Kemampuan Rasio Keuangan Dan Corporate Governance Memprediksi Peringkat Obligasi Pada Perusahaan Consumer Goods

This study aimed to examine empirically financial ratios (leverage, liquidity, solvability, and profitability)and corporate governance for distinguishing bond rating company in investment grade and non-investmentgrade companies. The population of this research was consumer goods companies listed at Indonesia StockExchange. The research used purposive sampling, polls financial data and analysis with logistic regression.Research finding indicated that corporate governance and financial ratios were the current ratio (CR), longtermdebt ratio (LTDR), total assets and turnover (TAT), return on assets (ROA), independent commissioners(kind), managerial ownership (KM), institutional ownership and audit quality (KA) that were simultaneouslyable to predict bond rating company as investment grade companies and non investment grade companies

An ensemble multi-model technique for predicting chronic kidney disease

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier