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Inferensi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 0216308X     EISSN : 27213862     DOI : http://dx.doi.org/10.12962/j27213862
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences
The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims; and any approach in data science. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where the original methodology is involved and original contributions to the foundations of statistical science. It also sometimes publishes review and expository articles on specific topics, which are expected to bring valuable information for researchers interested in the fields selected. The journal contributes to broadening the coverage of statistics and data analysis in publishing articles based on innovative ideas. The journal is also unique in combining traditional statistical science and relatively new data science. All articles are refereed by experts.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 110 Documents
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Aplikasi Model ARIMAX dengan Efek Variasi Kalender untuk Peramalan Trend Pencarian Kata Kunci “Zalora” pada Data Google Trends Andrea Tri Rian Dani; Sri Wahyuningsih; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sri Wigantono; Hardina Sandariria; Qonita Qurrota A'yun; Muhammad Aldani Zen
Inferensi Vol 6, No 2 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i2.15793

Abstract

ARIMAX is a method in time series analysis that is used to model an event by adding exogenous variables as additional information. Currently, the ARIMAX model can be applied to time series data that has calendar variation effects. In short, calendar variations occur due to changes in the composition of the calendar. The purpose of this study is to apply the ARIMAX model with the effects of calendar variations to forecast search trends for the keyword "Zalora". Data were collected starting from January 2018 to November 2022 in the form of a weekly series. Based on the results of the analysis, the ARIMAX model is obtained with calendar variation effects with ARIMA residuals (1,1,1). Forecasting accuracy using the Mean Absolute Percentage Error (MAPE) of 10.47%. Forecasting results for the next 24 periods tend to fluctuate and it is estimated that in April 2023 there will be an increase in search trends for the keyword "Zalora".
Perbandingan Performa Bandwidth CV, AICc, dan BIC pada Model Geographically Weighted Regression (Aplikasi pada Data Pengangguran di Pulau Jawa) Carisa Putri Salsabila Purnamasari; Yekti Widyaningsih
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19130

Abstract

Unemployment is a social phenomenon, a problem faced by every region in Indonesia. One way that can be carried out to reduce the unemployment rate is analyzing the factors that affect the open unemployment rate. Rather than using linear regression analysis, Geographically Weighted Regression (GWR) was preferable since it gave a better representative model by effectively resolve spatial heterogeneity problem which is generally exist in spatial data of social phenomenon. Spatial heterogeneity show that linear regression analysis will give a misleading interpretation results in some locations. GWR solve this problem by generating a single model in each observation location so the regression parameters can be different at each observation location. Parameter estimation in the GWR model uses weights based on the location of each observation so that the estimate model applies only to this location. The weighting determination depends on the bandwidth value. Bandwidth is a circle with radius ℎ from the center point of the observation location which is used as the basis for determining the weight of each observation location. Smaller bandwidth value will result a large variance. It can happen because when the bandwidth is very small, there will be a small number observations in the radius h, which can makes the estimate model is very rough (undersmoothing) because it uses few observations, and vice versa. Therefore, choosing the optimum bandwidth is very important in determining the weights where it can affect the accuracy of the model formed. This study aims to compare the performance of the GWR model using the Cross Validation (CV), Akaike Information Criterion Corrected (AICc), and Bayesian Information Criterion (BIC) bandwidth methods in the formation of Fixed Gaussian Kernel weighted function which is applied to unemployment data in districts/cities in Java. The results show that the GWR model with CV bandwidth is better at explaining district/city unemployment data on Java Island in 2020 which it has the smallest RMSE value, 1.0904, and the largest R2 and Adjusted-R2 values, namely 0.8539011 and 0.7937159, respectively.
Analisis Sentimen Ulasan Pengguna Aplikasi E-Samsat Provinsi Jawa Barat Menggunakan Metode BiGRU Rahma Kania Dewi; Bertho Tantular; Jadi Suprijadi; Anindya Apriliyanti Pravitasari
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19113

