Pub Date : 2023-11-01DOI: 10.20473/jisebi.9.2.305-319
Eva Hariyanti, Made Balin Janeswari, Malvin Mikhael Moningka, Fikri Maulana Aziz, Annisa Rahma Putri, Oxy Setyo Hapsari, Nyoman Agus Arya Dwija Sutha, Yohannes Alexander Agusti Sinaga, Manik Prasanthi Bendesa
Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review
{"title":"Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review","authors":"Eva Hariyanti, Made Balin Janeswari, Malvin Mikhael Moningka, Fikri Maulana Aziz, Annisa Rahma Putri, Oxy Setyo Hapsari, Nyoman Agus Arya Dwija Sutha, Yohannes Alexander Agusti Sinaga, Manik Prasanthi Bendesa","doi":"10.20473/jisebi.9.2.305-319","DOIUrl":"https://doi.org/10.20473/jisebi.9.2.305-319","url":null,"abstract":"Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135510235","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}
Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,
{"title":"Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs","authors":"Kunal Vermani, Amandeep Noliya, Sunil Kumar, Kamlesh Dutta","doi":"10.20473/jisebi.9.2.136-146","DOIUrl":"https://doi.org/10.20473/jisebi.9.2.136-146","url":null,"abstract":"Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135510372","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.16-27
Nur Alifiah, D. Kurniasari, Amanto Amanto, W. Warsono
Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.
{"title":"Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation","authors":"Nur Alifiah, D. Kurniasari, Amanto Amanto, W. Warsono","doi":"10.20473/jisebi.9.1.16-27","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.16-27","url":null,"abstract":"Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19.\u0000Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province\u0000Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance.\u0000Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases.\u0000Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022.\u0000 \u0000Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85258052","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.84-94
S. Salsabila, Salsabila Mazya Permataning Tyas, Yasinta Romadhona, D. Purwitasari
Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately. Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.
{"title":"Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19","authors":"S. Salsabila, Salsabila Mazya Permataning Tyas, Yasinta Romadhona, D. Purwitasari","doi":"10.20473/jisebi.9.1.84-94","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.84-94","url":null,"abstract":"Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions.\u0000Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation.\u0000Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation.\u0000Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them.\u0000Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately.\u0000 \u0000Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85638575","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.95-107
Halim Wildan Awalurahman, Ibrahim Hafizhan Witsqa, I. K. Raharjana, A. Basori
Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored. Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security. Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria. Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing. Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review
{"title":"Security Aspect in Software Testing Perspective: A Systematic Literature Review","authors":"Halim Wildan Awalurahman, Ibrahim Hafizhan Witsqa, I. K. Raharjana, A. Basori","doi":"10.20473/jisebi.9.1.95-107","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.95-107","url":null,"abstract":"Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored.\u0000Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security.\u0000Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria.\u0000Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing.\u0000Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing\u0000 \u0000Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89557744","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.28-37
Muhamad Adhytia Wana Putra Rahmadhan, D. I. Sensuse, Ryan Randy Suryono, Kautsarina Kautsarina
Background: Gamification is a trend that has emerged with the growth of e-commerce. Given the wide range of human characteristics, determining which gamification elements perform well and what impact those gamification elements have can be challenging. Objective: This study aims to conduct a systematic literature review to broadly review the impact that can be caused by the application of gamification elements in e-commerce. This study also attempts to identify the current trends in using gamification elements. Methods: This study was carried out based on the Kitchenham approach and analyzes 25 research papers extracted from a total of 550 papers. The articles were gathered from ACM, Emerald, ScienceDirect, and Scopus and were published between 2016 and 2021. Results: This study found that the trend of research in the field of gamification in e-commerce continues to grow every year. Also, this study found that the most frequently used gamification elements are achievement-oriented (such as rewards, points, badges, and leaderboards). Meanwhile, immersion-related gamification elements (such as avatars, fantasy, etc.) are emerging as a new trend for new gamification elements to be incorporated in e-commerce. This study also found three major themes, namely consumer loyalty, consumer engagement, and user behavior, as a result of the application of gamification in e-commerce. Conclusion: This study helps to improve knowledge of various gamification elements, trends, and impacts on e-commerce. Future studies need to examine the challenges that may arise in the application of gamification elements to the three major themes found in this study and find potential solutions to overcome them. Keywords: E-Commerce, Gamification, Gamification trends and applications, Kitchenham, Systematic literature review.
