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Organized by: Data Science Department Published by: UPN "Veteran" Jawa Timur Jl. Rungkut Madya, Gunung Anyar, Kecamatan Gunung Anyar, Kota Surabaya, Jawa Timur 60294 phone. +62 819-9947-1017 Fax. (031) 8706369 Email: ijdasea@upnjatim.ac.id
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International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : 27989208     EISSN : 28071689     DOI : https://doi.org/10.33005/
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Focus and Scope The IJDASEA International Journal of Data Science, Engineering, and Analytics publishes original papers in the field of computer science which covers the following scope: 1. Theoretical Foundations: Probabilistic and Statistical Models and Theories Optimization Methods Data Compression and Sampling Statistical Learning Computer Education Deep Learning Financial Modeling Forecasting Classification and Clustering Scientific Data and Big Data Analytics Artificial Intelligence Data Pre-Processing, Sampling and Reduction High Dimensional Data, Feature Selection and Feature Transformation High Performance Computing for Data Analytics Architecture, Management and Process for Data Science 2. Machine Learning : Biomedical Knowledge Discovery, Analysis of Micro-Array and Gene Deletion Data Machine Learning for High-Performance Computing Spatial Data Data And Knowledge Visualization Big Data Visualization, Modeling and Analytics Multimedia/Stream/Text/Visual Analytics Database Technology 3. Computational Data Science: Databases Big Data Computational Theories for Big Data Analysis Computational Intelligence for Pattern Recognition and Medical Imaging Intelligent Information Retrieval Probabilistic And İnformation - Theoretical Methods Time Series Analysis Data Acquisition, Integration, Cleaning Semantic Based Data Mining Data Wrangling Optimization for Data Analytics Computer Architecture for Data Analytics Computer Graphics for Data Analytics Computer Application for Data Analytics 4. Applications: Biomedical Informatics Applications Computational Neuroscience Applications Information Retrieval Applications Healthcare Applications Collaborative Filtering Applications Human Activity Recognition Applications Natural Language Processing Applications Web Search Applications Image Analysis Applications Parallel and Distributed Data Applications Spatial Data Mining Applications Multimedia Data Mining Applications Pre-Processing Techniques Applications Data And Information Networks Applications Data And Information Privacy and Security Applications Data And Information Semantics Applications Data Management in Smart Grid Applications Data Mining Algorithms Applications Data Mining Systems Applications Data Structures and Data Management Applications Database and Information System Performance Applications Statistical and Scientific Databases Applications Temporal, Spatial and High Dimensional Databases Natural Language Processing Applications Modeling and Simulation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
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Classification of Drought Impact by Drought Vulnerability Indicators in Probolinggo Regrency Using Naive Bayes Sri Hidayati
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.31

Abstract

Drought in Probolinggo is a big problem because most of the people in this work as farmers. Drought is a natural phenomenon, difficult to define due to differences in hydrometeorological variables and socio economic factors along with the stochastic nature of water demand in various regions. Resident vulnerability to drought hazard is varie. Vulnerability can be measured using vulnerability indicators such as economic factors, social factors, and ecological factors. This research used several vulnerability indicators to classified the impact of drought in three villages in Probolinggo Regency (Sumberkare, Tandonsentul, and Tegalsono). The classification method used in this research is Naïve Bayes. The 10-fold cross validation method was used to train the developed predictive model and the performance of the models evaluated. The accuracy of drought impact by the naive bayes is 85,90 %. Naïve Bayes classifier classify indicators of the impact of drought accurately.
Metric Comparison For Text Classification Amri Muhaimin; Tresna Maulana Fahrudin; Trimono; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.34

Abstract

Text classifications have been popular in recent years. To classify the text, the first step that needs to be done is to convert the text into some value. Some values that can be used, such as Term Frequencies, Inverse Document Frequencies, Term Frequencies – Inverse Document Frequencies, and Frequency of the word itself. This study aims to get which metric value is best in text classification. The method used is Naïve Bayes, Logistic Regression, and Random Forest. The evaluation score that is used is accuracy and Area Under Curve value. It comes out that some metric values produce similar evaluation scores. Another finding is that Random Forest is the best method among others, also the best metric for text classification is Term Frequencies – Inverse Document Frequencies.
The Economic Sentiment from The Coronavirus Perspective in Indonesia Khalilur Rahman; Muhammad Basorudin
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.35