Abstract

Organizing the facilitation of local revenue tasks and public services is one of the main tasks, functions, detailed unit tasks, and work procedures of the West Java Provincial Revenue Agency. One of the public services for the community in improving service to the West Java community is to launch an e-samsat innovation in providing annual Motor Vehicle Tax (PKB) payment services and updating ownership status through an Android-based smartphone application called Samsat Mobile Jawa Barat (SAMBARA) and can be downloaded for free on the Google Play Store. Service satisfaction is an important aspect in service development, therefore research was conducted. This study analyzes the sentiment of the Samsat Mobile Jawa Barat (SAMBARA) application on the Google Play Store by categorizing user reviews into three groups: Positive, Negative, and Neutral. The method chosen is the Bidirectional Gated Recurrent Unit (BiGRU). BiGRU is able to predict user reviews with an accuracy of up to 87.37%, which is considered good and can be used to help the development of service applications in West Java.
Determinan Kepemilikan Jaminan Kecelakaan Kerja pada Pekerja Informal di Provinsi Jawa Timur Hery Wahyu; Lia Yuliana
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19124

Abstract

The informal sector is often seen as workers with low wages, difficult and dangerous jobs, and low protection. The high rate of work accidents makes the importance of work accident insurance for all workers, especially informal workers. Work Accident Insurance is a benefit in the form of cash and health services provided when a participant experiences a work accident or illness caused by the work environment. This study aims to determine the factors that influence Occupational Accident Benefit ownership for informal sector workers in East Java Province in 2021 using the binary logistic regression method. The data used comes from August 2021 SAKERNAS data. The results of this study show that the variables of the area of residence, gender, income, age, marital status, education, and the number of household members affect Occupational Accident Benefit ownership status. Informal sector workers who have a greater tendency to have Occupational Accident Insurance are informal workers with the characteristics of residing in urban areas, male gender, having income greater than or equal to the provincial minimum wage, have been married, having an age range of 15- 25 years old, minimum education is high school, and the maximum number of household members is 4 people
Aplikasi Pengelompokan Data Runtun Waktu dengan Algoritma K-Medoids Muhammad Aldani Zen; Sri Wahyuningsih; Andrea Tri Rian Dani
Inferensi Vol 6, No 2 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i2.15864

Abstract

The development of information technology will always be accompanied by the storage and accumulation of massive quantities of digital information. Cluster analysis is one of many data processing problems that require the selection of an appropriate algorithm when dealing with large data sets. Cluster analysis is a collection of techniques for dividing a set of observation objects into clusters. Cluster analysis is applicable to time series data, the processing of which differs slightly from that of cross-section data. Clustering time series is a technique for processing multivariable time series data. K-Medoids is the clustering algorithm used for time series clustering. The objective of this study is to obtain optimal K-values in determining the number of clusters based on silhouette coefficients and grouping outcomes using the K-Medoids algorithm. In this study, the dynamic time-warping distance is utilized as the similarity metric. This study provides cooking oil price data for 34 Indonesian provinces from October 2017 to October 2022. The optimal K value is determined for two clusters based on the results of the analysis, with 19 provinces joining cluster 1, where the cluster with cooking oil prices was below cluster 2 and 15 provinces joining cluster 2 which is the cluster with the highest cooking oil prices.
Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection Ichwanul kahfi Prasetya; Devi Putri Isnawarty; Abdullah Fahmi; Salman Alfarizi Pradana Andikaputra; Wibawati Wibawati
Inferensi Vol 7, No 1 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i1.18755

Abstract

Credit card is a transaction tool using a card which is a substitute for legitimate cash in transactions. The use of computer technology is needed for various kinds of electronic transactions. In the world of technology, the term machine learning is not new and technological developments are increasingly rapid in recent years. Statistical process control method (SPC) is one of the measuring instruments used to improve the performance of public services. Hotelling T^2 control chart is a method in SPC that can be used to control the process. Methods that are widely used in the detection and classification of documents one of them is Naive Bayes Classifier (NBC) which has several advantages, among others, simple, fast and high accuracy. Those two methods will be used to detecting o2utlier of this dataset. The study used the credit card fraud registry with some PCA as independent variables. The size of fraud transaction is very small which represented only 0.172% of the 284,807 transactions. This research will use Area Under Curve (AUC) as the performance goodness test. A comparison of the accuracy of NBC and Hotelling's T2 predictions shows that the performance of the T2 Hotelling method is better in detecting outliers than the NBC method
Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach Ghisella Asy Sifa; Marcelena Vicky Galena; M. Fariz Fadillah Mardianto; Elly Pusporani
Inferensi Vol 7, No 1 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i1.20168