{"title":"Trends and Applications of Gamification in E-Commerce: A Systematic Literature Review","authors":"Muhamad Adhytia Wana Putra Rahmadhan, D. I. Sensuse, Ryan Randy Suryono, Kautsarina Kautsarina","doi":"10.20473/jisebi.9.1.28-37","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.28-37","url":null,"abstract":"Background: Gamification is a trend that has emerged with the growth of e-commerce. Given the wide range of human characteristics, determining which gamification elements perform well and what impact those gamification elements have can be challenging.\u0000Objective: This study aims to conduct a systematic literature review to broadly review the impact that can be caused by the application of gamification elements in e-commerce. This study also attempts to identify the current trends in using gamification elements.\u0000Methods: This study was carried out based on the Kitchenham approach and analyzes 25 research papers extracted from a total of 550 papers. The articles were gathered from ACM, Emerald, ScienceDirect, and Scopus and were published between 2016 and 2021.\u0000Results: This study found that the trend of research in the field of gamification in e-commerce continues to grow every year. Also, this study found that the most frequently used gamification elements are achievement-oriented (such as rewards, points, badges, and leaderboards). Meanwhile, immersion-related gamification elements (such as avatars, fantasy, etc.) are emerging as a new trend for new gamification elements to be incorporated in e-commerce. This study also found three major themes, namely consumer loyalty, consumer engagement, and user behavior, as a result of the application of gamification in e-commerce.\u0000Conclusion: This study helps to improve knowledge of various gamification elements, trends, and impacts on e-commerce. Future studies need to examine the challenges that may arise in the application of gamification elements to the three major themes found in this study and find potential solutions to overcome them.\u0000 \u0000Keywords: E-Commerce, Gamification, Gamification trends and applications, Kitchenham, Systematic literature review.\u0000 ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89802633","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.38-46
Mala Rosa Aprillya, E. Suryani
Background: The food security policy is an effort to ensure stable food availability and stable access of the community to food. As the population increases, this will affect the fulfillment of food needs in the future. Therefore, increase in rice production is needed to support food security. Objective: Conduct an analysis of the factors affecting the quality of rice production by using a dynamic system simulation that can be used as a basis for formulating policy strategies. Method: Simulation using System Dynamics (SD) is a method used to study and analyze complex systems by modeling non-linear behavior. Then several scenarios were carried out for the best decision-making using a computer. Result: The results of the scenario show that increasing the quality of paddy production in order to meet food needs in the future is doable by boosting the rendement of paddy as it will upgrade rice production which will contribute greatly to rice production. Conclusion: From the simulation results, the study can be used to increase the quality of rice production to maintain food security by improving the harvesting mechanism to increase yields. For further research, the use of Smart Agriculture can be considered to increase production of rice. Keywords: Food security, Rice production, Rice production, System dynamics
{"title":"Simulation of System Dynamics for Improving The Quality of Paddy Production in Supporting Food Security","authors":"Mala Rosa Aprillya, E. Suryani","doi":"10.20473/jisebi.9.1.38-46","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.38-46","url":null,"abstract":"Background: The food security policy is an effort to ensure stable food availability and stable access of the community to food. As the population increases, this will affect the fulfillment of food needs in the future. Therefore, increase in rice production is needed to support food security.\u0000Objective: Conduct an analysis of the factors affecting the quality of rice production by using a dynamic system simulation that can be used as a basis for formulating policy strategies.\u0000Method: Simulation using System Dynamics (SD) is a method used to study and analyze complex systems by modeling non-linear behavior. Then several scenarios were carried out for the best decision-making using a computer.\u0000Result: The results of the scenario show that increasing the quality of paddy production in order to meet food needs in the future is doable by boosting the rendement of paddy as it will upgrade rice production which will contribute greatly to rice production.\u0000Conclusion: From the simulation results, the study can be used to increase the quality of rice production to maintain food security by improving the harvesting mechanism to increase yields. For further research, the use of Smart Agriculture can be considered to increase production of rice.\u0000 \u0000Keywords: Food security, Rice production, Rice production, System dynamics","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84444091","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.108-118
Vivine Nurcahyawati, Z. Mustaffa
Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,
{"title":"Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation","authors":"Vivine Nurcahyawati, Z. Mustaffa","doi":"10.20473/jisebi.9.1.108-118","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.108-118","url":null,"abstract":"Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world.\u0000Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications.\u0000Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation.\u0000Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed.\u0000Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance.\u0000 \u0000Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"212 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90479081","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.1-15
A. Biswas, Md. Saiful Islam
Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming. Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.
