Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.
{"title":"Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion","authors":"Alifia Revan Prananda, I. Thalib","doi":"10.20473/jisebi.6.1.1-8","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.1-8","url":null,"abstract":"Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81620638","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.70-78
F. Retrialisca, U. Chotijah
Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI.
{"title":"The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model","authors":"F. Retrialisca, U. Chotijah","doi":"10.20473/jisebi.6.1.70-78","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.70-78","url":null,"abstract":"Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of \"As-is\" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI. ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76443451","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.55-69
Radityo Prasetianto Wibowo, Wiwik Anggraeni, Tresnaning Arifiyah, Edwin Riksakomara, F. Samopa, Pujiadi Pujiadi, Siti Aminatus Zehroh, Nur Aini Lestari
Background: Indonesia has 150 dengue cases every month, and more than one person dies every day from 2017 to 2020. One of the factors of Dengue Hemorrhagic Fever (DHF) patients dying is due to the late handling of patients in hospitals or clinics. Health Office of Malang Regency recorded 1,114 cases of DHF that occurred during 2016, and the number of patients room available is limited. Therefore, Malang Regency is used as a case study in this research. Objective: This study aims to make a dashboard to display the predictions, visualize the distribution of DHF patients, and give mitigation recommendations for handling DHF patients in Malang Health Office. Methods: This study used the Business Intelligence (BI) Development method, which consists of two main phases, namely the making of Business Intelligence and the use of Business Intelligence. This research used the making of the BI phase, which consists of four stages, which are BI development strategies, identification and preparation of data sources, selecting BI tools, and designing and implementing BI. In the Extract, Load, and Transform process, this study used essential transformation and forecast. Results: BI method has succeeded in building the dashboard. The dashboard displays the visualization of Dengue Hemorrhagic Fever predicted results, detail of Dengue Fever Patient number, Dengue Fever patient trends per year and predictions 2 Monthly patient, and mitigation recommendation for each Community Health Office. Conclusion: We have built the BI Dashboard using the BI development method. It needs some treatment to get better performance. These are improving ETL performance using data virtualization technology, considering the use of cloud computing technology, conducting further evaluations by understanding the critical success factors to determine the level of success and weaknesses.
{"title":"Business Intelligence Development in Distributed Information Systems to Visualized Predicting and Give Recommendation for Handling Dengue Hemorrhagic Fever","authors":"Radityo Prasetianto Wibowo, Wiwik Anggraeni, Tresnaning Arifiyah, Edwin Riksakomara, F. Samopa, Pujiadi Pujiadi, Siti Aminatus Zehroh, Nur Aini Lestari","doi":"10.20473/jisebi.6.1.55-69","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.55-69","url":null,"abstract":"Background: Indonesia has 150 dengue cases every month, and more than one person dies every day from 2017 to 2020. One of the factors of Dengue Hemorrhagic Fever (DHF) patients dying is due to the late handling of patients in hospitals or clinics. Health Office of Malang Regency recorded 1,114 cases of DHF that occurred during 2016, and the number of patients room available is limited. Therefore, Malang Regency is used as a case study in this research. Objective: This study aims to make a dashboard to display the predictions, visualize the distribution of DHF patients, and give mitigation recommendations for handling DHF patients in Malang Health Office. Methods: This study used the Business Intelligence (BI) Development method, which consists of two main phases, namely the making of Business Intelligence and the use of Business Intelligence. This research used the making of the BI phase, which consists of four stages, which are BI development strategies, identification and preparation of data sources, selecting BI tools, and designing and implementing BI. In the Extract, Load, and Transform process, this study used essential transformation and forecast. Results: BI method has succeeded in building the dashboard. The dashboard displays the visualization of Dengue Hemorrhagic Fever predicted results, detail of Dengue Fever Patient number, Dengue Fever patient trends per year and predictions 2 Monthly patient, and mitigation recommendation for each Community Health Office. Conclusion: We have built the BI Dashboard using the BI development method. It needs some treatment to get better performance. These are improving ETL performance using data virtualization technology, considering the use of cloud computing technology, conducting further evaluations by understanding the critical success factors to determine the level of success and weaknesses.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"16 1","pages":"55-69"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83142754","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.79-88
N. Dina, Nyoman Juniarta
Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies. The reviews can be used as an evaluation tool for each technology company to understand their employee’s perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing. Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company. Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis. Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception. Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.
