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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, 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.
Arjuna Subject : -
Articles 25 Documents
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Search results for , issue "Vol 11, No 1 (2024): February 2024" : 25 Documents clear
Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data Haikal, Husnul Aris; Wigena, Aji Hamim; Sadik, Kusman; Efriwati, Efriwati
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48565

Abstract

Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment. 
Analysis Impact of Rapid Application Development Method on Development Cycle and User Satisfaction: A Case Study on Web-Based Registration Service Riadi, Imam; Yudhana, Anton; Elvina, Ade
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49590

Abstract

Purpose: This research was conducted to respond to obstacles and inefficiencies in the new student registration system at RA Plus Rabbani. Currently, the conventional method of using physical documents for registration is vulnerable to damage and data loss. Therefore, the proposed solution is implementing a website-based online registration system using the Rapid Application Development (RAD) method. This aims to simplify the process, increase accessibility for prospective students, and reduce the costs and time required.Methods: This research commenced by identifying constraints within the conventional student registration system at RA Plus Rabbani through observations and interviews. The development, following the RAD methodology, involved testing with PHPUnit and Blackbox Testing to ensure the functionality of the system aligned with specifications. In addition, usability evaluation was conducted based on the ISO 9126 standard.Result: The research results show that testing on MVC indicated a 100% success rate for each architectural feature. Referring to expectations with a “valid” conclusion on functionality using Blackbox testing, based on ISO 9126 percentage displayed, it is known that the criterion with the most significant value is the understandability characteristic with a value of 83%. Novelty: This research makes a significant contribution by improving student registration services at RA Plus Rabbani through the implementation of various testing techniques, following the research flow offered by RAD. The study also provides substantial references for further research in web-based system development.
Real-Time Web-Based Monitoring System for Temperature, Humidity, and Solar Panels in Ramie Drying Facilities Hidayat, Sidiq Syamsul; Shabiya, Kiara Izzatus; Kadiran, Sri Anggraeni; Mujahidin, Irfan; Prabowo, M. Cahyo Ardi; Nursyahid, Arif; Wasito, Endro; Helmy, Helmy
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47234

Abstract

Purpose: To address the manual monitoring challenges in processing ramie fibers, especially during drying. The purpose is to create a monitoring system that oversees room temperature, humidity, and the status of solar panels, crucial factors in ramie productivity.Methods: Real-time web-based system development that monitors room temperature, humidity, and the performance of solar panels in a ramie drying room using the Internet of Things architecture ESP32 with communication through GSM SIM 800L in rural areas.Results: The system can display real-time information such as temperature data, humidity, and electrical energy parameters derived from the solar panel's utilization in the ramie drying room. By doing so, users gain efficiency and effectiveness in obtaining information, significantly enhancing ramie fiber productivity.Novelty:  Integration of sensor instruments, low-power ESP32 microcontrollers, GSM Telecommunication, Solar Cell Energy as a power source, and a real-time web-based Monitoring Information System implemented in a ramie drying dome. This simplifies the monitoring process and optimizes limited resources such as space, energy, telecommunications, and human resources, which are typically constrained infrastructure in the ramie fiber agricultural system.
Examination of the Factors Impacting the Interest of Residents in Semarang City in Mobile Health Applications: An UTAUT Analysis Perdana, Muhamad Putra; Abidin, Zaenal
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48130