{"title":"A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification","authors":"A. Biswas, Md. Saiful Islam","doi":"10.20473/jisebi.9.1.1-15","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.1-15","url":null,"abstract":"Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained.\u0000Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model.\u0000Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16.\u0000Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others.\u0000Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming.\u0000 \u0000Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90443033","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 : 2023-04-28DOI: 10.20473/jisebi.9.1.47-69
Rima Alviani, B. Purwandari, I. Eitiveni, Mardiana Purwaningsih
Background: The utilization of virtual healthcare services, particularly telemedicine, has been accelerated by the COVID-19 pandemic. Although the pandemic is no longer the primary concern, telemedicine still holds potential for long-term adoption. However, implementing telemedicine in Indonesia as an online platform for remote healthcare delivery still faces issues, despite its potential. Further investigation is required to identify the factors that affect its adoption and develop strategies to surmount implementation challenges. Objective: This study aims to examine and enrich knowledge about the adoption of telemedicine in Indonesia. Methods: A cross-sectional survey was conducted through an online questionnaire to collect data. Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was employed by integrating with several factors, such as eHealth Literacy, Privacy Concerns, and Trust. Gender and age were considered as moderating variables. Data samples were analyzed using Partial Least Square – Structural Equation Modeling (PLS–SEM). Results: The findings suggest that performance expectancy, effort expectancy, social influence, eHealth literacy, and trust have a significant impact on adults’ behavioral intention to use telemedicine. However, facilitating condition, price value, and privacy concern do not show any significant effects on adults’ Behavioral Intention to Use Telemedicine. Conclusion: This study highlights the importance of understanding adoption factors to develop effective strategies. Results show performance expectancy, effort expectancy, social influence, eHealth literacy, and trust are significant factors, while facilitating condition, price value, and privacy concern are not. The UTAUT2 model is a good predictive tool for healthcare adoption. To increase usage intention, several aspects must be considered in the implementation of telemedicine. Keywords: Adoption, Behavioral Intention to Use, Telemedicine, UTAUT2, Virtual Healthcare.
{"title":"Factors Affecting Adoption of Telemedicine for Virtual Healthcare Services in Indonesia","authors":"Rima Alviani, B. Purwandari, I. Eitiveni, Mardiana Purwaningsih","doi":"10.20473/jisebi.9.1.47-69","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.47-69","url":null,"abstract":"Background: The utilization of virtual healthcare services, particularly telemedicine, has been accelerated by the COVID-19 pandemic. Although the pandemic is no longer the primary concern, telemedicine still holds potential for long-term adoption. However, implementing telemedicine in Indonesia as an online platform for remote healthcare delivery still faces issues, despite its potential. Further investigation is required to identify the factors that affect its adoption and develop strategies to surmount implementation challenges.\u0000Objective: This study aims to examine and enrich knowledge about the adoption of telemedicine in Indonesia.\u0000Methods: A cross-sectional survey was conducted through an online questionnaire to collect data. Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was employed by integrating with several factors, such as eHealth Literacy, Privacy Concerns, and Trust. Gender and age were considered as moderating variables. Data samples were analyzed using Partial Least Square – Structural Equation Modeling (PLS–SEM).\u0000Results: The findings suggest that performance expectancy, effort expectancy, social influence, eHealth literacy, and trust have a significant impact on adults’ behavioral intention to use telemedicine. However, facilitating condition, price value, and privacy concern do not show any significant effects on adults’ Behavioral Intention to Use Telemedicine. \u0000Conclusion: This study highlights the importance of understanding adoption factors to develop effective strategies. Results show performance expectancy, effort expectancy, social influence, eHealth literacy, and trust are significant factors, while facilitating condition, price value, and privacy concern are not. The UTAUT2 model is a good predictive tool for healthcare adoption. To increase usage intention, several aspects must be considered in the implementation of telemedicine.\u0000 \u0000Keywords: Adoption, Behavioral Intention to Use, Telemedicine, UTAUT2, Virtual Healthcare.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78590246","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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