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Hasih Pratiwi
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hpratiwi@mipa.uns.ac.id
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+6282134673512
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ijas@mipa.uns.ac.id
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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Kota surakarta,
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 8 Documents
 1 
Search results for , issue "Vol 4, No 1 (2021)" : 8 Documents clear
Back Matter Vol 4 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.51603

Abstract

Analisis Dampak Covid-19 Terhadap Indeks Harga Konsumen dengan K-Means dan Regresi Berganda Firli Azizah; Muhammad Athoillah
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.46329

Abstract

The Indonesian economy during the global pandemic entered the brink of economic recession. This problem occurs because the state of public consumption has decreased due to the limited space for community movement and sluggish economic activities due to preventing the transmission of Covid-19. This affects the decline in public consumption in economic activities. In this case, it can be seen from the statistical news published by the official website of the Badan Pusat Statistik (BPS) which reports that the inflation rate in the previous months was around 0.10%, while in April 2020 it decreased by 0.08%. Based on these, a K-means grouping study was conducted by dividing the cluster into 3 parts and modeling using multiple regression methods. In this study, the variable used was the price index. The results of the K-means cluster analysis with the division of 3 clusters, namely cluster 3 (high CPI cluster) consisting of 66 cities, cluster 1 (moderate CPI cluster) consisting of 2 cities, and cluster 2 (low CPI cluster) consisting of 22 cities. Furthermore, the multiple regression results obtained 12 variables that have a significant effect on the Consumer Price Index (CPI). The results of regression modeling are the highest coefficient is food at 0.236 and the lowest coefficients are cigarettes and tobacco at 0.008. Therefore can be concluded that the grouping of the CPI indicator obtained 75% of cities with high index prices, especially in big cities such that economic activity, in general, was still consumptive during the pandemic and multiple regression modeling resulted from 37 indicator variables, only 12 indicator variables had a significant effect on the CPI.Keywords: k-means, CPI, multiple regression, and price index
Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine Bella Audina; Mohamat Fatekurohman; Abduh Riski
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.47953

Abstract

Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the Support Vector Machine (SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the Radial Basic Function kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the Free Cash Flow. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.Keywords: cash flow, forecasting, time series, support vector machine.
Classification of Tweets for Video Streaming Services’ Content Recommendation on Twitter Kiki Ferawati; Sa'idah Zahrotul Jannah
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.49051

Abstract

Streaming services were popular platforms often visited by internet users. However, the abundance of content can be confusing for its users, prompting them to look for a recommendation from other people. Some of the users looked for content to enjoy with the help of Twitter. However, there were irrelevant tweets shown in the results, showing sentences not related at all to the content in the streaming services platform. This study addressed the classification of relevant and irrelevant tweets for streaming services’ content recommendation using random forests and the Convolutional Neural Network (CNN). The result showed that the CNN performed better in the test set with higher accuracy of 94% but slower in running time compared to the random forest. There were indeed distinctive characteristics between the two categories of the tweets. Finally, based on the resulting classification, users could identify the right words to use and avoid while searching on Twitter.Keywords: text mining, streaming services, classification, random forest, CNN
Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous Tito Tatag Prakoso; Etik Zukhronah; Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.45795

Abstract

Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.Keywords: time series regression, seasonal, calendar variations, SARIMAX, forecasting
Structural Equation Modeling (SEM) untuk Mengukur Pengaruh Pelayanan, Harga, dan Keselamatan terhadap Tingkat Kepuasan Pengguna Jasa Angkutan Umum Selama Pandemi Covid-19 di Kota Ambon Zakheus Putlely; Yopi Andry Lesnussa; Abraham Z Wattimena; Muhammad Yahya Matdoan
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.45784

Abstract

Structural Equation Modeling (SEM) is a statistical analysis technique used to build and test statistical models in the form of causal models. Large-Scale Social Restrictions (PSBB) are government policies to break the chain of spreading the corona virus (Covid-19). This policy certainly has an impact on drivers of public transport services. This research shows that the passengers are very satisfied with the travel safety factor. Meanwhile, service factors and passenger public transport fares are in the satisfied category. Furthermore, the variable service quality (MP), the price of public transportation (H), and passenger safety (KP) have an influence on passenger satisfaction. Because the t-value is greater than 1.96 (for the real level of 5%). The influence of service quality, price and safety variables on passenger satisfaction is 78.1%, the remaining 21.9% is influenced by other variables outside the research.Keywords: covid-19, structural equation modeling, satisfaction.
Front Matter Vol 4 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.51602

Abstract

Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure Husna Afanyn Khoirunissa; Amanda Rizky Widyaningrum; Annisa Priliya Ayu Maharani
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.41461

Abstract

The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.Keywords: bank customer, random forest, logistic regression, multilayer perceptron

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