Article Per Year (5 Year)
p-Index From 2020 - 2025
1.649
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Islamic Khazanah Gunung Djati Conference Series
Dian Sa’adillah Maylawati
Department Of Informatics, UIN Sunan Gunung Djati Bandung
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Data science for digital culture improvement in higher education using K-means clustering and text analytics Dian Sa'adillah Maylawati; Tedi Priatna; Hamdan Sugilar; Muhammad Ali Ramdhani
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1246.5 KB) | DOI: 10.11591/ijece.v10i5.pp4569-4580

Abstract

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.
Augmented reality using features accelerated segment test for learning tajweed Adi Putra Andriyandi; Wahyudin Darmalaksana; Dian Sa’adillah Maylawati; Ferli Septi Irwansyah; Teddy Mantoro; Muhammad Ali Ramdhani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.14750

Abstract

Currently, education forms students to think creatively and critically, this can be supported by the multimedia technology for education, including Islamic religious education. Islam requires all of its Muslim to read the Qur'an. Tajweed is an important because it is related to the articulation of reading the Qur'an properly and correctly. This article discusses the application of augmented reality (AR) as one of the multimedia technologies that can be used as an interactive educational medium to help study the tajweed of Qur'an. The method used in this research is Features from accelerated segment test (FAST) corner detection. The testing result with 31 tajweed objects show that FAST is able to recognize all Tajweed objects and display their AR. Besides, based on a survey with questionnaires to several students, the result shows that 88.2% of students responded very well and judged that it was sufficient to help study the tajweed.
Latent semantic analysis and cosine similarity for hadith search engine Wahyudin Darmalaksana; Cepy Slamet; Wildan Budiawan Zulfikar; Imam Fahmi Fadillah; Dian Sa’adillah Maylawati; Hapid Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.14874

Abstract

Search engine technology was used to find information as needed easily, quickly and efficiently, including in searching the information about the hadith which was a second guideline of life for muslim besides the Holy Qur'an. This study was aim to build a specialized search engine to find information about a complete and eleven hadith in Indonesian language. In this research, search engines worked by using latent semantic analysis (LSA) and cosine similarity based on the keywords entered. The LSA and cosine similarity methods were used in forming structured representations of text data as well as calculating the similarity of the keyword text entered with hadith text data, so the hadith information was issued in accordance with what was searched. Based on the results of the test conducted 50 times, it indicated that the LSA and cosine similarity had a success rate in finding high hadith information with an average recall value was 87.83%, although from all information obtained level of precision hadith was found semantically not many, it was indicated by the average precision value was 36.25%.
Society's Perspectives on Contemporary Islamic Law in Indonesia through Social Media Analysis Technology: A Preliminary Study Dian Sa'adillah Maylawati; Siah Khosyi'ah; Achmad Kholiq
International Journal of Islamic Khazanah Vol 12, No 1 (2022): IJIK
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (661.395 KB) | DOI: 10.15575/ijik.v12i1.15865

Abstract

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.
Prediction of the COVID-19 Vaccination Target Achievement with Exponential Regression Teja Endra Eng Tju; Dian Sa’adillah Maylawati; Ghifari Munawar; Suharjanto Utomo
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1051

Abstract

The achievement of the national COVID-19 vaccination target in Indonesia is often reported to be uncertain with various existing obstacles. Prediction with exponential regression modeling is done by adopting part of the SKKNI Data Science with the stages of Data Understanding, Data Preparation, Modeling, Model Evaluation. The vaccination dataset from the Ministry of Health of the Republic of Indonesia for the period from January 13, 2021 to October 10, 2021, was randomly separated into training data of 0.8 parts and testing data of 0.2 parts. The optimal parameters of the exponential function are found using the scipy.optimize library in IPython. The model obtained was evaluated using MAE, RMSE, and R-Squared metrics on normalized training data, training data, test data, and recent data for seven days from 11 to 17 October 2021. The prediction results show that the vaccination target will be achieved 100 percent on January 18, 2022, while on December 31, 2021, only 80 percent will be achieved. From the recent data, it appears that more acceleration is needed, especially if it is desired to be achieved in December 2021 as determined by President Joko Widodo, there will be a shortfall of 20 percent based on the prediction results. 
Weighted inverse document frequency and vector space model for hadith search engine Septya Egho Pratama; Wahyudin Darmalaksana; Dian Sa'adillah Maylawati; Hamdan Sugilar; Teddy Mantoro; Muhammad Ali Ramdhani
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp1004-1014

