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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 Kualitas Tidur Penduduk Usia Produktif di Indonesia dengan Model Regresi Logistik Ordinal Nadiya Azhar Mufid; Kismiantini Kismiantini
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.v7i2.86659

Abstract

The prevalence of poor sleep quality in the productive age population in Indonesia is quite high. This study aims to analyze factors that influence the sleep quality level of the productive age population in Indonesia by an ordinal logistic regression model based on the Fifth Indonesian Family Life Survey (IFLS5). In this study, the response variable used is sleep quality with an ordinal scale of 5 categories and 12 predictor variables with 1 continuous predictor variable that is age and 11 categorical predictors including education, job status, smoking habit, health, gender, marital status, physical activity, religious, depression level, life satisfaction, and economic level with data of 28.743 respondents. The results of this study indicated that the ordinal logistic regression model with proportional odds model was more suitable to be used to analyze the sleep quality level of productive age population in Indonesia than non-proportional odds model. Based on the analysis result, it was found that among 12 predictor variables, variables that had a significant effect on sleep quality level were education, job status, smoking habit, health, age, depression level, life satisfaction, and economic.Keywords: IFLS, sleep quality, ordinal regression, productive age population 
Pemetaan Daerah Rawan Bencana di Pulau Sulawesi menggunakan Metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Havinka Angel Salsabilla; Nandini Lova Diani; Abimanyu Arya Ramadhan; M. Al Haris
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.106040

Abstract

Indonesia terletak pada pertemuan tiga lempeng tektonik aktif sehingga memiliki tingkat kerawanan yang tinggi terhadap bencana alam seperti gempa bumi, banjir, letusan gunung api, dan tanah longsor. Pulau Sulawesi merupakan salah satu wilayah dengan aktivitas seismik dan hidrometeorologi yang tinggi, sehingga identifikasi daerah rawan bencana menjadi penting dalam upaya pengurangan risiko dan perencanaan mitigasi yang efektif. Penelitian ini bertujuan untuk memetakan daerah rawan bencana di Pulau Sulawesi menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN merupakan metode klasterisasi berbasis kepadatan yang mampu mengidentifikasi pola spasial tanpa harus menentukan jumlah klaster di awal serta dapat mendeteksi data pencilan (outlier). Data yang digunakan adalah data sekunder dari Badan Nasional Penanggulangan Bencana (BNPB) tahun 2020–2024 yang mencakup kejadian bencana di seluruh kabupaten/kota di Pulau Sulawesi. Variabel yang dianalisis meliputi frekuensi kejadian banjir, tanah longsor, cuaca ekstrem, kekeringan, gempa bumi, letusan gunung api, dan gelombang pasang. Sebelum proses klasterisasi, data dinormalisasi menggunakan metode Min–Max. Hasil terbaik diperoleh pada parameter ε = 0,28 dan MinPts = 5, yang menghasilkan dua klaster utama dan satu kelompok noise. Klaster 1 menunjukkan wilayah dengan tingkat kejadian bencana tertinggi, terutama banjir, tanah longsor, dan cuaca ekstrem. Klaster 0 mencakup wilayah dengan intensitas bencana sedang, sedangkan kelompok noise terdiri atas wilayah dengan tingkat kejadian bencana yang rendah atau pola bencana yang tidak jelas. Penerapan algoritma DBSCAN terbukti efektif dalam pemetaan kerawanan bencana karena mampu menangani distribusi spasial yang tidak merata serta mengungkap pola tersembunyi. Hasil penelitian ini diharapkan dapat menjadi dasar dalam pengembangan strategi mitigasi bencana yang lebih terarah. Penelitian selanjutnya disarankan untuk menambahkan indikator kerentanan sosial-ekonomi serta memperluas cakupan data.Kata kunci: DBSCAN; Sulawesi; Klasterisasi Spasial; Pemetaan Bencana; Mitigasi RisikoIndonesia is located at the confluence of three active tectonic plates, making it highly vulnerable to natural disasters such as earthquakes, floods, volcanic eruptions, and landslides. Sulawesi Island is one of the regions with the highest seismic and hydro-meteorological activity in Indonesia, so identifying its disaster-prone areas is crucial for effective risk reduction and mitigation planning. This study aims to map disaster-prone areas in Sulawesi Island using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a density-based clustering method that is able to identify spatial patterns without determining the number of clusters from the start, as well as detect outlier data. The data used is secondary data from National Disaster Management Authority (BNPB) for 2020–2024 covering disaster events in all districts/cities in Sulawesi. The variables analyzed include the frequency of floods, landslides, extreme weather, droughts, earthquakes, volcanic eruptions, and tidal waves. The data was normalized using the Min-Max method before the clustering process. The best results were obtained at parameters ε = 0.28 and MinPts = 5, resulting in two main clusters and one noise group. Cluster 1 shows areas with the highest disaster occurrences, especially floods, landslides, and extreme weather. Cluster 0 includes areas with moderate disaster intensity, while the noise group consists of areas with low or unclear disaster patterns. The application of DBSCAN has proven effective for disaster vulnerability because it is able to handle uneven spatial distribution and reveal hidden patterns. These results are expected to be the basis for developing more targeted disaster mitigation strategies. Further research is recommended to add socio-economic vulnerability indicators and expand data coverage.Keywords: DBSCAN; Sulawesi; Spatial Clustering; Disaster Mapping; Risk Mitigation 
Penentuan Rate Asuransi Kendaraan Bermotor Menggunakan Kredibilitas Bayesian Rahmila Dapa; Iut Tri Utami
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.v6i1.79813

