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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) 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 Jurnal Teknik Informatika (JUTIF) 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 Sains dan Teknologi Jurnal Masyarakat Indonesia Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Management of Information System Journal Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Kemitraan dan Pengabdian
Lubis, Arif Ridho
Politeknik Negeri Medan
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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.
Fuzzy Mamdani-Based Vegetable Crop Recommendation System with Historical Climate Pattern Analysis in Deli Serdang Regency Meryatul Husna; Mhd Ikhsan P Siregar; Fachry Ferdiansyah Sembiring; Arif Ridho Lubis
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11923

Abstract

Climate variability poses significant challenges to short-cycle vegetable farming, leading to crop failure and economic losses. This study develops a Decision Support System (DSS) to recommend suitable vegetable crops based on historical climate pattern analysis in Deli Serdang Regency. The system utilizes meteorological data from BMKG spanning January 2022 to December 2024, including average temperature, rainfall, and humidity. Historical pattern analysis employs a three-month rolling mean to predict climate conditions for the upcoming planting period. The Fuzzy Mamdani method is implemented as the inference engine to determine crop suitability scores by processing uncertainty in growing requirements. The system was tested across four planting periods (January, April, July, and October) and successfully generated differentiated recommendations with fuzzy scores ranging from 50% to 88%. Results demonstrate that the system effectively adapts recommendations to seasonal climate variations, providing farmers with data-driven decision support to reduce planting risks and improve crop success rates. Future enhancements include real-time climate data integration and expansion of input variables such as soil type and solar radiation intensity.
Co-Authors A, Azanuddin Achmad Yani Adam, Hikmah Adwin Adha, Lilis Tiara Al Khowarizmi Ali Basrah Pulungan Alif Noorachmad Muttaqin Alkhowarizmi Arif Hamied Nababan Ariyani, Tika Azhar, Muhammad Fauzan Bister Purba Dini Oktarina Dwi Handayani Donny Sanjaya Efori Bu'ulolo Elviawaty Muisa Zamzami Fachry Ferdiansyah Sembiring Fahdi Saidi Lubis Fatmi, Yulia Fawwaz, Mohammad Faris Faza, Sharfina Ferry Fachrizal - Firjatullah, Muhammad Gabriel Ardi Hutagalung Gunawan Gunawan Habibi Ramdani Safitri Harefa, Hafid Rahman Haryadi - Haryadi Haryadi Hidayatullah, Rafly Artha Hikmah Adwin Adam Husna, Meryatul Ilham Ramadhan Nasution Imani, Muhammad Rayyan Indri Sulistianingsih Irvan, Irvan Julham Julham Julham Julham Kamil, Idham Lampson Pindahaman Purba Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mayang Mughnyanti Mhd Faris Pratama Mhd Ikhsan P Siregar Michael J Watts Mughnyanti, Mayang Muhammad Basri Muhammad Luthfi Hamzah Muhammad Rafif Rasyidi Muharman Lubis 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 Rian Syahputra Rina Anugrahwaty Rinaldy, Muhammad Eri Riza Sulaiman Rizki Syahputra Romi Fadillah Rahmat Salam, Azrizal Sarah Purnamawati Sekar Arini Syafli Selvida, Desilia Sembiring, Boni Oktaviani Sibarani, Yous Syafli, Sekar Arini Syamsul Arifin Tasril, Virdyra Tessya Fakhta Tri Nasution Tomi Mulhartono Virdyra Tasril Weno Syechu Yulia Fatmi Yulia Fatmi Yusuf, Kadri Yuyun Yusnida Lase