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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence BIOS: Jurnal Informatika dan Sains
Jelita Astrid Gulo
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Computer Science & IT Engineering

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Implementation of the Frame-Based LSB Steganography Method for Embedding Secret Messages in Digital Video Media Jhonatan Antonius Purba; Theresya Simanjuntak; Jelita Astrid Gulo
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 1 (2025): BIOS: Jurnal Informatika dan Sains, April 2025
Publisher : Sean Institute

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Abstract

The advancement of information technology has created a growing need to protect confidential data from unauthorized access. Steganography is a technique for hiding information within digital media so that its existence cannot be detected. This study implements a Frame-Based Least Significant Bit (LSB) steganography method to embed secret messages into digital video media in AVI format. This method works by extracting frames from the video, modifying the least significant bit of pixel values in the blue channel of each frame, and then reconstructing them back into a video. The implementation was carried out using the Python programming language, with the OpenCV library for video manipulation and CustomTkinter for the user interface. The testing results show that the system is capable of embedding and extracting messages with a 100% success rate in AVI videos using the FFV1 (lossless) codec. The embedding capacity depends on the video resolution and the number of frames. A video with a resolution of 1920×1080 at 30 fps for 10 seconds can store up to 207,360,000 bits or approximately 24 MB of data. This method preserves the visual quality of the video with changes that are imperceptible to the human eye.
Student Grouping Based on Grades and Attendance Using K-Means Theresya Simanjuntak; Jelita Astrid Gulo; Sardo Pardingotan Sipayung
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 1 (2026): Maret 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i1.7283

Abstract

Student grouping based on academic performance is needed to support decision-making in more targeted academic guidance programs. This research implemented K-Means Clustering algorithm to group students based on academic scores and attendance rates. The dataset consisted of 50 student samples with score and attendance percentage attributes ranging from 0-100. Optimal cluster determination used Elbow Method and Silhouette Score with K values varying from 2 to 6. Experimental results showed K=3 produced optimal separation with highest Silhouette Score of 0.72 and WCSS 8,230. Three clusters formed represented high-achieving students (30%), average-performing students (40%), and students requiring special attention (30%). The algorithm converged in average of 8-12 iterations with 90% consistency on multiple runs. Correlation analysis showed very strong relationship between scores and attendance (r=0.89). Interactive visualization system was developed using React.js and Recharts to facilitate result interpretation. This research provided practical contribution in form of clustering framework for early warning identification of at-risk students and academic intervention program recommendations.
Co-Authors Jhonatan Antonius Purba Sipayung, Sardo Pardingotan Theresya Simanjuntak