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Dr. Muhammad Ahsan
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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 8 Documents
 1 
Search results for , issue "Vol 6, No 1 (2023)" : 8 Documents clear
Determinan Utang Luar Negeri Indonesia Tahun 1981-2020 Rina Dwi Octavianti; Budyanra Budyanra
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14336

Abstract

Indonesia is one of the developing countries that is actively advancing development. However, limited funding for development is the main obstacle for developing countries, including Indonesia, in increasing development. To overcome these cost problems, a large injection of funds is needed, one of which comes from foreign debt. Although foreign debt helps to advance development, its uncontrolled increase will result in default on debt payments until a crisis occurs. Theoretically, foreign debt is influenced by various faktors including foreign investment (FDI), gross domestic product (GDP), exchange rates, imports, exports, and foreign exchange reserves. This study aims to analyze the effect of FDI, GDP, exchange rate, imports, exports, and foreign exchange reserves on Indonesia's foreign debt in the long term and short term. The analytical method used in this study is the Error Correction Mechanism (ECM) for the period 1981-2020. The results of the study indicate that in the long term and short term the variabels of FDI, foreign exchange reserves, and the exchange rate have a significant and positive effect on Indonesia's foreign debt. Meanwhile, the variabels of GDP, imports, and exports have no significant effect on Indonesia's foreign debt in the long and short term.
Pemodelan Faktor-Faktor yang Mempengaruhi Jumlah Kasus Diabetes Melitus di Jawa Timur Menggunakan Geographically Weighted Generalized Poisson Regression dan Geographically Weighted Negative Binomial Regression Elvira Dian Safire; Purhadi Purhadi
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.12623

Abstract

Diabetes mellitus is a chronic disease of metabolic disorders characterized by blood-sugar levels exceeding normal limits. The province contributing the largest number of cases of diabetes mellitus in Indonesia in 2019 is East Java Province. To know the factors that affect the number of cases of diabetes mellitus is used approach with Geographically Weighted Generalized Poisson Regression (GWGPR) and graphically Weighted Negative Binomial Regression (GWNBR) methods. The highest number of people with diabetes mellitus in East Java is in Surabaya with 94076 cases and the lowest is in Batu City which is 3344 cases. GWGPR and GWNBR modeling both resulted in 4 groups for significants variables in each district/city. The AICc value comparison of the GWGPR and GWNBR models shows almost the same value. Sehingga menunjukkan bahwa model GWGPR dan GWNBR sudah sesuai. So, it shows that the GWGPR and GWNBR models are appropriate. The GWGPR model has a smaller AICc value than the GWNBR model, so the GWGPR method is best suited to model the number of cases of diabetes mellitus in districts/cities in East Java compared to GWNBR method.
Analisis Regresi Spline Truncated pada Indeks Pembangunan Manusia (IPM) di Provinsi Jawa Timur tahun 2021 Ardiana Fatma Dewi; Kurnia Ahadiyah
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14107

Abstract

The increase in the achievement of the Human Development Index cannot be separated from the improvement of each of its constituent components. Currently, the components of the HDI also show an increase from year to year. To be able to participate in the development process, of course, Indonesian people are needed who are not only superior in terms of quantity, but also superior in terms of quality. HDI is used as a tool to achieve national goals, so that many things are related between humans and the development around them. This is to find out what factors can affect the HDI in East Java so that the provincial government can pay attention to several programs which can later be used to continue to maintain and improve development so that it can become an achievement for the Province of East Java. One of the analyzes that can be used is modeling, one of which is regression analysis. Nonparametric regression is a regression that is flexible in use because it can find its own data pattern. One of the truncated spline approaches to nonparametric regression can be used to predict the Human Development Index (HDI). HDI and several factors that influence it will be estimated at various knot points to get the best model. In the Spline Truncated nonparametric regression modeling which is applied to HDI data in East Java Province in 2021 several knot points are tried, namely 1 knot point, 2 knot point, and 3 knot point. The results obtained showed that the best model was found in the 3 knots experiment with a minimum GCV value of 5.40 and an R2 value of 89.875%.
Peningkatan kualitas layanan perbankan digital melalui pengelompokan tweet menggunakan DBSCAN Syeni Agustin Ningtias; Alfisyahrina Hapsery
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.12839

Abstract

Digitalization in the banking sector allows customers to obtain banking services independently without having to come directly to the bank. Digital banking services enable customers to obtain information, communicate, register, open accounts, banking transactions and close accounts, including obtaining other information and transactions outside of banking products. Banking is intensively providing services or promotions through social media, one of which is by using Twitter social media. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. DBSCAN clustering is done by combining the Eps and MinPts values to produce the highest silhouette coefficient. The highest silhouette coefficient values from BRI, Mandiri and BCA banking service tweets produce 33, 14, and 39 clusters, respectively, with different Eps and MinPts values. Based on the results of wordcloud, it shows that banking services need to be improved in terms of checking DM on accounts, customers ask the admin to immediately respond to complaints related to ATM cards, disruptions to mobile banking and some say thank you for the services that have been provided.
Penerapan Bagan Kendali MEWMA-MEWMV pada Pengendalian Kualitas Lulusan Prodi Statistika FMIPA Universitas Syiah Kuala Misbahul Jannah; Evi Ramadhani; Latifah Rahayu Siregar
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14457

