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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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Hybrid Holt Winter-Prophet method to forecast the num-ber of foreign tourist arrivals through Bali's Ngurah Rai Airport Damaliana, Aviolla Terza; Hindrayani , Kartika Maulida; Fahrudin, Tresna Maulana
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.v3i2.8

Abstract

The Indonesian is an archipelago rich in culture and natural resources. The Government of Indonesia utilizes this wealth by maximizing the tourism potential to earn sizeable foreign exchange. As a major destination, the Indonesian government needs a strategy to ensure foreign tourists continue to increase in terms of health, cleanliness, a sustainable environment and infrastructure. When we can forecast the number of foreign tourists, it is hoped that the government can establish appropriate policies to develop tourism. Based on this, an appropriate forecasting method is needed. This study will use a hybrid model with the Holt-Winter and the Prophet method. The data used is the number of foreign tourists to Bali through Ngurah Rai Airport from January 2009 to December 2019. This study will use stages based on the OSEMN Framework. These stages are Obtain, Scrub, Explore, Model, and Interpret. The result of this study is that the MAPE value for the Hybrid Method is 2.5880%. This result means the Hybrid Holt Winter-Prophet is better than the Holt Winter Method
Application of Design Thinking on BPD Bali Mobile Banking Kadek Dwi Natasya Pradnyani; Anindo Saka Fitri; Abdul Rezha Efrat Najaf
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 4 No. 01 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 1,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v4i01.9

Abstract

The banking company, which is one of the driving factors of economic growth in the country, is continuously innovating for its customers by leveraging technological advancements. This development is also utilized by Bank BPD Bali through the launch of BPD Bali Mobile Banking. When developing this, considering the functionality, usability, needs, and user comfort of the application is crucial, including creating a good and attractive UI/UX design to ensure user satisfaction. In the evaluation of BPD Bali's mobile banking, the obtained SUS score is 57.19. Therefore, a redesign of BPD Bali's mobile banking using Design Thinking is necessary. This method allows for true empathy with the target users and effectively developing solutions for user problems and needs. By going through 5 stages, Empathize, Define, Ideate, Prototype, and Test, the final result obtained is a prototype recommendation, which has been tested using SUS. In the prototype testing, a SUS score of 79.69 was obtained, which can be interpreted as users being able to accept the prototype recommendation.
Prediction of The Islamic Stock Price Index and Risk of Loss Using The Long Short-Term Memory (LSTM) and Value At Risk (VaR) Taufik, Ikbar Athallah; Trimono, Trimono; Muhaimin, Amri
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 4 No. 01 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 1,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v4i01.16

Abstract

Investment aims to increase the value of capital or earn additional income through asset growth, dividends or profits. One investment instrument that is in demand, especially among the Muslim community, is Islamic stocks, which are in accordance with Islamic principles that focus on a healthy economy. This research is focused on predicting Islamic stock prices using the Long Short-Term Memory (LSTM) method and measuring risk with Value at Risk (VaR) using the Cornish-Fisher Expansion (ECF) method. Stock price data from the food sector (PT Indofood), technology sector (Telkom Indonesia), and construction sector (Indocement) for the period 2018-2023 were analyzed. The results show that the ADAM model provides the best performance with the lowest prediction error rates for INTP and TLKM stocks (around 1.22%, 1.98%, and 1.41%). In addition, the SGD model shows limitations in accurate predictions with an error rate above 12%. VaR analysis reveals a slightly higher level of risk in INTP stocks, with a VaR value of around 2.85% at the 95% confidence level. Meanwhile, TLKM stock shows a lower level of risk, with a VaR of around 2.25% at the same confidence level. An in-depth understanding of the risk and growth characteristics of each stock, as well as the selection of the optimization model, are key in making wise investment decisions.
Navigating The Duality: Privacy And Security Concerns In Knowledge Management Systems (KMS) Satibi, Iswanda Fauzan; Ragil Tri Atmi
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 4 No. 2 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v4i2.23

Abstract

This study delves into the intricate relationship between privacy and security within Knowledge Management Systems (KMS). It identifies and analyzes distinct employee perceptions of these intertwined issues within a company's KMS environment. By exploring these perspectives, the authors aim to dispel the confusion surrounding privacy and security in KMS settings. The research employed an explanatory sequential mixed method design. First, a questionnaire survey was distributed directly to KM staff across three companies. This was followed by semi-structured interviews with four KM staff members. The findings reveal a high level of employee awareness regarding the importance of KMS and, consequently, the significance of personal information privacy and security. The study further distinguishes between privacy and security concerns within KMS. Privacy concerns, differentiated across three dimensions: confidentiality, trust, and behavior, are primarily viewed from the organizational layer. Security aspects, on the other hand, are seen as aligned with the ICT layer, governed by legal frameworks and KMS architecture.
Wayang’s Images Recognition using Vision Transformer Sihananto, Andreas Nugroho; Al Haromainy , Muhammad Muharrom; Fauzi, Zaky Ahmad; Reza, Reno Alfa; Putra, Gredy Christian Hendrawan; Christianty, Theressa Marry
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 4 No. 2 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v4i2.24

