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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 15 Documents
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Search results for , issue "Vol 4, No 4: DECEMBER 2023" : 15 Documents clear
Text Mining Application With K-Means Clustering to Identify Sentiments and Popular Topics: A Case Study of The Three Largest Online Marketplaces in Indonesia Andree E Widjaja; Andy Fransisko; Calandra Alencia Haryani; Hery Hery
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.134

Abstract

The number of internets and social media users, which continues to increase at a very fast rate, has resulted in the emergence of new business opportunities in Indonesia. One of those indications is the emergence of marketplace companies in Indonesia. The presence of these online marketplaces provides people with more online marketplace choices according to their preferences. One of the factors that became the basis for this election was reading comments or reviews from consumers on the marketplace posted on social media. This research was conducted using text mining method with k-means clustering algorithm to systematically identify the sentiments and topics that are widely discussed by online marketplace consumers in Indonesia. The data was processed by the k-means algorithm in the form of comments or reviews from three online marketplaces (Tokopedia, Shopee and Bukalapak) on Twitter. The amount of data for each marketplace referred to was 1500 data tweets. The results showed that the three online marketplaces were associated to different topics, even though they are in the same industry. These differences arise due to the fact that most consumers discuss the topics of programs held by their respective online marketplaces. The main topics related to Tokopedia are “belanja” (“shopping”) and “terimakasih” (“thank you”); while for Shopee “pilih” (“choose”) and “jongho”, and for Bukalapak “pra-kerja” (“pre-employment”). In addition, the sentiment analysis carried out shows that the sentiment of the three online marketplaces is predominantly neutral.
Applied Regression Modelling to Recommend Microfinance Development Policies Nguyen Quoc Huy; Lu Phi Nga; Phan Thanh Tam
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.139

Abstract

Microfinance plays an essential role in the socio-economic development of each country through support for poverty reduction. In Vietnam, hunger eradication and poverty reduction under the National Target Program have received attention and implementation in recent years. However, during 2020-2021, Vietnam had several difficulties and hurdles for microfinance organizations, exacerbated by the Covid-19 outbreak, which was hurting the country and all sectors of social life. Microfinance is an excellent instrument for long-term poverty reduction since it teaches the poor how to do business and save and provides essential information. However, microfinance has not yet reached its full potential in our nation. One of the suggested reasons is the legal framework impediment. Thus, the research examines the State's policies for microfinance operations using a survey of 260 staffs related to microfinance activities from 30 microfinance organizations and 30 commercial banks in Vietnam, with data processed using SPSS 20.0. Finally, the study's value suggests ideas for removing barriers to continued microfinance activity development in Vietnam.
Multiple Choice Question Difficulty Level Classification with Multi Class Confusion Matrix in the Online Question Bank of Education Gallery Pariang Sonang Siregar; Rindi Genesa Hatika; B. Herawan Hayadi
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.132

Abstract

The importance of test question planning as a critical element in improving the quality of education is undeniable as it helps teachers evaluate student understanding. The creation of questions must consider the level of difficulty, which is often divided into three categories: easy, medium, and difficult. Predicting the difficulty level of questions has great importance as it helps teachers create test questions that match students' abilities. In this study, we view the identification of item difficulty as a classification problem. The data used includes questions from elementary and junior high school, with various machine learning methods applied to perform classification. We tested Random Forest, Logistic Regression, SVM, Gaussian, and Dense NN, considering embedding, lexical, and syntactic features. The evaluation results show that the best method in identifying the difficulty level of questions in subjects is using Random Forest, resulting in an accuracy of 84%. Meanwhile, in other cases, the best method is also Random Forest, with an accuracy of 80%. Our research shows that the use of feature embedding and TF-IDF has a significant positive impact on the accuracy of the resulting model.
Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction Chandra Lukita; Lalu Darmawan Bakti; Umi Rusilowati; Asep Sutarman; Untung Rahardja
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.131

Abstract

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.
Applying Structural Equation Modeling for Accessing Mobile Banking Service Quality and Customer Satisfaction: A Case Study in Vietnam Nguyen Quoc Huy; Lu Phi Nga; Phan Thanh Tam
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.137

Abstract

Mobile Banking allows customers to use mobile devices and smartphones to conduct banking transactions anytime, anywhere. On the other hand, Mobile banking is a service product that brings high business efficiency, does not cost much, creates initiative for users, reduces pressure on over-the-counter transactions and has little risk, so developing developing mobile banking services brings great benefits to banks. Therefore, using scientific and technological achievements, particularly information technology, electronics, and telecommunications, has had a significant impact on daily life, the economy, and society, changing people's awareness and production and business methods in a wide range of fields and industries, including financial-banking services. In order to address the aforementioned analytical concerns, the authors performed a survey of 650 individual consumers who use mobile banking services at ten commercial banks in Vietnam. The authors employed structural equation modeling and data processing tools SPSS 20.0, Amos. Customer satisfaction is influenced by five elements, according to the findings: dependability, responsiveness, empathy, competence, and tangibles. The findings of the article had a significant reliability influence on individual customer satisfaction, with a significance level of sig 0.01. Finally, the study uniqueness validates ideas regarding customer satisfaction and service quality drivers, as well as the need of flexibly implementing customer satisfaction research policies.

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