Abstract

Fluctuations in the rupiah exchange rate against the United States Dollar from 2020 to early 2024 have been analyzed using classical and modern time series approaches. In this study, the classical time series approach based on Gaussian Kernel successfully provides predictions with an RMSE value of 57.5722 and a MAPE of 0.29%. Meanwhile, the modern approach with RBF Kernel SVR shows an RMSE value of 74.9201 and a MAPE of 0.41%. The results of the model performance comparison show the superiority of the classical approach with the Gaussian Kernel in predicting the rupiah exchange rate against the US Dollar as an impact of the Federal Funds Rate (FFR) policy. Therefore, it is recommended to use the classical time series method based on the Gaussian Kernel in dealing with the impact of the FFR policy to improve the accuracy of predicting the Rupiah exchange rate against the United States Dollar. This research supports the achievement of the 8th Sustainable Development Goals (SDGs) related to economic and social matters while providing a better understanding of currency exchange rate fluctuations and providing recommendations that can help in managing economic risks related to global monetary policy.
Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java Yunita Dwi Ayu Ningtias; Yudhie Andriyana; Anindya Apriliyanti Pravitasari
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20309

Abstract

Poverty is one of the crucial problems that has a negative impact on all sectors. As a developing country, Indonesia has a fairly high poverty rate. The government's efforts to overcome the problem of poverty can be circumvented by detecting the factors that influence it to determine the policies taken by using statistical modeling. There is a spatial effect on poverty in West Java Province. Spatial Data Analysis is the only statistical model that can explain the relationship between an area and the surrounding area. If the response variable contains a lag that correlates with each other, it is called a Spatial Autoregressive with Autoregressive Disturbances (SARAR) model. The Generalized Method of Moment (GMM) approach is used to get an estimator from the model. This method is applied to obtain the factors that influence poverty in West Java Province. The results of this study indicate that the GMM SARAR poverty modeling with customized weights provides relatively better estimation results. In addition, the relationship between locations (spatial lag dependence) is positive and significant. Expected Years of Schooling and Per capita Expenditure have a negative and significant effect on the increase in the percentage of poor people in West Java.
Penerapan Metode Hybrid Dekomposisi-Arima dalam Peramalan Jumlah Wisatawan Mancanegara Aswi Aswi; Ina Rahma; Muhammad Fahmuddin
Inferensi Vol 7, No 1 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i1.18738

Abstract

The Decomposition-ARIMA hybrid method is a combination of two methods used to predict future events in time series data. This method separates the data into three components: the seasonal component, the trend component, and the random component. The decomposition method is employed to forecast the seasonal and the trend components in a data series, while the ARIMA method is utilized to predict the random component within the data series. A tourist is an individual who visits an area for a specific period, making use of its facilities and infrastructure. In order to ascertain the growth of the number of foreign tourists, this study employs the decomposition-ARIMA hybrid method. The aim is to derive forecasting results from the data on the count of foreign tourists from January 2022 to December 2022. The research finding indicates that the best ARIMA model is ARIMA (0, 1, 1) with a Mean Absolute Percentage Error (MAPE) of 8.5% signifying a very high forecast accuracy.
Nonparametric Regression Modeling with Multivariable Fourier Series Estimator on Average Length of Schooling in Central Java in 2023 Ludia Ni'matuzzahroh; Andrea Tri Rian Dani
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20219

Abstract

One of the benchmarks to see the quality of education and human resources in Indonesia is the average length of schooling. If the school average is higher, it can positively impact Indonesian society, enabling it to compete globally. There are several factors, both economic and educational factors, that influence the low average length of schooling in Central Java Province. Therefore, this research aims to model and determine what variables can influence the average length of schooling in Central Java in 2023 using a nonparametric regression approach with a multivariable Fourier series estimator. This approach is used when the form of the relationship pattern is unknown and tends to have recurring patterns. The Fourier series estimator depends on the number of oscillations, so in this study, 1 to 4 oscillations were tried, where the minimum GCV value determined the optimal oscillation. The best model was obtained on the analysis results, producing the smallest GCV value, namely the model with 3 oscillations with a GCV value of 1.027. The results of simultaneous and partial hypothesis testing showed that all predictor variables used in this research were proven to influence the Average Length of Schooling. This is also supported by the coefficient of determination value of 85.464%.

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