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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.
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Articles 15 Documents
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Search results for , issue "Vol 10, No 4 (2023): November 2023" : 15 Documents clear
Mapping of Social Vulnerability to Natural Hazards in Geodemographic Analysis Using Fuzzy Geographically Weighted Clustering Deden Istiawan; Ratri Wulandari; Sulastri Sulastri
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v%vi%i.47418

Abstract

Purpose: Assessing social vulnerability is essential for addressing environmental risks by developing suitable adaptation strategies and fostering a resilience mindset. However, relying solely on an index-based approach to measure social vulnerability has limitations as it only provides a broad overview. It is essential to recognize that various regions are influenced by distinct factors contributing to social vulnerability. This study aims to pinpoint specific community factors that impact vulnerability to natural disasters in various districts across Indonesia.Methods: In this research, we determine the optimal number of clusters with the Cluster Validity Index (CVI). Furthermore, this research applies clustering analysis of social vulnerability to natural disasters at the district level using the Fuzzy Geographically Weighted Clustering (FGWC) algorithm.Results: This research highlights varying social vulnerability profiles across Indonesia's diverse districts. Specifically, districts on the western side of Sumatra Island, such as Nias and Mentawai, and those in the eastern regions of Indonesia, including Nusa Tenggara, West Sulawesi, Central Sulawesi, North Sulawesi, the Southern Maluku Islands, and Papua, exhibit the most noticeable vulnerability. This vulnerability is particularly evident in socioeconomic indicators, family composition, housing conditions, and educational access.Novelty: The results of this study provide valuable support for the government as a policymaker. By identifying priority areas and tailoring policies to address critical social vulnerability issues in each district, especially in the most vulnerable areas, the research offers a practical framework for targeted and effective disaster risk reduction and mitigation efforts.
Impact of Online Gaming on the Academic Performance of DEBESMSCAT-Cawayan Campus Students Ricky Capinig Gabrito; Roger Yatan Ibañez Jr.; Jacob Frederick Palabiano Velza
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v%vi%i.45007

Abstract

Purpose: This study investigated the effects of online gaming on the academic performance of students of DEBESMSCAT-Cawayan Campus.Methods: A descriptive research design was employed, and a survey questionnaire was distributed to 75 student online gamers who were selected through a census approach. Statistical analysis techniques such as frequency and percentage were used to analyze the data.Result: Mobile Legends was found to be the most popular game among the respondents. The majority of students spent 1-2 hours playing online games per day and incurred costs associated with gaming. However, most respondents believed that their gaming activities did not significantly hinder their ability to perform tasks in school or at home. The effects of online games on academic performance were perceived positively by the respondents. They believed that online gaming had a positive impact on test scores, overall grades, submission of school activities, time in studying, concentration in studies, participation in learning activities, interaction with people, interest in class discussions, willingness to go to school, and interest in school activities.Novelty: This study provided a comprehensive overview of the perceptions of students regarding the effects of online games on their academic performance. The study suggested that online gaming could have both positive and negative effects on academic performance, depending on how it was managed by students. The study also contributed to the existing body of knowledge on the subject and may have informed future research and interventions aimed at supporting students in managing their gaming activities while maintaining their academic performance.
Design of an Arduino Mega-Based Walking Cane Assistive Device to Improve the Quality of Life for the Elderly in the Riau Islands Province Dimas Akmarul Putera; Aulia Agung Dermawan; Dwi Ely Kurniawan; Alvendo Wahyu Aranski; Rafi Dio
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47793

Abstract

Purpose: The number of people aged 60 or older is expected to increase globally by nearly three times from 737 million in 2009 to 2 million by 2050. Indonesia's population is projected to reach 28 million by 2045, making up nearly one-third of the total population. In 2020, the population aged 60 or older reached 29.52 million, with 29.52 million from the last two houses being occupied by a house due to numerous housing programs that require a close relationship between people. The study suggests that a house with a strong sense of belonging is a risky group required attention. The study uses a smartphone with a 180° panorama camera and GPS to help locating the house and performing its activities.Methods: Anthropometry can be defined as the study of body dimensions, namely body size, shape, strength and work capacity for the purpose of body design and composition and prototyping. Results: This research has resulted in elderly sticks that use sensors to help elderly mobility. This stick can detect the environment nearby the ESP32-Cam with the password "/Capture_photo" and is able to send information about the location of the elderly by sending a message to the GSM Number connected to the Arduino Mega with the password "Hello" and the device will send a message containing the location of the elderly.Novelty: Based on previous research reviewed by researchers, it was found that similar research using Camera and GPS Technology to Improve the Quality of Life of the Elderly has not been carried out in the Riau Islands of Batam.
JSON and MySQL Databases for Spatial Visualization of Polygon and Multipolygon Data in Geographic Information Systems: A Comparative Study M. Zakki Abdillah; Devi Astri Nawangnugraeni; Solikhin Solikhin; Toni Wijanarko Adi Putra
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47393

