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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Journal Zetroem Brilliance: Research of Artificial Intelligence Jurnal Manajemen Perbankan Keuangan Nitro Journal of Natural Science and Technology Adpertisi: Bidang Sains dan Teknologi Journal of Technology Research in Information System and Engineering Journal of Training and Community Service Adpertisi Journal of Information Technology and its Utilization International Conference on Social Science
Resha, Muhammad
Universitas Teknologi Akba Makassar
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Other

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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
Z-Score-Free Stunting Prediction from Basic Anthropometric Measurements Using Machine Learning 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 commonly relies on the WHO height-for-age Z-Score, which requires reference standards, calculation tools, and correct interpretation. These requirements can reduce screening efficiency in resource-limited community health services. This study proposes and validates a Z-Score-free machine learning framework for toddler stunting screening using only basic anthropometric measurements: age, sex, weight, and height. The scientific novelty lies in the deliberate exclusion of Z-Score-derived variables, weight-for-age status, weight-for-height status, and other nutritional-status indicators from the predictor set to prevent data leakage. Four yearly datasets from Jeneponto Regency, Indonesia, were combined, producing 40,071 labeled toddler records after removing one blank row. SMOTE was applied only to training data, and missing numeric values were handled through median imputation inside the training pipeline. Four algorithms were evaluated: XGBoost, Random Forest, LightGBM, and Logistic Regression. Under the valid Z-Score-free pipeline, Random Forest achieved the highest hold-out accuracy and F1-score (95.81% accuracy, 94.86% F1-score), while LightGBM achieved the highest ROC-AUC (99.28%) and recall (95.83%). XGBoost remained highly competitive (95.00% accuracy, 93.90% F1-score, and 99.12% ROC-AUC), but it did not outperform all competing classifiers on this dataset. Stratified 5-fold cross-validation confirmed the same overall pattern, with Random Forest producing the highest mean F1-score. SHAP analysis showed that height and age were the dominant contributors to the XGBoost decision process, aligning with the biological basis of stunting as impaired linear growth relative to age. 
Co-Authors Amiruddin, Amran Amran Amiruddin Andi Sumardin, Andi Andri Wijaya T Andryanto A Askar Askar Askar Askar Asnimar Asnimar Asrul Asrul Asrul Asrul Bakry Bakry Bakry, Bakry Fadli Tamrin Fauziah Suhardi , Rifdah Tami Febri Hidayat Saputra Herman Herman Ilham Ilham Lilis Sugianti Lukman Syafie M. Sabir, Fitriana Markani, Markani Mashud Mashud Maslihatin, Tatik Matulatan, Tekad Maulidinnawati, Andi Muhajirin, Muhajirin Muhammad Arafah Neneng Awaliah Pasnur Pasnur Pasnur Pasnur Rika, Rika Suryadi Syamsu Suryadi Syamsu, Suryadi Tasmil Tasmil Wijaya T, Andri Yusuf , Andi M