{"title":"Aspect based Sentiment Analysis of Employee’s Review Experience","authors":"N. Dina, Nyoman Juniarta","doi":"10.20473/jisebi.6.1.79-88","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.79-88","url":null,"abstract":"Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies. The reviews can be used as an evaluation tool for each technology company to understand their employee’s perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing. Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company. Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis. Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception. Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"6 1","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91312795","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.18-26
Tanweer Alam
Background: The wireless networks make it easier for users to connect with each other in the sense of the Internet of Things (IoT) system. The cloud and MANET convergence offer the services for cloud access within MANET of devices connected. Objective: The main objective of this research is to establish a cloud-based ad-hoc network architecture for the communication among smart devices under the 5G based Internet of Things architecture. Methods: The methods are applied to discover the smart devices using probability-based model, hidden Markov model and gradient-based model. Results: A cloud-MANET architecture of the smart device is constructed with cloud and MANET computation. The framework allows MANET users to access and deliver cloud services through their connected devices, where all simulations, error handling, and resource management are implemented. Conclusion: The MANET service has been launched as well as linked to the cloud by the mobile device. The author used the amazon cloud storage service. This research produces a conceptual model that is based on the ubiquitous method. It is shown the success in this area and expectations for future scope.
{"title":"Device-to-Device Communications in Cloud, MANET and Internet of Things Integrated Architecture","authors":"Tanweer Alam","doi":"10.20473/jisebi.6.1.18-26","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.18-26","url":null,"abstract":"Background: The wireless networks make it easier for users to connect with each other in the sense of the Internet of Things (IoT) system. The cloud and MANET convergence offer the services for cloud access within MANET of devices connected. Objective: The main objective of this research is to establish a cloud-based ad-hoc network architecture for the communication among smart devices under the 5G based Internet of Things architecture. Methods: The methods are applied to discover the smart devices using probability-based model, hidden Markov model and gradient-based model. Results: A cloud-MANET architecture of the smart device is constructed with cloud and MANET computation. The framework allows MANET users to access and deliver cloud services through their connected devices, where all simulations, error handling, and resource management are implemented. Conclusion: The MANET service has been launched as well as linked to the cloud by the mobile device. The author used the amazon cloud storage service. This research produces a conceptual model that is based on the ubiquitous method. It is shown the success in this area and expectations for future scope.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78528410","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.27-36
I. K. Raharjana, Fadel Harris, Army Justitia
Background: Testing using Behavior-Driven Development (BDD) techniques is one of the practices of Agile software development. This technique composes a test-case based on a use case scenario, for web application acceptance tests.Objective: In this study, we developed a tool to generate test case codes from BDD scenario definitions to help and facilitate practitioners to conduct testing.Methods: The generated test case code is made according to the codeception framework format so that it can be directly executed by the tester. The procedure is performed as follows: map the correlation of the language used in BDD (gherkin language) and the code syntax of the test code in the codeception framework, designed the GUIs in such a way that users can easily transform the Use Case Scenario, built the tool so that it can generate test cases codes. Evaluation is done by gathering respondents; ask to run the application and gathering feedback from respondents.Results: This tool can generate a codeception test-case file based on the BDD scenario. Generated test cases can be directly used on codeception tools. The results of the evaluation show that the tools can help entry-level programmers in developing automated tests.Conclusion: The tool can help user especially entry-level programmers to generate BDD test-case and make easy for the users for testing the web applications.
{"title":"Tool for Generating Behavior-Driven Development Test-Cases","authors":"I. K. Raharjana, Fadel Harris, Army Justitia","doi":"10.20473/jisebi.6.1.27-36","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.27-36","url":null,"abstract":"Background: Testing using Behavior-Driven Development (BDD) techniques is one of the practices of Agile software development. This technique composes a test-case based on a use case scenario, for web application acceptance tests.Objective: In this study, we developed a tool to generate test case codes from BDD scenario definitions to help and facilitate practitioners to conduct testing.Methods: The generated test case code is made according to the codeception framework format so that it can be directly executed by the tester. The procedure is performed as follows: map the correlation of the language used in BDD (gherkin language) and the code syntax of the test code in the codeception framework, designed the GUIs in such a way that users can easily transform the Use Case Scenario, built the tool so that it can generate test cases codes. Evaluation is done by gathering respondents; ask to run the application and gathering feedback from respondents.Results: This tool can generate a codeception test-case file based on the BDD scenario. Generated test cases can be directly used on codeception tools. The results of the evaluation show that the tools can help entry-level programmers in developing automated tests.Conclusion: The tool can help user especially entry-level programmers to generate BDD test-case and make easy for the users for testing the web applications.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84310203","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 : 2020-04-27DOI: 10.20473/jisebi.6.1.9-17
Mardhiya Hayaty, Sumarni Adi, A. D. Hartanto
Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.
