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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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Perbandingan Metode Dekomposisi Multiplikatif dan Metode Prophet dalam Meramalkan Nilai Tukar USD ke Rupiah Mohammad Dwitiar Nalole; Agusyarif Rezka Nuha; La Ode Nashar; Novianita Achmad; Asriadi Asriadi
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.v8i1.96572

Abstract

Nilai tukar antara USD dan Rupiah memiliki dampak signifikan terhadap perekonomian Indonesia, sehingga peramalan yang akurat menjadi sangat penting dalam pengambilan keputusan ekonomi. Penelitian ini membandingkan metode Dekomposisi Multiplikatif dan metode Prophet dalam meramalkan nilai tukar USD terhadap Rupiah. Data nilai tukar bulanan dari tahun 2018 hingga 2023 digunakan, dengan 80% sebagai data pelatihan dan 20% sebagai data pengujian. Model akhir untuk metode Dekomposisi Multiplikatif menggabungkan komponen tren, musiman, dan siklus secara multiplikatif, sehingga memungkinkan representasi perilaku nilai tukar yang lebih rinci dari waktu ke waktu. Sebaliknya, metode Prophet menghasilkan model akhir yang menggabungkan komponen tren dan musiman secara aditif, serta mampu mengakomodasi perubahan tren secara dinamis melalui deteksi otomatis terhadap changepoints. Akurasi kedua metode dievaluasi menggunakan Mean Absolute Percentage Error (MAPE). Hasil menunjukkan bahwa metode Dekomposisi Multiplikatif memiliki nilai MAPE sebesar 1,21%, sedangkan metode Prophet memiliki nilai MAPE sebesar 5,43%. Temuan ini menunjukkan bahwa metode Dekomposisi Multiplikatif lebih akurat dalam meramalkan nilai tukar untuk periode yang diberikan, sehingga lebih sesuai untuk dataset ini yang menunjukkan pola musiman yang kuat.Kata kunci: Nilai Tukar; Peramalan; Dekomposisi Multiplikatif; Prophet; MAPE.The exchange rate between USD and Rupiah has a significant impact on Indonesia's economy, making accurate forecasting essential for economic decision-making. This study compares the Multiplicative Decomposition method and the Prophet method in forecasting the USD to Rupiah exchange rate. Monthly exchange rate data from 2018 to 2023 was used, with an 80% training set and a 20% test set. The final model for the Multiplicative Decomposition method combines the trend, seasonal, and cycle components multiplicatively, allowing for a detailed representation of the exchange rate's behavior over time. In contrast, the Prophet method produces a final model that incorporates trend and seasonal components additively, while also accommodating dynamic changes in the trend through automatic detection of changepoints.. The accuracy of both methods was evaluated using Mean Absolute Percentage Error (MAPE). Results show that the Multiplicative Decomposition method had a MAPE of 1.21%, while the Prophet method had a MAPE of 5.43%. These findings indicate that the Multiplicative Decomposition method is more accurate in forecasting the exchange rate for the given period, making it more suitable for this dataset, which exhibits strong seasonal patterns.Keywords: Exchange rate; forecasting; Multiplicative Decomposition; Prophet; MAPE.
Machine Learning Predictive Modeling of Agricultural Sustainability Indicators Raden Roro Shafira Meisy Sudarsono; Harimukti Wandebori
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.v6i1.64245

Abstract

Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.Keywords: Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture
Pemodelan Data Kemiskinan di Pulau Sumatera dengan Regresi Multilevel Spline Linear Truncated Muhammad Ridzky Davala; Nurul Mutiara Annisa; Siswanto Siswanto; Anisa Kalondeng
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.v7i1.80768

Abstract

Poverty is one of the world's biggest challenges that is still a problem, both in developing and developed countries, including Indonesia. Around 27.5 million people live below the national poverty line in Indonesia. Because it is the largest archipelago, poverty problems in each region also vary, including on the Sumatra Island. One of the efforts to alleviate poverty can be done through identifying factors that affect the percentage of poor population using truncated linear spline multilevel regression model. Multilevel modeling is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of individual and group-level factors to the response variable. The predictor variables considered are per capita expenditure, open unemployment rate, and human development index at the district/city level (level-1), as well as population growth rate and economic growth rate at the provincial level (level-2). The results of this study show that the best multilevel regression model at level-1 uses three knot points, while at level-2 it uses two knot points. The factors that affect PPM in Sumatra Island in 2021 at level-1 are per capita expenditure and at level-2 are population growth rate and economic growth rate. The factors that affect percentage of poor population in Sumatra Island in 2021 are expected to provide a more in-depth view of the socio-economic conditions on the island of Sumatra.
Penalized Spline Semiparametric Regression for Bivariate Response in Modeling Macro Poverty Indicators Cinta Rizki Oktarina; Idhia Sriliana; Sigit Nugroho
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.v8i1.94370