Abstract

Statistical technique used to analyze quality problems and improve process performance is Statistical Process Control (SPC). In this study, multivariate control chart is used to control the quality of the graduates of Statistics Study Program, FMIPA USK, namely using Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to control process average, Multivariate Exponentially Weighted Moving Variance (MEWMV) to control process variability, and analysis Process Capability to assess the entire process. Data used is secondary data, namely GPA data, duration of thesis preparation, and length of study for graduates of Statistics Study Program, FMIPA USK in 2016-2021 as many as 122 people. Results of the study, using MEWMA control chart, it was found that the average process for quality of graduates was statistically controlled in phase II. Selection of the most optimal weighting is λ=0.9. Meanwhile, for application of the MEWMV control chart, it was found that the process variability in the quality of graduates was also statistically controlled. The selection of the most optimal weights is λ =0.9 and ω=0.3. The results of the calculation of process capability, multivariately the GPA variable, length of the final project preparation, and length of the study show that all three are not capable.
Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) dalam Peramalan Data Curah Hujan di Kota Makassar Nurul Ilmi; Aswi Aswi; Muhammad Kasim Aidid
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14347

Abstract

Modeling of rainfall data using time series data involving location elements has not been widely carried out. One model that involves elements of time and location is Space Time Autoregressive (STAR). The development of the STAR model which assumes that each location has heterogeneous characteristics is the Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) model. The purpose of this research is to get the best GSTARIMA model and forecast rainfall data in Makassar City based on the best GSTARIMA model. This model incorporates time and geographic dependencies with different parameters for each location. The data used is Makassar city's monthly rainfall data at the Bawil IV/Panaikang, Biring Romang/Panakkukang and Stammar Paotere rain stations from January 2017 to September 2021. Autoregressive (AR) and Moving Average (MA) orders were identified using the Space Time Autocorrelation plot. Function (STACF) and Space Time Partial Autocorrelation Function (STPACF). The spatial order used in this study is spatial order 1 with an inverse distance weighting matrix and normalized cross-correlation. Parameters were estimated using the Generalized Least Squares (GLS) method. The best model for predicting rainfall in the city of Makassar is the GSTARIMA (1,0,0) (1,1,0)12  model using an inverse distance weighting matrix with the smallest average Root Mean Square Error (RMSE) of 132.9661.
Pengelompokan Kemiskinan di Indonesia Menggunakan Time Series Based Clustering Dedi Setiawan; Amalia Zahra
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14969

Abstract

Indonesia has a strong commitment in achieving the 2030 SDGs, which one of the targets is reducing poverty. Poverty itself is defined as the inability of an individual or group to meet the basic needs of both food and non-food. During the pandemic, Indonesia experienced an increase in poverty percentage, which peaked in September 2020 with 10.19%. The latest data in March 2022, showed that the number has decreased by 0.75% or equivalent of 1.79 million people. However, the decrease does not encompass all provinces, there are several provinces that still suffer from increasing poverty. Therefore, it is necessary to group the provinces in Indonesia based on its percentage of poverty, in order to provide more appropriate treatment. The analysis method used in this research is the time series-based clustering with dtw distance. The clustering algorithm used is hierarchical cluster complete linkage. Based on the analysis result, the use of dtw distance can increase the silhouette coefficient value to 0.75 compared to using the Euclidean distance. The silhouette coefficient is one of the parameters used to determine the goodness of the clustering results, where the value of 0.75 can already be said to be very good clustering results. The optimum result of the clustering is a total of 3 groups with low, medium, and high poverty categories, where NTT, Papua, and West Papua provinces have the highest poverty rates but have significant progress in reducing poverty.
Prediksi Harga Ekspor Non Migas di Indonesia Berdasarkan Metode Estimator Deret Fourier dan Support Vector Regression Chaerobby Fakhri Fauzaan Purwoko; Sediono Sediono; Toha Saifudin; M Fariz Fadillah Mardianto
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.15558

Abstract

Economic growth is one of the indicators in the Sustainable Development Goals (SDGs) on increasing economic activity.  One of the activities that supports the running of the economy is trade between countries, such as exports.  In Indonesia, non-oil and gas exports have played an important role in total exports in recent years, including coal exports being the main export.  Therefore, price predictions for Indonesia's non-oil and gas exports are very important as material for evaluating policies to encourage economic growth.  This is the main focus of this research.  In this study, non-oil and gas export price forecasts are made taking into account current issues such as the COVID-19 pandemic and the Russia-Ukraine war.  The accuracy of the model obtained from the Fourier series estimator and Support Vector Regression (SVR) is investigated by comparing the Mean Absolute Percentage Error (MAPE) value to predict Indonesia's non-oil and gas export prices.  The results of the study show that the COVID-19 pandemic and the Russia-Ukraine war have had a significant impact on non-oil and gas export prices. The SVR model with the Radial Basis Function (RBF) kernel shows better accuracy than the Fourier series estimator model of the cos sin function, with MAPE values of 9.29 and 15.26% for each test data, respectively.  Therefore, this study is expected to be the basis for formulating policies related to regulating non-oil and gas export processes to support economic growth in Indonesia.

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