{"title":"Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media","authors":"Mardhiya Hayaty, Sumarni Adi, A. D. Hartanto","doi":"10.20473/jisebi.6.1.9-17","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.9-17","url":null,"abstract":"Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74192343","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 : 2019-10-24DOI: 10.20473/jisebi.5.2.163-170
P. W. Wirawan, D. E. Riyanto, D. Nugraheni, Yasmin Yasmin
Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher
{"title":"Graph Database Schema for Multimodal Transportation in Semarang","authors":"P. W. Wirawan, D. E. Riyanto, D. Nugraheni, Yasmin Yasmin","doi":"10.20473/jisebi.5.2.163-170","DOIUrl":"https://doi.org/10.20473/jisebi.5.2.163-170","url":null,"abstract":"Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"209 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73971642","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 : 2019-10-24DOI: 10.20473/jisebi.5.2.208-218
Wahyuri Wahyuri, U. Athiyah, Ira Puspitasari, Y. Nita
Background: Drug sampling and testing in the context of post-marketing control is an important component to ensure drug safety in the supply chains. The results are used by the Indonesian National Agency for Drug and Food Control (NA-FDC) for conducting public warnings, evaluating the Good Manufacturing Practice (GMP) and Good Distribution Practice (GDP) implementation, and enforcing the law against drug violation.Objective: This study aimed to identify and analyze drug distribution patterns to provide an overview of drug sampling in the public sector. Methods: The data was collected from Balai Besar Pengawas Obat dan Makanan (BBPOM) Palangka Raya’s database. The collected data were the drug sampling data from Integrated Information Reporting Systems (IIRS) application from 2014 to 2018. Next, we employed CRISP-DM methodology to analyze the data and to identify the pattern. K-means clustering model was selected for data modeling.Results: The dataset contained five attributes, i.e., drug name, therapeutic classes, district/city, sample category, and evaluation of drug surveillance. The drug distribution pattern formed three clusters. First cluster contained 522 drug items in eight therapeutic classes and spread over ten districts, second cluster contained 1542 drug items in five therapeutic classes and spread over five districts, and third cluster contained 503 drug items in eleven therapeutic classes and spread across nine districts.Conclusion: To conclude, the applied data mining technique has improved the decision on the drug sampling planning. It also provides in-depth information on the improvement of drug post-marketing control performance in Central Kalimantan Province.Keywords: Clustering, CRISP-DM, Data Mining, Drug distribution patterns, Drug quality control, Drug sampling
背景:在上市后控制的背景下，药品抽样和检测是确保供应链中药品安全的重要组成部分。印度尼西亚国家药品和食品管理局(NA-FDC)使用这些结果进行公众警告，评估良好生产规范(GMP)和良好销售规范(GDP)的实施，以及执法打击毒品违法行为。目的:本研究旨在识别和分析药物分布模式，以提供公共部门药物抽样的概述。方法:数据来自Balai Besar Pengawas Obat dan Makanan (BBPOM) Palangka Raya数据库。收集的数据为2014 - 2018年综合信息报告系统(IIRS)申请的药品抽样数据。接下来，我们采用CRISP-DM方法分析数据并确定模式。采用K-means聚类模型进行数据建模。结果:该数据集包含药物名称、治疗类别、地区/城市、样本类别和药物监测评价5个属性。药品分布格局形成3个集群。第一聚类包含8个治疗类522种药物，分布在10个地区;第二聚类包含5个治疗类1542种药物，分布在5个地区;第三聚类包含11个治疗类503种药物，分布在9个地区。结论:数据挖掘技术的应用提高了药品抽样计划决策的准确性。它还提供了关于加里曼丹省中部药品上市后管制绩效改进的深入信息。关键词:聚类，CRISP-DM，数据挖掘，药品分布模式，药品质量控制，药品抽样
{"title":"Clustering of Drug Sampling Data to Determine Drug Distribution Patterns with K-Means Method : Study on Central Kalimantan Province, Indonesia","authors":"Wahyuri Wahyuri, U. Athiyah, Ira Puspitasari, Y. Nita","doi":"10.20473/jisebi.5.2.208-218","DOIUrl":"https://doi.org/10.20473/jisebi.5.2.208-218","url":null,"abstract":"Background: Drug sampling and testing in the context of post-marketing control is an important component to ensure drug safety in the supply chains. The results are used by the Indonesian National Agency for Drug and Food Control (NA-FDC) for conducting public warnings, evaluating the Good Manufacturing Practice (GMP) and Good Distribution Practice (GDP) implementation, and enforcing the law against drug violation.Objective: This study aimed to identify and analyze drug distribution patterns to provide an overview of drug sampling in the public sector. Methods: The data was collected from Balai Besar Pengawas Obat dan Makanan (BBPOM) Palangka Raya’s database. The collected data were the drug sampling data from Integrated Information Reporting Systems (IIRS) application from 2014 to 2018. Next, we employed CRISP-DM methodology to analyze the data and to identify the pattern. K-means clustering model was selected for data modeling.Results: The dataset contained five attributes, i.e., drug name, therapeutic classes, district/city, sample category, and evaluation of drug surveillance. The drug distribution pattern formed three clusters. First cluster contained 522 drug items in eight therapeutic classes and spread over ten districts, second cluster contained 1542 drug items in five therapeutic classes and spread over five districts, and third cluster contained 503 drug items in eleven therapeutic classes and spread across nine districts.Conclusion: To conclude, the applied data mining technique has improved the decision on the drug sampling planning. It also provides in-depth information on the improvement of drug post-marketing control performance in Central Kalimantan Province.Keywords: Clustering, CRISP-DM, Data Mining, Drug distribution patterns, Drug quality control, Drug sampling","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84538687","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 : 2019-10-24DOI: 10.20473/jisebi.5.2.171-182
