earnings, popularity, runtime, and audience ratings to align closely with each user's specific preferences. Methods: The Sort Filter Skyline (SFS) algorithm is employed, designed to identify and recommend items different from others within the dataset. Initially, the data undergoes preparation through pre-processing before being analyzed to compute entropy using the entropy formula. Before carrying out the dominance test, the SFS algorithm organizes the data based on entropy values. Result: In this research, 176 skyline objects were identified from a dataset containing 4,803 movies, including well-known titles like "Avatar" and "Titanic." The Skyline Filter Sort (SFS) algorithm pinpointed these objects within 4 seconds. Additionally, evaluation results using synthetic data, as depicted in the data visualization, revealed that the number of attributes increased from 1 to 7. The dataset size grew, and the execution time also rose—from 18 seconds to 170 minutes. Despite this increase, the algorithm demonstrated efficient performance with optimized processing times. Novelty: This study showcases the successful application of the SFS algorithm for generating personalized movie recommendations while tackling the difficulty of aligning viewer preferences with the extensive selection of films available. The findings offer important insights into enhancing recommendation systems by implementing algorithms efficiently and managing execution time complexity, contributing fresh perspectives to the field.
Behavioral Intention and Adoption of Clinical Informatics Tools: A Study in Regional Public Municipality Clinics Mdunge, Eric Njabulo; Ezeji , Ijeoma Noella; Evans , Neil
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8794

Abstract

Purpose: The healthcare sector is vital for addressing public health emergencies and improving overall health outcomes. This paper aims to explore the factors that influence the adoption of clinical informatics by healthcare professionals in public clinics within a KwaZulu Natal municipality in South Africa. The paper adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand determinants of behavioral intention to use, and actual usage of these clinical informatics tools by the sampled healthcare professionals. Methods: The paper adopted a mixed-methods approach to answer the research questions, both primary quantitative and secondary qualitative data was collected and analysed. Surveys and interviews were conducted with 67 sampled healthcare professionals including doctors, nurses, administrative staff and 10 managers to obtain the primary quantitative data and secondary qualitative data respectively. SPSS was used to analyse the quantitative data and thematic content analysis was used to analyse for the qualitative data. Result: Results indicate that the constructs of UTAUT positively influence healthcare workers’ intention to use and use of clinical informatics tools, these include in the order of their influence, performance expectancy, effort expectancy, social influence and facilitating conditions. While clinical informatics tools are available and frequently used by healthcare workers, significant challenges are mentioned to impede their effective adoption. These challenges include insufficient hardware, lack of awareness and training about ICT applications, and limited access to data network infrastructure such as Wi-Fi hotspots and routers. Novelty: The novelty of this research lies in its comprehensive analysis of both the behavioral and infrastructural factors affecting the adoption of clinical informatics in a specific municipal context. Conducted in a municipality that serves both rural and urban communities, this study provides practical recommendations to overcome identified adoption barriers. The findings contribute to developing strategies for the adoption and provision of clinical informatics tools to improve healthcare delivery in South Africa.
Combining Multiple Text Representations for Improved Automatic Evaluation of Indonesian Essay Answers Wibowo, Moh Edi; Rokhman, Nur; Sihabudin, Agus
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9440

