A relative tolerance relation of rough set in incomplete information

University is an educational institution that has objectives to increase student retention and also to make sure students graduate on time. Student learning performance can be predicted using data mining techniques e.g. the application of finding essential association rules on student learning base on demographic data by the university in order to achieve these objectives. However, the complete data i.e. the dataset without missing values to generate interesting rules for the detection system, is the key requirement for any mining technique. Furthermore, it is problematic to capture complete information from the nature of student data, due to high computational time to scan the datasets. To overcome these problems, this paper introduces a relative tolerance relation of rough set (RTRS). The novelty of RTRS is that, unlike previous rough set approaches that use tolerance relation, non-symmetric similarity relation, and limited tolerance relation, it is based on limited tolerance relation by taking account into consideration the relatively precision between two objects and therefore this is the first work that uses relatively precision. Moreover, this paper presents the mathematical properties of the RTRS approach and compares the performance and the existing approaches by using real-world student dataset for classifying university’s student performance. The results show that the proposed approach outperformed the existing approaches in terms of computational time and accuracy

Filosofi, Teori dan Implementasi N-Soft Sets pada Data Mining

Pengambilan keputusan merupakan proses yang penting dalam kehidupan individu atau kelompok. N-soft set merupakan inovasi dalam teori pengambilan keputusan khususnya dalam mengatasi masalah ketidakpastian dengan merepresentasikan data dalam berbagai bentuk. Beberapa penelitian telah mengombinasikan N-soft set dengan teori-teori lain namun belum ada penelitian yang menggabungkan N-soft set dengan data mining. Oleh karena itu, Penelitian ini membahas filosofi kolabrasi antara N-soft set dan data mining serta pendekatan yang diperlukan untuk mengimplementasikan N-soft set dalam metode data mining. N-soft set dapat diaplikasikan untuk memodelkan ketidakpastian dan ambiguitas dalam data dan data mining dapat mengidentifikasi pola dan tren. Metode pencarian pola dalam data mining seperti Association Rule Mining, Clustering, dan Classification juga dapat menggunakan representasi N-soft set. Hal ini memungkinkan identifikasi pola yang relevan dan hubungan yang ada dalam data dengan mempertimbangkan tingkat keanggotaan objek dalam kelas-kelas yang relevan. Penggabungan N-soft set dengan data mining dapat menghasilkan pemodelan yang lebih representatif sesuai kebutuhan pengguna dengan mempertimbangkan lebih banyak faktor dalam pengambilan keputusan. Hal ini disebabkan pola data yang digali telah memiliki tanda atau pengenal sehingga memudahkan untuk mengidentifikasi faktor atau fitur. Penggunaan N-soft set pada data mining diharapkan dapat memberikan wawasan baru dalam pengambilan berbagai jenis keputusan dengan mempertimbangkan ketidakpastian, ambiguitas, dan kompleksitas data

A relative tolerance relation of rough set with reduct and core approach, and application to incomplete information systems

Data mining concepts and methods can be applied in various fields. Many methods have been proposed and one of those methods is the classical 'rough set theory' which is used to analyze the complete data. However, the Rough Set classical theory cannot overcome the incomplete data. The simplest method for operating an incomplete data is removing unknown objects. Besides, the continuation of Rough Set theory is called tolerance relation which is less convincing decision in terms of approximation. As a result, a similarity relation is proposed to improve the results obtained through a tolerance relation technique. However, when applying the similarity relation, little information will be lost. Therefore, a limited tolerance relation has been introduced. However, little information will also be lost as limited tolerance relation does not take into account the accuracy of the similarity between the two objects. Hence, this study proposed a new method called Relative Tolerance Relation of Rough Set with Reduct and Core (RTRS) which is based on limited tolerance relation that takes into account relative similarity precision between two objects. Several incomplete datasets have been used for data classification and comparison of our approach with existing baseline approaches, such as the Tolerance Relation, Limited Tolerance Relation, and NonSymmetric Similarity Relations approaches are made based on two different scenarios. In the first scenario, the datasets are given the same weighting for all attributes. In the second scenario, each attribute is given a different weighting. Once the classification process is complete, the proposed approach will eliminate redundant attributes to develop an efficient reduce set and formulate the basic attribute specified in the incomplete information system. Several datasets have been tested and the rules generated from the proposes approach give better accuracy. Generally, the findings show that the RTRS method is better compared to the other methods as discussed in this study

Trends in Science and Technology for Sustainable Living

Dalam buku ini, dibahas mengenai perkembangan tren kajian dalam sains dan teknologi yang mendukung pembangunan berkelanjutan untuk mewujudkan kehidupan berkelanjutan. Pembangunan berkelanjutan mempunyai prinsip pembangunan yang bertujuan memenuhi kebutuhan generasi saat ini tetapi tidak mengurangi ataupun mengorbankan kemampuan generasi selanjutnya dalam memenuhi kebutuhan mereka; sehingga kehidupan yang baik akan terus berlanjut dalam waktu yang lama. Pembangunan berkelanjutan saat ini berfokus pada tiga hal, yaitu pembangunan keberlanjutan ekonomi dan sosial, serta perlindungan terhadap lingkungan untuk generasi mendatang. Ketiganya saling berhubungan dan mendukung dalam mencapai tujuan pembangunan serta stabilitas lingkungan dan sosial. Oleh karena itu, keseimbangan yang baik dalam aspek lingkungan,ekonomi, dan sosial harus dicapai untuk membentuk kehidupan berkelanjutan