Abstract

Semiparametric spline regression has become an increasingly popular method for modeling data due to its flexibility and objectivity, especially as a parameter estimation method. Spline functions are highly effective in semiparametric regression because they offer unique statistical interpretations by segmenting each predictor variable in relation to the response variable. Bivariate semiparametric regression can be applied to data where observations tend to have disparities between regions, making it suitable for poverty data, particularly the poverty depth index and the poverty severity index. The objective of this research is to analyze the models of the poverty depth index and poverty severity index, as well as to perform segmentation and interpretation of these models. This study utilized observations from 60 districts/cities in the southern part of Sumatra. Several predictor variables were considered, including the percentage of households with a floor area of ≤19 m², labor force participation rate, and life expectancy as parametric components, while the nonparametric components included the average length of schooling and the percentage of households with tap water sources. The estimation methods used were penalized least squares and penalized weighted least squares, involving a full search algorithm for selecting the number and location of knots. The results of the study indicated that the penalized weighted least squares method was the best estimator, with an MSE value of 0.3122 and two knots for each predictor, yielding GCV values of 4.3604 and 4.0794.Keywords: semiparametric regression; bivariate response; poverty; knot; penalized weighted least square
Model Simulation of Continuous Time Markov Chain Susceptible Infected Recovered-Bacterial Population for Cholera Disease Aulia Maulani Syifa Nur Hidayati; Respatiwulan Respatiwulan; Sri Subanti
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.v6i1.71801

Abstract

Epidemic is an outbreak of an infectious disease rapidly in a population at a certain place and time. Epidemic models are used to explains the spread pattern of disease. The continuous time Markov chain susceptible infected recovered-bacterial population in the aquatic reservoir (CTMC SIR-B) model is a stochastic model, which considers the effect of bacterial population. The human population are classified into 3 groups. There are susceptible, infected, and recovered groups. Then, there are bacterial population which can infectious the cholera disease to human. CTMC SIR-B model considers treatment and water sanitation parameters. The spread of cholera disease can be modeled as CTMC SIR-B. Cholera is an acute intestinal infectious disease caused by the bacterium Vibrio cholerae. Cholera can be transmitted through the human digestive system. The symptoms of cholera disease are diarrhea, vomiting, and dehydration. The dehydration if not handled properly, may cause death. The aims of this research are to build and simulate the CTMC SIR-B model for cholera disease. The result of the model simulation shows that there is no significant difference between various values of treatment and water sanitation parameters. The pattern of the cholera disease spread describes that the transmission of cholera can occur from human to human even though there is no population of bacteria in the aquatic reservoir.Keywords: cholera; ctmc sir-b; epidemic model; stochastic. 
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
Analisis Spasial Kemiskinan di Pulau Jawa Tahun 2022 dengan Metode Geographically Weighted Regression (GWR) Wisly Ryan Eliezer
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.v7i2.87342

Abstract

Poverty is a multifaceted problem that poses a challenge for developing countries across the world, including Indonesia. Poverty is one of the global and national obligations stated in the first Sustainable Development Goals (SDGs), namely "Without Poverty". This study seeks to examine the factors that determine poverty in Java in 2022 while accounting for regional effects. Geographically Weighted Regression (GWR) is the methodology employed. The findings revealed that geographic/spatial characteristics had a substantial impact on poverty rates, and the GWR model generated a more accurate assessment measure than the global model. The structure of the parameters varies by location, with the variable coefficients of the health, salary, and credit indicators fluctuating, whilst the coefficients of the Education and Inflation Indicators remain similar throughout districts and cities. The government must implement proper measures to eliminate poverty not just nationally, but also in each district/city in Indonesia, particularly on Java Island. Policies might include improving human resources in education and health, monetary policies to sustain market pricing and determine minimum salaries, and policies to infuse credit assistance money into people's companies.Keywords: poverty, Geographically Weighted Regression (GWR), spatial analysis
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
Analisis Autoregressive Integrated Moving Average (ARIMA) dengan Intervensi Double Input pada Prediksi Harga Saham Gita Arinda Maulidya; Neva Satyahadewi; Nur'ainul Miftahul Huda
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.v7i1.85229

