Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.49975
Yesy Diah Rosita, Lady Silk Moonlight
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.
{"title":"Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios","authors":"Yesy Diah Rosita, Lady Silk Moonlight","doi":"10.15294/sji.v11i1.49975","DOIUrl":"https://doi.org/10.15294/sji.v11i1.49975","url":null,"abstract":"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"4 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140410162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.48721
Adlina Khairunnisa, K. Notodiputro, B. Sartono
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.
{"title":"A Comparative Study of Random Forest and Double Random Forest Models from View Points of Their Interpretability","authors":"Adlina Khairunnisa, K. Notodiputro, B. Sartono","doi":"10.15294/sji.v11i1.48721","DOIUrl":"https://doi.org/10.15294/sji.v11i1.48721","url":null,"abstract":"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"73 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140408683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.48475
Aaqila Dhiyaanisafa Goenawan, Sri Hartati
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).
目的：比较两种能够从手势识别印尼手语系统或 Sistem Isyarat Bahasa Indonesia (SIBI) 字体并将其实时转化为书写信息的模型或原型程序：数据集由 Kaggle 网站上提供的多个 SIBI 字母图像的手势图像数据集组合而成。地标被分为 26 个标志类别，经过预处理后共有 19826 行数据，然后分为 67% 的训练数据和 33% 的测试数据。接下来，K-NN 算法和随机森林算法被应用到不同的程序中，并在模型评估和实时性两个不同的测试中进行测试。最后，对结果进行比较，以了解 K-Nearest Neighbors (K-NN) 算法和随机森林算法准确率的提高情况：使用随机森林算法对构建和训练的模型进行评估，结果显示精确度、召回率、准确度和 F1 分数均达到 99.88%。新颖性：从结果可以看出，使用随机森林算法建立的模型更优越，但在实时测试中，K-近邻算法的效果更好。因此，应增加图像数据及其多样性，以提高识别准确率。此外，还可以增加程序识别手势的功能，不仅可以识别单手或双手，还可以识别带动作的手势，这样程序就可以识别静态和动态的字母（要求手部动作）。
{"title":"The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time","authors":"Aaqila Dhiyaanisafa Goenawan, Sri Hartati","doi":"10.15294/sji.v11i1.48475","DOIUrl":"https://doi.org/10.15294/sji.v11i1.48475","url":null,"abstract":"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).","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.48565
Husnul Aris Haikal, A. Wigena, Kusman Sadik, Efriwati Efriwati
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.
{"title":"Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data","authors":"Husnul Aris Haikal, A. Wigena, Kusman Sadik, Efriwati Efriwati","doi":"10.15294/sji.v11i1.48565","DOIUrl":"https://doi.org/10.15294/sji.v11i1.48565","url":null,"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. ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.50274
Rini Muzayanah, Apri Dwi Lestari, Jumanto Jumanto, Budi Prasetiyo, Dwika Ananda Agustina Pertiwi, M. A. Muslim
Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem 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 techniquesused. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, andRandom Oversampling.Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Lineresampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTEresampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by acombination 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 theXGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lendingplatforms so that the sorting algorithm can work optimally and produce optimal accuracy.
{"title":"Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction","authors":"Rini Muzayanah, Apri Dwi Lestari, Jumanto Jumanto, Budi Prasetiyo, Dwika Ananda Agustina Pertiwi, M. A. Muslim","doi":"10.15294/sji.v11i1.50274","DOIUrl":"https://doi.org/10.15294/sji.v11i1.50274","url":null,"abstract":"Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem 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 techniquesused. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, andRandom Oversampling.Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Lineresampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTEresampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by acombination 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 theXGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lendingplatforms so that the sorting algorithm can work optimally and produce optimal accuracy.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"12 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140413420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.48145
Fhara Elvina Pingky Nadya, M.Firdaus Ibadi Ferdiansyah, Vinna Rahmayanti Setyaning Nastiti, Christian Sri Kusuma Aditya
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.
{"title":"Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree","authors":"Fhara Elvina Pingky Nadya, M.Firdaus Ibadi Ferdiansyah, Vinna Rahmayanti Setyaning Nastiti, Christian Sri Kusuma Aditya","doi":"10.15294/sji.v11i1.48145","DOIUrl":"https://doi.org/10.15294/sji.v11i1.48145","url":null,"abstract":"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"32 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140412672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Indonesian News Text Summarization Using MBART Algorithm","authors":"Rahma Hayuning Astuti, Muljono Muljono, Sutriawan Sutriawan","doi":"10.15294/sji.v11i1.49224","DOIUrl":"https://doi.org/10.15294/sji.v11i1.49224","url":null,"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"1997 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.47364
Erna Zuni Astuti, C. A. Sari, E. H. Rachmawanto, Rabei Raad Ali
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.
{"title":"Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS","authors":"Erna Zuni Astuti, C. A. Sari, E. H. Rachmawanto, Rabei Raad Ali","doi":"10.15294/sji.v11i1.47364","DOIUrl":"https://doi.org/10.15294/sji.v11i1.47364","url":null,"abstract":"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140413592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.50077
Ni Kadek Emik Sapitri, Umu Sa’adah, Nur Shofianah
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.
目的：微阵列数据分析结果对癌症诊断非常重要，尤其是卵巢癌等早期无症状癌症。分析微阵列数据的挑战之一是不平衡数据问题。遗憾的是，根据微阵列数据进行癌症分类的研究往往忽视了这一挑战，因此没有使用适当的评估指标。这使得结果偏向于多数类。本研究使用流行的评价指标 "准确率 "和适用于不平衡数据的评价指标 "平衡准确率（BA）"，从混淆矩阵中获取有关卵巢癌分类中准确率和平衡准确率值的信息：本研究使用分类回归树（CART）作为分类器。通过剪枝优化 CART。根据 CART 复杂性分析和混淆矩阵的结果确定 CART 最佳值：本研究中的混淆矩阵和 CART 解释表明，低复杂度的 CART 仍能很好地预测大多数类别的受访者。新颖性：确保所使用的评价指标与所处理数据的特征相匹配非常重要。这项研究说明了准确率和 BA 之间的区别。研究得出结论，在不进行重采样的情况下对不平衡数据集进行分类，可以使用 BA 作为评价指标，因为根据结果，BA 对两类数据都更公平。
{"title":"Knowledge Discovery from Confusion Matrix of Pruned CART in Imbalanced Microarray Data Ovarian Cancer Classification","authors":"Ni Kadek Emik Sapitri, Umu Sa’adah, Nur Shofianah","doi":"10.15294/sji.v11i1.50077","DOIUrl":"https://doi.org/10.15294/sji.v11i1.50077","url":null,"abstract":"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.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"4 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140410528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.15294/sji.v11i1.48183
A. Editya, Neny Kurniati, Mochammad Machlul Alamin, Anggay Luri Pramana, A. Lisdiyanto
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.
{"title":"Forensic Analysis of Drones Attacker Detection Using Deep Learning","authors":"A. Editya, Neny Kurniati, Mochammad Machlul Alamin, Anggay Luri Pramana, A. Lisdiyanto","doi":"10.15294/sji.v11i1.48183","DOIUrl":"https://doi.org/10.15294/sji.v11i1.48183","url":null,"abstract":"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. ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"22 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140412115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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