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All Journal Jurnal Media Infotama Jurnal Media Computer Science
Ockhy Jey Fhiter
Universitas Dehasen Bengkulu
Computer Science & IT

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Implemetasi Machine Learning Metode K-Nearest Neighbor Untuk Memprediksi Status Kelulusan Mahasiswa Program Studi Informatika Universitas Dehasen Bengkulu AL Kausar AH; Devi Sartika; Ockhy Jey Fhiter
Jurnal Media Infotama Vol 22 No 2 (2026): Oktober 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i2.12098

Abstract

Student graduation rates are a key performance indicator that’s really crucial for the quality and reputation of a university. However, study delays and the risk of dropping out are still serious challenges for the Informatics Study Program at Dehasen University Bengkulu. This research focuses on tackling these issues by developing an early prediction system for student graduation status to help support more proactive academic decision-making. This research uses an Educational Data Mining (EDM) approach with the Knowledge Discovery in Databases (KDD) framework to process student academic datasets. It implements the K-Nearest Neighbor (KNN) algorithm. Based on the testing results with the hyperparameter K=5, the KNN model achieved an accuracy rate of 50%, which indicates that the decision boundary between students who pass and those who don't cannot be significantly separated by the numeric academic variables currently used. This finding suggests that further development is needed by adding latent variables or sociological data, as well as more specific feature selection techniques to better capture student behavior patterns and improve the model's performance in the future.
Analysis Of The Image Quality Of Public Works And Housing Infrastructure Documentation Using The Discrete Cosine Transform Method Belly Prayoga; Devi Sartika; Ockhy Jey Fhiter
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12135

Abstract

Visual documentation of Public Works and Public Housing (PUPR) infrastructure projects produces high-resolution images that function as progress evidence, technical verification, and audit archives; however, large file sizes create storage and transmission constraints. This study aims to analyze the quality of infrastructure documentation images using a web-based Discrete Cosine Transform (DCT) approach. The research employed a quantitative experimental design supported by the Waterfall development model. Infrastructure documentation images were processed through DCT transformation, quantization, reconstruction, and evaluation using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), compression ratio, and processing time. The system was implemented using PHP and MySQL. Testing on five images produced an average MSE of 45.29 and an average PSNR of 34.86 dB, indicating that reconstructed images remained acceptable for visual documentation because most PSNR values exceeded 30 dB. Nevertheless, the average compression ratio of -68.98% indicated data expansion under the current implementation. These findings confirm that DCT is effective in preserving visual quality, but quantization parameters, output encoding strategy, and file-format handling require further optimization to achieve actual compression efficiency.
Co-Authors AL Kausar AH Belly Prayoga Devi Sartika