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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,
Jawa tengah
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 77 Documents
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Penerapan Model Geographically Weighted Regression(GWR) Pada Produksi Ubi Jalar Yuliana Susanti
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24114

Abstract

Sweet potatoes are a major source of carbohydrate, after rice, corn, and cassava. Sweet potato is consumed as an additional or side meal, except in Irian Jaya and Maluku, sweet potato is used as staple food. The main problem faced in increasing sweet potato production is still relies on certain areas, namely Java Island, as the main producer of sweet potato. Differences in production is what often causes the needs of sweet potato in various regions can not be fulfilled and there is a difference price of sweet potato. To fulfill the needs of sweet potato in Java, mapping areas of sweet potato production need to be made so that areas with potential for producing sweet potato can be developed while areas with insufficient quantities of sweet potato production may be given special attention. Due to differences in production in some areas of Java which depend on soil conditions, altitude, rainfall and temperatures, a model of sweet potato production will be developed using the GWR model. Based on the Geographically weighted regression model for each regencies / cities in Java Island, it can be concluded that the largest sweet potato production coming from Kuningan with R2 equal 99.86%.Keywords : Geographically weighted regression, model, sweet potato
Back Matter Vol 2 No 2 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.38496

Abstract

Back Matter Vol 3 No 2 2020 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.48002

Abstract

Aplikasi Model Cox Proportional Hazard pada Pasien Stroke RSD Balung Kabupaten Jember Tutik Qomaria; Mohamad Fatekurohman; Dian Anggraeni
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34907

Abstract

According to the World Health Organization (WHO) cardiovascular disease is a disease caused by impaired heart and blood vessel function. There are many types of cardiovascular disease, but the most common and most well-known are coronary heart disease and stroke. Stroke is a syndrome characterized by symptoms and / or rapidly developing clinical signs in the form of focal and global brain functional disorders lasting more than 24 hours (unless there are surgical interventions or bringing death), which are not caused by other causes besides vascular causes. The number of stroke patients in Indonesia in 2013 based on the diagnosis of health personnel (Nakes) was 1.236.825 (7,0%), while based on the diagnosis of symptoms was 2.137.941 (12,1%). In this study the factors that can affect the survival of stroke sufferers were analyzed using the Cox proportional hazard regression model, the dependent variable was the length of time the patient was treated and the independent variables were gender, age, hypertension status, cholesterol status, Diabetes Militus (DM) status, stroke type, and Body Mass Index (BMI). The result showed that age, DM status, and type of stroke were the most influential factors on the survival of stroke patients at Balung Regional Hospital.Keywords : stroke disease, survival analysis, Cox proportional hazard model
Faktor-Faktor yang Memengaruhi Tingkat Kriminalitas di Indonesia Tahun 2018 Andrian Dwi Putra; Gracilia Stevi Martha; Muhammad Fikram; Risni Julaeni Yuhan
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.41917

Abstract

Crime still often occurs easily in daily life in Indonesia. This study aims to determine factors that influence the level of crime in Indonesia in 2018 and the magnitude of the influence of each factor. The data used in this study are secondary data obtained from the Statistics Indonesia (BPS). The method used in this study is path analysis, and the variables used are population, education, unemployment rate, poverty rate, and crime. As a result, population and poverty influenced crime while education influenced poverty significantly.Keywords: crime, path coefficient, path analysis
Analisis Premi Asuransi Jiwa Menggunakan Model Cox Proportional Hazard Firda Anisa Fajarini; Mohamat Fatekurohman
Indonesian Journal of Applied Statistics Vol 1, No 2 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i2.25280

Abstract

Cox proportional hazard model is a regression model that is used to see the factors that cause an event. The survival analysis used in this research is the period of time the client is able to pay the life insurance premium using Cox proportional hazard model with Breslow method.The purpose of this research is to know how sex, age, insured money, job, method of payment of premium, premium, and type of product can influence the level of ability of client to make payment of life insurance premium based on customer data from PT. BRI Life Insurance Branch of Jember in 2007.The result of this research is the final model of Cox proportional hazard obtained from several variables which have significant influence with simultaneous and partial significance test is the variable of insured money (X3), variable of payment method of premium (X5), premium variable (X6) , and insurance product variable (X7) . The four variables are said to have a significant effect on the model, so that the final model of Cox proportional hazard is obtained that consists of the parameter estimation (β) value of each variable Keywords : survival analysis; cox proportional hazard model; breslow method; life insurance.
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

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