Scientific Journal of Informaticsn starting in 2024 migrates to better secure from various unwanted things, including journal hacking and so on. To submit, the author please visit the new website page of our journal at the link https://journal.unnes.ac.id/journals/sji MIGRATION OFFICIAL STATEMENT HERE Starting vol 11 no 2 has moved to https://journal.unnes.ac.id/journals/sji 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, with a particular emphasis on the Indonesian context and global perspective. The SJI publishes 4 issues in a calendar year (February, May, August, November). The advantage of this journal is the fast response, for good quality articles, the following is the estimated processing time: (1). Initial Decision for Review: 1 -10 days after submission, (2). Decision after review: 6 - 8 weeks after submission, (3). online publication time: 1-4 weeks after acceptance. You can also contact us through the contacts on this website. Indexing & Abstracting: DOAJ, EBSCO, IPI, SINTA, ROAD, GARUDA, DIMENSIONS
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Applying Structural Equational Model (SEM) to Analysis of Perceived Social Media Influence on Intention to Buy Online Store by Consumer Trust and Hedonic Brand Image
Purpose: This research aims to examine the influence of social media perceptions which directly influence repurchase intentions which are mediated by consumer trust and hedonic brand image in an online shop by applying the Structural Equational Model (SEM) with Partial Lease Square (Smart PLS) technique. Methods: This research examines the effect of social media perception intervention on repurchase intentions which is analyzed with a population taken from consumers in four marketplaces, including Lazora.Com, Lazada.com, and Blibli.com. bukalapak.com. The number of samples in this research was 80 respondents. The sampling technique uses accidental, namely distributing questionnaires to consumers who meet in WhatsApp groups. The data was tested with the variable social media perception, purchase intention, consumer trust, and hedonic brand image. Data were analyzed using the Structural Equational Model (SEM) with Partial Lease Square (Smart PLS) technique. Result: The research results show that the indicator variables proposed to test the influence of perceived social media on repurchase decisions. The Q2 predictive relevance value shows that the perceived social and consumer trust contributes 0.7647 towards the intention to buy. The rest which is 0.2353 is affected by the other variable. Therefore, social media perceptions do not influence repurchase intentions, but social media perceptions influence repurchase intentions which are mediated by consumer trust and Hedonic Brand Image in an online shop. Research limitation: We found that there are perceived social media influence repurchase intention to buy mediated by consumer trust and Hedonic Brand Image in an online store. This will contribute to other research, creating Consumer trust and a Hedonic Brand image is very important to increase repurchase intention to buy in an online store. Practical implications – The findings of this study may contribute to consumer behavior models, online stores, and Tripple duties. Novelty: The novelty of this research is that consumer trust and hedonic brand Image can increase the intention to buy in an online store.
Copyright (c) 2023 Scientific Journal of Informatics
Damerau-Levenshtein Distance Algorithm Based on Abstract Syntax Tree to Detect Code Plagiarism
Purpose: This research aimed to detect source code plagiarism based on Abstract Syntax Tree using Damerau-Levenshtein Distance algorithm, which is expected to streamline the inaccuracies and time-consumption associated with the manual process. Methods: Damerau-Levenshtein Distance algorithm was used to determine the similarity between source code files and calculate F-Measure. The dataset, which consisted of 178 source code files from 20 coursework assignments, was obtained from GitHub by Lawton Nichols in 2019. Damerau-Levenshtein Distance algorithm was used to compute the minimum cost required to transform one line of code into another. Furthermore, ANTLR detected AST, which was processed through preprocessing, including node pruning, function and variable sorting, and log output removal. Result: The result showed that the two methods took 5.704 seconds and 0.996 seconds to complete. The lowest and highest values obtained using F-Measure were 0.16 and 0.8, respectively. Therefore, the system performed detection processes quickly and effectively detected common forms of code plagiarism with difficulty in the more complex forms. Novelty: In conclusion, this research used AST and Damerau-Levenshtein Distance algorithm to calculate the 5 levels of similarity in Java programming language source code. For further development, preprocessing steps were needed to prune unnecessary nodes and detect equivalent but differently syntaxed code.
Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF
Purpose: Freedom in social media gives rise to the possibility of disturbing users through the sentences they send, which is limited by the Electronic Information and Transactions Law (UU ITE). This research aims to find an effective method for classifying hate speech text data, especially in Indonesian, with many categories expected to minimize this case. Methods: This study used 1.000 data from Twitter with five labels, including religion, race, physical, gender and other (invective or slander). The process started with several steps of preprocessing, data transformation using TF-IDF-ICSρF term weighting and data mining using an Improved KNN algorithm. Then, the results were compared with the TF-IDF and KNN methods to evaluate the differences. Result: Using TF-IDF-ICSρF and Improved KNN algorithms gets an average accuracy value of 88.11%, 17.81% higher compared with the same data and parameters to the K-Nearest Neighbor and TF-IDF algorithms, which get results of 70.30%. Novelty: Based on the comparison results, TF-IDF-ICSρF and Improved KNN methods can effectively classify hate speech sentences that have many labels with fairly good accuracy.
Copyright (c) 2024 Scientific Journal of Informatics
A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19
Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily. Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model. Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%. Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.
E-Dharmagita Learning Model Innovation with Mobile and Multimedia Technology
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.
Smart System on Two-dimensional Shapes Recognition Application for Kindergarten Students
Abstract. Kindergarten-aged children are going through an important period of cognitive development, such as the ability to think concretely, including recognizing simple geometric shapes such as circles, triangles, and squares. However, many children find it difficult to understand the basic concepts of two-dimensional shapes. Purpose: It is necessary to develop prototype learning aids in the form of intelligent systems in two-dimensional shapes applications for kindergarten students, which utilize information technology and object visualization directly through cameras on smartphones. This is expected to increase children's learning motivation and help strengthen their understanding of two-dimensional shapes. Methods: The research combines Waterfall and Agile methodologies, tailoring them to four stages: plan and discovery, analysis and design, application development, and testing. Testing gathers accuracy with 120 smartphone-collected data points for square, triangle, circle, and pentagon shapes. Also, usability testing based on learnability, efficiency, memorability, error handling, and satisfaction, was obtained from six kindergarten teacher questionnaires and quantitatively processed. Results: The application achieves an accuracy rate of approximately 79%. Notably, accuracy decreases with fewer corners, mainly due to low resolution or lack of focus, resulting in simplified detected shapes. Regarding usability, it is evident that the application has received positive feedback from users, particularly kindergarten teachers, who have given it an average score of 78.83. Novelty: Distinguished from previous research, the novelty of this study resides in its ability to capture objects through a camera, eliminating the need for predefined shapes within the application, and innovating by creating an educational tool aligned with the kindergarten curriculum to recognize two-dimensional shapes. The research contribution is the creation of an innovative learning tool for kindergarteners, merging smartphone technology with real-world objects to teach two-dimensional shapes, thus integrating technology into early childhood education effectively, which has urgency in efforts to improve the quality of learning.
Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies
Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses. Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation. Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410. Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.
Real-Time Web-Based Monitoring System for Temperature, Humidity, and Solar Panels in Ramie Drying Facilities
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.
Analysis Impact of Rapid Application Development Method on Development Cycle and User Satisfaction: A Case Study on Web-Based Registration Service
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.