Abstract

Purpose: Spatial data is used to display digital maps. Geographic information systems' access performance depends on spatial data formats. This study compared JSON and MySQL database data display speeds. Open-source RDBMSs work with various programming languages. JSON displays data in text format. The purpose of this study is to select spatial data for polygon and multipolygon Geographic Information Systems (GIS).Design of study: access speed to the GIS determined the method. This study evaluated how effectively JSON and MySQL displayed digital maps in GIS using two types of geographical data. JSON was in the server directory, and MySQL was on the database server. To measure performance, these two spatial data sets were compared using the same server parameters. Testers employed various tools, operating systems, devices, and browsers.Result: JSON data is stored on a live server and is easier to access while having more data. This test compares file size and speed on three online devices. This test generates JSON as the fastest geographic data, with an average access time of 3.9 seconds and 8.5 MB loaded. MySQL, which averages 9.7 seconds, loads 6.3 MB of files. Despite its larger file size, JSON is faster for spatial data, according to tests.Originality: Its comparison of JSON and MySQL databases based on its application for geographical data display in GIS is unique. This test offers geographic data in JSON faster than MYSQL. JSON can be used to choose location data that GIS can readily access. 
Exploring Long Short-Term Memory and Gated Recurrent Unit Networks for Emotion Classification from Electroencephalography Signals Dian Palupi Rini; Winda Kurnia Sari; Novi Yusliani; Deris Stiawan; Aspirani Utari
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47734

Abstract

This study delves into comparing LSTM and GRU, two recurrent neural network (RNN) models, for classifying emotion data through electroencephalography (EEG) signals. Both models adeptly handle sequential data challenges, showcasing their unique strengths. In EEG emotion dataset experiments, LSTM demonstrated superior performance in emotion classification compared to GRU, despite GRU’s quicker training processes. Evaluation metrics encompassing accuracy, recall, F1-score, and area under the curve (AUC) underscored LSTM’s dominance, which was particularly evident in the ROC curve analysis. This research sheds light on the nuanced capabilities of these RNN models, offering valuable insights into their efficacy in emotion classification tasks based on EEG data. The study explores parameters, such as the number of layers, neurons, and the utilization of dropout, providing a detailed analysis of their impact on emotion recognition accuracy.Purpose: The proposed model in this study is the result of optimizing LSTM and GRU networks through careful parameter tuning to find the best model for classifying EEG emotion data. The experimental results indicate that the LSTM model can achieve an accuracy level of up to 100%.Methods: To improve the accuracy of the LSTM and GRU methods in this research, hyperparameter tuning techniques were applied, such as adding layers, dense layers, flattening layers, selecting the number of neurons, and introducing dropout to mitigate the risk of overfitting. The goal was to find the best model for both methods.Results: The proposed model in this study is capable of classifying EEG emotion data very effectively. The experimental results demonstrate that the LSTM model achieves a maximum accuracy of 100%, while the GRU model achieves a highest accuracy of approximately 98%.Novelty: The novelty of this research lies in the optimization of hyperparameters for both LSTM and GRU methods, leading to the development of novel architectures capable of effectively classifying EEG emotion data.
Optimized Handwriting-based Parkinson's Disease Classification Using Ensemble Modeling and VGG19 Feature Extraction Jumanto Unjung; Maylinna Rahayu Ningsih
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47108

