Pub Date : 2023-05-06DOI: 10.30595/juita.v11i1.16166
Leli Fitriani, D. Tresnawati, Muhammad Bagja Sukriyansah
In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.
{"title":"Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation","authors":"Leli Fitriani, D. Tresnawati, Muhammad Bagja Sukriyansah","doi":"10.30595/juita.v11i1.16166","DOIUrl":"https://doi.org/10.30595/juita.v11i1.16166","url":null,"abstract":"In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115277899","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 : 2023-05-06DOI: 10.30595/juita.v11i1.15341
Dwi Intan Af’idah, P. Anggraeni, Muhammad Rizki, Aji Setiawan, Sharfina Febbi Handayani
The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.
{"title":"Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory","authors":"Dwi Intan Af’idah, P. Anggraeni, Muhammad Rizki, Aji Setiawan, Sharfina Febbi Handayani","doi":"10.30595/juita.v11i1.15341","DOIUrl":"https://doi.org/10.30595/juita.v11i1.15341","url":null,"abstract":"The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132601384","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 : 2023-05-06DOI: 10.30595/juita.v11i1.16191
C. Huda, B. Etikasari, P. S. D. Puspitasari
Food is an essential need for every living creature. Choosing the wrong food leads to serious problems e.g. indigestion, obesity, diabetes mellitus, stroke, including heart disease that causes death. To prevent those diseases from harming the body, people should be concerned about food consumption, for example by consuming organic food. Organic food is obtained by cultivating plants in a greenhouse to increase production, minimize risk, prevent disease, and be safer against environmental risk. However, some obstacles faced by farmers such as disease or pests, water supply, temperature, and so on. Based on some previous research, the problem is dominated by soil moisture since the farmer has to water all plants manually. It has affected crop yields directly. If this phenomenon is not handled properly, farmers are threatened with losses so organic farming becomes a catastrophe. Therefore, in this research, an IoT technology is proposed to increase soil moisture in real time. The proposed system is also equipped with a Web-based information system to expose the cultivation phase, and market crops, as well as a tool for buyers as interaction media through the feedback provided. In the end, the proposed system is adequate to increase the productivity of vegetable cultivation grown in a greenhouse. Based on some experiments that have been done, the proposed method is capable to work optimally and effectively meet user needs by 95.55%.
{"title":"A Smart Greenhouse Production System Utilizes an IoT Technology","authors":"C. Huda, B. Etikasari, P. S. D. Puspitasari","doi":"10.30595/juita.v11i1.16191","DOIUrl":"https://doi.org/10.30595/juita.v11i1.16191","url":null,"abstract":"Food is an essential need for every living creature. Choosing the wrong food leads to serious problems e.g. indigestion, obesity, diabetes mellitus, stroke, including heart disease that causes death. To prevent those diseases from harming the body, people should be concerned about food consumption, for example by consuming organic food. Organic food is obtained by cultivating plants in a greenhouse to increase production, minimize risk, prevent disease, and be safer against environmental risk. However, some obstacles faced by farmers such as disease or pests, water supply, temperature, and so on. Based on some previous research, the problem is dominated by soil moisture since the farmer has to water all plants manually. It has affected crop yields directly. If this phenomenon is not handled properly, farmers are threatened with losses so organic farming becomes a catastrophe. Therefore, in this research, an IoT technology is proposed to increase soil moisture in real time. The proposed system is also equipped with a Web-based information system to expose the cultivation phase, and market crops, as well as a tool for buyers as interaction media through the feedback provided. In the end, the proposed system is adequate to increase the productivity of vegetable cultivation grown in a greenhouse. Based on some experiments that have been done, the proposed method is capable to work optimally and effectively meet user needs by 95.55%.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132060927","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}
Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with 𝑲 = 𝟏𝟎 to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟗𝟔. At the same time, the model with the lowest performance is SVM with RBF, namely𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟖𝟔. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is 𝑨𝑼𝑪 = 𝟎.𝟗𝟐𝟕 to 𝟎.𝟗𝟗𝟑.
