Data Analytics for Effectiveness Evaluation of Islamic Higher Educationusing K-Means Algorithm

The aim of this research is to utilize data analytics technology in evaluating the development of Indonesian national curriculum based on Indonesian National Qualification Framework, especially in universities. This research uses Exploratory Data Analysis (EDA) and several clusterization method, among others K-Means, K-Means++, MiniBatch K-Means, and MiniBatch K-Means++. The result of this research is not to measure the accuracy of clasterization result, but to discover the insight and interpretasion information from data collections that related with national curriculum in Indonesia. Based on the EDA and claterization methods with 30 variables of quetions and 67 students as respondent, MiniBatch K-Means with 2 cluster has the best pattern that reliable with highest Silhouette Coefficient value. However, on average K-Means++ has better interpretation than the others, with the average of Silhouette Coefficient value is highest. From that result, thisresearch found that generally around 77,67% students can understand and feel the application of the Indonesian national curriculum well, but specifically only about 19.4% of students really understand and feel the impact of the curriculum very well. This is important to be evaluated by curriculum users in this case students and tertiary educational institution to improve the quality of academic services in the application of the Indonesian national qualification network

Data science for digital culture improvement in higher education using K-means clustering and text analytics

This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education

Enhanced sentence extraction through neuro-fuzzy technique for text document summarization

A summary system comprises a subtraction of text documents to generate a new form that delivers the essentials contents of the documents. Due to the hassle of documents overload, getting the right information and effectively-developed summaries are essential in retrieving information. Reduction of information allows users to find the information needed quickly without the need to read the full document collection, in particular, multi documents. In the recent past, soft computing-based approaches have gained popularity in its ability to determine important information across documents. A number of studies have modelled summarization systems based on fuzzy logic reasoning in order to select important sentences to be included in the summary. Although past studies support the benefits of employing fuzzy based reasoning for extracting important sentences from the document, there is a limitation concerning this method. Human or linguistic experts are required to determine the rules for the fuzzy system. Furthermore, the membership functions need to be manually tuned. These can be a very tedious and time-consuming process. Moreover, the performance of the fuzzy system can be affected by the choice of rules and parameters of membership function. Therefore, this study proposes a text summarization model based on classification using neuro-fuzzy approach. A classifier is first trained to identify summary sentences. Then, we use the proposed model to score and filter high-quality summary sentences. We compare the performance of our proposed model with the existing approaches, which are based on fuzzy logic and neural network techniques. In this study, we also evaluate the performance of sentence scoring and clustering in the process of generating text summaries. The proposed neuro-fuzzy model was used to score the sentences and clustering were performed using K-Means and Hierarchical Clustering (HC) approaches. The proposed approach showed improved results compared to the previous techniques in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus. However, it was found that no improvements in the quality of the generated summaries obtained by simply performing clustering