Abstract

Purpose: Parkinson's is a neurological disorder that causes muscles to weaken and arms and legs to tremor over time. The discovery and identification of the stages of Parkinson's disease can substantially benefit the treatment of its symptoms. Many studies have been conducted in classifying hand-drawn -based Parkinson's disease but the resulting performance is still not optimal. The purpose of this research is to improve the performance of handwritten Parkinson's disease classification accuracy using an ensemble soft voting model.Methods: The model adopted the Parkinson's Disease Augmented Data of Handwritten dataset taken from the Kaggle Repository, where the dataset contains Healthy and Parkinson's classes with a total of 3264 images. Then, the dataset was augmented again, resulting in 2612 images in the training data and 652 images in the validation data. After that, the dataset was optimized with VGG19 feature extraction and fine-tuning and then modeled with the Ensemble Learning model. The model was evaluated with a confusion matrix, and the classification report was viewed.Result: The results of the proposed evaluation test discovered the effectiveness of the Ensemble Learning model with Fine Tuning VGG19 feature extraction by producing an accuracy of 98.9%.Novelty: This research has successfully contributed to the proposed ensemble model for Parkinson's handwriting and improved the accuracy performance of the model.
Plasmodium falciparum Identification Using Otsu Thresholding Segmentation Method Based on Microscopic Blood Image Nurul Huda; Alfa Yuliana Dewi; Adiyah Mahiruna
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47924

Abstract

Purpose: Malaria, particularly the Falciparum strain, is a deadly disease caused by Plasmodium falciparum. Malaria diagnosis heavily relies on a microscopist's expertise. To expedite identification, machine learning research has been extensively conducted. However, it has not yet achieved high accuracy, partly due to the use of microscopic images as the dataset. Otsu thresholding, an optimal image segmentation method, maximizes pixel variance to separate the foreground (objects of interest) from the background, which is especially effective in various lighting conditions. Otsu thresholding aims to enhance accuracy and reliability in detecting and classifying Plasmodium falciparum parasites in blood samples.Methods: Dataset of microscopic images of thin blood smears with Plasmodium falciparum, taken from several sources, such as the UC Irvine Machine Learning Repository (UCI), National Institutes of Health (NIH), Kaggle, and Public Health Image Library (PHIL). The methodology combines various image processing techniques, including illumination correction, contrast enhancement, and noise filtering, to prepare the images effectively. The subsequent segmentation using Otsu thresholding method isolates the parasite regions of interest. The classification process involving CNN and SVM evaluates the performance of accuracy to identify different stages of Plasmodium falciparum parasites.Results: The research found the effectiveness of the Otsu thresholding segmentation method in identifying Plasmodium falciparum parasites in microscopic images. By utilizing Convolutional Neural Network (CNN) and Support Vector Machine (SVM) classifiers, the study achieved impressive accuracy rates, with the CNN achieving 96.5% accuracy and the SVM achieving 95.7%.Novelty: This research recorded significant improvement over previous studies that utilized feature extraction and selection methods. At the same time, previous research achieved an accuracy of 82.67. The key innovation here is the adoption of the Otsu thresholding segmentation method, which enhances the identification of Plasmodium falciparum parasites in microscopic images by integrating traditional image processing techniques like Otsu thresholding with modern machine learning methods like CNN and SVM. This significant improvement suggests that the proposed approach offers a more robust and reliable solution for malaria diagnosis.
Zachman Enterprise Architecture Planning (Study Case: E –Government’s General Election Services on Karadenan Sub-District in Bogor Regency Armada Rizki; Elisa Susanti; Wina Erwina
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47691

Abstract

Purpose: This study examines enterprise architecture planning using the Zachman Framework as an effort to automate citizen administration in Bogor Regency. Furthermore, this planning is a solution in the simultaneous step of preparing voter lists down in general election commission data, especially connected to the Citizen Administration Bureau in Karadenan Sub District as a pilot plant project. This study aims to analyze enterprise architecture planning for citizen administration involved in the General Election Commission on collection, updating, and changing citizen data in Karadenan Sub District in Bogor Regency.Methods: Data analysis was carried out from 122 heads of neighborhood units and 19 heads of residential units in Karadenan Subdistrict Bogor Regency. After that, using a qualitative method using the Zachman Framework, an Enterprise Architecture Planning of Citizen Administration was designed.Result: According to the results, this study provides a new role model-based service scheme for citizen administration, especially in election conditions.Novelty: The results of this study are valuable for the government in implementing new digital citizen administration services, especially empowering general elections by providing high-quality data on voters.
Voting Classifier Technique and Count Vectorizer with N-gram to Identifying Hate Speech and Abusive Tweets in Indonesian Riza Arifudin; Dandi Indra Wijaya; Budi Warsito; Adi Wibowo
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.46633

