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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 123 Documents
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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
Prediksi Jumlah Permintaan Darah UTD PMI Kota Pontianak Menggunakan ARIMA-Kalman Filter Lyra Mauditia; Nurfitri Imro'ah; Wirda Andani
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.85958

Abstract

Ensuring a sufficient supply of blood is a crucial aspect of providing health services. However, the large demand for blood is sometimes difficult to fulfill for one of the work units in the Indonesian Red Cross (PMI), namely the Blood Transfusion Unit. Therefore, blood demand prediction is needed to assist the blod transfuse unit in preparing sufficient blood stock. This study uses the ARIMA-Kalman Filter model to anticipate the quantity of blood demand for Blood Transfusion Unit PMI. The observations modeled in this study are daily observations of the amount of blood demand with the period January 1 to December 26, 2023 as an in-sample of 360 observations and blood demand for the period 27 to 31 December 2023 which amounted to 5 observations as an out-sample used to evaluate the model. The analysis’s findings indicate that the model obtained for predicting the amount of blood demand is the ARIMA (0,0,2) model, then the model parameters are estimated using Kalman Filter. The model used fulfills the diagnostic test and obtained a MAPE value of 15.021% in predicting out-sample data. Thus it can be concluded that the model used is in the very good category and is suitable for prediction. Furthermore, predictions are made for the next three days on the number of blood requests at Blood Transfusion Unit PMI Pontianak City to help health services prepare blood stocks for patients in need.
Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pekerjaan Layak dengan Menggunakan K-Medoids Clustering Edy Widodo; Shifa Qonita; Safira Feri Amalina; Sifa Nurul Aoliya
Indonesian Journal of Applied Statistics Vol 8, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i1.97996

Abstract

Pekerjaan layak memainkan peran krusial dalam mengurangi kemiskinan dan menjadi salah satu tujuan utama dalam pembangunan berkelanjutan. Studi ini dilakukan untuk mengkaji berbagai indikator yang berperan dalam menentukan pekerjaan layak di Indonesia dan mengklasifikasikan tingkat kemampuan kerja di 34 provinsi berdasarkan indikator yang terkait dengan sustainable development goals (SDGs), khususnya Tujuan 8 tentang pekerjaan layak dan pertumbuhan ekonomi. Data yang digunakan meliputi indikator-indikator seperti tingkat pengangguran terbuka, proporsi pekerja informal, persentase anak usia 10-17 tahun yang bekerja, perusahaan yang menerapkan norma K3, dan persentase pemuda usia 15–24 tahun yang saat ini tidak terdaftar dalam pendidikan, pekerjaan, atau pelatihan (NEET). Teknik analisis yang digunakan adalah metode principal component analysis (PCA) dan metode k-medoids clustering. Berdasarkan analisis PCA, diperoleh 2 faktor utama, yaitu faktor kesejahteraan dan ketenagakerjaan serta faktor pengangguran dan keselamatan kerja. Secara bersama-sama, kedua faktor ini menyumbang 73,9516% dari keseluruhan varians dalam data. Analisis cluster dilakukan dengan menggunakan 2 faktor utama ini. Berdasarkan analisis pengelompokan menggunakan metode k-medoids, dihasilkan 3 cluster. Cluster 1 yang terdiri dari 20 provinsi merupakan wilayah dengan indikator pekerjaan layak yang moderat, cluster 2 yang terdiri dari 5 provinsi merupakan wilayah dengan indikator pekerjaan layak yang tinggi, dan cluster 3 yang terdiri dari 9 provinsi merupakan wilayah dengan indikator pekerjaan layak yang tinggi.Kata kunci: Pekerjaan layak; sustainable development goals (SDGs); k-medoids; principal component analysis (PCA).Decent work plays a crucial role in reducing poverty and serves as one of the key objectives in sustainable development. This study was conducted to examine various indicators that play a role in determining decent work in Indonesia and to classify the level of workability in 34 provinces based on indicators related to Sustainable Development Goals (SDGs), particularly Goal 8 on decent work and economic growth. The data used includes indicators such as the open unemployment rate, proportion of informal workers, percentage of children aged 10-17 years who work, companies that apply OSH norms, and the percentage of youth aged 15–24 who are not currently enrolled in education, work, or training (NEET). The analysis techniques used are Principal Component Analysis (PCA) method and K-Medoids Clustering method. Based on PCA analysis, 2 main factors were obtained, namely the welfare and employment factor and the unemployment and job safety factor. Together, these two factors account for 73.9516% of the overall variance in the data. Cluster analysis was conducted using these 2 main factors. Based on clustering analysis using the K-Medoids method, 3 clusters were generated. Cluster 1 consisting of 20 provinces is a region with moderate decent work indicators, cluster 2 consisting of 5 provinces is a region with high decent work indicators, and cluster 3 consisting of 9 provinces is a region with high decent work indicators.Keywords: decent work; sustainable development goals (SDGs); k-medoids; principal component analysis (PCA).
Aplikasi Metode Market Basket Analysis dengan Algoritma Apriori untuk Mengetahui Pola Belanja Konsumen pada Online Shop Amerta Fashion Iut Tri Utami; Rahmila Dapa
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.79963

