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Hasih Pratiwi
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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 8 Documents
 1 
Search results for , issue "Vol 6, No 2 (2023)" : 8 Documents clear
Regresi Data Panel dalam Analisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Daerah Istimewa Yogyakarta Tahun 2017-2021 Agung Pamuji; Ezra Putranda Setiawan; Amir Mishbahul Munir
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.80010

Abstract

Open unemployment is one of the most important problems in a country's economy. High unemployment rates can cause serious economic impacts, such as reduced productivity, poverty, and social instability. This problem is one that currently being faced by Indonesia, including in the Province of Special Region of Yogyakarta. This study aims to analyze the factors that influence the open unemployment rate in the Special Region of Yogyakarta Province in the 2017-2021 period. The factors tested include the Human Development Index (HDI), Regional Gross Domestic Product (Regional GDP), and District/City Minimum Wage. This study uses the panel data regression method using annual data from each district/city in the Special Region of Yogyakarta Province during the 2017-2021 period. The results showed that the Human Development Index (HDI) and District/City Minimum Wage variables positively and significantly affected the open unemployment rate in Special Region of Yogyakarta Province during the study period. Meanwhile, the GRDP variable has an insignificant effect on the open unemployment rate in the Special Region of Yogyakarta Province.Keywords: Data Panel; TPT; IPM; UMK; PDRB
Analisis Penyakit Tuberkulosis (TBC) pada Provinsi Jawa Timur Tahun 2021 Menggunakan Geographically Weighted Regression (GWR) Novi Rahmawati; Faldianus Karno; Elvira Mustikawati Putri Hermanto
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.78593

Abstract

Tuberculosis (TB) is an infectious disease with the highest mortality rate in the world. In East Java, TB cases will experience a significant increase in 2021. This study uses the Geographically Weighted Regression (GWR) method to analyze the variables that affect TB cases in East Java that year. The variables used are the number of TB cases, the number of smokers, the number poor population, and population density. The GWR results show that the number of poor people has a significant effect on all districts/cities in East Java. Meanwhile, population density has a significant effect in most areas (except Pacitan, Bondowoso, Situbondo). This research provides input for the government to reduce the number of TB cases in East Java. Efforts need to be made to increase employment and reduce regional disparities so that the burden of this disease can be controlled more effectively.Keywords: TBC, GWR, Kernel Bisquare
Pemodelan Data Time Series Menggunakan Pendekatan Regresi Polinomial Lokal Pada Data Harga Saham MDKA Febrian Adri Nur Fauzi; Rukun Santoso; Di Asih I Maruddani
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.80118

Abstract

Investment is an important way to manage finances for profit. One of the most popular investments in Indonesia is buying and selling shares. In addition to getting profits, they also have risks.  Therefore, analyzing stock prices before buying and selling is an important key in stock investing. Investors should buy stocks at a low price and sell them at a high price. One of the methods used is parametric regression analysis, but it has assumptions that must be met. A more flexible alternative is local polynomial regression without any particular assumptions. PT Merdeka Copper Gold Tbk with MDKA stock code is a company engaged in the mining and industrialization of gold, silver, and other associated minerals. The study of modeling the lowest daily price of MDKA shares using local polynomial regression showed excellent results. The high coefficient of determination exceeding 67% on the in-sample data indicates strong model performance, and the Mean Absolute Percentage Error (MAPE) value on the out-of-sample data is less than 10%, ensuring excellent model accuracy.Keywords: local polynomial regression; MDKA shares; time series
Analisis Structural Equation Modeling Partial Least Square pada Kinerja Pegawai PT. Bank Pembangunan Daerah Jambi Siti Nurhalizah; Gusmi Kholijah; Z Gusmanely
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.78921

Abstract

Human resource management is required to be able to continue to develop themselves in order to have high performance and be able to excel at work. The value of employee performance is important which causes the company to require employee performance to be improved. The object of research conducted at the Head Office of Bank Jambi found that there are employees who have not been able to complete the assigned tasks effectively and efficiently. The purpose of this study was to analyze the direct and indirect effects of competency variables and work discipline on employee performance variables with work motivation variables as intervening variables at Bank Jambi. Based on these problems, the results of the analysis and discussion using SEM-PLS are (1) competence and work discipline have a positive and significant effect on work motivation at Bank Jambi. Work motivation can be explained by competence and work discipline by 65.7% and 34.3% is explained by other variables outside those studied, (2) competence, work discipline, and work motivation have a direct positive and significant effect on the performance of Bank Jambi employees. Employee performance can be explained by competence, work discipline, and work motivation by 72.1% and 27.9% explained by other variables outside the study, and (3) competence and work discipline have a positive and significant indirect effect on employee performance with work motivation as an intervening variable.Keywords: Human resources; competence; work discipline; work motivation; employee performance
Pemodelan Regresi Semiparametrik B Spline (Studi Kasus: Pengaruh Harga Emas dan Minyak Mentah Dunia Terhadap Indeks Harga Saham Gabungan) M. Pratama Aryansah; Suparti Suparti
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79365