Abstract

multiple-choices, regarding students’ learning achievement. When the number of students in a class is huge, however, examinations using essay questions become hard to conduct and take a long evaluation time. Automatic essay evaluation has, therefore, become a potential approach in this situation. Various methods have been proposed, however, optimal solutions for such evaluation in the Indonesian language are less known. Furthermore, with the rapid development of machine learning approaches, in particular deep learning approaches, the investigation of such optimal solutions becomes more necessary. Method: To address the aforementioned issue, this study proposed the investigation of text representation approaches for optimal automatic evaluation of Indonesian essay answers. The investigation compared pre-trained word embedding methods such as Word2vec, GloVe, FastText, and RoBERTa, as well as compared text encoding methods such as long short-term memories (LSTMs) and transformers. LSTMs are able to capture temporal semantics by employing state variables, while transformers are able to capture long-term dependency between parts of their input sequences. Additionally, we investigated classification-based and similarity-based training to build text encoders. We expected that these training approaches allowed encoders to extract different views of information. We compared classification results produced by different text encoders and combinations of text encoders. Result: We evaluated various text representation approaches using the UKARA dataset. Our experiments showed that the FastText word embedding method outperformed the Word2vec, GloVe, and RoBERTa methods. The FastText method achieved an F1-score of 75.43% on validation sets, while the Word2vec, GloVe, and RoBERTa methods achieved F1-scores of 69.56%, 74.53%, and 72.87%, respectively. In addition, the experiments showed that combinations of text encoders outperformed individual encoders. The combination of the LSTM encoder, the transformer encoder, and the TF-IDF encoder obtained an F1-score of 77.22% in the best case, which is better than the best F1-scores of the individual LSTM encoders (75.35%), the best combination of transformer encoders (71.49%), and the individual TF-IDF encoder (76.69%). We observed that LSTM encoders produced better performance when they were built using classification-based training. Meanwhile, the transformer encoders obtained better performance when built using similarity-based training. Novelty: The novelty proposed in this research is the optimal combination of text encoders specifically constructed for the evaluation of essay answers in the Indonesian language. Our experiments showed that the combination of three encoders - namely the LSTM encoder built using classification-based training, the transformer encoder built using classification-based and similarity-based training, and the TF-IDF encoder - obtained the best classification performance.
Modified Mixed Effects Random Forest in Small Area Estimation Using PCA and Rotation Forest with Correlated Auxiliary Variables Ananda, Rizki; Notodiputro, Khairil Anwar; Aidi, Muhammad Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10633

Abstract

Purpose: The per capita expenditure data in Jambi Province, Indonesia have been plagued with severe multicollinearity problems. To address the issue, this study developed an effective small area estimation (SAE) method, which is essential for formulating comprehensive regional development policies in Jambi Province. By modifying the mixed effects random forest (MERF) method, we introduced PCA-MERF (which applies principal component analysis prior to MERF) and MERoF (which replaces the standard random forest with rotation forest) to handle multicollinearity more effectively. Data from the National Socioeconomic Survey (Susenas) in March 2021 and Village Potential (PODES) in 2021 were utilized. The methods were evaluated using metrics such as root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of variation (CV), and their ability to capture random area effects. The random effect block (REB) bootstrap approach was employed to obtain MSE estimates for evaluating area-level estimate quality. Result: The results showed that MERoF outperformed both MERF and PCA-MERF, particularly in unit-level (village) estimation. Additionally, MERoF demonstrated superior capability in capturing variation between subdistricts compared to MERF and PCA-MERF. PCA-MERF performed better than MERF and MERoF at the area level (subdistrict). All three methods showed acceptable performance with RRMSE and CV values ranging between 8% and 10%, indicating precise and reliable predictions for per capita expenditure in small areas. These modifications to MERF prove effective and advantageous for small-area estimation in datasets with significant multicollinearity. Novelty: This research introduces a novel semi-parametric, tree-based SAE approach, enhancing the precision of per capita expenditure estimates and supporting more informative regional policy decisions, thus filling a gap in current SAE methodologies.
cPanel Server Hosting Security Against Malware and DDoS Attacks on the Open Journal System Platform Anshari Nur, Muh Nadzirin; Hijriani
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.11605

