Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Masyarakat Unama
Rasywir, Errisya
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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

Found 2 Documents
Search

 1 
BIMBINGAN TEKNIS DALAM MENGHADAPI OLIMPIADE SAIN NASIONAL BIDANG INFORMATIKA PADA SMA NEGERI 2 KOTA JAMBI (MATERI PEMOGRAMAN WEB) Rasywir, Errisya; Irawan; Pahlevi, Riza; Salwa, Shakira
Jurnal Pengabdian Masyarakat UNAMA Vol 3 No 2 (2024): JPMU Volume 3 Nomor 2 Oktober 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2024.3.2.1823

Abstract

In dealing with OSN in the field of informatics, not all schools provide technical guidance. This is important to do considering that in ICT material, the output of material taught at school does not yet produce a simple application, while one of the assessments is that OSN participants are required to present the application. The importance of carrying out this technical guidance lies in the need to prepare participants optimally in facing OSN in the Informatics Field. Laravel was chosen as the focus of learning because it is a popular PHP framework and provides an organized structure and advanced features for developing web applications. With technical guidance, it is hoped that participants will be able to master web programming skills using Laravel, so that they can produce simple applications that can be applied in schools or the community. Thus, they are not only ready to take OSN Informatics well, but also have practical skills that can be useful in everyday life.
Comparison of robust machine learning algorithms on outliers and imbalanced spam data Abidin, Dodo Zaenal; Jasmir, Jasmir; Rasywir, Errisya; Siswanto, Agus
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1130-1144

Abstract

Effective spam detection is essential for data security, user experience, and organizational trust. However, outliers and class imbalance can impact machine learning models for spam classification. Previous studies focused on feature selection and ensemble learning but have not explicitly examined their combined effects. This study evaluates the performance of random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) under four experimental scenarios: (i) without synthetic minority over-sampling technique (SMOTE) and outliers, (ii) without SMOTE but with outliers, (iii) with SMOTE and without outliers, and (iv) with SMOTE and with outliers. Results show that XGBoost achieves the highest accuracy (96%), an area under the curve-receiver operating characteristic (AUCROC) of 0.9928, and the fastest computation time (0.6184 seconds) under the SMOTE and outlier-free scenario. Additionally, RF attained an AUCROC of 0.9920, while GB achieved 0.9876 but required more processing time. These findings emphasize the need to address class imbalance and outliers in spam detection models. This study contributes to developing more robust spam filtering techniques and provides a benchmark for future improvements. By systematically evaluating these factors, it lays a foundation for designing more effective spam detection frameworks adaptable to real-world imbalanced and noisy data conditions.
Co-Authors Abidin, Dodo Zaenal Agus Siswanto Irawan Jasmir, Jasmir Riza Pahlevi, Riza Salwa, Shakira