Abstract

Hadith is the second source of Islamic law after Qur’an which make many types and references of hadith need to be studied. However, there are not many Muslims know about it and many even have difficulties in studying hadiths. This study aims to build a hadith search engine from reliable source by utilizing Information Retrieval techniques. The structured representation of the text that used is Bag of Word (1-term) with the Weighted Inverse Document Frequency (WIDF) method to calculate the frequency of occurrence of each term before being converted in vector form with the Vector Space Model (VSM). Based on the experiment results using 380 texts of hadith, the recall value of WIDF and VSM is 96%, while precision value is just around 35.46%. This is because the structured representation for text that used is bag of words (1-gram) that can not maintain the meaning of text well).
Comparison of Classification Algorithms for Sentiment Analysis on Movie Comments Dian Sa'adillah Maylawati; Melani Nur Mudyawati; Muhammad Humam Wahisyam; Riki Ahmad Maulana
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The film industry is growing rapidly nowadays, various genres and storylines are nicely packaged to convey messages and entertain audiences. Sentiment analysis technology can be used for the advancement of the film industry as well as film recommendations that need to be presented next. This study aims to compare several algorithms used for sentiment analysis of movie reviews or comments. The algorithms used in this study are K-Nearest Neighbor (k-NN), Naïve Bayes Classifier (NBC), and Logistic Regression. The experimental results using 25,000 film comment datasets show that Logistic Regression has the highest accuracy rate with an accuracy of 89%, compared to Naïve Bayes' accuracy of 86%, while k-NN is 65.22%.
Combination of Graph-based Approach and Sequential Pattern Mining for Extractive Text Summarization with Indonesian Language Dian Sa'adillah Maylawati; Yogan Jaya Kumar; Fauziah Binti Kasmin
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.21495

Abstract

The great challenge in Indonesian automatic text summarization research is producing readable summaries. The quality of text summary can be reached if the meaning of the text can be maintained properly. As a result, the purpose of this study is to improve the quality of extractive Indonesian automatic text summarization by taking into account the quality of structured text representation. This study employs Sequential Pattern Mining (SPM) to generate a sequence of words as a structured representation of text and a graph-based approach to generate automatic text summarization. The SPM algorithm used is PrefixSpan, and the graph-based approach uses the Bellman-Ford algorithm. The results of an experiment using the IndoSum dataset show that combining SPM and Bellman-Ford can improve the precision, recall, and f-measure of ROUGE-1, ROUGE-2, and ROUGE-L. When Bellman-Ford is combined with SPM, the F-measure of ROUGE-1 increases from 0.2299 to 0.3342. The ROUGE-2 f-measure increases from 0.1342 to 0.2191, and the ROUGE-L f-measure increases from 0.1904 to 0.2878. This result demonstrates that SPM can improve the performance of the Bellman-Ford algorithm in producing Indonesian text summaries.
Co-Authors Achmad Kholiq Adi Putra Andriyandi Ali, Hapid Cepy Slamet Fauziah Binti Kasmin Ghifari Munawar Hamdan Sugilar Imam Fahmi Fadillah Melani Nur Mudyawati Muhammad Ali Ramdhani Muhammad Humam Wahisyam Riki Ahmad Maulana Septya Egho Pratama Siah Khosyi'ah Suharjanto Utomo Teddy Mantoro Tedi Priatna Tju, Teja Endra Eng Wahyudin Darmalaksana Wildan Budiawan Zulfikar Yogan Jaya Kumar