Pub Date : 2020-10-27DOI: 10.20473/jisebi.6.2.123-132
M. A. Afrianto, Meditya Wasesa
Background: Literature in the peer-to-peer accommodation has put a substantial focus on accommodation listings' price determinants. Developing prediction models related to the demand for accommodation listings is vital in revenue management because accurate price and demand forecasts will help determine the best revenue management responses. Objective: This study aims to develop prediction models to determine the booking likelihood of accommodation listings. Methods: Using an Airbnb dataset, we developed four machine learning models, namely Logistics Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest Classifiers. We assessed the models using the AUC-ROC score and the model development time by using the ten-fold three-way split and the ten-fold cross-validation procedures. Results: In terms of average AUC-ROC score, the Random Forest Classifiers outperformed other evaluated models. In three-ways split procedure, it had a 15.03% higher AUC-ROC score than Decision Tree, 2.93 % higher than KNN, and 2.38% higher than Logistics Regression. In the cross-validation procedure, it has a 26,99% higher AUC-ROC score than Decision Tree, 4.41 % higher than KNN, and 3.31% higher than Logistics Regression. It should be noted that the Decision Tree model has the lowest AUC-ROC score, but it has the smallest model development time. Conclusion: The performance of random forest models in predicting booking likelihood of accommodation listings is the most superior. The model can be used by peer-to-peer accommodation owners to improve their revenue management responses.
{"title":"Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers","authors":"M. A. Afrianto, Meditya Wasesa","doi":"10.20473/jisebi.6.2.123-132","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.123-132","url":null,"abstract":"Background: Literature in the peer-to-peer accommodation has put a substantial focus on accommodation listings' price determinants. Developing prediction models related to the demand for accommodation listings is vital in revenue management because accurate price and demand forecasts will help determine the best revenue management responses. Objective: This study aims to develop prediction models to determine the booking likelihood of accommodation listings. Methods: Using an Airbnb dataset, we developed four machine learning models, namely Logistics Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest Classifiers. We assessed the models using the AUC-ROC score and the model development time by using the ten-fold three-way split and the ten-fold cross-validation procedures. Results: In terms of average AUC-ROC score, the Random Forest Classifiers outperformed other evaluated models. In three-ways split procedure, it had a 15.03% higher AUC-ROC score than Decision Tree, 2.93 % higher than KNN, and 2.38% higher than Logistics Regression. In the cross-validation procedure, it has a 26,99% higher AUC-ROC score than Decision Tree, 4.41 % higher than KNN, and 3.31% higher than Logistics Regression. It should be noted that the Decision Tree model has the lowest AUC-ROC score, but it has the smallest model development time. Conclusion: The performance of random forest models in predicting booking likelihood of accommodation listings is the most superior. The model can be used by peer-to-peer accommodation owners to improve their revenue management responses.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"35 1","pages":"123-132"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84657303","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.143-150
M. Wijanto, Rachmi Rachmadiany, Oscar Karnalim
Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise. Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal. Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model. Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP. Conclusion: An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed.
{"title":"Thesis Supervisor Recommendation with Representative Content and Information Retrieval","authors":"M. Wijanto, Rachmi Rachmadiany, Oscar Karnalim","doi":"10.20473/jisebi.6.2.143-150","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.143-150","url":null,"abstract":"Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise. Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal. Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model. Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP. Conclusion: An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"1 1","pages":"143-150"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77935515","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.151-158
M. K. Harahap, N. Khairina
Background: The confidentiality of a message may at times be compromised. Steganography can hide such a message in certain media. Steganographic media such as digital images have many pixels that can accommodate secret messages. However, the length of secret messages may not match with the number of image pixels so the messages cannot be inserted into the digital images. Objective: This research aims to see the dynamics between an image size and a secret message’s length in order to prevent out of range messages entered in an image. Methods: This research will combine the Least Significant Bit (LSB) method and the Stretch technique in hiding secret messages. The LSB method uses the 8 th bit to hide secret messages. The Stretch technique dynamically enlarges the image size according to the length of the secret messages. Images will be enlarged horizontally on the rightmost image pixel block until n blocks of image pixels. Results: This study compares an original image size and a stego image size and examines a secret message’s length that can be accommodated by the stego image, as well as the Mean Square Error and Structure Similarity Index. The test is done by comparing the size change of the original image with the stego image from the Stretch results, where each original image tested always changes dynamically according to the increasing number of secret message characters. From the MSE and SSIM test results, the success was only with the first image, while the second image to the fourth image remained erroneous because they also did not have the same resolution. Conclusion: The combination of LSB steganography and the Stretch technique can enlarge an image automatically according to the number of secret messages to be inserted. For further research development, image stretch must not only be done horizontally but also vertically.
