Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JURNAL PENDIDIKAN TAMBUSAI Building of Informatics, Technology and Science
Abror, Irfan Fiqry
Unknown Affiliation
Computer Science & IT Social Sciences

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

Found 2 Documents
Search

 1 
Penerapan Metode GA-NM Pada Algoritma SVM Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Abror, Irfan Fiqry; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6756

Abstract

Class imbalance is a common challenge in data analysis, especially when the number of instances in the majority class significantly exceeds that in the minority class. This imbalance can cause classification models to favor the majority class, resulting in low accuracy in identifying the minority class. In this study, the Support Vector Machine (SVM) method combined with Near Miss and Genetic Algorithm (GA) is used to address the class imbalance problem in the scholarship recipient data of the Kartu Indonesia Pintar (KIP) program at Universitas Muhammadiyah Kalimantan Timur. The dataset consists of 1,075 records with 27 features representing the socio-economic factors of the scholarship recipients. Near Miss was applied to undersample the majority class, producing a more balanced data distribution. Subsequently, the SVM algorithm was utilized as the primary classification model, with feature selection and parameter optimization conducted using GA. The results indicate that the combination of SVM, Near Miss, and GA improved classification performance in identifying the minority class. The initial accuracy obtained without the method was 60.55% and after implementation it increased to 76.88%. This approach not only enhances the overall accuracy of the model but also ensures more stable performance, particularly for the minority class. Therefore, this study is expected to provide a significant contribution to the development of a more accurate and efficient scholarship selection system, as well as serve as a reference for future research in data mining and machine learning.
Pembuatan Aplikasi Surety Bond Calculator Berbasis Android pada PT Jasa Raharja Putera Marketing Office Samarinda Abror, Irfan Fiqry; Rahim, Abdul
Jurnal Pendidikan Tambusai Vol. 8 No. 1 (2024): April 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v8i1.13490

Abstract

Penelitian ini berfokus pada pembuatan aplikasi Surety Bond Calculator berbasis Android pada PT Jasa Raharja Putera Marketing Office Samarinda. Tujuan penelitian ini adalah meningkatkan kualitas pelayanan PT Jasa Raharja Putera dengan cara memberikan nasabah nilai premi sebelum membuat Jaminan Surety Bond secara cepat dan benar. Prototype adalah metode yang digunakan untuk merancang aplikasi ini sedangkan pengumpulan data dilakukan dengan Teknik observasi, wawancara, dan studi literatur. Hasil dari penelitian ini menujukan aplikasi Surety Bond Calculator berjalan baik dengan menunjukan nilai premi yang sesuai dengan perhitungan secara manual.
Co-Authors Abdul Rahim Rudiman, Rudiman Tagfirul Azima Yoga Siswa, Muspidar Mulyadi