{"title":"NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization","authors":"Aji Gautama Putrada, Nur Alamsyah, Muhamad Nurkamal Fauzan, Syafrial Fachri Pane","doi":"10.30595/juita.v11i1.15140","DOIUrl":"https://doi.org/10.30595/juita.v11i1.15140","url":null,"abstract":"Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with 𝑲 = 𝟏𝟎 to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟗𝟔. At the same time, the model with the lowest performance is SVM with RBF, namely𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟖𝟔. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is 𝑨𝑼𝑪 = 𝟎.𝟗𝟐𝟕 to 𝟎.𝟗𝟗𝟑.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129495665","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 : 2023-05-06DOI: 10.30595/juita.v11i1.15506
S. Sudarman, Asmarani Pratama Y. Hadad, Abdallah M. H. Abbas
Institut Agama Islam Ternate (IAIN) is one of the universities that run business processes in terms of educational services. IAIN Ternate has implemented information systems and information technology (IS/IT) to support its business processes. However, the implementation of IS/IT has not been equipped with strategic planning, so all forms of procurement, development, and maintenance of IS/IT are only available upon request and are not yet aligned with the organization's strategic plan. This research was conducted to design organizational needs into an IS/IT strategic plan at IAIN Ternate using the Ward and Peppard Framework. This research was conducted using interviews and focus group discussions (FGD) with the leaders and staff of the units at IAIN Ternate. The tools used for the analysis of the organization's internal environment are SWOT and value chain; for the analysis of the organization's external environment, PESTLE and McFarlan Grid are used to map the application portfolio. Based on the results of the study, IAIN Ternate has utilized IS and IT. To achieve the vision, mission, and objectives of IAIN Ternate, it is recommended that IS/IT become a priority for development. The IT Strategic Plan consists of an IS strategy, an IT strategy, and an IS/IT management strategy.
Agama Islam Ternate学院(IAIN)是在教育服务方面运行业务流程的大学之一。IAIN Ternate已经实施了信息系统和信息技术(IS/IT)来支持其业务流程。然而，IS/IT的实施还没有配备战略规划，因此所有形式的采购、开发和维护IS/IT只能根据要求提供，尚未与组织的战略计划保持一致。本研究在IAIN Ternate使用Ward和Peppard框架将组织需求设计成IS/IT战略计划。这项研究是通过与IAIN Ternate各单位的领导和工作人员进行访谈和焦点小组讨论(FGD)进行的。用于分析组织内部环境的工具是SWOT和价值链;对于组织外部环境的分析，使用PESTLE和McFarlan网格来映射应用程序组合。根据研究结果，IAIN Ternate已经利用了IS和IT。为了实现IAIN Ternate的愿景、使命和目标，建议将信息系统/信息技术作为发展的优先事项。IT战略计划包括一个IS战略、一个IT战略和一个IS/IT管理战略。
{"title":"Information System Strategic Planning at Institut Agama Islam Negeri Ternate","authors":"S. Sudarman, Asmarani Pratama Y. Hadad, Abdallah M. H. Abbas","doi":"10.30595/juita.v11i1.15506","DOIUrl":"https://doi.org/10.30595/juita.v11i1.15506","url":null,"abstract":"Institut Agama Islam Ternate (IAIN) is one of the universities that run business processes in terms of educational services. IAIN Ternate has implemented information systems and information technology (IS/IT) to support its business processes. However, the implementation of IS/IT has not been equipped with strategic planning, so all forms of procurement, development, and maintenance of IS/IT are only available upon request and are not yet aligned with the organization's strategic plan. This research was conducted to design organizational needs into an IS/IT strategic plan at IAIN Ternate using the Ward and Peppard Framework. This research was conducted using interviews and focus group discussions (FGD) with the leaders and staff of the units at IAIN Ternate. The tools used for the analysis of the organization's internal environment are SWOT and value chain; for the analysis of the organization's external environment, PESTLE and McFarlan Grid are used to map the application portfolio. Based on the results of the study, IAIN Ternate has utilized IS and IT. To achieve the vision, mission, and objectives of IAIN Ternate, it is recommended that IS/IT become a priority for development. The IT Strategic Plan consists of an IS strategy, an IT strategy, and an IS/IT management strategy.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128554480","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}
Abstract - The Faculty of Information Technology currently carries out performance evaluations at the end of each semester and involves students as sources of data evaluation. The evaluation activity took place online on the website ss.fti.uajm.ac.id. With the number of active students, the number of evaluations that need to be read and the number read by faculty stakeholders also increases. This is inversely proportional to the time that stakeholders need time to read, evaluate, and categorize comments entered by students as part of the performance evaluation. In this study, a multi-classification of student comments related to evaluations at the Faculty of Information Technology UAJM will be carried out. Text pre-processing will use the Sastrawi library which includes stopword removal, stemming, and transformation of text into TFIDF form. The results of the pre-processing text will be used as input on Naive Bayes and using three scenarios to evaluate the classifier model. The average accuracy values of the Naive Bayes algorithm for category and sentiment labels are 79% and 81%, respectively. Furthermore, the expected result of this research is to reduce the time for FTI UAJM stakeholders to read and comment/suggest faster because the evaluation results are obtained in real-time.
