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International Journal of Data Science, Engineering, and Analytics (IJDASEA)

ijdasea
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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 28 Documents

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Model Selection For Forecasting Rainfall Dataset Amri Muhaimin; Hendri Prabowo; Suhartono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

The objective of this research is to obtain the best method for forecasting rainfall in the Wonorejo reservoir in Surabaya. Time series and causal approaches 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-forward 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 obtained, 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.

Geometric Brownian Motion and Value at Risk For Analysis Stock Price Of Bumi Serpong Damai Ltd Trimono Trimono; Di Asih I Maruddani; Prisma Hardi Aji Riyantoko; I Gede Susrama Mas Diyasa
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

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 Tasya Ardhian Nisaa; Shavira Maya Ningrum; Berlianda Adha Haque
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

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 Susy Rahmawati; Miftahul Nuril Silviyah; Nur Syifa’ul Husna
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

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 apriori algorithm, with a dataset from the Kaggle web. Using anaconda software features with the Python programming language is expected to create knowledge overwriting consumer buying patterns. In conclusion, this pattern can be used to support industry in managing its company activities.

Selection of Notification Based on Priority Scale with Fuzzy Algorithm Mohammad Faisal Riftiarrasyid; Sherli Nur Diana; Aulia Istiqomah; Sumiati Ratna Sari
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

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 sort out notifications that are considered important. It is proven that the system can sort notifications based on the given metrics.

Visitor Forecasting Wisata Bahari Lamongan (WBL) Using Hybrid Particle Swarm Optimization (PSO) and Seasonal ARIMA Dinita Rahmalia
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

The revenue of city is determined by some factors, one of them is tourism sector. A problem of tourism sector is forecasting visitors Wisata Bahari Lamongan (WBL). Because data of the number of visitors WBL are fluctuating and seasonal, then it is required Seasonal ARIMA method. In the Seasonal ARIMA method, there are some parameters that should be optimized for producing forecasting with small mean square error (MSE). In this research, Seasonal ARIMA parameters will be optimized by Particle Swarm Optimization (PSO). PSO is optimization algorithm inspired by behavior of birds group in searching food. Based on simulation results, PSO algorithm can optimize Seasonal ARIMA parameter which is optimal and it can produce forecasting result with small MSE.

The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA Model Regi Muzio Ponziani
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.

Analysis and Development of KEBI 1.0 Checker Framework as an Application of Indonesian Spelling Error Detection Tresna Maulana Fahrudin; Ilmatus Sa'diyah; Latipah; Ibnu Zahy Atha Illah; Cagiva Chaedar Beylirna; Burhan Syarif Acarya
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

At educational institutions, especially at University, writing scientific papers is a skill that must be possessed by academics such as educators and students. However, writing scientific papers is not easy, there are many provisions and rules that need to be fulfilled. Several studies show that there are still many academics who make mistakes in writing their scientific papers. Some of the mistakes made include punctuation errors, typographic writing errors and the use of non-standard words in Indonesian. Researchers in Indonesia have developed various spelling error detection applications in Indonesian-language scientific papers. This study tries to analyze the development of an application framework for detecting Indonesian spelling errors from various assessment indicators. This study tries to compare the application framework for detecting spelling errors between other studies with proposed application that named KEBI 1.0 Checker. KEBI 1.0 Checker as a spelling error detection application has 3 main features, namely detecting errors in the use of punctuation marks, writing typography, and using non-standard words in accordance with the standards of the Big Indonesian Dictionary and the General Guidelines for Indonesian Spelling. In addition, this study tries to objectively examine the complexity of the features, advantages and disadvantages, methods and the level of accuracy of each application. The results of the analysis show that KEBI 1.0 Checker has the completeness of features, fast computation time, easy application access, and an attractive user interface. However, it is still necessary to improve the precision in correcting spelling errors in typographic words.

Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data Amri Muhaimin; Prismahardi Aji Riyantoko; Hendri Prabowo; Trimono Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.

Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association) Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Kartika Maulida Hindrayani; Amri Muhaimin; Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.

Page 1 of 3 | Total Record : 28

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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

Location
Kota surabaya,

Jawa timur

INDONESIA

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email ijdasea@upnjatim.ac.id

Phone +6285854329315

Journal Mail Official ijdasea@upnjatim.ac.id

Editorial Address 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

Location Kota surabaya, Jawa timur INDONESIA

Abstract

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Contact Name
-

Contact Email
ijdasea@upnjatim.ac.id

Phone
+6285854329315

Journal Mail Official
ijdasea@upnjatim.ac.id

Editorial Address
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

Location
Kota surabaya,

Jawa timur

INDONESIA