IMPLEMENTASI PEMBELAJARAN KITAB TALIM AL-MUTA ALLIM DALAM PEMBENTUKAN AKHLAK SANTRI DI PONDOK PESANTREN ALKHAIRAAT MADINATUL ILMI DOLO

The purpose of this study is to describe the implementation or application of instructional books Ta‘lim al-Muta‘allim the moral formation of students at boarding school Alkhairaat Madinatul Ilmi Dolo. The qualitative method is used as a method of approach in this study by using observation data collection, interviews, and documentation are analyzed by using the method of data reduction, data presentation, and data verification. The research indicate is result of learning book Ta‘lim al-Muta‘allim  is one book that discusses the presentation on moral learning that must be owned by the students in their studies. Terpokus description on what attitudes must be done by the students in studying good relationship with the teacher (Kiai), with fellow students, as well as how it should be enacting the books (the book) he learned it. In other words, this book is a guideline or code of conduct that students succeed in their studies either in accordance with laid down by Islam. By studying the book Ta'li m al-Muta'allim such, the students should be able to practice and broadcast both inside and outside the boarding school / community. As for the implementation of Learning book Ta‘lim al-Muta‘allim  the moral formation of students at boarding school Alkhairaat Madinatul Ilmi Dolo is providing direct guidance of teaching through the book, namely by providing appropriate role models that are suggested or taught by the book Ta‘lim al-Muta‘allim. Therefore, application of the Book Ta‘lim al-Muta‘allim  between teachers and students in learning is usually not optimal, the influence of the factors of teachers, for example exemplary. So, it should be an important practice, not only wants his students to master learning the book but have a direct practice.

Advancements in Genetic Programming for Data Classification

This thesis aims to advance the state of the art in data classification using Genetic programming (GP). GP is an evolutionary algorithm that has several outstanding features making it ideal for complex problems like data classification. However, it suffers from a few limitations that reduce its significance. This thesis targets at proposing optimal solutions to these GP limitations. The problems covered in this thesis are: 1. Increase in GP tree complexity during evolution that results in long training time. 2. Lack of convergence to a single (optimal) solution. 3. Lack of methodology to handle mixed data-type without type transformation. 4. Search of a better method for multi-class classification. Through this work, we have proposed a method which achieves significant reduction in bloat for classification task. Moreover, we have presented a Particle Swarm Optimization based hybrid approach to increase performance of GP evolved classifiers. The approach offers better performance in less computational effort. Another approach introduces a new two layered paradigm for mixed type data classification with an added feature that uses data in its original form instead of any transformation or pre-processing. The last but not the least contribution is an efficient binary encoding method for multi-class classification problems. The method involves smaller number of GP evolutions, reducing the computation and suffers from fewer conflicts yielding better results. All of the proposed methods have been tested and our experiments conclude the efficiency of proposed approaches.