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All Journal Journal Zetroem Brilliance: Research of Artificial Intelligence International Conference on Social Science
Muhammad Resha
Universitas Teknologi Akba Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Implementation of Random Shuffle Algorithm to Randomize Questions in Anti-Corruption Prevention Game Askar; Pasnur; Asrul; Amran Amiruddin; Muhammad Resha; Andri Wijaya T
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3126

Abstract

Socializing anti-corruption education in Indonesia is crucial to fostering public aversion towards corruption. Games are a highly effective medium for disseminating anti-corruption prevention during school years. This research aims to implement anti-corruption education within a game using the shuffle random algorithm to ensure the successful randomization of anti-corruption questions. This data was obtained through a literature review. The method used in this study is the Multimedia Development Life Cycle (MDLC). The results of this research indicate that the testing yielded a 100% accuracy rate for the shuffle random algorithm in randomizing the questions. The accuracy rate demonstrates that the shuffle random algorithm can effectively randomize the questions and is easy to implement.
THE EFFECT OF LIFE INSURANCE CLAIM FILING SYSTEM DEVELOPMENT ON INCREASING CUSTOMER SERVICE PERFORMANCE (CASE STUDY: PT. ASURANSI ALLIANZ LIFE INDONESIA KPM. MAKASSAR) Markani Pato; Lilis Sugianti; Askar Askar; Febri Hidayat Saputra; Asrul Asrul; Mashud Mashud; Asnimar Asnimar; Muhammad Resha; Neneng Awaliah
Proceedings of the 1st International Conference on Social Science (ICSS) Vol. 2 No. 1 (2023): Proceedings of the 2nd International Conference on Social Science (ICSS)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/icss.v2i1.87

Abstract

Customer comfort is absolute and needs to be fully attentive to improving service performance, especially companies in the service sector. Submission of life insurance claim pt. Allianz Life Indonesia Insurance still uses conventional methods where when the customer wants to submit an insurance claim, the customer must visit the branch office, website or contact the head office to get information on what requirements are needed to submit a claim. This problem requires a flexible system application that helps customers in submitting claims and companies in processing customer data. This study aims to create a claim submission information system for PT. Allianz Life Indonesia Insurance KPM. Makassar to make it easier for customers to reach claim submissions without having to visit a branch office. Data obtained by: 1. Field research, 2. Interview with PT. Allianz Life Insurance Indonesia. System development using RAD (Rapid Application Development) model. System development using the RAD (Rapid Application Development) model. This model has 3 stages, namely 1. Requirement Planning, 2. Design 3. Implementation. The results showed that the results of testing with the UAT (User Acceptance Testing) technique were obtained by 82,75% of 26 respondents including admins, pimipinan and customers. The test results, it shows that the information system for submitting life insurance claims is declared feasible and can improve customer service performance
Predicting Independent Z-Score Stunting Through Fundamental Anthropometric Measurements Utilizing Extreme Gradient Boosting (XGBoost) Muhammad Resha; Apriana Toding
ZETROEM Vol 8 No 2 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i2.8736

Abstract

Early detection of stunting generally relies on the WHO standard Z-Score calculation, which requires specific instruments, thus limiting diagnostic efficiency in resource-constrained areas. This study proposes a data-centric Machine Learning approach to precisely predict stunting status using only basic anthropometric data (age, sex, weight, and height) without involving Z-Score derived variables. The study evaluated a large-scale post-pandemic dataset consisting of 40,071 medical records of toddlers from Jeneponto Regency, Indonesia.Given the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Comprehensive benchmarking experiments were conducted using XGBoost, Random Forest, LightGBM, and Logistic Regression algorithms. The empirical evaluation results demonstrated that the XGBoost algorithm produced the best classification performance with an accuracy of 97.88%, outperforming Random Forest (95.77%) and registering a significant margin of advantage of 15.36% compared to the baseline linear model.Feature Importance analysis confirmed that Height (52.41%) and Age (28.15%) were the most fundamental predictors. These findings indicate that tree-ensemble-based architectures are capable of capturing complex non-linear correlations between basic anthropometric parameters. The implications of this study offer a scientific foundation for the development of lightweight, low-cost, and automated stunting diagnosis systems independent of conventional Z-Score calculations at integrated health posts in developing regions
Co-Authors Amran Amiruddin Andri Wijaya T Askar Askar Askar Asnimar Asnimar Asrul Asrul Asrul Febri Hidayat Saputra Lilis Sugianti Markani, Markani Mashud Mashud Neneng Awaliah Pasnur