{"title":"Dynamic Steganography Least Significant Bit with Stretch on Pixels Neighborhood","authors":"M. K. Harahap, N. Khairina","doi":"10.20473/jisebi.6.2.151-158","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.151-158","url":null,"abstract":"Background: The confidentiality of a message may at times be compromised. Steganography can hide such a message in certain media. Steganographic media such as digital images have many pixels that can accommodate secret messages. However, the length of secret messages may not match with the number of image pixels so the messages cannot be inserted into the digital images. Objective: This research aims to see the dynamics between an image size and a secret message’s length in order to prevent out of range messages entered in an image. Methods: This research will combine the Least Significant Bit (LSB) method and the Stretch technique in hiding secret messages. The LSB method uses the 8 th bit to hide secret messages. The Stretch technique dynamically enlarges the image size according to the length of the secret messages. Images will be enlarged horizontally on the rightmost image pixel block until n blocks of image pixels. Results: This study compares an original image size and a stego image size and examines a secret message’s length that can be accommodated by the stego image, as well as the Mean Square Error and Structure Similarity Index. The test is done by comparing the size change of the original image with the stego image from the Stretch results, where each original image tested always changes dynamically according to the increasing number of secret message characters. From the MSE and SSIM test results, the success was only with the first image, while the second image to the fourth image remained erroneous because they also did not have the same resolution. Conclusion: The combination of LSB steganography and the Stretch technique can enlarge an image automatically according to the number of secret messages to be inserted. For further research development, image stretch must not only be done horizontally but also vertically.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"10 1","pages":"151-158"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74377794","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.133-142
Tora Fahrudin, Kastaman Kastaman, Sherin Nadya Meideni, Padma Edhitya Chairunnisafa Priyono, Muhammad Galang Fathirkina, Samira Samira
Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking. Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API. Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application. Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule with conjunction “dan”; has the same attribute before and after tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence. Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.
{"title":"Smart Dissemination by Using Natural Language Processing Technology","authors":"Tora Fahrudin, Kastaman Kastaman, Sherin Nadya Meideni, Padma Edhitya Chairunnisafa Priyono, Muhammad Galang Fathirkina, Samira Samira","doi":"10.20473/jisebi.6.2.133-142","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.133-142","url":null,"abstract":"Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking. Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API. Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application. Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule with conjunction “dan”; has the same attribute before and after tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence. Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"1 1","pages":"133-142"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89094191","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.159-168
Eko Wahyu Tyas Darmaningrat, H. M. Astuti, Fadhila Alfi
Background : Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia. Objective : This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia. Methods : This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM). Results : The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has. Conclusion : The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.
{"title":"Information Privacy Concerns Among Instagram Users: The Case of Indonesian College Students","authors":"Eko Wahyu Tyas Darmaningrat, H. M. Astuti, Fadhila Alfi","doi":"10.20473/jisebi.6.2.159-168","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.159-168","url":null,"abstract":"Background : Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia. Objective : This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia. Methods : This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM). Results : The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has. Conclusion : The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"7 1","pages":"159-168"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75417518","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.99-111
A. Biswas, Sabrina Abedin, Md. Ahasan Kabir
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).
{"title":"Moving Object Detection Using Ultrasonic Radar with Proper Distance, Direction, and Object Shape Analysis","authors":"A. Biswas, Sabrina Abedin, Md. Ahasan Kabir","doi":"10.20473/jisebi.6.2.99-111","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.99-111","url":null,"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).","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80682097","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.112-122
Pulung Hendro Prastyo, Amin Siddiq Sumi, A. Dian, A. E. Permanasari
Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.
{"title":"Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel","authors":"Pulung Hendro Prastyo, Amin Siddiq Sumi, A. Dian, A. E. Permanasari","doi":"10.20473/jisebi.6.2.112-122","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.112-122","url":null,"abstract":"Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately. ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86014509","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 : 2020-10-27DOI: 10.20473/jisebi.6.2.89-98
Khalid Khalid, I. S. Rozas, Dwi Rolliawati
Background: The Internet use according to Indonesian Internet Services Provider Association (APJII) can be an indicator for parents and educators to monitor students’ mental development and learning behaviors.Objective: This study aims to analyze trends and patterns of the Internet use among students during the school holidays.Methods: This study uses data from XYZ operator, one of the most affordable mobile service providers in Indonesia in 2019. The data was analyzed by using Online Analytical Processing (OLAP).Result: The results shows that the use of 3G and 4G data increased significantly during the school holidays, compared to school days. The highest increase of the Internet traffic is during the semester break, occurred at the rate of 22 to 24 hours a day, with the peak reaching 20.87% at 10:00.Conclusion: The research findings can inform relevant parties, both parents and school teachers in guiding their children to use the Internet.
{"title":"Trends and Patterns of The Internet Use During School Holidays","authors":"Khalid Khalid, I. S. Rozas, Dwi Rolliawati","doi":"10.20473/jisebi.6.2.89-98","DOIUrl":"https://doi.org/10.20473/jisebi.6.2.89-98","url":null,"abstract":"Background: The Internet use according to Indonesian Internet Services Provider Association (APJII) can be an indicator for parents and educators to monitor students’ mental development and learning behaviors.Objective: This study aims to analyze trends and patterns of the Internet use among students during the school holidays.Methods: This study uses data from XYZ operator, one of the most affordable mobile service providers in Indonesia in 2019. The data was analyzed by using Online Analytical Processing (OLAP).Result: The results shows that the use of 3G and 4G data increased significantly during the school holidays, compared to school days. The highest increase of the Internet traffic is during the semester break, occurred at the rate of 22 to 24 hours a day, with the peak reaching 20.87% at 10:00.Conclusion: The research findings can inform relevant parties, both parents and school teachers in guiding their children to use the Internet.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81099000","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.6.1.37-45
Doni S. Pambudi, L. Hidayah
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.
{"title":"Foot 3D Reconstruction and Measurement using Depth Data","authors":"Doni S. Pambudi, L. Hidayah","doi":"10.20473/jisebi.6.1.37-45","DOIUrl":"https://doi.org/10.20473/jisebi.6.1.37-45","url":null,"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.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"28 1","pages":"37-45"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73526041","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.6.1.46-54
Hadi Helmi Md Zuraini, W. Ismail, R. Hendradi, Army Justitia
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.
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