{"title":"The Automatic Classification System for Academic Performance Evaluation at the Faculty of Information Technology Atma Jaya University of Makassar","authors":"Erick Alfons Lisangan, Dwi Marisa Midyanti, Chairul Mukmin, Astrid Lestari Tungadi","doi":"10.30595/juita.v11i1.14116","DOIUrl":"https://doi.org/10.30595/juita.v11i1.14116","url":null,"abstract":"Abstract - The Faculty of Information Technology currently carries out performance evaluations at the end of each semester and involves students as sources of data evaluation. The evaluation activity took place online on the website ss.fti.uajm.ac.id. With the number of active students, the number of evaluations that need to be read and the number read by faculty stakeholders also increases. This is inversely proportional to the time that stakeholders need time to read, evaluate, and categorize comments entered by students as part of the performance evaluation. In this study, a multi-classification of student comments related to evaluations at the Faculty of Information Technology UAJM will be carried out. Text pre-processing will use the Sastrawi library which includes stopword removal, stemming, and transformation of text into TFIDF form. The results of the pre-processing text will be used as input on Naive Bayes and using three scenarios to evaluate the classifier model. The average accuracy values of the Naive Bayes algorithm for category and sentiment labels are 79% and 81%, respectively. Furthermore, the expected result of this research is to reduce the time for FTI UAJM stakeholders to read and comment/suggest faster because the evaluation results are obtained in real-time.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294260","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 : 2023-05-06DOI: 10.30595/juita.v11i1.15498
Anief Fauzan Rozi, Adi Wibowo, B. Warsito
Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.
{"title":"Resampling Technique for Imbalanced Class Handling on Educational Dataset","authors":"Anief Fauzan Rozi, Adi Wibowo, B. Warsito","doi":"10.30595/juita.v11i1.15498","DOIUrl":"https://doi.org/10.30595/juita.v11i1.15498","url":null,"abstract":"Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116474880","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 : 2023-05-06DOI: 10.30595/juita.v11i1.15457
Cynthia Wulandari, I. Sumertajaya, M. Aidi
Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of the biclustering algorithms that is relatively fast and can be used as a reference for the comparison of several algorithms is the BCBimax algorithm. The BCBimax algorithm works by finding a sub-matrix containing element 1 of the formed binary data matrix. The selection of thresholds in the binarization process and the minimum combination of rows and columns are essential in finding the optimal bicluster. Capture fisheries have an important role in supporting sustainable growth in Indonesia, so information on the potential of fish species that have similarities in several provinces is needed in optimally mapping the potential. The BCBimax algorithm found 11 optimal biclusters in grouping capture fisheries data. The median of each variable is used as a threshold in the binarization process, and the minimum combination of row 2 and maximum column 2 is chosen to find the optimal bicluster result. The optimal average value of Mean Square Residual bicluster obtained is 0.405403 with the similarity of bicluster results (Liu and Wang index) which is different for each bicluster combination produced. All the bicluster results grouped the provinces and types of fish that had the same potential simultaneously.
