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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 2, No 1 (2019)" : 8 Documents clear
Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier Lingga Aji Andika; Pratiwi Amalia Nur Azizah; Respatiwulan Respatiwulan
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.29998

Abstract

Indonesia is one of the countries that adheres to a democratic system. In the course of a democratic system it is marked by periodic general elections. In 2019 Indonesia held a general election simultaneously to elect the President, DPR, DPRD and DPD. After the election, a lot of opinion arise within the community, including on social media twitter. One of the topics discussed was the results of the quick count of the presidential election. Therefore, a method that can be used to analyze sentiment from the quick count opinion is needed, that is naive Bayes method. The aims of this study are to find the best naive Bayes model and to classify sentiments. The result shows the best accuracy of 82.90% with α = 0.05. The classification obtained is 34.5% (471) positive tweets and 65.5% (895) negative tweets on the results of the quick count.Keywords : sentiment analysis, naive Bayes classifier, elections, quick count
Classification of Human Development Index Using K-Means Retno Tri Vulandari; Sri Siswanti; Andriani Kusumaningrum Kusumawijaya; Kumaratih Sandradewi
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.28566

Abstract

Human development progress in Central Java. It is characterized by a continued rise in the human development index (HDI) of Central Java. HDI is an important indicator for measuring success in the effort to build the quality of human life. HDI explains how residents can access the development results in obtaining a long and healthy life, knowledge, education, decent standard of living and so on. HDI is affected by four factors, namely life expectancy, expected years of schooling, means years of schooling, and expenditure per capita. Currently the Central bureau of statistics do grouping HDI, using calculation formula then known how the value HDI each regency or city in Central Java. In this research we classified the regency or city in Central Java based on the HDI be high, middle, and under estimate area. We used cluster analysis. Cluster analysis is a multivariate technique which has the main purpose to classify objects based on their characteristics. Cluster analysis classifies the object, so that each object that has similar characteristics to be clumped into a single cluster (group). One of the cluster analysis method is k-means. The result of this research, there are three groups, high estimate area, middle estimate area, and under estimate area. The first group or the under estimate area contained 12 regencies, namely Cilacap, Purbalingga, Purworejo, Wonosobo, Grobogan, Blora, Rembang, Pati, Jepara, Demak, Pekalongan, and Brebes. The second group or the middle estimate area contained 8 regencies, namely Banjarnegara, Kebumen, Magelang, Temanggung, Wonogiri, Batang, Pemalang, and Tegal. The third group or the high estimate area contained 11 regencies, namely Banyumas, Kudus, Boyolali, Klaten, Sukoharjo, Karanganyar, Sragen, Semarang, Kendal, Surakarta, and Salatiga.Keywords : cluster analysis, k-means, the human development index.
Front Matter Vol 2 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

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Abstract

Faktor-Faktor yang Mempengaruhi Kriminalitas di Indonesia Tahun 2011-2016 dengan Regresi Data Panel Kosmaryati Kosmaryati; Chandra Arinda Handayani; Refinanda Nur Isfahani; Edy Widodo
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.27932

Abstract

Criminality in Indonesia is increasing every year, therefore an effort is needed to reduce criminality in Indonesia, one of which can be used by knowing which factors influence the increase of criminality. This paper discusses the factors that influence criminality by using panel data regression analysis. Unemployment, domestic violence cases, narcotics cases, embezzlement cases, and fraud cases have positive effect on the amount of criminality with R2 of 0,85823 or 85,823%.Keywords : panel regression analysis, crime, Indonesia
Back Matter Vol 2 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

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Abstract

Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR) Akbar Maulana; Renny Meilawati; Vita Widiastuti
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.26170

Abstract

The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is geographically weighted regression (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.Keywords: human development index, ordinary least square, geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare
Regresi Data Panel untuk Mengetahui Faktor-Faktor yang Mempengaruhi PDRB di Provinsi DIY Tahun 2011-2015 Dea Aulia Nandita; Lalu Bayu Alamsyah; Enggar Prima Jati; Edy Widodo
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.28950

Abstract

Population growth can encourage and hinder economic growth. This study aims to analyze the factors that influence gross domestic product (GDP) in Daerah Istimewa Yogyakarta (DIY) using panel data regression. This study uses three independent variables, namely number of population, number of poor population, and investment, while the dependent variable is GDP. We use secondary data obtained from Badan Pusat Statistik (BPS). The results obtained from the regression analysis of the data series time panel are generalized least square (GLS), while for the cross section data panel shows the REM model is more suitable than PLS and FEM. Based on the validity test of the influence or t-test, the variable that shows significant to the economic rate which is categorized as GRDP in the Daerah Istimewa Yogyakarta in 2011-2015 is the variable Total population and Investment which has a positive relationship.Keywords : economic growth rate, panel data regression, gross regional domestic product
Peramalan Tingkat Penghunian Tempat Tidur Hotel Bintang Tiga Kota Surakarta Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) Shindy Dwi Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.31428

Abstract

Surakarta is a cultural city that is now starting to attract domestic and foreign tourists. This makes many tourists visit the city of Surakarta so that it affects the occupancy rate of hotels in Surakarta. The occupancy rate of hotels in Surakarta has fluctuations from each year. The uncertainty of hotel occupancy rates in Surakarta will certainly affect investors to choose policies in the hotel industry so that hotel occupancy rates in Surakarta City need to be estimated for the next year. In this study, the Autoregressive Integrated Moving Average (ARIMA) method was used to forecast hotel occupancy rates in Surakarta from January to May 2018. By using the best model IMA (1.1), it was concluded that the occupancy rate of three-star Surakarta hotels increased every the month.Keywords : occupancy rate of hotel, forecasting, ARIMA.

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