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Injury Prevention Strategy Training in Esports Players: A Holistic Approach to Physical and Mental Health Saefullah, Rifki; Yohandoko, Setyo Luthfi Okta; Lianingsih, Nestia
International Journal of Ethno-Sciences and Education Research Vol 4, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v4i3.711

Abstract

Esports has grown into a global industry that attracts the attention of millions of players around the world. However, the popularity and intensity of the game has brought a risk of injury to esports players, especially related to physical problems such as hand joint injuries and poor posture. This article describes various injuries that often occur in esports players, including injuries to hand joints, posture disorders, eye disorders, sleep disorders, and mental health. We also explain the factors that can cause this injury, such as repetitive movements, high pressure, unergonomic positions, lack of rest, joint stiffness and psychological stress. Next, we present comprehensive solutions to prevent injuries in esports players, including physical exercise, stretching exercises, use of ergonomic equipment, attention to body posture, playing time management, and mental health care. Emphasis is placed on a holistic approach that includes both physical and mental health.
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.
Analysis of Queueing Systems in Fast Food Restaurants Using the M/M/c Model: A Case Study during Peak Hours Hidayana, Rizki Apriva; Yohandoko, Setyo Luthfi Okta
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.341

Abstract

This study evaluates the queueing system of a fast-food restaurant using the M/M/c model to optimize the number of service counters (servers) for reducing customer waiting times during peak hours. The analysis involved simulating different configurations with 1, 2, and 3 servers, considering a customer arrival rate of 20 customers per minute and a service rate of 25 customers per minute. Results demonstrate a clear relationship between the number of servers and system performance. A single-server system resulted in an average total time of 12 seconds per customer in the system, highlighting significant delays during peak times. Introducing a second server reduced the average waiting time in the system to 4.44 seconds, striking an effective balance between service efficiency and resource utilization. However, adding a third server showed minimal improvement, as the system's utility ratio declined significantly, suggesting underutilized resources. Based on these findings, a two-server configuration is identified as the optimal solution, efficiently managing the customer arrival rate while maintaining a balanced utility ratio. This study emphasizes the practical value of combining queueing models and simulations to improve operational efficiency in fast-food service systems. The insights can guide decision-making processes for restaurant managers aiming to enhance customer satisfaction and optimize resource allocation during high-demand periods.
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.
A Legal and Criminological Analysis with Prevention Measures in Mind for the General Elections Lianingsih, Nestia; Yohandoko, Setyo Luthfi Okta; Laksito, Grida Saktian
International Journal of Humanities, Law, and Politics Vol. 2 No. 2 (2024): International Journal of Humanities, Law, and Politics
Publisher : Communication in Research and Publications (CRP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhlp.v2i2.49

Abstract

The crime of money politics is a serious challenge in the election system, affecting the integrity of democracy and public trust. This article analyzes the phenomenon of money politics from a legal and criminological perspective, and identifies the driving factors and their impact on the political process. Apart from that, the article also offers prevention strategies that involve strict regulations, law enforcement officials with integrity, fair trials, the active role of the Election Supervisory Agency (Bawaslu), and comprehensive political education. It is hoped that these steps can help overcome and prevent criminal acts of money politics to ensure clean and democratic general elections
Utilization of Used Rubber Bands as Sports Equipment for Skipping Rope Pirdaus, Dede Irman; Yohandoko, Setyo Luthfi Okta; Lianingsih, Nestia
International Journal of Health, Medicine, and Sports Vol. 2 No. 3 (2024): International Journal of Health, Medicine, and Sports
Publisher : Corespub

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhms.v2i3.123

Abstract

This study explores the potential of reusing discarded rubber bands as an alternative material to create an affordable and environmentally friendly jump rope. The study emphasizes the importance of physical activity, particularly jump rope, for maintaining health and fitness. By utilizing discarded rubber bands, the study aims to address waste reduction and the need for accessible sports equipment. The methodology involves collecting, cleaning, and assembling discarded rubber bands into a functional jump rope. Fitness measurements, including resting heart rate, maximum jumps per minute, and time to exhaustion, were recorded before and after the exercise period. Results showed improvements in all measured parameters: decreased mean resting heart rate (from 72.4 to 68 bpm), increased maximum jumps per minute (from 84.8 to 110), and longer time to exhaustion (from 5.2 to 7.2 minutes). These findings suggest that the rubber band jump rope effectively contributed to the cardiovascular health, stamina, and endurance of the participants. The study concludes that the repurposed rubber band jump rope offers a viable, cost-effective, and environmentally friendly alternative to traditional sports equipment. This innovation has the potential to contribute to environmental sustainability, social inclusivity in fitness, and economic accessibility to sports equipment.