Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal G-Tech : Jurnal Teknologi Terapan JPEG
Abdillah, Refa Maulana
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification of Remission Data for Prisoners in Tangerang Class IIA Correctional Institution using K-Nearest Neighbor Algorithm Abdillah, Refa Maulana; Agung, Halim; Ramdhan, Syaipul
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7532

Abstract

The classification of remission eligibility for prisoners is a critical issue in correctional institutions, as it directly impacts prison management and the rehabilitation process. Special remission is a reduction of sentence granted to prisoners based on specific criteria, including religious status and the type of remission granted. This research aims to address the challenge of classifying special remission data for prisoners at the Class IIA Tangerang Correctional Facility using the K-Nearest Neighbor (KNN) algorithm. The dataset used in this study includes four indicators: Length of Sentence, Remaining Sentence, Crime Type, and Risk Dimension, which are analyzed to predict the remission status to be granted. The KNN model, with a parameter of k=1, achieved an accuracy of 93.94%. However, the model struggled to accurately classify the "No Remission" class, resulting in failures to detect prisoners who are not eligible for remission. The data processing steps included converting categorical data into numerical format, data normalization, and splitting the data into training and testing sets. Model evaluation was conducted using Confusion Matrix, Precision, Recall, and F1-Score. The findings suggest that while the KNN algorithm can be effectively used to classify remission status, further improvements are needed to address class imbalance and optimize results.
Pelatihan Penggunaan Aplikasi Pembelajaran Interaktif untuk Meningkatkan Minat Belajar Anak di Panti Asuhan LKSA Al-Izan’ni Sofia, Detin; Saepudin, Muhamad; Safitri, Nur Hafidzah; Abdillah, Refa Maulana; Suryantiko, Sandy
JURNAL PENGABDIAN GLOBAL Vol 4, No 2 (2025): Jurnal Pengabdian Global (JPEG)
Publisher : JURNAL PENGABDIAN GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/jpeg.v4i2.16005

Abstract

Kegiatan Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan untuk menjawab permasalahan rendahnya minat belajar anak-anak di Panti Asuhan LKSA Al-Izan’ni, yang dipengaruhi oleh keterbatasan metode pembelajaran, minimnya pemanfaatan teknologi interaktif, serta kurangnya keterampilan pengasuh dalam menggunakan aplikasi digital. Solusi yang ditawarkan berupa pelatihan penggunaan aplikasi pembelajaran interaktif, pendampingan, serta evaluasi implementasi dalam proses belajar. Metode pelaksanaan meliputi tahap persiapan, pelatihan pengasuh, implementasi aplikasi, hingga monitoring dan evaluasi. Hasil kegiatan menunjukkan peningkatan minat belajar anak-anak melalui penggunaan aplikasi seperti Duolingo dan Quizizz yang dikombinasikan dengan metode gamifikasi, kuis, dan video edukasi. Selain itu, keterampilan pengasuh dalam mendukung pembelajaran interaktif juga meningkat sehingga menciptakan lingkungan belajar yang lebih menarik, menyenangkan, dan efektif. Dengan demikian, program ini berhasil memberikan kontribusi nyata dalam mendukung kualitas pendidikan anak-anak di Panti Asuhan LKSA Al-Izan’ni.
Co-Authors Halim Agung Ramdhan, Syaipul Saepudin, Muhamad Safitri, Nur Hafidzah SOFIA, DETIN Suryantiko, Sandy