Abstract

This paper uses Credibility to determine new rate based on data of historical claim in a motor vehicle insurance in Bandung, Indonesia. Rate is formed based on past loss through experience rating. Credibility is one of the examples of experience rating that considers group historical claims. One of the credibility methods is Bayesian credibility that considers rate as a random variable. Bayesian credibility is used based on claim frequency and claim severity from a group of policy holders in order to create new rates. In this paper, claim frequency followed the Poisson distribution while claim severity followed the Lognormal distribution. Result of analysis showed that rate values based on claim frequency and severity are higher than the rate values that were used back in 2010.Keywords: bayesian credibility; rate; claim frequency; claim severity
Forecasting on Closing Stock Price Data Using Fuzzy Time Series Sri Subanti; Asti Rahmaningrum
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.v7i1.54309

Abstract

The stock prices move up and down during trading time which is obtained from time series data. Investors need to estimate the fluctuation of stock prices in the future day to make the best investment decision. Fuzzy time series can be used as an alternative by investors in making stock price predictions. The advantage of this forecasting method compared to others is that it can formulate a problem based on expert knowledge or empirical data. This research aims to apply fuzzy time series in estimating the future value of closing stock price on the LQ45 Index. Three different methods will be applied to the data which are Chen, Lee, and Cheng. The data of the LQ45 Index will be obtained during the period of January, 4th until April 30th, 2021. The LQ45 index is chosen by many investors because it has high returns. All three model were applied and has a different rule in the calculation stage. The results show that all three models give different forecasting values and different performance of accuracy. The Lee method has the lowest values of accuracy, meanwhile the Cheng method has the highest value of accuracy. It can be concluded that Lee method is the best model indicated by the lowest value of RMSE, MAD, and MAPE for estimating the closing stock price of the LQ45 index.
Comparison of Hard Clustering and Soft Clustering Methods in Grouping Regencies/Cities in West Java Province Based on Regional Vulnerability Indicators to the Impact of Hydrometeorological Disasters in 2021 Hanifah Vida Indrasari; Yuliagnis Transver Wijaya
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.v8i1.80562

