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Hasih Pratiwi
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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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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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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
Laboratory Clustering using K-Means, K-Medoids, and Model-Based Clustering Niswatul Qona'ah; Alvita Rachma Devi; I Made Gde Meranggi Dana
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.40823

Abstract

Institut Teknologi Sepuluh Nopember (ITS) is an institute which has about 100 laboratories to support some academic activity like teaching, research and society service. This study is clustering the laboratory in ITS based on the productivity of laboratory in carrying out its function. The methods used to group laboratory are K-Means, K-Medoids, and Model-Based Clustering. K-Means and K-Medoids are non-hierarchy clustering method that the number of cluster can be given at first. The difference of them is datapoints that selected by K-Medoids as centers (medoids or exemplars) and mean for K-Means. Whereas, Model-Based Clustering is a clustering method that takes into account statistical models. This statistical method is quite developed and considered to have advantages over other classical method. Comparison of each cluster method using Integrated Convergent Divergent Random (ICDR). The best method based on ICDR is Model-Based Clustering.Keywords : K-Means, K-Medoids, Laboratory, Model-Based Clustering
Pengeluaran Pariwisata dan Karakteristik Sosial Demografi Rumah Tangga di Provinsi Jawa Tengah Sri Subanti; Arif Rahman Hakim
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.v1i1.24120

Abstract

The study about tourism expenditure had been one of the important things in the formulation of tourism development, such as marketing analysis, strategies, and policies. Based on this condition, the purpose of our paper wants to know about the determinants of tourism expenditure at households level based on their demographic characteristics. The findings of this paper, (1) the important factors affecting household tourism expenditure are marital status, sex, household income per capita, education for heads of households, the length of study for household members in average, number of households, urban-rural, and industrial origin for head of household; (2) variables that are positively related to tourism expenditure are marital status, age, education, number of household, household income per capita, the length of study for household members in average, urban-rural, and home ownership. This paper suggest that the local governments should give an attention on households demographic characteristics to formulate the tourism marketing and the tourism policies.Keywords : tourism expenditure, demographic characteristics, households
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.
Estimating Bark Eating Caterpillars Indarbela quadrinotata (walker) in Populus deltoides Using Ranked Set Sampling Arvind Kumar; Girish Chandra; Sanjay Kumar
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.35370

Abstract

The problem of bark eating caterpillar, Indarbela quadrinotata infestation has been observed from variety of horticulture and forest tree species in India. The estimation of infestation of this caterpillar using conventional sampling methods was found difficult because counting the number of caterpillar in each tree is practically not feasible. Ranked set sampling (RSS) is a cost efficient method which provides improved estimators of mean and variance when actual measurement of the observations is difficult to obtain but a reasonable ranking of the units in the sample is relatively easy. In the present study, poplar, Populus deltoides plantation of Western Uttar Pradesh and Uttarakhand was taken for the assessment of Indarbela quadrinotata infestation. The RSS estimator of population mean and variance have been discussed and compared with the corresponding estimators from simple random sampling (SRS). The relative precision (RP) of RSS procedure with respect to the SRS for four different set sizes of k = 3, 5, 7, and 10 has been deliberated. It was seen that RP increase with the increment in k. The method of RSS was found suitable for the assessment of insect pest infestation.Keywords: Indarbela quadrinotata, Populus deltoides, simple random sampling, ranked set sample, order statistics.
Analisis Faktor-Faktor yang Mempengaruhi Penyebaran Penyakit Demam Berdarah Dengue (Dbd) di Provinsi Jawa Tengah dengan Metode Spatial Autoregressive Model dan Spatial Durbin Model Arkadina Prismatika Noviandini Taryono; Dwi Ispriyanti; Alan Prahutama
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.v1i1.24026

Abstract

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. Moran’s I test results stated that there are spatial dependencies between dependent and independent variables. The best model produced is the SAR model because it has the smallest AIC value of 49.61
Analisis Faktor Indeks Harga Konsumen Kota Semarang Novia Nafisah; Respatiwulan Respatiwulan
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.v2i2.34903

Abstract

The Consumer Price Index (CPI) can describe consumption patterns in the community. The CPI is also used to calculate inflation rates that reflect a country's economic conditions. The CPI for sub-expenditure consists of 7 groups divided into 35 sub-groups. Factor analysis on CPI was conducted to reduce variables, to identify underlying factors, and to classify variables in the Semarang City CPI expenditure group from January 2014 to August 2017. As the result, there is only one underlying factor, namely the primary needs of urban communities with cumulative variance value of 88.509%, eigenvalues of 23.012 consisting of 27 subgroup variables.Keywords : Consumer Price Index (CPI), factor analysis, eigen value
Valuasi One Period Coupon Bond dengan Aset Mengikuti Model Geometric Brownian Motion with Jump Diffusion Meiliawati Aniska; Di Asih I Maruddani; Suparti Suparti
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.v3i2.43149

Abstract

One period coupon bond gives coupon once a bond life together with the principal debt. If the firm’s asset value on maturity date is insufficient to meet the debtholder’s claim, then the firm is stated as default. The well-known model for predicting default probability is KMV-Merton model. Under this model, it is assumed that the return on the firm’s assets is distributed normally and their behaviour can be described with the Geometric Brownian Motion (GBM) formula. In practice, most of the financial data tend to have heavy-tailed distribution. It indicates that the data contain some extreme values. GBM with Jump is a popular model to capture the extreme values. In this paper, we evaluate a corporate bond which has some extreme condition in their asset value and predicts the default probability in the maturity date. Empirical studies were carried out on bond that is issued by CIMB Niaga Bank that has a payment due in November 2020. The result shows that modelling the asset value is more appropriate by using GBM with Jump rather than GBM modelling. Estimation to CIMB Niaga Bank equity in November 2020 is IDR 246,533,573,844,229.00. The liability of this company is IDR 4,205,751,155,771.00. The prediction of CIMB Niaga Bank default probability is 1.065812 ´ 10-8 at the bond maturity. It indicates that the company is considered capable of fulfilling the obligations at the maturity date.Keywords: jump diffusion, extreme value, probability default, equity, liability
Pendekatan Regresi Data Panel untuk Pemodelan Jumlah Angkatan Kerja dan Penanaman Modal Luar Negeri terhadap PDRB Provinsi di Indonesia Muhammad Syukron; Hafidz Muhammad Fahri
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.v1i2.26172

Abstract

Indonesia is a country with great economic potency. Indonesia has a vast area and abundant natural products, but until now Indonesia is still a developing country. The Indonesian economy is defeated by other countries such as Japan, China and South Korea even by the neighboring country, Singapore. Increasing the national economy can be started from improving the regional economy which can be measured by gross regional domestic product (GRDP). Indonesia will experience a demographic bonus in 2045 so that the population of productive age is expected to contribute a lot to economic growth. The large number of productive age population must be balanced with the availability of jobs so that this momentum can be fully utilized. Foreign investment can be a solution when domestic capital is insufficient in financing economic activities. In addressing this phenomenon, a statistical analysis of panel data regression was conducted to see the relationship between independent variables, namely the number of labor force and realization of foreign investment, and a dependent variable, namely GRDP at constant prices in 2010 for every province in Indonesia. We use time series data in 2015-2017 and cross-sectional data of 34 provinces in Indonesia taken from BPS official website. The estimation result shows that both independent variables partially and fully have a significant effect on the GRDP with an adjusted R2 of 99.86%. Keywords: Labor force; regression; panel data; foreign capital; GRDP.
Front Matter Vol 2 No 1 Hasih 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

Abstract

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