Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Innovation Information Technology and Application (JINITA)
Riyadi Purwanto
(SCOPUS ID :57209985994), Politeknik Negeri Cilacap
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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

Found 2 Documents
Search

 1 
A Comparative Analysis of KIP-K Acceptance Prediction Based on School Type Using XGBoost, Random Forest, and SVM-RBF: Evaluation Through Accuracy and Data Visualization Riyadi Purwanto; Fajar Mahardika; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/10.35970/jinita.v7i2.2948

Abstract

The Indonesia Smart College Card (Kartu Indonesia Pintar-Kuliah / KIP-K) is a national initiative aimed at expanding access to higher education for students from socioeconomically disadvantaged backgrounds. This study, conducted at Politeknik Negeri Cilacap, investigates the prediction of KIP-K acceptance based on the type of high school attended by applicants. A comparative analysis was carried out using three supervised machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine with Radial Basis Function (SVM-RBF). The dataset, sourced from institutional admission records between 2022 and 2024, comprises information on school types (public, private, vocational, madrasah, and others), demographic attributes, and the KIP-K acceptance status. The data were split into training and testing sets using a 50:50 stratified sampling technique to preserve class distribution. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Additionally, confusion matrices, ROC curves, and feature importance visualizations were used to enhance model interpretability. The experimental results demonstrate that the XGBoost algorithm consistently outperformed the other models across all performance metrics. Specifically, XGBoost exhibited the highest discriminatory power with an AUC of 0.93, followed by Random Forest (0.90) and SVM-RBF (0.85). These findings affirm the suitability of tree-based ensemble methods for classification tasks in educational domains and emphasize the predictive relevance of school type in determining KIP-K eligibility. The study presents a data-driven decision support framework that can contribute to more objective, transparent, and equitable scholarship allocation practices, particularly within the context of vocational higher education institutions in Indonesia
User Sentiment toward Halodoc Psychological Consultation Services Using RNN and LSTM Analysis Riyadi Purwanto; Randi Afif; Fajar Mahardika; Dwi Novia Prasetyanti; Cahya Vikasari; Rostika Listyaningrum
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3288

Abstract

In the digital era, online psychological consultation services such as HaloDoc are increasingly used to provide accessible mental health support; however, the rise in users does not always reflect service satisfaction, making sentiment analysis of user reviews essential for understanding public perception. This study aims to classify positive and negative sentiments from Indonesian-language user reviews of HaloDoc’s psychological consultation service and to compare the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms. Using a quantitative approach with text mining and sentiment analysis, data were collected from Google Play Store reviews and processed through case folding, filtering, tokenizing, and stemming, then manually labeled and split into 80% training and 20% testing data. Models were developed using Keras with RNN and LSTM architectures and evaluated using confusion matrix metrics, including accuracy, precision, recall, and F1-score. The results show that RNN achieved 95% accuracy, while LSTM reached 91%; although RNN performed better in accuracy, LSTM demonstrated more stable performance and superior capability in capturing complex contextual information, particularly in longer reviews with varied emotional expressions. Overall, the findings indicate that LSTM is more effective for Indonesian-language sentiment classification in the digital psychology domain and can serve as a foundation for developing automated systems to assess user satisfaction with online psychological services in Indonesia
Co-Authors Cahya Vikasari Dwi Novia Prasetyanti Mahardika, Fajar Muhammad Nur Faiz Randi Afif Rostika Listyaningrum