Abstract

Purpose: The study aims is to examine the determinants that impact the level of public interest in utilizing mobile health (m-health) applications in Semarang City, Indonesia. Our specific objective is to identify the critical factors that facilitate or impede the public's adoption of these applications.Methods: This study objective was pursued using a comprehensive approach. A study model was developed utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) as its foundation. This model encompasses essential variables including performance expectancy, effort expectancy, social influence, facilitating conditions, price value, and perceived trust. The process of data collecting was carried out by means of a survey that was disseminated across widely used social media channels. The study was conducted using a sample size of 257 participants who are residents of Semarang City. The data that was collected underwent a thorough analysis utilizing the Partial Least Squares - Structural Equation Model (PLS-SEM) approach.Results: The research conducted in our study resulted in several significant findings. The study revealed that several factors, namely performance expectancy, social influence, price value, and perceived trust, had a notable and beneficial impact on users' inclination towards using m-health applications. On the other hand, the variables of effort expectancy and facilitating conditions did not exhibit a statistically significant influence on the level of public interest in these applications. Furthermore, a substantial correlation was found between the behavioral intention and the actual usage behavior of inhabitants of Semarang City in their adoption of m-health applications.Novelty: The research presented in this study is distinguished by its comprehensive analysis of the various factors that impact the adoption of mobile health (m-health) applications in Semarang City. Through the incorporation and expansion of variables such as price value and perceived trust, our study provides a comprehensive and nuanced comprehension of this particular occurrence by adapting and extending the UTAUT model. Our work emphasizes the importance of performance expectancy and social influence, while also suggesting the need for additional investigation into the roles of effort expectancy and facilitating conditions. Additionally, our study offers valuable information regarding the influence of age and gender as moderators in these associations. The results of this study have significant practical implications for healthcare professionals and policymakers who are interested in promoting the use of mobile health (m-health) technologies among the public. Additionally, these findings can serve as a valuable guide for future research endeavors in this particular area of study. 
YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions Panja, Eben; Hendry, Hendry; Dewi, Christine
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49038

Abstract

Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.
Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection Ridwan, Mohammad; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49424

Abstract

Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.
YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition Dewanto, Muhammad Ridho; Farid, Mifta Nur; Rafdi Syah, Muhammad Abby; Firdaus, Aji Akbar; Arof, Hamzah
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48723

Abstract

Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.
Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification Andrian, Rico; Herwanto, Hans Christian; Taufik, Rahman; Kurniawan, Didik
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49451

Abstract

Purpose: The rich cultural heritage of Indonesia includes the intricate art of batik, which varies across regions with unique patterns and motifs. This study focuses on Lampung batik, a distinctive type of batik, representing Lampung Province, Indonesia. Leveraging Convolutional Neural Network (CNN) architectures, namely LeNet-5 and MobileNet, the research compares their effectiveness in recognizing and classifying Lampung batik motifs. Data augmentation techniques, including rotation, brightness, and zoom, were employed to enhance the dataset and improve model performance.Methods: The study collected 500 Lampung batik images categorized into 10 classes which were then augmented and divided into training, validation, and testing sets. The model was created using a Deep Learning approach, LeNet And MobileNet. Both models were trained using identical hyperparameters and evaluated based on their accuracy in classifying Lampung batik motifs.Results: The results demonstrate an accuracy of 99.33% for LeNet-5 and 98.00% for MobileNet, outperforming previous studies. LeNet-5, particularly with augmentation, exhibited superior precision and recall in classifying Lampung batik motifs. This research underscores the efficacy of CNN architectures, coupled with data augmentation techniques, in accurately identifying intricate cultural artifacts like Lampung batik.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.
E-Dharmagita Learning Model Innovation with Mobile and Multimedia Technology Sudana, Oka; Sukma, Kd. Vigyan Melati; Wirdiani, Ayu; Putri, Gusti Agung Ayu
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.46653

Abstract

Purpose: Yadnya ceremony is a sacred sacrifice performed with sincere and wholehearted devotion by Hindus to God, spiritual leaders or teachers, fellow humans, the ancestors, and Bhuta_Kala. The implementation of the Yadnya ceremony is_usually accompanied by a spiritual chant of the Hindus known as Dharmagita. Nowadays, a lot of young generations do not know and are even more indifferent to the Dharmagita. Using books as a source of Dharmagita information is less attractive since there are no audio examples or recordings of correct songs that can be listened to. The Android-based e-Dharmagita application was built to overcome these problems to facilitate the younger generation in getting more interesting information about Dharmagita.Methods: e-Dharmagita was constructed with the support of mobile and multimedia technology. In addition, the information presented was related to the Yadnya Ceremony by implementing a complex Tree Algorithm.Results: This research produced an e-Dharmagita Application, of which user acceptance testing using the PSSUQ Method resulted in a score incudes in the excellent category. Therefore, the application could be accepted well in the implementation the Information Technology in Balinese Culture and Hinduism, especially the innovation of a more modern and attractive Dharmagita learning model.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.
Indonesian News Text Summarization Using MBART Algorithm Astuti, Rahma Hayuning; Muljono, Muljono; Sutriawan, Sutriawan
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49224

Abstract

Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.

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