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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Science and Technology
David Asirvatham
Taylor's University
Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Classification of instagram fake users using supervised machine learning algorithms Kristo Radion Purba; David Asirvatham; Raja Kumar Murugesan
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.165 KB) | DOI: 10.11591/ijece.v10i3.pp2763-2772

Abstract

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review Eshrak Alaros; Mohsen Marjani; Dalia Abdulkareem Shafiq; David Asirvatham
Indonesian Journal of Science and Technology Vol 8, No 2 (2023): (ONLINE FIRST) IJOST: September 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v8i2.55814

Abstract

Consumer/customer relationship management (CRM) can potentially influence business as it predicts changes in people’s perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers’ intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics.
Co-Authors Dalia Abdulkareem Shafiq Eshrak Alaros Kristo Radion Purba Mohsen Marjani Raja Kumar Murugesan