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INDONESIA
Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 246 Documents
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Foot 3D Reconstruction and Measurement using Depth Data Doni Setio Pambudi; Lailatul Hidayah
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.37-45

Abstract

Background: The need for shoes with non-standard sizes is increasing, but this is not followed by the competence to measure the foot effectively. The high cost of such an instrument in the market has led to the development of a precise yet affordable measurement system.Objective: This research attempts to solve the measuring problem by employing an automatic instrument utilizing a depth image sensor that is available on the market at an affordable price.Methods: Data from several Realsense sensors that have been preprocessed are combined using transformation techniques and noise cleaning is performed afterward. Finally the 3D model of the foot is ready and hence the length and width can be obtained.Results: The experimental results show that the proposed method produces a measurement error of 0.351 cm in foot length, and 0.355 cm in foot width.Conclusion: The result shows that multiple angles of a static Realsense sensor can produce a good 3D foot model automatically. This proposed system configuration can reduce complexity as well as being an affordable solution.  
Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion Alifia Revan Prananda; Irfandy Thalib
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.1-8

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.
Students Activity Recognition by Heart Rate Monitoring in Classroom using K-Means Classification Hadi Helmi Md Zuraini; Waidah Ismail; Rimuljo Hendradi; Army Justitia
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.46-54

Abstract

Background: Heartbeat playing the main roles in our life. With the heartbeat, the anxiety level can be known. Most of the heartbeat is used in the exercise. Heart rate measurement is unique and uncontrollable by any human being.Objective: This research aims to learn student’s actions by monitoring the heart rate. In this paper, we are measuring the student reaction and action in classroom can give impact on teacher’s way of delivery when in the teaching session. In monitoring, student’s behavior may give feedback whether the teaching session have positive or negative outcome.Methods: The method we use is K-Means algorithm. Firstly, we need to know the student’s normal heartbeat as benchmark. We used Hexiware for collecting data from students’ hear beat. We perform the classification where K is benchmark students’ heartbeat. K-Means algorithm performs classification of the heart rate measurement of students.Results: We did the testing for five students in different subjects. It shows that all students have anxiety during the testing and presentation. Its consistency because we tested 5 students with mixes activities in the classroom, where the student has quiz, presentation and only teaching.Conclusion: Heart rate during studying in the classroom can change the education world in improving the efficiency of knowledge transfer between student and teacher. This research may act as basic way in monitoring student behavior in the classroom. We have tested for 5 students. Three students have their anxiety in classroom during the exam, presentation, and question. Two students have normal rate during the seminar and lecturer. The drawback, Hexiware is capturing average of ten minutes and tested in different classes and students. In future, we need just measure one student for all the subjects and Hexiware need to configure in one minute. 
Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media Mardhiya Hayaty; Sumarni Adi; Anggit Dwi Hartanto
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.9-17

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.
Business Intelligence Development in Distributed Information Systems to Visualized Predicting and Give Recommendation for Handling Dengue Hemorrhagic Fever Radityo Prasetianto Wibowo; Wiwik Anggraeni; Tresnaning Arifiyah; Edwin Riksakomara; Febriliyan Samopa; Pujiadi Pujiadi; Siti Aminatus Zehroh; Nur Aini Lestari
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.55-69

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.
Device-to-Device Communications in Cloud, MANET and Internet of Things Integrated Architecture Tanweer Alam
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.18-26

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.
The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model Fitri Retrialisca; Umi Chotijah
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.70-78

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. 
Tool for Generating Behavior-Driven Development Test-Cases Indra Kharisma Raharjana; Fadel Harris; Army Justitia
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.27-36

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.
Moving Object Detection Using Ultrasonic Radar with Proper Distance, Direction, and Object Shape Analysis Angona Biswas; Sabrina Abedin; Md. Ahasan Kabir
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.99-111

Abstract

Background: In its early development, radar (radio detection and ranging) was primarily used by the navy, the military, and the aviation services, as well as space organizations for security and monitoring purposes. Nowadays, the demand of radar is expanding. Research has been conducted to overcome the limitations of radar.Objective: One of the current limitations to detect moving object. The current paper aims to fill the gap in the literature by using a radar system in the identification of moving object, capturing the distance, direction, radar pulse duration and object shape simultaneously. Velocity or the object’s speed towards or away from the radar was determined by using an algorithm to obtain the precision.Methods: The accuracy of distance measurement and angle is ensured by comparing the real values and the values obtained by the radar. The objects under study consist of metal and non-metal. Novelty of this work is the accurate detection of moving objects with suitable algorithms using only one Arduino UNO and one ultrasonic sensor.Results: The experiment design yielded much better efficiency than previous works. The proposed method predicted the exact speed of the object detected by the radar system. The experiment has successfully proven the accuracy of moving object sensor.Conclusion: Besides proper distance and velocity, a large set of data was taken to find the accuracy of the radar for objects of different shapes. For a cylindrical object, the radar provided 100% efficiency in a constant environment when the object was 5 cm away. The accuracy decreased to 30% when the distance was 17 cm away. The limitation of this system is that it was unable to detect small object or if the object was very close (1 cm).
Comparative Analysis of Image Classification Algorithms for Face Mask Detection Mohammad Farid Naufal; Selvia Ferdiana Kusuma; Zefanya Ardya Prayuska; Ang Alexander Yoshua; Yohanes Albert Lauwoto; Nicky Setyawan Dinata; David Sugiarto
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.56-66

Abstract

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.  

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