Abstract

Indonesia is an archipelagic country with a high incidence of hydrometeorological disasters and the number is increasing every year. One of the provinces in Indonesia with the highest number of hydrometeorological disasters is West Java Province, where 98.97 percent are hydrometeorological disasters. This is also supported by the characteristics of the area which is dominated by mountains, high rainfall, has 40 watersheds, and has six faults that are suspected to be still active so that it is vulnerable to hydrometeorological disasters. Research on regional vulnerability to hydrometeorological disasters can be carried out by grouping regions based on the same level of vulnerability using the clustering method. The purpose of this study was to group regencies or cities in West Java Province based on indicators of regional vulnerability to the impacts of hydrometeorological disasters in 2021. The clustering method used is hard clustering (single linkage, complete linkage, average linkage, ward's method and k-means) and soft clustering (Fuzzy C-Means). The most optimal method for grouping regencies or cities in West Java Province is the complete linkage method with a total of 4 clusters. The result is that all the resulting clusters are vulnerable to the characteristics of social vulnerability.Keywords: cluster analysis; hard clustering; natural disasters; regional vulnerability; soft clustering.
Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP) Deby Fakhriyana; Indira Ihnu Brilliant
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.v6i1.79907

Abstract

In Indonesia, crude oil plays a significant role in the country’s economy as it serves as a source of income and meets the country's energy needs. Therefore, fluctuations in crude oil prices have a significant impact on the economic activities of the society. Forecasting the price of Indonesian crude oil is thus crucial. The international price of crude oil in Indonesia is known as the Indonesian Crude Oil Price (ICP). One commonly used statistical method for forecasting is the ARIMA method. However, the ARIMA method has certain assumptions that need to be fulfil, and many real-world data cannot meet these assumptions. Hence, forecasting using the Fuzzy Time Series (FTS) method, which does not rely on assumptions, is employed. Some popular FTS methods include the Cheng FTS method and the Markov Chain FTS method. This study implements the Cheng FTS and Markov Chain FTS methods on the ICP data from May 2018 to June 2023 to determine the most appropriate method for forecasting. The analysis results using the Cheng FTS method on the testing data yield a Mean Absolute Percentage Error (MAPE) value of 4,083%, while the Markov Chain FTS method has MAPE value of 4,585%. The Cheng FTS method selected as the appropriate model for forecasting the ICP data since it has a smaller MAPE value. Using the Cheng FTS method, the predicted ICP value for July 2023 is US$72,907 per barrel.Keywords: ICP; FTS Cheng; FTS Markov Chain; MAPE
Application of the Mixed Geographically Weighted Regression Model to Identify Influencing Factors for Literacy Development Index of Indonesian Society's in 2022 Zulhijrah Zulhijrah; Ruliana Ruliana; Aswi Aswi
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.v7i2.88784

Abstract

The mixed geographically weighted regression (MGWR) method is a combination of a linear regression model and a geographically weighted regression (GWR) model. The MGWR model can produce parameter estimates that have global parameter estimates, and other parameters that have local parameters according to the observation location. This method can be used in epidemiological studies that are influenced by spatial heterogeneity. The aim of this research is to determine and model the factors that influence the Community Literacy Development Index (CLDI) in Indonesia based on MGWR modeling. The data used in this research is CLDI data in Indonesia in 2022 along with the factors that are thought to influence it. The results of this research indicate that the MGWR model outperforms both the linear regression and GWR models, as it yields the lowest Akaike information criterion (AIC) value and an ?² value of 96.54%. Based on the modeling results, several factors influencing CLDI were identified, including the percentage of libraries, the adequacy ratio of library collections, the average length of schooling, and the level of participation in organized learning. Keywords: Literacy; literacy development index; mixed geographically weighted regression; spatial
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
Grouping Indonesian Province Farmers’ Term of Trade Using Dynamic Time Warping Imtikhanah Anis Mahmudiati; Rohmatul Fajriyah
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.v7i2.94456