Abstract

The increases of the world gold and crude oil prices have a big role as a main factor that effect composite stock price index, the effect can make investors to buy stock from Bursa Efek Indonesia. Regression semiparametric used in this research for a purpose to get combined parametric and nonparametric with B Spline approach. B Spline is a development of spline to overcome weaknesses in making singular matrix at a high order spline with many knot points and close together. Variable parametric component is composite stock price index with crude oil price, and variable nonparametric component is composite stock price index with gold price that got obtained from January 2015 until December 2022. The result from this research is best regression semiparametric B-Spline modelling can be obtained using some combination of order and knot points. The optimal point is obtained on 2nd order using 4 knot point (1.135;1.319,15;1.320,75;1.323,25) with a minimum GCV value is 100.227,8. The best measure of goodness with a coefficient of determination value (R-Square) obtained a value 78,8%, because the value is more than 67% make it as a strong model. MAPE value is 3,37% that has a value less than 10 %, make this model have a perfect forecasting ability.Keywords: Gold; Crude Oil; Composite Stock Price Index; Semiparametric B Spline; GCV
Klasifikasi Menggunakan Algoritma K-Nearest Neighbor pada Imbalance Class Data dengan SMOTE. (Studi Kasus: Nasabah Bank Perkreditan Rakyat “X”) Salsabilla Rizka Ardhana; Tatik Widiharih; Bagus Arya Saputra
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79389

Abstract

Rural Banks (Bank Perkreditan Rakyat/BPR) provide financial services to micro-businesses and low repayment communities, especially in rural areas. The main activity of the bank is lending. Customer credit classification is expected to assist BPR in anticipating potential bad loans. K-Nearest Neighbor classify current and potential bad credit status based on customer data from BPR “X” in Central Java in October 2022. K-Nearest Neighbor is effective against a large amount of training data and works based on the nearest neighbor. There is an imbalance class data which causes the classification process to focus more on the majority class. Imbalance class data is handled using Synthetic Minority Oversampling Technique (SMOTE) as an oversampling approach. Classification with the addition of SMOTE can improve the evaluation of classification accuracy, especially G-mean. G-mean is the most comprehensive measurement in term of  accuracy, sensitivity and specificity in evaluating classification performance on imbalance class data. The results of this research were able to increase g-mean to 58.55% and sensitivity to 45.46% by implementing SMOTE. Based on the classification results, it is concluded that K-Nearest Neighbor with SMOTE at k = 19 and a proportion of training data to test data of 70:30 is a more appropriate classification model to use for customer credit status. Keywords: Credit Status; K-Nearest Neighbor; Imbalance Class Data; SMOTE
Penerapan Model Log-Logistik Proporsional Hazard Untuk Menentukan Faktor yang Mempengaruhi Kondisi Financial Distress Sudarno Sudarno; Di Asih I Maruddani
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79812

Abstract

Every company is likely to experience an up or down phase in its financial performance. A decline in financial performance is a condition of financial distress. Financial distress is an event of a continuous decline in a company's financial performance over a certain period of time. The variables in this research are the response variable in the form of the time of company experiences financial distress, while the covariates are the solvency ratio, liquidity ratio, growth ratio, profitability ratio, company size and activity ratio. The aim and objective of the research is to obtain the property and significance of covariates when a company experiences financial distress. How to determine covariates that are significant to financial distress. The model used is a log-logistic proportional hazard regression model. The log-logistic model is a regression model in the form of a maximum extreme function with right asymptotics and non-negative random variables, while the Cox proportional hazards model is a survival model with the independent variables being time and covariates, between time and covariates being independent. The results of this research are that companies in the infrastructure, utilities and transportation sectors experience financial distress, influenced by solvency ratios, liquidity ratios and profitability ratios. The solvency ratio and profitability ratio have a positive effect, while the liquidity ratio has a negative effect on the timing of financial distress. The contribution of these factors to companies experiencing financial distress is 1.1% (for liquidity ratio), 3.4% (for solvency ratio), and 95.5% (for profitability ratio).Keywords: financial distress; log-logistic; portional hazard; profitability ratio
A Study on the Effects of Selected Micronutrients, Locations, and Their Interaction on Cassava Yield Based on the Two-Way ANOVA Model with Interaction Emmanuel W. Okereke; T M Pokalas; B C Ufondu
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.71440

Abstract

The paper investigated the effects of fertilizer (Zinc, Iron and Manganese), location of farm land and their interaction on cassava yield. The secondary data used for the study were collected from the National Cereal Research Institute of Nigeria Outstanding Farm, Nung Udo, Uyo, Akwa Ibom State. The data comprised of cassava yield (Hectares) for 2016 planting season, five separate farms where three types of fertilizer were applied. The two-way analysis of variance (ANOVA) technique with interaction was used in the analysis of the data. Furthermore, the Tukey HSD test was conducted to compare the treatment means. The result of the study showed that there is significant mean difference in the yield of cassava based on the three types of fertilizer applied. On the basis of farm locations, the result shows that there is no significant mean difference in cassava yield while the interaction between fertilizer and farm location affects cassava yield significantly at 5% level of significance.Keywords: two-way analysis of variance with interaction; Tukey test; fertilizer; cassava yield; farm location

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