Abstract

Purpose: The objective of this study is to identify hate speech and abusive tweets in Indonesian using a Voting Classifier technique and Count Vectorizer with N-grams. Voting Classifier technique involves combining multiple classifiers like Random Forest and Support Vector Machines to improve classification accuracy.Methods: This research begins by preprocessing the data. Voting classifier uses Support Vector Machine algorithm and Random Forest algorithm. Support Vector Machine and Random Forest serve as the estimators for the voting classifier. As for feature extraction, N-gram and count vectorizer were employed. The effectiveness of the suggested procedures is the desired outcome.Result: Combining the Voting Classifier approach with Count Vectorizer feature extraction and using 1 gram of N-grams, or 82.50%, resulted in the best accuracy. From this study, it can be inferred that the approach employed to identify hate speech and abusive tweets is extremely practical.Novelty: Combining multiple classifiers and using feature extraction techniques like count vectorizer and N-gram with machine learning algorithms can be used for sentiment analysis to differentiate between hate speech and abusive tweets.
Revolutionizing Healthcare: Comprehensive Evaluation and Optimization of SVM Kernels for Precise General Health Diagnosis Wardatus Sholihah; Ade Silvia Handayani; Sarjana Sarjana
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.46430

Abstract

Purpose: This study is driven by a two-fold objective. Firstly, it seeks to optimize the Support Vector Machine (SVM) algorithms in machine learning, comprehensively evaluating diverse SVM kernel variants to enhance their versatility and applicability across various domains, which is beyond the healthcare sector. Secondly, in the context of general health diagnosis, it aims to assess the suitability of SVM kernels for achieving precision in predictive modeling. The choice of SVM is rooted in its effectiveness, proven in classification and regression within data mining. SVMs excel in high-dimensional problem classification, demonstrating superior accuracy, making them invaluable in refining machine learning methodologies and advancing diagnostic systems, promising implications for healthcare and beyond. The chosen SVM model, distinguished by its exceptional performance, is then implemented in real-world applications, particularly in wireless, non-invasive healthcare devices. This deployment signifies a substantial stride toward advancing healthcare practices and holds promising implications for various fields.Methods: Data for this study was collected from publicly accessible datasets on Kaggle, encompassing a comprehensive array of general health-related information. This dataset, comprised of clinical data and vital signs data, underwent meticulous preprocessing, such as data cleaning, feature extraction, and categorization of health status into ‘healthy’ and ‘requiring further attention’. Subsequently, predictive models were constructed employing Support Vector Machine (SVM) algorithms with various kernel functions, such as Linear, RBF, Polynomial, and Sigmoid. They were trained and tested on the preprocessed dataset to assess their efficacy in general health diagnosis. Model performance was rigorously evaluated using established metrics, including accuracy, precision, recall, F-1 score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve, and cross-validation. The selection of the most efficacious SVM kernel was governed by stringent adherence to industry standards and best practices, ensuring optimal integration into health diagnostic systems. The chosen model was tested using new datasets obtained from wireless non-invasive healthcare devices and the pre-existing AHD application. Hyperparameter tuning was meticulously executed to maximize accuracy, ensuring the effectiveness of the evaluation process.Results: The results demonstrate that the Polynomial kernel was selected as the body health diagnostic model instead of the Linear, RBF, and Sigmoid kernels. This kernel has a training time of 0.8 seconds, a testing time of 0.1 seconds, accuracy scores of 97%, precision of 97%, recall of 97%, F-1 score of 97% for training metrics, and accuracy scores of 99%, precision of 99%, recall of 99%, and F-1 score of 99% for testing metrics. The accuracy of the polynomial kernel model decreased to 0.88 on new datasets; adjusting the hyperparameter C to C = 100 resulted in the highest accuracy of 0.945.Novelty: This study introduces a pioneering approach by rigorously optimizing Support Vector Machine (SVM) algorithms, notably the innovative application of the Polynomial kernel in general health diagnosis. Unlike traditional kernels, the Polynomial kernel exhibited exceptional accuracy (up to 99%) and precision. Furthermore, the study’s unique methodology, combining industry standards and meticulous hyperparameter tuning, ensures seamless integration into real-world healthcare systems. The deployment of this optimized model in wireless non-invasive healthcare devices signifies a groundbreaking advancement, highlighting a novel synthesis of theoretical innovation and practical implementation in machine learning for healthcare.

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