Salman Dziyaul Azmi, R. Kusumaningrum
Background: The Rapid growth of technological developments in Indonesia had resulted in a growing amount of information. Therefore, a new information retrieval environment is necessary for finding documents that are in accordance with the user’s information needs.Objective: The purpose of this study is to uncover the differences between using Relevance Feedback (RF) with genetic algorithm and standard information retrieval systems without relevance feedback for the Indonesian language documents.Methods: The standard Information Retrieval (IR) System uses Sastrawi stemmer and Vector Space Model, while Genetic Algorithm-based (GA-based) relevance feedback uses Roulette-wheel selection and crossover recombination. The evaluation metrics are Mean Average Precision (MAP) and average recall based on user judgments.Results: By using two Indonesian language document datasets, namely abstract thesis and news dataset, the results show 15.2% and 28.6% increase in the corresponding MAP values for both datasets as opposed to the standard Information Retrieval System. A respective 7.1% and 10.5% improvement on the recall value at 10th position was also observed for both datasets. The best obtained genetic algorithm parameters for abstract thesis datasets were a population size of 20 with 0.7 crossover probability and 0.2 mutation probability, while for news dataset, the best obtained genetic algorithm parameters were a population size of 10 with 0.5 crossover probability and 0.2 mutation probability.Conclusion: Genetic Algorithm-based relevance feedback increases both values of MAP and average recall at 10th position of retrieved document. Generally, the best genetic algorithm parameters are as follows, mutation probability is 0.2, whereas the size of population size and crossover probability depends on the size of dataset and length of the query.Keywords: Genetic Algorithm, Information Retrieval, Indonesian language document, Mean Average Precision, Relevance Feedback
{"title":"Relevance Feedback using Genetic Algorithm on Information Retrieval for Indonesian Language Documents","authors":"Salman Dziyaul Azmi, R. Kusumaningrum","doi":"10.20473/jisebi.5.2.171-182","DOIUrl":"https://doi.org/10.20473/jisebi.5.2.171-182","url":null,"abstract":"Background: The Rapid growth of technological developments in Indonesia had resulted in a growing amount of information. Therefore, a new information retrieval environment is necessary for finding documents that are in accordance with the user’s information needs.Objective: The purpose of this study is to uncover the differences between using Relevance Feedback (RF) with genetic algorithm and standard information retrieval systems without relevance feedback for the Indonesian language documents.Methods: The standard Information Retrieval (IR) System uses Sastrawi stemmer and Vector Space Model, while Genetic Algorithm-based (GA-based) relevance feedback uses Roulette-wheel selection and crossover recombination. The evaluation metrics are Mean Average Precision (MAP) and average recall based on user judgments.Results: By using two Indonesian language document datasets, namely abstract thesis and news dataset, the results show 15.2% and 28.6% increase in the corresponding MAP values for both datasets as opposed to the standard Information Retrieval System. A respective 7.1% and 10.5% improvement on the recall value at 10th position was also observed for both datasets. The best obtained genetic algorithm parameters for abstract thesis datasets were a population size of 20 with 0.7 crossover probability and 0.2 mutation probability, while for news dataset, the best obtained genetic algorithm parameters were a population size of 10 with 0.5 crossover probability and 0.2 mutation probability.Conclusion: Genetic Algorithm-based relevance feedback increases both values of MAP and average recall at 10th position of retrieved document. Generally, the best genetic algorithm parameters are as follows, mutation probability is 0.2, whereas the size of population size and crossover probability depends on the size of dataset and length of the query.Keywords: Genetic Algorithm, Information Retrieval, Indonesian language document, Mean Average Precision, Relevance Feedback ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89830941","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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