Abstract

Due to its complex nature and outdated perception, Wayang is a traditional Indonesian art form influenced by Hindu-Buddhism. However, it is difficult for the younger generation to recognize the various types of Wayang. In an effort to preserve Wayang culture, this study evaluates the performance of four deep learning models in recognizing types of Wayang namely, Vision Transformer (ViT), ResNet34, YOLOv5-cls, and YOLOv8-cls. These models were trained and assessed using a dataset of 232 images representing six Wayang types and using matrix such as accuracy, recall, precision, and F1 score. ViT demonstrated efficiency and adaptability despite high computational requirements, achieving the best accuracy (91.3%), showing high adaptability despite substantial computational requirements. Meanwhile, YOLOv5-cls and YOLOv8-cls offered a good balance betwwen accuracy and efficiency. This study suggest that deep learning models can play an essentialrole in Wayang by enhancing recognition accessibility, thus helping younger generations appreciate this tradisional art form.
Geometric Brownian Motion and Value at Risk For Anal-ysis Stock Price Of Bumi Serpong Damai Ltd I Maruddani , Di Asih; Trimono; Aji Riyantoko, Prismahardi; Susrama Masdiyasa, I Gede
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.v1i1.25

Abstract

Investment is one of the activities that last actually attractive to the people of Indonesia. One of the most widely traded financial assets in the capital market is stocks. Stock prices frequently experience challenges to predict changes, so they can increase or decrease at any time. One method that can be applied to predict stock prices is GBM. Then, the risk can be measured using the VaR risk measure. The GBM model is determined to be accurate in predicting the stock price of BSDE.JK, with a MAPE value of 5.17%. By using VaR-HS and VaR CFE, the prediction of risk of loss at the 95% confidence level for the period 06/07/21 is -0.0597 and -0.0623.
Diagnosis of Diabetes Using Naïve Bayes Classifier Method Ardhian Nisaa , Tasya; Maya Ningrum , Shavira; Adha Haque , Berlianda
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.v1i1.26

Abstract

Not a few people suffer from diabetes, diabetes is usually caused by genetic inheritance from parents and grandparents. Not only from heredity but many criteria or characteristics can determine a person has diabetes. This research was conducted by looking for a dataset on Kaggle that contains criteria for someone diagnosed or undiagnosed with diabetes such as age, gender, weakness, polyuria, polydipsia, and others. Furthermore, from these criteria, predictions are calculated using the Naive Bayes classification method where this method is one of the data mining techniques. This prediction calculation uses the Python programming language. From these criteria, each criterion is grouped with similarities and the results of the program that have been made can diagnose someone with diabetes. The prediction calculations that have been carried out have resulted in 90% accuracy, 93% precision, 89% recall, 92% specificity, and 91% F1-Score.
Implementation of Data Mining in Shopping Cart Analysis using the Apriori Algorithm Rahmawati, Susy; Nuril Silviyah , Miftahul; Husna , Nur Syifa’ul
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.v1i1.27

Abstract

Market basket analysis is one of the techniques of knowledge mining used in a broad dataset or database to find a collection of items that are interwoven. Generally used in a sale, the most relevant shopping cart data is used. This methodology has been widely applied in different multinational or foreign industries and is very useful in consumer buying preferences. Technology advances change business trends dramatically, shifting customer demands require increased surgical accuracy of business. In this research, the writer wants to analyze the shopping cart using a priori algorithm, with a dataset from the Kaggle web. Using anaconda software features with the Python programming language which is expected to create knowledge overwriting consumer buying patterns. In conclusion, this pattern can be used to support an industry in managing its company activities.
Selection of Notification Based on Priority Scale with Fuzzy Algorithm Faisal Riftiarrasyid , Mohammad; Nur Diana, Sherli; Istiqomah, Aulia; Ratna Sari, Sumiati
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.v1i1.28

Abstract

Notification is one method that works as a marker that there is information waiting to be read. But along with the times, notifications are increasingly filled with information that is considered less important for device users. So there needs to be a breakthrough to overcome this. This study aims to design a system that can help users to sort out notifications that are considered important and not. It is proven that the system can sort notifications based on the given metrics.

Page 4 of 4 | Total Record : 39

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