双聚类是一种同时聚类技术，通过寻找行和列之间具有相同相似性的子矩阵。BCBimax算法是比较快的一种双聚类算法，可以作为几种算法比较的参考。BCBimax算法的工作原理是找到包含所形成的二进制数据矩阵的元素1的子矩阵。二值化过程中阈值的选择以及行和列的最小组合对于找到最佳双聚类至关重要。捕捞渔业在支持印度尼西亚的可持续增长方面发挥着重要作用，因此需要关于几个省份具有相似性的鱼类品种潜力的信息，以最佳方式绘制潜力图。BCBimax算法在捕捞渔业数据分组中找到了11个最优双聚类。在二值化过程中，使用每个变量的中位数作为阈值，选择第2行和第2列的最小组合来找到最优的双聚类结果。得到的均方残差双聚类的最优平均值为0.405403，双聚类结果的相似度(Liu and Wang指数)对产生的每个双聚类组合有所不同。所有双聚类结果将同时具有相同潜力的省份和鱼类类型分组。
{"title":"Evaluation of Bicluster Analysis Results in Capture Fisheries Using the BCBimax Algorithm","authors":"Cynthia Wulandari, I. Sumertajaya, M. Aidi","doi":"10.30595/juita.v11i1.15457","DOIUrl":"https://doi.org/10.30595/juita.v11i1.15457","url":null,"abstract":"Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of the biclustering algorithms that is relatively fast and can be used as a reference for the comparison of several algorithms is the BCBimax algorithm. The BCBimax algorithm works by finding a sub-matrix containing element 1 of the formed binary data matrix. The selection of thresholds in the binarization process and the minimum combination of rows and columns are essential in finding the optimal bicluster. Capture fisheries have an important role in supporting sustainable growth in Indonesia, so information on the potential of fish species that have similarities in several provinces is needed in optimally mapping the potential. The BCBimax algorithm found 11 optimal biclusters in grouping capture fisheries data. The median of each variable is used as a threshold in the binarization process, and the minimum combination of row 2 and maximum column 2 is chosen to find the optimal bicluster result. The optimal average value of Mean Square Residual bicluster obtained is 0.405403 with the similarity of bicluster results (Liu and Wang index) which is different for each bicluster combination produced. All the bicluster results grouped the provinces and types of fish that had the same potential simultaneously.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998342","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 : 2022-11-14DOI: 10.30595/juita.v10i2.14939
A. Sanmorino, R. Gustriansyah, Juhaini Alie
In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.
{"title":"DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine","authors":"A. Sanmorino, R. Gustriansyah, Juhaini Alie","doi":"10.30595/juita.v10i2.14939","DOIUrl":"https://doi.org/10.30595/juita.v10i2.14939","url":null,"abstract":"In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883447","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 : 2022-11-14DOI: 10.30595/juita.v10i2.14360
Retno Waluyo, T. Hariguna, Agung Purwo Wicaksono
Science and technology can be collaborated to create an application that can help to track the contacts of COVID-19 patients. Smartphone-based contact tracing applications have been adopted by more than 50 countries. One of which is in Indonesia. In March 2020, Indonesia launched a mobile application to track the contact of COVID-19 patients namely PeduliLindungi. During its usage, users find some issues about PeduliLindungi, such as potential data leaks, data misuse, and data inaccuracies. This research is aimed to develop a conceptual model to analyze factors that affect user intentions in using the PeduliLindungi application. The proposed conceptual model is the integration of EUCS, DeLone and McLean that is equipped by the system security variables. There were 288 respondents. The data is processed using SmartPLS 3.0. According to the results of the analysis, the proposed conceptual model has 83.1 percent for its accuracy. User satisfaction and system security give a positive and significant impact on user intentions. The variables of content, accuracy, format, ease of use, and timeliness give a positive and significant impact on user satisfaction. On the other hand, the system security has no positive and significant impact on user satisfaction. Meanwhile, user satisfaction and system security itself affects the user's intentions in using the PeduliLindungi application.
{"title":"Analysis of Factor in User Intention to Use the Covid-19 Tracking Application","authors":"Retno Waluyo, T. Hariguna, Agung Purwo Wicaksono","doi":"10.30595/juita.v10i2.14360","DOIUrl":"https://doi.org/10.30595/juita.v10i2.14360","url":null,"abstract":"Science and technology can be collaborated to create an application that can help to track the contacts of COVID-19 patients. Smartphone-based contact tracing applications have been adopted by more than 50 countries. One of which is in Indonesia. In March 2020, Indonesia launched a mobile application to track the contact of COVID-19 patients namely PeduliLindungi. During its usage, users find some issues about PeduliLindungi, such as potential data leaks, data misuse, and data inaccuracies. This research is aimed to develop a conceptual model to analyze factors that affect user intentions in using the PeduliLindungi application. The proposed conceptual model is the integration of EUCS, DeLone and McLean that is equipped by the system security variables. There were 288 respondents. The data is processed using SmartPLS 3.0. According to the results of the analysis, the proposed conceptual model has 83.1 percent for its accuracy. User satisfaction and system security give a positive and significant impact on user intentions. The variables of content, accuracy, format, ease of use, and timeliness give a positive and significant impact on user satisfaction. On the other hand, the system security has no positive and significant impact on user satisfaction. Meanwhile, user satisfaction and system security itself affects the user's intentions in using the PeduliLindungi application.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422360","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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