Abstract

The Coronavirus is becoming a pandemic that gives many impacts not only in the health aspect but also in the social-economics aspect. The Coronavirus spread drastically since the first cases in December 2019. Based on World Health Organization data, the Coronavirus has infected 10,402,389 people and killed 507,000 people all around the world until June 2020. In order to minimize the Coronavirus risk, many countries have adopted strict quarantine policies which impacted on their limited activities including Indonesia. However, this policy made the economy fall down, increased unemployment, and poverty. The Coronavirus has also impacted the national statistics office to collect data directly. As the alternative, big data and sentiment analysis have great potential to become a relevant solution. Sentiment data from online news can be used as supporting data for the economy. By using sentiment analysis, the Indonesia economy and health is in a worrying situation currently. Coronavirus gives negative sentiment for economic factors such as inflation, inflation rate, and composite stock price index (IHSG), high subjectivity index and low polarity index.
A Fraud Detection Implementation Of Decision Tree C4.5 Algorithm For Fraud Detection On Anonymous Credit Card Transaction Ulfa Nur Ulfa Mauludina; Dhian Satria Yudha Kartika; Ananda Devi Muri Utomo
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.36

Abstract

The development of technology today makes credit cards seen as a solution to problems that are not difficult and practical in conducting transactions at a bank. Not only is it easy to use when making payments, but using a credit card also doesn't require many requirements. However, with the increase in the use of credit cards, there are several emergencies of criminal acts that can cause losses for customers and banks. This study uses a dataset from the Kaggle website, which amounts to 56,962 original data from a bank in Europe. Data Mining has been reviewed as the best solution to solving this problem, so in this study, the Decision Tree C4.5 method will be used in detecting fraud in credit card transactions. Keywords: Credit Card. Fraud, Data Mining
Sales Analysis at Supermart to Determine Marketing Strategy Using Apriori Algorithm Priyandini Pramithasari; Anindya Dewi Nariswari; Dhian Satria Yudha Kartika
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.37

Abstract

Supermart as a place to shop must have many products that are sold to consumers. Supermart has sales transaction data that occurs every time and the longer the data produces a collection of transaction data. So, it is very unfortunate if it is not analyzed again. In this study, the data used is sales data at supermarts from 2015 to 2018 as many as 9994 supermart transaction data. This data analysis can use data mining methods with a priori algorithms so that they can find new patterns that can help and support companies in understanding business better, and can predict future results. Using Association Rules with Apriori Algorithm, Supermart Grocery can find out the products with the number of sales and the relationship between products with other products or called Itemset. With the support and confidence values, researchers can find out whether the association rules are important or not, and the higher the support and confidence values, the more accurate the association rules will be. The results of the study show that the association rules with the a priori algorithm can be applied by the company in supporting decision-making in the company so that it can determine the right marketing strategy. Thus, supermart companies can analyze consumer purchasing patterns in buying a product based on a combination of 2 items/categories, so that supermarts can make bundles of these items/categories.
Simple Sentiment Analysis Using LSTM and BERT Algoritmhs for Classifying Spam and Non-Spam Data Prismahardi Aji Riyantoko; Dwi Arman Prasetya; Tahta Dari Timur
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.40

Abstract

Sentiment analysis has become a useful tool for doing data analysis and classification based on words, phrases, or documents. Previously, researchers conducted extensive research on sentiment analysis using a variety of algorithms and models. Based on previous research, the results of the sentiment analysis have a negative impact on model performance and data type. At the moment, researchers are using the LSTM and BERT models to classify SMS data into spam and non-spam. The researcher using TD-IDF and GloVe algorithm to determine the weighting of the values represented in vectors in each word to optimize the results of value accuracy. Regardless of the results obtained, the methods BERT and LSTM have a value accuracy sensitivity of 99.35% and 98.22%, respectively. The results present that the completion of spam and non-spam dataset classification is very effective and efficient. Tests were also carried out using disaster twitter data, but the level of accuracy of the values decreased. Therefore, it can be supposed that the different types of datasets considerably affect the performance of the temptation model.
Urban Village Clustering in Surabaya City based on Live Birth Rate using K-Means with Principle Component Analysis Regita Putri Permata; Rifdatun Ni’mah; Amri Muhaimin
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.41

Abstract

Pregnancy and childbirth are important times in a mother's life. Mothers and children are vulnerable so their health efforts should be prioritized. The health level is a useful indicator to see the health efforts achievement or success of an area. The Surabaya City Government is very concerned about the health and safety of mothers and babies problem. Therefore, this study aims to map and classify urban villages in Surabaya based on the number of live births and pregnant women using the K-Means algorithm and feature reduction techniques using Principal Component Analysis. Two main components can be formed as the result of the variable reduction. The most optimal grouping of urban villages in the city of Surabaya is 3 groups/clusters. Based on the number of live births and pregnant women, those consisted of 3 clusters, in which cluster 0 consisted of 99 villages, cluster 1 consisted of 42 villages, and cluster 2 consisted of 12 villages
Model Selection for Forecasting Rainfall Dataset Muhaimin, Amri; Prabowo, Hendri; Suhartono
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v1i1.2