Abstract

In this era of globalization, society has been facilitated in various ways due to advances in technology, one of which is the use of the internet. One of the goals of the continuous development of technology is to meet human needs. According to a global survey conducted by Nielsen Online in 2009, more than 85% of the world's population has used the internet for buying and selling transactions. The increase in purchases made online, especially during this pandemic period, shows that people have begun to be technology literate. The large number and rapid growth of data often overwhelm business actors in processing data so that they are left unorganized, even though the data obtained can produce useful information to support decisions or assist in determining marketing strategies. This difficulty also occur at Online Shop Amerta Fashion. This study uses one of the data mining methods, namely Market Basket Analysis with a priori algorithm which goes through the stages of data collection, formation of association rules, and concluding. Market basket analysis can determine consumer buying patterns and also find out which items are selling well. The results obtained is that consumers of Online Shop Amerta Fashion didn’t only buy 1 item in a transaction. Boyfriend Jeans Highwaist and Wrap Top Summer are 2 types of items that are popularly purchased by consumers so that they become the most influential items in the overall purchasing transactions from Online Shop Amerta Fashion from January 4, 2021 to June 18, 2021.Keywords: data mining; association rules; market basket analysis; Apriori; Online Shop Amerta Fashion
Analisis Kemiskinan di Sulawesi Selatan dengan Regresi Nonparametrik Berbasis B-Spline Rafli Setiawan Nasir; Muhammad Agung Wahid; Domi Rico Arung Padang; Anna Islamiyati; Raupong Raupong
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.80716

Abstract

Poverty is one of the problems faced by Indonesia, including in the province of South Sulawesi. This study aims to identify and understand the complex relationship between factors influencing poverty in South Sulawesi using nonparametric B-Spline regression. The data used are secondary data obtained from the publication of the Central Statistics Agency in the form of Data and Information on Poverty in Districts/Cities in Indonesia in 2022. The variables used are the percentage of poor people as the dependent variable, and the percentage of per capita expenditure on food, poverty depth index, and poverty severity index as independent variables. The best B-Spline model was obtained using order 2 for each independent variable, and one knot for each independent variable at a certain point. This model provides a Generalized Cross-Validation (GCV) value of 10.199728. The results of the analysis show that the measure of the goodness of the model obtained or R2 which means that the percentage of per capita expenditure on food, poverty depth index, and poverty severity index greatly influences the percentage of poor people in South Sulawesi. The relationship between the independent variables and the dependent variables is non-linear and varies. The B-Spline model can produce an accurate and flexible picture of the relationship pattern and variability in poverty data in South Sulawesi. This study can provide in-depth insights and recommendations for the government in the form of poverty alleviation policies based on local data and non-linear analysis, by targeting specific interventions according to the unique conditions of each region in South Sulawesi to increase the effectiveness of poverty alleviation.
Penerapan Teknik Soft Voting Ensemble pada Klasifikasi Rating Film Alina Selia Rizka; Virgania Sari
Indonesian Journal of Applied Statistics Vol 8, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i1.100904