Community Satisfaction with Online Services in East Lombok Regency: (Case Study: BAKSO Application)
Purpose: This study analyzes the use of the BAKSO (Create Online Population Administration) application for users, namely employees of the East Lombok Regency Population and Civil Registration Office and the Admin of each village. The measurement uses variables consisting of infrastructure, ICT, bureaucracy, leadership, and implementation of digital government. Methods: The method used is a quantitative type, primary data in the form of a survey of 115 respondents from East Lombok Regency Population and Civil Registration Office office employees and village admins who used the BAKSO application (Create Online Population Administration). Using the Likert Scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree). The analysis technique for this study uses SmartPLS 3. Results: The results of this study show that infrastructure and ICT variables have a positive and significant influence on the implementation of digital government in the implementation of the BAKSO application. Meanwhile, bureaucratic and leadership variables have little impact and are substantial in implementing the BAKSO application in East Lombok Regency. Novelty: This study is unique because it examines users who are also employees who use the BAKSO application (Make Online Population Administration) with a measuring indicator, namely the online service index (OSI); most of the previous research on this theory was only oriented to assessing public satisfaction with online services. This research provides a new perspective on using the Online Service Index (OSI) on the scope of application-based online services from an employee perspective. In addition, the empirical contribution lies in the professionalism of employees in using the BAKSO application. So that later, it can provide space for the government to pay attention to human resources in running Online Service Index (OSI)-based application services.
Examination of the Factors Impacting the Interest of Residents in Semarang City in Mobile Health Applications: An UTAUT Analysis
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.
Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection
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.
YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions
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.
YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition
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
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.
Indonesian News Text Summarization Using MBART Algorithm
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.
Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data
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.
Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS
Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages. Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm. Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.
Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree
Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models. Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%. Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.
Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios
Purpose: This paper aims to investigate and compare the performance of LSTM Neural Network and Support Vector Machines (SVM) in predicting the USD exchange rate using three different training data scenarios: 45%, 55%, and 75%. The study employs a dataset from the Indonesian Central Bureau of Statistics (BPS) for the period of January 1 to June 30, 2021, encompassing attributes USD Selling Rate. Methods: The methods involve implementing LSTM and SVM algorithms within the Python programming language using Google Colaboratory. Three distinct training data scenarios are explored to evaluate the models' robustness. Performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, are employed for evaluation. Result: Results reveal that LSTM demonstrates superior prediction accuracy compared to SVM across all scenarios, even though it incurs a longer training time. Notably, in the 75% training data scenario, LSTM achieves an MAE of 49.52, RMSE of 63.08, and R-squared of 0.37906, outperforming SVM with MAE of 138.33, RMSE of 161.58, and R-squared of 0.34277. Novelty: This study innovatively compares LSTM Neural Network and Support Vector Machines (SVM) for USD exchange rate prediction across different training scenarios (45%, 55%, and 75%). Unlike previous research focusing on individual models, this study systematically evaluates both methods, highlighting the nuanced balance between prediction accuracy and training time. The findings offer novel insights into LSTM and SVM applicability in currency forecasting, providing valuable guidance for researchers and practitioners in model selection based on specific predictive task requirements.
A Comparative Study of Random Forest and Double Random Forest Models from View Points of Their Interpretability
Purpose: This study aims to compare the performance of ensemble trees such as Random Forest (RF) and Double Random Forest (DRF) from view points of interpretability of the models. Both models have strong predictive performance but the inner working of the models is not human understandable. Model interpretability is required to explain the relationship between the predictors and the response. We apply association rules to simplify the essence of the models. Methods: This study compares interpretability of RF and DRF using association rules. Each decision tree formed from each model is converted into if-then rules by following the path from root node to leaf nodes. The data was selected in such a way that they were underfit data. This is due to the fact that DRF has been shown by other researchers to overcome the underfitting problem faced by RF. A Simulation study has been conducted to evaluate the extracted rules from RF and DRF. The rules extracted from both models are compared in terms of model interpretability based on support and confidence values. Association rules may also be applied to identify the characteristics of poor people who are working in Yogyakarta. Result: The simulation results revealed that the interpretability of DRF outperformed RF especially in the case of modelling underfit data. On the other hand, using empirical data we have been able to characterize the profile of poor people who are working in Yogyakarta based on the most frequent rules. Novelty: Research on interpretable DRF is still rare, especially the interpretation model using association rules. Previous studies focused only on interpreting the random forest model using association rules. In this study, the rules extracted from the random forest and double random forest models are compared based on the quality of the rules extracted.
