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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 115 Documents
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Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA Henderi Henderi; B Herawan Hayadi; Sofa Sofiana; Padeli Padeli; Didik Setiyadi
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.102

Abstract

This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.
Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering Riya Widayanti; Mochamad Heru Riza Chakim; Chandra Lukita; Untung Rahardja; Ninda Lutfiani
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.115

Abstract

This innovative study introduces a novel enhancement to recommendation systems through a synergistic integration of Collaborative Filtering (CF) and Content-Based Filtering (CBF) techniques, termed the hybrid CF-CBF approach. By seamlessly amalgamating the strengths of CF's user interaction insights and CBF's content analysis prowess, this approach pioneers a more refined and personalized recommendation paradigm. The research encompassed meticulous phases, including comprehensive data acquisition, efficient storage management, meticulous data refinement, and the skillful application of CF and CBF methodologies. The findings markedly highlight the prowess of the hybrid approach in generating recommendations that exhibit enhanced diversity and precision, surpassing the outcomes obtained from either technique in isolation. Remarkably, the hybrid CF-CBF approach effectively addresses the inherent shortcomings of individual methods, such as CF's vulnerability to the "cold start" problem and CBF's limitation in fostering recommendation diversity. By fostering a harmonious synergy, this novel approach transcends these limitations and provides a holistic solution. Furthermore, the interplay of CF and CBF augments the recommender system's cognitive grasp of user preferences, subsequently enriching the quality of recommendations provided. In conclusion, this research stands as a pioneering contribution to the evolution of recommendation systems by championing the hybrid CF-CBF approach. By ingeniously fusing two distinct techniques, the study engenders a breakthrough in personalized recommendations, thereby propelling the advancement of more sophisticated and effective recommendation systems.
Bank Soundness Level Prediction: ANFIS vs Deep Learning Satia Nur Maharani; Bambang Sugeng; Makaryanawati Makaryanawati; Mohammad Mahbubi Ali
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.116

Abstract

The systemic nature of the risk of bankruptcy of financial institutions has become an important issue in maintaining the existence and stability of domestic and global finance. The use of statistics for bankruptcy prediction so far provides optimal benefits. However, this approach has limitations, especially since the model is built based on systematic relationships, so the linearity and normality aspects are often weaknesses. This can be overcome very efficiently through linear and non-linear patterns built by artificial intelligence models. One of the most popular of these techniques is the Artificial Neural Network (ANN). Many studies show that ANN and fuzzy set theory is more accurate, adaptive, and strong in predicting compared to statistical models. One technique to integrate ANN with fuzzy logic systems is through the Adaptive-Network-Based Fuzzy Inference System (ANFIS). ANFIS is an adaptive network that is functionally equivalent to fuzzy inference and has the advantages of ANN and fuzzy logic. One of the important features of ANFIS is its acclimatization capability where the membership function parameters can adapt and change in the learning procedure. Utilizing the ANN model and fuzzy logic for bankruptcy prediction is still very limited in Indonesia. Therefore, this study aims to construct a financial institution bankruptcy prediction model that is much more accurate, operational quickly, and effective through ANFIS as a hybrid of fuzzy logic and ANN. The results showed that ANFIS can be used to predict the bankruptcy of financial institutions with the best MAPE 0.140335507.
Incorporating Augmented Reality to Enhance Learning for Students with Learning Disabilities: A Focus on Spatial Orientation in Physical Navinee Intarapreecha; Thosporn Sangsawang
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.126

Abstract

This research endeavors to integrate Augmented Reality (AR) technology into the realm of physical education, with a specific emphasis on improving spatial orientation skills among students with learning disabilities. The study pursues three core objectives: (1) To assess the efficacy of utilizing AR-based instructional tools to enhance spatial orientation abilities; (2) To scrutinize the academic advancements of students with learning disabilities post-AR intervention; (3) To gauge the satisfaction levels of these students with the AR-enhanced learning experience. The study cohort comprises nine students with learning disabilities, drawn from an educational institution situated in Pathum Thani Province, Wat Pathum Nayok school, using a targeted sampling methodology. Data is gathered through immersive AR experiences within the context of physical education, with a focus on spatial awareness. The analytical approach encompasses a diverse array of statistical techniques, including percentages, means, and standard deviations. Furthermore, the t-test is deployed to statistically compare pre and post-learning outcomes, maintaining a significance level of α = 0.05. The research outcomes substantiate that AR-driven educational activities in physical education effectively enhance spatial orientation skills among students (E1/E2: 82.40/81.33). Preceding the intervention, students recorded an average score of 8.80 with a standard deviation of 2.33, which significantly escalated to 16.27 with a standard deviation of 1.48 following AR-assisted learning. The t-test underscores the statistically significant disparity (p 0.05) in scores prior and subsequent to the AR intervention. Furthermore, students with learning disabilities express considerable satisfaction with the application of AR in physical education, with an average satisfaction rating of 4.51. This research carries substantial implications, particularly within the realm of data science, as it pertains to the collection and analysis of data relating to students' educational achievements and satisfaction levels.
Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST Khodijah Hulliyah; Normi Sham Awang Abu Bakar; Saepul Aripiyanto; Dewi Khairani
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.104

Abstract

This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).
Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government Taqwa Hariguna; Athapol Ruangkanjanases
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.127

