Exploratory data analysis to reveal learning loss condition in Islamic religious education

One of the negative impacts of this prolonged distance teaching and learning activity during the pandemic is that students lag in absorbing lessons, known as learning loss. Islamic religious education (IRE) as value education, especially in Indonesia, can also cause learning loss. This study aims to identify possible indications of learning loss experienced by madrasa students in IRE. This study uses data science methods with an exploratory data analysis (EDA) approach. Respondents are students in the sixth grade of Madrasah Ibtidaiyah (MI), the ninth grade of Madrasah Tsanawiyah (MTs), and the twelfth grade of Madrasah Aliyah (MA). The total respondents in this study were 38,326 MI students, 29,350 MTs students, and 13,474 MA students. The results of the EDA found that most madrasas in Indonesia experienced indications of learning loss in IRE subjects during distance learning during the COVID-19 pandemic, both at the MI, MTS, and MA levels. This study found that the learning loss condition is influenced by various states and readiness for distance learning, both the preparedness of students to learn independently, the availability of digital content that is interesting and easy to understand, as well as the availability of facilities and technology for distance learning

Logical framework of information technology: Systematization of software development research

This article aims to present a comprehensive Logical Framework for Information Technology (IT) Research, specifically for developing customized IT applications or software. The methodology of writing this article uses a content analysis with the main source of literature review, Focus Discussion Group, and also based on the experience and knowledge of the authors as lecturers of Software Engineering and Software Project Management. This article shows that although current IT development approaches or methodologies (especially software development methodology) continue to develop, good IT design is carried out through six main stages, namely planning, analysis, design, construction, implementation, and maintenance. The success of IT implementation depends on the good process of all stages of IT design. The involvement of all actors/ stakeholders in IT design is essential to be accommodated at all stages of IT design. Quality also becomes the main goal and controls every process of IT development

Indonesian Citizens' Health Behavior in a Pandemics: Twitter Conversation Analysis using Latent Dirichlet Allocation

Health is an essential thing in carrying out human activities. The COVID-19 pandemic has made people aware of the importance of maintaining health and hygiene for individuals, families, and the surrounding community. All countries, including Indonesia, are impacted by the COVID-19 pandemic, which has undoubtedly changed health behavior in the community. This study aims to reveal changes in health behavior during the pandemic through conversations on social media such as Twitter. The study was conducted using the Latent Dirichlet Allocation (LDA) method to analyze changes in Indonesian citizens' health behavior during the pandemic through social media analysis. The results of social media analysis using LDA on 495,740 tweet data indicate that it is true that there has been a change in public health behavior. At the beginning of the pandemic, many people still did not believe that various hoaxes were spread, and it was difficult to comply with health protocols. Hence, the government massively appealed to make regulations to break the chain of the spread of COVID-19. However, at a critical time with many victims falling, the public became more aware, maintained health protocols, followed the vaccination program, and finally, people got used to coexistence with COVID-19. These results indicate that the Indonesian people are wiser in dealing with the COVID-19 pandemic and following the applicable health protocols

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

Society's Perspectives on Contemporary Islamic Law in Indonesia through Social Media Analysis Technology: A Preliminary Study

The social, cultural, and technological developments of society are unavoidable. This has an impact on the development of Islamic Law, which keeps all Muslim activities in the right corridor. Contemporary Islamic law, known as Contemporary Islamic Law, has also developed to answer new societal problems. Various views on Contemporary Islamic Law in solving multiple issues certainly reap various responses from the community and scholars. These views are often conveyed through social media such as Youtube, Instagram, Facebook, and Twitter. Therefore, this article aims to discuss a preliminary study of text analysis techniques used to find out the views of the community and Ulama on Contemporary Islamic Law issues computationally and automatically. This initial study reviews the methods and techniques that will be used, namely the Indonesian National Work Competency Standards (SKKNI) methodology for data science. This study will also use a sentiment analysis approach, topic modeling, and pattern analysis to find out people's views on issues of Contemporary Islamic Law through social media. The algorithm used for sentiment analysis is the Multinomial Naïve Bayes Classifier (MNBC), for topic modeling is Latent Dirichlet Allocation (LDA), while for pattern analysis using the Prefix-projected Sequential Pattern Mining (PrefixSpan) algorithm. The model generated from sentiment analysis, topic modeling, and pattern analysis will be evaluated by measuring the confusion matrix, coherence value, and silhouette coefficient value. In addition, analysis and interpretation of the model results will be carried out in-depth qualitatively by involving the views and thoughts of Islamic Law experts

Society's Perspectives on Contemporary Islamic Law in Indonesia through Social Media Analysis Technology: A Preliminary Study

The social, cultural, and technological developments of society are unavoidable. This has an impact on the development of Islamic Law, which keeps all Muslim activities in the right corridor. Contemporary Islamic law, known as Contemporary Islamic Law, has also developed to answer new societal problems. Various views on Contemporary Islamic Law in solving multiple issues certainly reap various responses from the community and scholars. These views are often conveyed through social media such as Youtube, Instagram, Facebook, and Twitter. Therefore, this article aims to discuss a preliminary study of text analysis techniques used to find out the views of the community and Ulama on Contemporary Islamic Law issues computationally and automatically. This initial study reviews the methods and techniques that will be used, namely the Indonesian National Work Competency Standards (SKKNI) methodology for data science. This study will also use a sentiment analysis approach, topic modeling, and pattern analysis to find out people's views on issues of Contemporary Islamic Law through social media. The algorithm used for sentiment analysis is the Multinomial Naïve Bayes Classifier (MNBC), for topic modeling is Latent Dirichlet Allocation (LDA), while for pattern analysis using the Prefix-projected Sequential Pattern Mining (PrefixSpan) algorithm. The model generated from sentiment analysis, topic modeling, and pattern analysis will be evaluated by measuring the confusion matrix, coherence value, and silhouette coefficient value. In addition, analysis and interpretation of the model results will be carried out in-depth qualitatively by involving the views and thoughts of Islamic Law experts

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

Deep sequential pattern mining for readability enhancement of Indonesian summarization

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect

Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization

Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text summarization research