Abstract

Intervention analysis is the time series analysis used in a time series model that experiences an intervention event. Intervention is an event that can cause time series data to change patterns caused by external or internal factors such as changes in government policy, advertising promotions, environmental regulations, and others. This research uses the ARIMA analysis method of double input step function intervention with daily data on the closing share prices of PT Adaro Energy Indonesia for the period 7 March 2022 to 7 March 2023 because in that period there are two points that are thought to be interventions that have an impact on changes in the ADRO’s share prices over a long period of time. The aim of this research is to analyze the intervention ARIMA model and predict the closing price of PT Adaro Energy Indonesia for the next five-days period. The ARIMA analysis steps are based on the ARIMA model through the process of stationarity data (variance and mean), order identification, parameter estimation, and diagnostic examination. The best ARIMA model used to predict ADRO's closing share price is the ARIMA (2,1,2) model, which is obtained based on the smallest AIC, MAPE, and RMSE values. The prediction results in this research show that the predictions produced for the next five-days period are classified as very good because they have a MAPE value on training data of 1,96% and a MAPE value on testing data of 1,74%.
Metode Geographically Weighted Logistic Regression untuk Memodelkan Kasus Kemiskinan di Indonesia Tahun 2022 Ane Nurahmi; Dwi Agustin Nuriani Sirodj
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.v8i1.92564

Abstract

Geographically Weighted Logistic Regression (GWLR) merupakan pengembangan dari model regresi logistik yang dirancang untuk menganalisis data spasial dengan variabel dependen kategorik. Penelitian ini bertujuan untuk memodelkan kasus kemiskinan di Indonesia pada tahun 2022 menggunakan fungsi pembobot Adaptive Gaussian Kernel serta mengidentifikasi faktor-faktor yang memengaruhinya. Hal ini penting mengingat garis kemiskinan nasional Indonesia (Rp 535.547/kapita/bulan) masih berada di bawah standar Bank Dunia (Rp 962.130/kapita/bulan). Tingkat kemiskinan di Indonesia menunjukkan variasi yang tinggi antarwilayah, yang dipengaruhi oleh perbedaan kondisi geografis dan karakteristik sosial ekonomi setempat. Dengan demikian, hubungan antara variabel-variabel penentu kemiskinan bersifat lokal dan bervariasi secara spasial. Pendekatan GWLR lebih tepat digunakan dibandingkan regresi logistik klasik karena mampu mengakomodasi heterogenitas spasial melalui pembobotan geografis. Kategori provinsi miskin ditetapkan berdasarkan nilai Head Count Index sebagai variabel dependen. Variabel independen yang dianalisis meliputi Pengeluaran Per Kapita Disesuaikan, Tingkat Pengangguran Terbuka, dan Upah Minimum Provinsi. Melalui penggunaan fungsi pembobot Adaptive Gaussian Kernel, diperoleh 34 model GWLR. Hasil penelitian menunjukkan bahwa Upah Minimum Provinsi berpengaruh signifikan terhadap tingkat kemiskinan pada delapan provinsi: Jawa Tengah, DI Yogyakarta, Jawa Timur, Bali, Nusa Tenggara Barat, Kalimantan Tengah, Kalimantan Selatan, dan Kalimantan Timur.Kata kunci: adaptive gaussian; geographically weighted logistic regression; kemiskinanGeographically Weighted Logistic Regression (GWLR) is a development of Logistic Regression for spatial data with a categorical dependent variable. The research aims to model poverty cases in Indonesia in 2022 using the Adaptive Gaussian Kernel weighting function and the factors that influence it, considering that Indonesia's poverty line is IDR 535,547/capita/month lower than the World Bank standard, IDR 962,130/capita/month. Poverty levels in Indonesia vary between regions due to different contributing factors based on geographical and socioeconomic conditions. Therefore, the relationship between variables that determine poverty is local and varies spatially, making the Geographically Weighted Logistic Regression (GWLR) method more appropriate than logistic regression because it is able to capture differences in influence between regions through geographical weighting. The poor province category is based on the Head Count Index value as the dependent variable. The dependent variables are adjusted Per Capita Expenditure, Open Unemployment Rate, and Provincial Minimum Wage. By using the Adaptive Gaussian Kernel weighting function, 34 models were obtained. The Provincial Minimum Wage has a significant effect on poverty cases in Indonesia in 8 provinces, namely the Provinces of Central Java, DI Yogyakarta, East Java, Bali, West Nusa Tenggara, Central Kalimantan, South Kalimantan and East Kalimantan.Keywords: adaptive gaussian; geographically weighted logistic regression; poverty

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