Abstract

This study explores the potential of text analysis and classification techniques to improve the operational efficiency and effectiveness of e-government, particularly within law enforcement agencies. It aims to automate the analysis of textual crime reports and deliver timely decision support to policymakers. Given the increasing volume of anonymous and digitized crime reports, conventional crime analysts encounter challenges in efficiently processing these reports, which often lack the filtering or guidance found in detective-led interviews, resulting in a surplus of irrelevant information. Our research involves the development of a Decision Support System (DSS) that integrates Natural Language Processing (NLP) methods, similarity metrics, and machine learning, specifically the Naïve Bayes' classifier, to facilitate crime analysis and categorize reports as pertaining to the same or different crimes. We present a crucial algorithm within the DSS and its evaluation through two studies featuring both small and large datasets, comparing our system's performance with that of a human expert. In the first study, which encompasses ten sets of crime reports covering 2 to 5 crimes each, the binary logistic regression yielded the highest algorithm accuracy at 89%, with the Naive Bayes' classifier trailing slightly at 87%. Notably, the human expert achieved superior performance at 96% when provided with sufficient time. In the second study, featuring two datasets comprising 40 and 60 crime reports discussing 16 distinct crime types for each dataset, our system exhibited the highest classification accuracy at 94.82%, surpassing the crime analyst's accuracy of 93.74%. These findings underscore the potential of our system to augment human analysts' capabilities and enhance the efficiency of law enforcement agencies in the processing and categorization of crime reports.
LSTM-Based Machine Translation for Madurese-Indonesian Danang Arbian Sulistyo; Aji Prasetya Wibawa; Didik Dwi Prasetya; Fadhli Almu'iini Ahda
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.113

Abstract

Madurese is one of the regional languages in Indonesia, which dominates East Java and Madura Island in particular. The use of Madurese as a daily language has declined significantly due to a language shift in children and adolescents, some of which are caused by a sense of prestige and difficulty in learning Madurese. The scarcity of research or scientific titles that raises the Madurese language also helps reduce literacy in the language. Our research focuses on creating a translation machine for Madurese to Indonesian to maintain and preserve the existence of the Madurese language so that learning can be done through digital media. This study use the latest dataset for the Madurese-Indonesian language by using a corpus of 30,000 Madura-Indonesian sentence pairs from the online Bible. This study scrapped online Bible pages to organize the corpus based on the Indonesian and Madurese bilingual Bible. Then This study manually process text to match the two languages' scrapping results, normalization, and tokenization to remove non-printable characters and punctuation from the corpus. To perform neural machine translation (NMT), This study connected the RNN encoder with the RNN decoder of the language model, while for training and testing, This study used a sequential model with LSTM, while the BLEU measure was used to assess the accuracy of the translation results. This study used the SoftMax optimization function with Adam Optimizer and added some settings, including using 128 layers in the training process and adding a Dropout layer so that This study got the average evaluation result for BLEU-1 is 0.798068, BLEU-2 is 0.680932, BLEU-3 is 0.623489, and for BLEU-4 is 0.523546 from five tests conducted. Given the language differences between Madurese and Indonesian, this can be the best approach for machine translation of Indonesian to Madurese.
Utilizing the Delphi Technique to Develop a Self-Regulated Learning Model Yongmei Li; Thosporn Sangsawang; Kitipoom Vipahasna
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.124

Abstract

This study combines learning process theories within the context of data science education in Sichuan Province, China, and develops a customized instructional model for the self-regulated International Higher Education (IHE) Model. In collaboration with 17 experts, selected through purposive sampling, and involving 100 instructors within Sichuan, China, this research explores an instructional model designed to foster self-regulated learning in the field of data science. The Delphi data collection method is employed to investigate the relevance of various learning theories within international higher education in Sichuan Province, China, with a specific emphasis on the data science discipline. The Self-Regulated Learning in International Higher Education (SLR-IHE) model, informed by survey questionnaires, addresses pertinent challenges encountered in data science education, including issues related to English language proficiency, faculty training, curriculum development, faculty mobility, cross-border regulations, and funding constraints. The findings of this study lead to the development of an International Higher Education (IHE) Model for Sichuan Province, China, using the Delphi Technique, which consists of four distinct instructional modules. Through a linear regression analysis of the SLR-IHE model, it becomes evident that the self-regulated learning process in data science education comprises four essential stages, each contributing to the acquisition of distinct goals. These stages include: (1) activating prior knowledge; (2) fostering idea exchange and iterative improvement; (3) building organizational knowledge through understanding, memorization, analysis, and transfer; and (4) generating innovative ideas through reflexive thinking and initiating creative thought processes. These stages collectively support the achievement of specific goals associated with Self-Managed Learning (SML), Self-Regulated Learning (SRL), Self-Paced Learning (SPL), and Self-Directed Learning (SDL) in the context of data science education. This comprehensive instructional model for data science education within the framework of international higher education development in Sichuan Province, China, emphasizes globalization, collaborative efforts, and economic growth as key drivers for enhancing the quality of education in the field of data science.
Assessing of The Continuance Intentions to Use Fintech Payments, an Integrating Expectation Confirmation Model Tubagus Asep Nurdin; Mohammad Benny Alexandri; Widya Sumadinata; Ria Arifianti
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.105

Abstract

This study aims to identify the factors influencing users' continuance intention to use FinTech payment applications. An online questionnaire was administered to 361 FinTech users during the pandemic using Google Forms to achieve the objective. The Expectation-Confirmation Model (ECM) was extended to include perceived trust, social influence, and functional benefits and was used to analyze the data obtained from the survey. The study results indicate that prior expectation confirmation and perceived usefulness of the application after use are crucial for increasing users' continuance intention to use the service. Additionally, perceived trust and social influence positively influence users' continuance intention to use the service and can be strengthened through personalized experiences and positive interactions. This study provides valuable insights for researchers and practitioners in the field of FinTech payments.
Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students Aammuay Luaensutthi; Thosporn Sangsawang
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.125

Abstract

The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma’s teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma’s teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers’ and students’ satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers’ learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between   the pre-test and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students’ learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students’ satisfaction gained a very high level with an average of 4.50, SD.=.44.

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