ComTech Binus

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K-Means Clustering to Identity Twitter Build Operate Transfer (BOT) on Influential Accounts

Abstract
Twitter is a popular social media with hundreds of millions of users, but some are not human. About 48 million accounts are created by Build Operate Transfer (BOT), which represents up to 15% of all accounts. BOTs are created for various purposes, one of which is to post information about news automatically. However, BOTs have also been abused, such as spreading hoaxes or influencing public perception of a topic. The research aimed to determine which Twitter accounts were identified as BOT accounts based on predefined attributes. The research used tweet data from 213 Twitter accounts. The accounts used as test data were accounts that had influence. After that, the data were clustered using k-means using the attributes of retweets + replies count, followers count, account age, friends count, status count, digits count in name, username length, name similarity, name ratio, and likes count. The results show the optimal number of clustering at k = 3 on the Sum of Squared Errors (SSE) evaluation and the Elbow method and the best quality and cluster power at k = 2 on the silhouette coefficient. It shows that the clustered accounts with the highest number of members on each attribute are places for accounts with high BOT scores from several aspects of the BOT score type.

Keywords: K-Means clustering, Twitter accounts, Build Operate Transfer (BOT), influential accounts

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/10620

Mobile-Based Car Diagnostic Application Using Onboard Diagnostic-II Scanner

Abstract
Mobile applications today serve as versatile tools across diverse sectors, enhancing human productivity through specialized software on electronic devices. Implementation of the mobile application can also be applied to vehicles, with inspection and checking functions assisted by the Onboard Diagnostic-II (OBD-II) scanner. The research aimed to develop an integrated mobile application that utilized the OBD-II scanner and Data Acquisition System (DAS) to monitor vehicle health and provide timely service reminders. Vehicle information was taken by the DAS process into a Diagnostic Trouble Code (DTC) from the vehicle itself. The method applied the waterfall model, which consisted of communication, planning, modeling, construction, and evaluation. The problem analysis and requirements gathering for developing the application involves the interview method and Google Forms-generated questionnaires with 101 responses. Then, the research used OBD-II series ELM327 and ELM 327 IC devices for testing. The research results in an application developed for vehicle diagnostics using a recommendation system through notifications that provide vehicle health information and service time reminders to users. This application consists of eight modules, with the main module being able to provide recommendations for vehicle owners. These recommendations are helpful for users to maintain the health of their vehicles regularly. Further research is recommended to enhance the development of the application, aiming to create a more comprehensive user interface.

Keywords: mobile-based application, car diagnostic, Onboard Diagnostic-II Scanner

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/9138

Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia

Abstract
The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.

Keyword: prediction model, tourism object ticket, ticket determination

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/7992
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Multiple Classifier System for Handling Imbalanced and Overlapping Datasets on Multiclass Classification

Abstract:
The performance of classification models suffer when the dataset contains imbalanced and overlapping data. These two conditions are already challenging separately and even more complex if they occur together. In the research, an ensemble method called a Multiple Classifier System was proposed to address these issues by combining K-Nearest Neighbour and Logistic Regression. The Synthetic Minority Oversampling Technique (SMOTE) method was also applied to balance the dataset. The One Versus One (OVO) decomposition technique helped the multiclass classification process. A simulation with 18 scenarios proves that the MCS-SMOTE model can handle these problems by providing good performance. The model’s performance is also tested using empirical data on Poverty in West Java in 2021. Empirical data also show that the proposed method performs well, with an accuracy rate of 80.09%, an F1 score of 0.782, and a G-Mean of 0.242. The areas with the highest poverty rates are Bogor, Bekasi City, Bandung City, Bekasi Regency, and Depok City, located near DKI Jakarta, the capital city. Based on existing predictor variables, poor households in West Java are more likely to occur when they do not have access to credit, the number of household members is more than three, multiple families live in one building, and the head of the household has not graduated from elementary school.

Keywords: Multiple Classifier System (MCS), imbalanced datasets, overlapping datasets, multiclass classification

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/11295
Visit our website: https://journal.binus.ac.id/index.php/comtech/index