Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Mamangan Social Science Journal
Isa Faqihuddin Hanif
Universitas Muhammadiyah Prof. DR. HAMKA, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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

Found 2 Documents
Search

 1 
Evaluation Of Prior Learning Recognition (RPL) Program In Higher Education Irdalisa Irdalisa; Benny Hendriana; Isnaini Handayani; Arum Fatayan; Isa Faqihuddin Hanif; Tri Wintolo Apoko
Jurnal Mamangan Vol 14, No 2 (2025): Special Issue
Publisher : LPPM Universitas PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22202/mamangan.v14i2.9995

Abstract

The purpose of this study was to evaluate how the Recognition of Prior Learning (RPL) program is implemented in Higher Education. The learning process in Indonesia is currently no longer limited to the classroom or formal academic environment. The acquisition of knowledge and skills obtained for the community can be through work experience, training, and non-formal activities. The Recognition of Prior Learning (RPL) program is designed to recognize and validate knowledge and skills obtained outside of formal education, by providing academic credits equivalent to the results of learning in the classroom. RPL serves as an important bridge for students to utilize their experiences and accelerate the completion of studies in higher education. This research method is descriptive-quantitative with 73 students as research subjects. The results showed that this RPL program received a positive response and became the choice of students in continuing their studies. Regarding the stages of the RPL program learning activities, 66.7% of newly registered students strongly agreed and 33.3% agreed that the learning activities encouraged students' curiosity because the material studied was linear with their field of work. Regarding the benefits of the RPL program, 84.2% of RPL students strongly agreed and 15.8% agreed that during the RPL program they gained a lot of new experiences and knowledge that could be implemented in their work. This RPL program contributes to workers to gain access to education, improve competency, obtain a bachelor's degree and support career plans. Thus, the implementation of RPL must be guaranteed in quality so that graduates have standardized graduate achievement quality
Comparison of VGG16 and Resnet50 Architecture using GLCM Feature Extraction In Detecting Monkeypox Najla Qurrata Aini Putri Yusrizal; Isa Faqihuddin Hanif
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.909

Abstract

The increasing number of monkeypox cases has become a global health issue that de-mands rapid and accurate diagnosis. In Indonesia, monkeypox cases reached 88 victims as of August, 2024. The complex symptoms of monkeypox, which often resemble those of other diseases, require advanced technology to distinguish them in a short amount of time. The purpose of this research is to enhance the accuracy and efficiency of the model in identifying monkeypox skin lesions automatically and quickly, thereby supporting the medical diagnosis process more effectively. This study proposes an innovative approach by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction with deep learning architectures ResNet50 and VGG16 for detecting monkeypox in skin lesion im-ages The results show a significant improvement in classification accuracy for both Res-Net50 and VGG16. The GLCM-VGG16 model achieved an accuracy of 95.75%, an im-provement of 18.57% from its original 77.18% without GLCM features. The GLCM-ResNet50 model reached an accuracy of 98.07%, marking a 44.82% increase from the initial 53.25%. The training time of models with GLCM features was also faster compared to models without GLCM. The integration of GLCM successfully captured unique texture characteristics in monkeypox lesions, thereby enhancing the model's ability to distinguish them from other skin diseases. These findings indicate that the combination of GLCM with CNN architectures can be an effective approach for accurately and efficiently detect-ing skin diseases.
Co-Authors Arum Fatayan Benny Hendriana Irdalisa Isnaini Handayani Najla Qurrata Aini Putri Yusrizal Tri Wintolo Apoko