Abstract

Pertumbuhan jumlah penonton film yang begitu pesat mendorong industri perfilman untuk terus berinovasi, sehingga menghasilkan beragam judul baru dengan genre dan karakteristik yang semakin bervariasi. Kondisi ini menyebabkan kompleksitas data yang tinggi, sehingga dibutuhkan metode klasifikasi yang efektif dan akurat untuk mengelompokkan rating film berdasarkan karakteristiknya. Penelitian ini bertujuan untuk meningkatkan kinerja klasifikasi rating film dengan menggunakan metode ensemble soft voting, yang menggabungkan tiga algoritma klasifikasi, yaitu k-nearest neighbor (KNN), decision tree (DT), dan support vector machine (SVM). Evaluasi dilakukan dengan membandingkan kinerja metode soft voting terhadap masing-masing metode individu berdasarkan metrik akurasi, presisi, sensitivitas, dan F1-score. Hasil penelitian menunjukkan bahwa metode soft voting memberikan kinerja klasifikasi yang lebih baik dibandingkan metode KNN, decision tree, dan SVM secara terpisah, dengan capaian akurasi sebesar 89,64%, presisi 85,63%, sensitivitas 89,64%, dan nilai F1-score sebesar 86,52%.Kata kunci: klasifikasi, ensemble learning, KNN, decision tree, SVMThe rapid growth in the number of movie viewers has driven the film industry to continuously innovate, resulting in a diverse range of new titles with increasingly varied genres and characteristics. This has led to significant data complexity, necessitating an effective and accurate classification method to categorize movie ratings based on their characteristics. This study aims to evaluate the performance of the Soft Voting ensemble method in classifying movie ratings. The classification results from soft voting are compared to those of individual models, namely k-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). The evaluation was conducted by comparing the performance of the soft voting method against each individual method based on accuracy, precision, sensitivity, and F1-score metrics. The results showed that the soft voting method provided better classification performance than the KNN, decision tree, and SVM methods individually, with an accuracy of 89.64%, a precision of 85.63%, a sensitivity of 89.64%, and an F1-score of 86.52%.Keywords: classification, ensemble learning, KNN, decision tree, SVM
Analisis Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA Suparti Suparti; Rukun Santoso
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.79385

Abstract

Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R2 or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R2 is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.Keywords: 3 time series; kernel regression; GCV; MDKA stock price.
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 Dampak Kebijakan Tarif Safeguard terhadap Impor Kain Tenun dari Kapas di Indonesia Tahun 2008-2022 dengan Pendekatan ARIMA Intervention Model Khoirunisa Maula Izzaty; Siskarossa Ika Oktora
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.85689

Abstract

The increase in the price of cotton raw materials as well as the low quality of products and productivity of the domestic industry has increased imports of woven fabric from cotton in Indonesia. The increase in imports resulted in serious losses to the domestic industry. Indonesian Textile Association submitted a petition to investigate imports of cotton woven fabric and resulting in a decision to implement a safeguard tariff policy on imports of cotton woven fabric. This research aims to analyze the impact of safeguard tariffs and the elimination of safeguard tariffs on the volume of imports of cotton woven fabrics. The method used is ARIMA multi-input intervention with interventions in the form of a safeguard tariff policy for 2011-2014, elimination of safeguard tariffs for 2014-2020, and a safeguard tariff policy for 2020-2022. The research results show that the safeguard policy in 2011-2014 and 2020-2020 had an impact on reducing the volume of imports of woven cotton fabrics. Meanwhile, the elimination of the safeguard policy in 2014-2020 had the impact of increasing the volume of imports of cotton woven fabrics. The results of this research can be used as material for the government's evaluation regarding safeguard tariff policies in protecting the domestic cotton woven fabric industry. Keywords: ARIMA intervention, cotton, woven fabric
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

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