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All Journal JRIIN :Jurnal Riset Informatika dan Inovasi JITCoS : Journal of Information Technology and Computer System
Fauhan Alfarizi Saragih
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Artificial Neural Network Fauhan Alfarizi Saragih; Lailan Sofinah Harahap; M. Rifqi Arrafi
Jurnal Riset Informatika dan Inovasi Vol 3 No 10 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Kelulusan mahasiswa tepat waktu merupakan salah satu indikator penting dalam menilai keberhasilan proses pembelajaran di perguruan tinggi. Tingginya angka keterlambatan kelulusan dapat berdampak pada efektivitas manajemen akademik serta mutu institusi pendidikan. Penelitian ini bertujuan untuk menerapkan metode Artificial Neural Network (ANN) dalam memprediksi tingkat kelulusan mahasiswa berdasarkan data akademik. Pendekatan yang digunakan adalah kuantitatif dengan metode klasifikasi. Data yang digunakan merupakan data sekunder akademik mahasiswa yang meliputi Indeks Prestasi Kumulatif (IPK), jumlah Satuan Kredit Semester (SKS), dan lama masa studi. Dataset dibagi menjadi data latih dan data uji dengan proporsi 80% dan 20%. Proses pelatihan model dilakukan menggunakan algoritma backpropagation. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, precision, recall, dan confusion matrix. Hasil penelitian menunjukkan bahwa model ANN mampu memprediksi tingkat kelulusan mahasiswa dengan nilai akurasi sebesar 82%, serta nilai precision dan recall yang relatif seimbang.
Comparative Analysis of K-Means Clustering and Otsu Thresholding Methods in Digital Fingerprint Image Segmentation Rifqi Arrafi; Dedek; Fauhan Alfarizi Saragih; Muhammad Rayhans Adrians; M. Haraji
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.115

Abstract

Image segmentation is an important stage in digital image processing that aims to separate the main object from the background so that image information can be analyzed more accurately. This study compares the performance of Otsu Thresholding and K-Means Clustering methods in digital fingerprint image segmentation using a fingerprint image from the FVC2000 dataset. Prior to segmentation, preprocessing was performed using Gaussian Blur and Histogram Equalization to improve image quality. The evaluation was conducted using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Experimental results on 20 fingerprint images from the FVC2000 dataset showed that the K-Means Clustering method achieved a lower average MSE value (5162.54) and a higher average PSNR value (11.34 dB) compared to Otsu Thresholding, which obtained an average MSE value of 8593.21 and an average PSNR value of 9.56 dB. Furthermore, Otsu Thresholding demonstrated significantly faster computational performance. These results indicate that K-Means provides better segmentation quality, whereas Otsu Thresholding is more computationally efficient.
Co-Authors Dedek Lailan Sofinah Harahap M. Haraji M. Rifqi Arrafi Muhammad Rayhans Adrians Rifqi Arrafi