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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 7 Documents
 1 
Search results for , issue "Vol 7, No 1 (2024)" : 7 Documents clear
Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection Prasetya, Ichwanul kahfi; Isnawarty, Devi Putri; Fahmi, Abdullah; Andikaputra, Salman Alfarizi Pradana; 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 Sifa, Ghisella Asy; Galena, Marcelena Vicky; Mardianto, M. Fariz Fadillah; Pusporani, Elly
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.
Penerapan Metode Hybrid Dekomposisi-Arima dalam Peramalan Jumlah Wisatawan Mancanegara Aswi, Aswi; Rahma, Ina; Fahmuddin, Muhammad
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.
Determinants of PM2.5 Concentration in DKI Jakarta Province: A VAR Model Approach Jayadri, Bertolomeus Laksana; Pangastuti, Mafitroh; Farhan, Muh; Kartiasih, Fitri
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.v7i1.19843

Abstract

Air pollution in the DKI Jakarta Province is a serious issue as it is related to public health and environmental concerns. Therefore, this research aims to analyze the causality of PM2.5 concentration with meteorological factors such as air temperature, humidity, rainfall, and wind speed. The data source used is from the MERRA-2 satellite, which provides information at a spatial resolution of 0.5° × 0.625°. The data covers the period from January 1, 1980, to November 1, 2023, with hourly time intervals. The research variables involve PM2.5 concentration as the response variable, as well as predictor variables such as air temperature, humidity, rainfall, and wind speed. The analytical method employed is the Vector Autoregressive (VAR) approach, as all variables are stationary at the level.  The constructed VAR model tends to indicate that meteorological variables have a low explanatory power for PM2.5 concentration, while changes in PM2.5 concentration itself have sustained impacts in both the short and long term. This suggests that the fluctuations in PM2.5 concentration in DKI Jakarta Province are not significantly influenced by meteorological factors.
Pengendalian Kualitas Semen PCC di PT Semen Bosowa Banyuwangi Menggunakan Maximum Half-Normal Multivariate Control Chart (Max-Half-Mchart) Loka, I Melda Puspita; Khusna, Hidayatul; Aksioma, Diaz Fitra
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.v7i1.16247

Abstract

Cement is a building adhesive material produced from clinker and has the main ingredients in the form of calcium silicate and additional gypsum. The Indonesian Cement Association (ASI) states that there will be an increase in domestic cement consumption by 5.5% in 2021. Competition in the industrial sector is quite tight, causing PT Semen Bosowa Banyuwangi maintain and improve the quality of its products continuously. One of the steps taken is to check for blaine, residual, and free lime through the laboratory before the cement is distributed. Since there are more than one quality characteristic of Portland Composite Cement (PCC) and each quality characteristic is monitored every shift, the control chart used is a multivariate control chart for individual observations in the form of a Max-Half-Mchart. The Max-Half-Mchart for individual observation can effectively monitor mean and process variability simultaneously. PCC cement quality control using the Max-Half-Mchart in phase I showed that the process was statistically controlled. In phase II, there were out of control observations identified as a shift in the average process. The multivariate process capability measurement results obtained a 〖MC〗_pk value of 1.053, which means that the overall production of PCC cement complies with company regulations.
Intrusion Detection Systems (IDSs) using Multivariate Control Chart Hotelling’s T2 with Dimensional Reduction of Factorial Analysis of Mixed Data (FAMD) and Autoencoder Rifki, Kevin Agung Fernanda; Rosyadi, Niam; Zenklinov, Amanatullah Pandu; Suhermi, Novri
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.v7i1.18751

Abstract

Traditional multivariate control charts for network intrusion detection encounter significant challenges including false alarms due to non-conforming network data traffic distributions, limitations in identifying outlier intrusions caused by masking effects, and handling diverse data types. This paper introduces a T2-based multivariate control chart that leverages dimensional reduction techniques using Factor Analysis of Mixed Data (FAMD) and Autoencoder to address these issues. FAMD reduces data with both quantitative and qualitative variables, while Autoencoder focuses on dimensionality reduction for quantitative variables, enhancing multivariate control chart performance. The proposed chart, a modified T2, is compared to conventional T2 with dimensionality reduction through FAMD and Autoencoder. Results from simulating data using UNSW-NB 15 demonstrate T2's superior performance with dimensionality reduction compared to conventional T2. Under various conditions, conventional control chart T achieves 64% accuracy, T2 with FAMD achieves 74%, and T2 with Autoencoder reaches 76%. Notably, T2 with FAMD excels in detecting normal activity as intrusion compared to Autoencoder. This approach holds promise for improving network intrusion detection accuracy, especially in mixed-data environments.
Modeling Life Expectancy Index in West Nusa Tenggara Province with Panel Regression Method Astuti, Alfira Mulya; Ashri, Erina Salsabila; Sabri, Sabri
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.v7i1.20148

Abstract

Health is a condition of total physical, mental, and social well-being, rather than simply the lack of disease or weakness. One way to assess health indicators in a region is by enhancing the development of the health sector, which may be quantified using the life expectancy index (LEI). This study seeks to analyze the impact of average years of schooling, the adjusted per capita expenditure, and the number of poor people on life expectancy in NTB province from 2011 to 2020. The study's individual observation units consist of 10 regencies/cities in NTB Province. The data were obtained from BPS NTB in a panel data format and processed using the panel regression method. The panel model selection indicates that the Random Effect Model is the most suitable to predict the life expectancy in NTB province. The average years of schooling and the adjusted per capita expenditure have a notable impact on the life expectancy in NTB province. The effect provided is a beneficial impact. The number of poor people has a limited impact on life expectancy. Simultaneously, the average years of schooling, the adjusted per capita expenditure, and the number of poor people in the province of NTB have a substantial impact on the life expectancy. 

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