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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara ARSY : Jurnal Aplikasi Riset kepada Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal Software, Hardware and Information Technology Journal of Mathematics and Scientific Computing With Applications Jurnal Masyarakat Indonesia Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Lubis, Arif Ridho
Politeknik Negeri Medan
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Predictive Analytics for IMDb Top TV Ratings: A Linear Regression Approach to the Data of Top 250 IMDb TV Shows Husna, Meryatul; Purba, Lampson Pindahaman; Rinaldy, Muhammad Eri; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7600

Abstract

In the era of a growing entertainment industry, understanding audience preferences and predicting the financial performance of entertainment products such as films and television shows has become increasingly important. Previous research has demonstrated various approaches in understanding the factors that influence the financial performance of entertainment products. However, there is still a need for research to investigate other aspects of film and television show evaluation. This study aims to explore the contribution of linear regression in analysing the ratings and financial performance of IMDb's top TV shows. Through the incorporation of various data-informed and interpretative approaches, it is expected to gain a deeper understanding of the factors that influence the success of a television show. Using data from the Top 250 IMDb TV Shows, a predictive analysis was conducted to understand the relationship between the number of episodes and IMDb ratings. The results of the information showed a negative relationship between the number of episodes and IMDb rating, with the linear regression model predicting a decrease in IMDb rating as the number of episodes increases. Implications of this research include recommendations for content creators to consider both quality and quantity of content in the development of TV shows.
Prediction of Cyberbullying in Social Media on Twitter Using Logistic Regression Prayudani, Santi; Adha, Lilis Tiara; Ariyani, Tika; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9842

Abstract

As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The first and most important research question of this study is to assess the extent to which the model is capable of correctly identifying the comments that contain features of cyberbullying and those that do not. The data set included comments from different social media sites and was preprocessed before further analysis was conducted on it. Exploratory Data Analysis was applied in the study to establish relationships and textual features with bullying behavior. As with any other model, after training and testing the model, the results were analyzed using parameters like precision, precision, gain, and F1 statistics. The outcomes of this study revealed that the use of logistic regression models can give a fairly satisfactory level of accuracy in identifying cyberbullying. In light of this, this study underscores the need to use machine learning algorithms to minimize negative actions in cyberspace.
Co-Authors A, Azanuddin Achmad Yani Adam, Hikmah Adwin Adha, Lilis Tiara Al Khowarizmi Ardi Hutagalung, Gabriel Ariyani, Tika Azhar, Muhammad Fauzan Dini Oktarina Dwi Handayani Elviawaty Muisa Zamzami Fatmi, Yulia Fawwaz, Mohammad Faris Faza, Sharfina Firjatullah, Muhammad Gabriel Ardi Hutagalung Gunawan Gunawan Habibi Ramdani Safitri Harefa, Hafid Rahman Haryadi - Hidayatullah, Rafly Artha Hikmah Adwin Adam Husna, Meryatul Idham Kamil Ilham Ramadhan Nasution Imani, Muhammad Rayyan Irvan, Irvan Julham Julham Julham Julham Kadri Yusuf Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mhd Faris Pratama Michael J Watts Muhammad Basri Muhammad Luthfi Hamzah Muharman Lubis Mulhartono, Tomi Muttaqin, Alif Noorachmad Nadi, Farhad Nst, Fifi Anggiani Br Nurhaflah Soraya Nurlinda Opim Salim Sitompul Prayudani, Santi Purba, Lampson Pindahaman Purnamawati, Sarah Putra, Purwa Hasan Raditiansyah, Farhan Rahmadani Rahmadani Rahmadani Rahmadani Rinaldy, Muhammad Eri Riza Sulaiman Rizki Syahputra Romi Fadillah Rahmat Salam, Azrizal Sarah Purnamawati Selvida, Desilia Sembiring, Boni Oktaviani Sibarani, Yous Syafli, Sekar Arini Syamsul Arifin Tessya Fakhta Tri Nasution Yulia Fatmi Yulia Fatmi Yuyun Yusnida Lase