Abstract

This study employs dynamic time warping (DTW) to analyze the farmer’s terms of trade (FTT) across 34 provinces in Indonesia, aiming to identify patterns and cluster similarities in time series data. DTW is recognized for its effectiveness in measuring flexible similarities under time distortions, making it particularly suitable for time series classification across various fields. The FTT is utilized to assess farmers' purchasing power by comparing the prices they receive for their products to the prices they pay for goods and services. K-Medoid clustering techniques were applied to group provinces based on their DTW distances, revealing three distinct clusters. The silhouette score indicates that three clusters as the optimum cluster for the FTT data. The findings show that the first and third clusters have low mean of FTT and the second cluster has the highest mean FTT. These indicates disparities in farmers’ income and purchasing power across regions where the government needs to enhance agricultural strategies and improve economic conditions for farmers in the first and third clusters.Keywords: Clustering; Dynamic Time Warping; Farmers Term of Trade; K-Medoid.
Penerapan GWR dan MGWR dengan Pembobot Kernel Adaptive Tricube pada Pemodelan Prevalensi Stunting di Provinsi Jawa Tengah Imas Fitri Ningrum; Sri Sulistijowati Handajani; Respatiwulan Respatiwulan
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.94657

Abstract

Jawa Tengah merupakan salah satu provinsi dengan prevalensi stunting yang tinggi di Indonesia pada tahun 2022 sebesar 20,8% dan hampir mendekati prevalensi stunting di Indonesia sebesar 21,6%. Wilayah di Jawa Tengah beragam dalam hal geografi, ekonomi, sosial budaya, kualitas sumber daya manusia, dan ketersediaan sumber daya alam. Regresi spasial digunakan untuk memodelkan faktor-faktor yang mempengaruhi prevalensi stunting di Jawa Tengah dengan mempertimbangkan pengaruh lokasi. Perbedaan karakteristik antar lokasi menyebabkan heterogenitas spasial, sehingga masalah tersebut diatasi dengan pemodelan menggunakan geographically weighted regression (GWR). Pemodelan dengan GWR memberikan hasil bahwa tidak ada heteroskedastisitas pada salah satu variabel, sehingga pemodelan juga dilakukan menggunakan mixed geographically weighted regression (MGWR) dengan kernel tricube tertimbang adaptif. Namun, dari analisis, model GWR memberikan hasil yang lebih baik daripada model MGWR. Unit sampel dalam penelitian ini adalah 35 kabupaten/kota di Provinsi Jawa Tengah. Model GWR untuk memodelkan prevalensi stunting di Jawa Tengah menghasilkan model yang lebih baik dengan nilai AIC yang lebih kecil dibandingkan dengan model MGWR. Hasil penelitian menunjukkan bahwa balita yang kekurangan gizi memiliki pengaruh positif terhadap stunting, sedangkan bayi baru lahir yang menerima IMD, balita yang menerima vitamin A, dan pengeluaran riil per kapita memiliki pengaruh negatif terhadap stunting.kata kunci: tricube adaptif; GWR; MGWR; stuntingCentral Java is one of the provinces with a high prevalence of stunting in Indonesia in 2022 at 20.8% and is almost close to the prevalence of stunting in Indonesia at 21.6%. The regions in Central Java are diverse in terms of geography, economy, socio-culture, quality of human resources, and availability of natural resources. Spatial regression was used to model the factors that influence the prevalence of stunting in Central Java by considering the influence of location. The characteristics between locations cause heterogeneity, so the modeling used is Geographically Weighted Regression (GWR). Because one variable is not locally significant, modeling is also carried out using Mixed Geographically Weighted Regression (MGWR) with adaptive tricube kernel weighted. However, from the analysis, the GWR model gave better results than the MGWR model. The GWR model for modeling stunting prevalence in Central Java produces a better model with an AIC value of 148.883 and R^2 of 88.01% compared to the MGWR model, which only provides an AIC value of 190.371 and R^2 value of 47.66%. Based on the analysis results with the GWR model using adaptive tricube weighted, the factors influencing the prevalence of stunting in Central Java Province are newborns getting early breastfeeding initiation (IMD), toddlers with malnutrition, toddlers getting vitamin A, and real expenditure per capita.Keywords: adaptive tricube; GWR; MGWR; stunting

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