Forensic Analysis of Drones Attacker Detection Using Deep Learning
Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers. Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers. Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5. Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.
Knowledge Discovery from Confusion Matrix of Pruned CART in Imbalanced Microarray Data Ovarian Cancer Classification
Purpose: The results of microarray data analysis is important in cancer diagnosis, especially in early stages asymptomatic cancers like ovarian cancer. One of the challenges in analyzing microarray data is the problem of imbalanced data. Unfortunately, research that carries out cancer classification from microarray data often ignores this challenge, so that it doesn’t use appropriate evaluation metrics. It makes the results biased towards the majority class. This study uses a popular evaluation metric “accuracy” and an evaluation metric that is suitable for imbalanced data “balanced accuracy (BA)” to gain information from the confusion matrix regarding accuracy and BA values in case of ovarian cancer classification. Methods: This study use Classification and Regression Tree (CART) as the classifier. CART optimized by pruning. CART optimal is determined from the results of CART complexity analysis and confusion matrix. Results: The confusion matrix and CART interpretations in this research show that CART with low complexity is still able to predict majority class respondents well. However, when none of the data in the minority class was classified correctly, the accuracy value was still quite high, namely 86.97% and 88.03% respectively at the training and testing stages, while the BA value at both stages was only 50%. Novelty: It is very important to ensure that the evaluation metrics used match the characteristics of the data being processed. This research illustrate the difference between accuracy and BA. It concluded that that classification of an imbalanced dataset without doing resampling can use BA as evaluation metric, because based on the results, BA is more fairly to both classes.
The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time
Purpose: Comparing 2 models or prototype programs which can recognize Indonesian Sign Language System or Sistem Isyarat Bahasa Indonesia (SIBI) fonts from hand gesture and translate it’s into writing Messages in real-time. Methods: After selecting datasets and reprocessed by the researcher into 1 dataset, which are a combination of several sign image datasets of the SIBI letters images available on the Kaggle website, the dataset is converted into landmarks. The landmarks are divided into 26 sign classes and preprocessed to a total of 19,826 rows of data, and then divided into 67% training data and 33% test data. Next, both K-NN and Random Forest algorithm are implemented into different program and get tested into 2 different tests, model evaluation and real-time. At the end, the result is compared to see the increase of accuracy level of both K-Nearest Neighbors (K-NN) and Random Forest algorithm. Result: The constructed and trained model is then evaluated and the results of Precision, Recall, Accuracy, and F1-Score are 99.88% using the Random Forest algorithm. The results of real-time program testing with the K-Nearest Neighbors algorithm get higher results, where the average accuracy value reaches 99%. Novelty: From the result shows that the model built with the Random Forest algorithm is superior, but the K-Nearest Neighbors algorithm is better in real-time testing. Therefore, image data and its diversity should be increased, in order to improve recognition accuracy. The program could be enhanced by adding a function where the program can recognize hand gesture, not only one or two hands but also can recognize a hand gesture with movements so the program can recognize static and dynamic letter (required hands movement).
Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction
Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform can cause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome this problem, appropriate resampling techniques are needed so that the classification algorithm can work optimally and provide results with optimal accuracy. This research aims to find the right resampling technique to overcome the problem of data imbalance in data lending on peer-to-peer landing platforms. Methods: This study uses the XGBoost classification algorithm to evaluate and compare the resampling techniques used. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, and Random Oversampling. Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Line resampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTE resampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by a combination of the XGBoost model with the SMOTE resampling technique. Novelty: It is hoped that from this research we can find the most suitable resampling technique combined with the XGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lending platforms so that the sorting algorithm can work optimally and produce optimal accuracy.