Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Journal of Embedded Systems, Security and Intelligent Systems
Fitri Rahmadhani
Unknown Affiliation
Computer Science & IT Social Sciences

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

Found 2 Documents
Search

 1 
PILIHAN RASIONAL PETANI GAMBIR DALAM MEMILIH PT. SUMATERA RESOURCES INTERNATIONAL SEBAGAI MITRA (STUDI DI DESA MANGGILANG KECAMATAN PANGKALAN KOTO BARU KABUPATEN LIMA PULUH KOTA) Fitri Rahmadhani; Yoskar Kadarisman
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Vol. 9: Edisi II Juli - Desember 2022
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research conducted inManggilangVillage, PangkalanKotoBaruDistrict, Lima Puluh KotaRegency.The purpose of this research is to find out: 1) the production process of processing gambier leaves into gambier and picking gambir leaves which are sold directly, 2) the rational choice of gambir farmers. This research uses descriptive quantitative method.The data collection techniques in this study were observation, questionnaires, and documentation.This study uses a census technique, where there are 60 respondents. The data analysis technique in this research is descriptive quantitative using SPSS version 26. Based on the results of the study, it can be concluded that the processing of leaves into gambier takes time to process. Meanwhile, the process of picking gambir leaves can be directly sold on that day. Most of the income earned is obtained from picking rather than processing gambier leaves into gambier. There are 51 respondents who experienced an increase in income by selling gambier leaves, the remaining 9 respondents did not experience a significant change in their income. Furthermore, 46 respondents also reduced the number of farm workers working on their land, but there were 14 other respondents who did not experience a change between processing gambier leaves into gambier and selling gambier leaves. Keyword: Rational Choice, Gambir Processing, Farmers
Prediction of Learning Outcomes of Programming Courses Using Random Forest and Feature Selection Andi Muh Wira Gunawan; Emalia Fatma Dianti; Erfina Fitri Adnur; Fathul Umam; Fatmawati; Fitri Rahmadhani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/1ctsxz88

Abstract

The achievement of learning outcomes in programming courses remains a challenge in higher education due to variations in students’ logical thinking skills, problem-solving abilities, and practical competencies. Conventional evaluation methods are generally retrospective and do not provide early identification of students at risk of not achieving course learning outcomes. Therefore, predictive modeling based on educational data can support data-driven academic decision-making. This study aims to develop a predictive model of learning outcomes in a programming course using the Random Forest algorithm combined with feature selection to improve model performance and interpretability. This research employed a computational experimental method with a quantitative approach. The dataset consisted of 180 student academic records, including assignment scores, quizzes, practicum, project, attendance, midterm exam, and final exam scores. The experiment compared a baseline Random Forest model using all features with a model applying feature selection based on feature importance. Data were divided into 80% training and 20% testing sets and evaluated using accuracy, precision, recall, and F1-score. The results showed that the baseline model achieved 83.33% accuracy, while the model with feature selection improved accuracy to 88.89% and increased recall performance. Final exam and practicum scores were identified as the most influential predictors. The findings indicate that integrating Random Forest and feature selection enhances prediction accuracy and provides meaningful insights for early intervention strategies in programming education.
Co-Authors Andi Muh Wira Gunawan Emalia Fatma Dianti Erfina Fitri Adnur Fathul Umam Fatmawati Fatmawati Yoskar Kadarisman