Abstract

Purpose: This research analyzes the security of cPanel in protecting Open Journal Systems (O.J.S.) from DDoS attacks and malware infections. Since contemporary threats to this environment are continuously evolving, this investigation seeks to offer empirical findings and applicable suggestions for platform managers of academic publishing platforms. Methods: The method applies the scope of the literature study and tests system-specific security using cpanel features to test Imunify360, SSL Manager, IP Blocker, Site Quality Monitoring, Awstats, and Jetbackup. This is then followed by observing the server logs on cPanel, where any suspicious activities or signs of attacks can be identified. It contributes to the detection of attack patterns and weaknesses in a system. Result: This indicates several default settings within cPanel were found to be vulnerable and could allow exploitation for DDoS purposes. Tools available in cPanel helped eliminate malware and strengthen defense against DDoS attacks. This is verified by the AWStats check, which means a quicker and more secure access time from the server. Novelty: This study combines many security features and tools from cPanel to implement a complete manner of detecting and improving its security. This method will not only help find out what the weak points are but also provide actionable solutions that can be employed to secure your application. The research results offer a practical guide for system administrators to enhance cPanel security configurations. This includes applying amended security settings and using more tools to safely sweep O.J.S. from online threats.
Optimizing Inventory Management: Data-Driven Insights from K-Means Clustering Analysis of Prescription Patterns Dermawan, Aulia Agung; Ansarullah Lawi; Putera, Dimas Akmarul; Kurniawan, Dwi Ely; Ummatin, Kuntum Khoiro; Jorvick Steve
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8690

Abstract

Purpose: The goal is to improve how inventory is managed in healthcare by using K-Means clustering to analyze prescription trends. This approach helps ensure better stock availability, streamlines operations, and ultimately increases sales opportunities. Methods: This research applied the K-Means clustering algorithm to analyze a comprehensive dataset of prescription behaviors from XYZ Clinic. By grouping similar prescriptions into clusters, this method highlighted patterns within the data. These insights led to the identification of unique prescription categories, enabling the creation of tailored recommendations for improving inventory management. Result: The analysis showed that Cluster 1 should be prioritized for inventory management due to its high sales potential and consistent prescription patterns. It is recommended to increase stock for the medications in Cluster 1 to improve inventory turnover and streamline clinical operations. These findings underscore the value of K-Means clustering in healthcare, especially for enhancing inventory management and operational efficiency. Novelty: This research presents a novel application of K-Means clustering in healthcare, focusing on prescription patterns and inventory management. While previous studies have primarily used K-Means clustering for areas such as risk assessment and logistics, this study provides valuable data-driven insights to improve inventory management strategies in healthcare. The results highlight how clustering methods can support better decision-making and resource allocation, ultimately leading to greater operational efficiency and improved patient care.
PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography Susanto, Ajib; Sinaga, Daurat; Mulyono, Ibnu Utomo Wahyu
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9561

Abstract

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.
Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote' Iedwan, Aisha Shakila; Mauliza, Nia; Pristyanto, Yoga; Hartanto, Anggit Dwi; Rohman, Arif Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10290

Abstract

Purpose: The purpose of this research is to analyze and compare the performance of two machine learning models, Support Vector Machine (SVM) and Multinomial Naive Bayes, in conducting sentiment analysis on YouTube comments related to the film "Dirty Vote." Methods: The study involved collecting YouTube comments and preprocessing the data through cleaning, labeling, and feature extraction using TF-IDF. The dataset was then divided into training and testing sets in an 80:20 ratio. Both the SVM and Multinomial Naive Bayes models were trained and tested, with their performance evaluated using accuracy, precision, recall, and F1-score metrics. Result: The results revealed that both models performed well in classifying sentiments, with SVM slightly outperforming Multinomial Naive Bayes in terms of accuracy and precision. Particularly, SVM showed superior performance in detecting positive comments, making it a more reliable model for this specific sentiment analysis task. Novelty: This study contributes to the field of sentiment analysis by providing a detailed comparative analysis of SVM and Multinomial Naive Bayes models on YouTube comments in the context of an Indonesian film. The findings highlight the strengths and weaknesses of each model, offering insights into their applicability for sentiment analysis tasks, particularly in analyzing social media content. This research also suggests potential future directions, including the exploration of advanced NLP techniques and different models to enhance sentiment analysis performance.

Page 2 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 13 No. 1: February 2026 Vol. 12 No. 4: November 2025 Vol. 12 No. 3: August 2025 Vol. 12 No. 2: May 2025 Vol. 12 No. 1: February 2025 Vol. 11 No. 4: November 2024 Vol. 11 No. 3: August 2024 Vol. 11 No. 2: May 2024