Abstract

The objective of this research is to obtain the best method for forecast- ing rainfall in the Wonorejo reservoir in Surabaya. Time series and causal ap- proaches using statistical methods and machine learning will be compared to forecast rainfall. Time series regression (TSR), autoregressive integrated moving average (ARIMA), linear regression (LR), and transfer function (TF) are used as a statistical method. Feedforward neural network (FFNN) and deep feed-for- ward neural network (DFFNN) is used as a machine learning method. Statistical methods are used to capture linear patterns, whereas the machine learning method is used to capture nonlinear patterns. Data about hourly rainfall in the Wonorejo reservoir is used as a case study. The data has a seasonal pattern, i.e. monthly seasonality. Based on the cross-validation and information criteria, the results showed that DFFNN using the time series approach has a more accurate forecast than other methods. In general, machine learning methods have better accuracy than statistical methods. Furthermore, additional information is ob- tained, through this research the parameter that best to make a neural network model is known. Moreover, these results are also not in line with the results of M3 and M4 competition, i.e. more complex methods do not necessarily produce better forecasts than simpler methods.
The IT Master Plan Development of Randegan Village Anita Wulansari; Tri Lathif Mardi Suryanto; Narti Apriyanti; Anindita Pratita; Usmanur Dian Iswanti; Lidya Bela Simarmata; vana Elfirdaus
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 3 No. 2 (2023): International Journal of Data Science, Engineering, and Analytics Vol 3, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v3i2.4

Abstract

This research focuses on the transformation of Randegan Village into a Smart Village through the integration of digital technology and community empowerment. The main objective of this research is the implementation of e-government in Randegan Village through the design of an IT Master Plan to achieve a smart village. The research design involved a comprehensive analysis of the Smart Village model and literature review by citing insights from the Smart City concept and successful implementation case studies from past research. To help achieve a smart village in Randegan Village, the research team aims to carry out sensing, understanding, controlling, in Randegan Village so that an IT master plan can be created which then has the potential to become a Smart Village Development Master Plan in Randegan Village. The result is that there is a need for cooperation between educational institutions and the community to increase technological literacy. Technology education programs are suggested to overcome challenges of technology adoption. Community involvement is considered very important in developing technology applications, to encourage their acceptance and benefits. The allocation of resources for technology infrastructure is considered important, and it is also important to work with government to ensure financial support and technical assistance. The results of this study are expected to help Randegan Village in determining policies and programs for developing village information systems so that later they can improve the quality of service to villagers, increasing economic growth, and quality of life in randegan village.
Strategic Insights into Educational Assessment: The Implementation and Constraints of SIMCPM in Monitor-ing Student Outcomes Seftin Fitri Ana Wati; Fitri, Anindo Saka; Vitianingsih, Anik Vega; Najaf, Abdul Rezha Efrat
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 3 No. 2 (2023): International Journal of Data Science, Engineering, and Analytics Vol 3, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v3i2.5

Abstract

In response to the evolving challenges in educational institutions, the Ministry of Education and Culture emphasizes the crucial role of effective information systems in achieving optimal educational objectives. This study introduces the Student Learning Achievement Information System (SIMCPM) as a strategic solution for systematically monitoring and evaluating student performance. The research explores the implementation of SIMCPM, focusing on its role in functional testing within educational environments. With a user-centric approach, the study investigates how SIMCPM can be integrated as an innovative tool for monitoring student learning achievements, specifically in displaying grade and attendance data. The methodology outlines the comprehensive approach to SIMCPM's development, emphasizing the use of Laravel 8 for back-end infrastructure and HTML, CSS, and JavaScript for UI/UX development. Data visualization development is highlighted, showcasing the integration of ApexCharts.JS for effective communication of educational metrics. Functionality testing ensures the reliability of the system, encompassing testing scenarios, integration testing, load and performance testing, and mobile and tablet functional testing. Results and discussion present the outcomes of SIMCPM's implementation, including data simulation, dashboard rendering, and functionality testing. The study introduces dashboard features for students, lecturers, and the Head of Study Program, emphasizing speed, efficiency, and data visualization quality. Functionality testing results confirm the robustness of the system. The subsequent section interprets the results, addressing implications, strengths, limitations, and potential improvements in the SIMCPM system. The conclusion recommends continuous testing with real-time data, user feedback integration, and potential enhancements such as predictive analytics and personalized learning recommendations to ensure sustained effectiveness in supporting academic processes. Overall, SIMCPM emerges as a promising tool for efficient academic management, subject to continuous refinement and innovation.

Page 3 of 4 | Total Record : 39

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