Article Per Year (5 Year)
p-Index From 2020 - 2025
0.961
P-Index
This Author published in this journals
All Journal International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research International Journal of Humanities, Law, and Politics International Journal of Health, Medicine, and Sports
Yohandoko, Setyo Luthfi Okta
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Quantitative Research and Modeling

 1 
Sentiment Analysis of Maxim App User Reviews in Indonesia Using Machine Learning Model Performance Comparison Saefullah, Rifki; Yohandoko, Setyo Luthfi Okta; Prabowo, Agung
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.762

Abstract

User reviews can vary widely in language and writing style, which can make accurate sentiment modeling difficult. Selecting the right machine learning model and comparing performance between models can be challenging, given that each model has its own strengths and weaknesses. The method used involved data collection by scraping 5000 reviews from the Google Play Store, followed by data pre-processing including data cleaning, tokenization, stemming, and feature engineering using TF-IDF. The data was divided into training (70%) and testing (30%) sets, with the SMOTE oversampling technique applied to address class imbalance. Three machine learning models were used: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The results showed that the majority of reviews were positive, with a high average app rating. Word cloud analysis revealed that “service”, “driver”, “price”, and “time” were the most frequently discussed aspects in the reviews. In terms of model performance, SVM performed the best with an accuracy of 91.3%, followed by Random Forest (89%) and Naive Bayes (78%). Maxim was generally well received by users in Indonesia, with the majority of reviews being positive. The SVM model proved to be the most effective in classifying review sentiment, outperforming other models in accuracy and precision.
Risk Analysis Using Poisson-Pareto Models to Estimate Reserve Funds for Catastrophic Diseases in National Health Insurance Yohandoko, Setyo Luthfi Okta; Pangestika, Almira Ajeng; Salih, Yasir
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.818

Abstract

Catastrophic diseases such as heart disease, cancer, stroke, and kidney failure pose significant financial burdens on national health insurance systems due to their high treatment costs and frequency. This study utilizes the Poisson-Pareto model to analyze aggregate claims and determine premium loading for these diseases, ensuring the financial sustainability of the National Health Insurance program. Using secondary data from 2018 to 2023, we estimate the parameters for frequency and severity distributions, calculate the expected aggregate claims, and derive the required premium loading at various confidence levels. The results show that heart disease accounts for the highest reserve fund allocation, while kidney failure requires the lowest. These findings emphasize the importance of preparing sufficient reserve funds to manage financial risks associated with catastrophic diseases. The proposed approach provides a robust framework for national health insurance providers to maintain financial stability and optimize resource allocation for high-cost diseases.
Co-Authors Agung Prabowo Hidayana, Rizki Apriva Laksito, Grida Saktian Lianingsih, Nestia Pangestika, Almira Ajeng Pirdaus, Dede Irman Saefullah, Rifki Salih, Yasir