Studi Komparasi Penyelesaian Wasiat Wajibah di Indonesia dan Malaysia

A will is a legal document that outlines how a person's assets will be distributed after their death.   In some countries, the distribution of assets is regulated by law and not solely based on the wishes of the deceased. This is known as a mandatory will, which limits the amount that can be given to certain parties to no more than one-third of the total assets. Indonesia and Malaysia are countries that regulate the matter of mandatory wills. The purpose of this journal is to identify the similarities and differences in the provisions of mandatory wills in Indonesia and Malaysia, to determine the Islamic legal basis for mandatory wills, and to examine the development of mandatory wills in both countries.

A Comparativestudy of State-Of-The-Artmachine Learning Classificationmethods

In this era of information and technology data mining has gained much fame. Millions of versatile data records in various forms such as text, digits and images are going to store in databases and online data repositories. Machine learning techniques are playing vital role in analyzing such bulk of data in better way. Health department is considered as one of the most significant domain of generating huge collection of data associated to patient?s care, diagnostics, analysis and recommendations in various contexts based on disease and medical situations. The analysis of health care data can be very helpful for diagnosis of patients and decision making. A number of comparative researches in machine learning techniques have been performed in the literature on health data; however most of these approaches have been limited to a single dataset analysis, focused on a small number of parameters evaluation such as accuracy measurement and lack of graphical representation of statistical performance metrics. There is need to use more parameters and multiple data sets in order to evaluate machine learning algorithms for their maximum performance. The purpose of this research work was to propose and conduct empirical analysis of multiple machine learning classifiers through accuracy, precision, sensitivity, specificity and F-measure parameters to measure their maximum performance on health data. In this regard Diabetes, Kidney, Liver, Lungs and Heart datasets have been analyzed using Na?ve Bayes, LMT, SMO, JRip and J48 Decision Tree classifiers. It has been concluded from analysis that J48 classifier has shown optimal functionality on health datasets having large number of attributes. It has shown high accuracy and F-measure value on CKD (Chronic Kidney Dataset) dataset that is the highest ratio among other classifiers. While in case of small datasets (Lung cancer) Na?ve Bayes and SMO has beaten other classifiers. In graphical representation ROC curve has proved that Na?ve Bayes classifiers presented maximum performance. Precision-Recall curve proved that J48 has beaten other classifiers. Graphical representation of the results of different statistical performance metrics of machine learning Algorithms have also been provided.