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All Journal Jurnal Statistika dan Matematika (Statmat) JURNAL REKAYASA KIMIA & LINGKUNGAN
Susanty, Wenny
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Other

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Optimization of the Condition of Palm Frond Torrefaction Process by Utilizing Liquid Torrefaction Product as Pre-treatment for Improve Product Quality Susanty, Wenny; Helwani, Zuchra; Bahruddin, Bahruddin
Jurnal Rekayasa Kimia & Lingkungan Vol 14, No 1 (2019): Jurnal Rekayasa Kimia & Lingkungan (June, 2019)
Publisher : Chemical Engineering Department, Syiah Kuala University, Banda Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23955/rkl.v14i1.13443

Abstract

AbstractPalm frond can be converted to solid fuel using torrefaction process as an alternative energy source. Torrefaction is the process to convert the biomass into solid fuel at a temperature range of 200-300oC in inert condition. Calorific value is the most important response in solid fuel. The aim of this research is to obtain the optimum condition of palm fronds torrefaction statistically was used Response Surface Methodology. Torrefaction of palm frond on fixed bed a horizontal reactor which is equipped with horizontal condenser and condensate trap with the condition process such as the temperature (225-275oC), time (15-45 min), and N2 flow rate (50-150 ml/min). This research methodology consist of drying, washing with liquid product of torrefaction, torrefaction, and analysis. The response variables were mass yield, calorific value, energy yield, and proximate. Design Expert Trial Version 7.0 Software was used for optimization of condition process with desirability. The optimized condition process were temperature of 275oC, time of 44 minute, and N2 flow rate of 50 ml/min.Keywords: solid fuel, design expert, optimization, palm frond, torrefaction
Analysis of Life Insurance Underwriting Risk Classification Using Ordinal Logistic Regression and XGboost Susanty, Wenny; Dewi Fortuna Silaban
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.59268

Abstract

The underwriting process in life insurance is a critical step in determining the risk classification of prospective policyholders, which impacts premium setting and the company’s sustainability. This study aims to analyze underwriting risk classification using the Ordinal Logistic Regression and XGBoost methods. The data used is the Prudential Life Insurance Assessment dataset, consisting of 59,381 training data points and 19,765 test data points with over 120 variables. The research methodology includes data preprocessing, variable selection using XGBoost, and modeling using Ordinal Logistic Regression and XGBoost. Model evaluation was conducted using the accuracy metric and Quadratic Weighted Kappa (QWK). The results indicate that variables related to health conditions and medical history, such as Medical_History, Medical_Keyword, and BMI, have a significant influence on risk classification. The Ordinal Logistic Regression model offers an advantage in interpreting relationships between variables, while XGBoost demonstrates fairly good classification performance with an accuracy of 0.568 and a QWK of 0.540. Overall, this study demonstrates that a combination of statistical and machine learning approaches can support a more effective underwriting process in the life insurance industry.
Co-Authors Bahruddin Bahruddin Dewi Fortuna Silaban Zuchra Helwani, Zuchra