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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 147 Documents
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Perbandingan Model Hybrid ARIMAX-FFNN-EGARCH dan Model Hybrid SETAR-EGARH untuk Peramalan (Studi Kasus: Data Cash Outflow dan Inflow Bank Indonesia Kota Kediri) Agus Suharsono; Marieta Monica; Jerry Dwi Trijoyo Purnomo
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12470

Abstract

Dalam kehidupan sehari-hari, perekonomian tak lepas dari kebutuhan akan uang. Terkait hal tersebut, dibutuhkan perencanaan pencetakan uang serta komposisi uang yang akan dicetak selama satu tahun kedepan oleh Bank Indonesia. Peramalan cash outflow dan inflow dapat digunakan untuk mengestimasikan kebutuhan uang masyarakat. Pada umumnya sering dijumpai permasalahan data deret waktu yang memiliki hubungan linier. Akan tetapi, terdapat pula data deret waktu dengan pola non-linier terutama pada bidang ekonomi. Kejadian tertentu atau terjadinya shock-shock yang menyebabkan adanya pola non-linier dan volatilitas pada data tersebut. Pemodelan non-linier yang digunakan dalam penelitian ini adalah model hybrid ARIMAX-FFNN-EGARCH dan hybrid SETAR-EGARCH. Kedua model diaplikasikan dan dibandingkan pada studi kasus data cash outflow dan inflow bulanan Kantor Perwakilan Bank Indonesia Kota Kediri. Hasil yang didapatkan yaitu penduga parameter Self-Exciting Threshold Autoregressive (SETAR) dengan metode pendugaan parameter Ordinary Least Square (OLS) terbukti memiliki sifat yang tidak bias, linier, dan memiliki varians minimum atau dapat dikatakan memenuhi sifat BLUE (Best Linear Unbiased Estimator). Model untuk peramalan data outflow dan inflow dengan kedua model dapat menangkap efek variasi kalender pola non-linier serta volatilitas yang tidak konstan. Pemodelan untuk peramalan di masa yang akan datang dapat menjadi pertimbangan penting bagi instansi terkait dalam mengambil kebijakan moneter selanjutnya.
Penerapan Keluarga Model Spline Truncated Polinomial pada Regresi Nonparametrik Andrea Tri Rian Dani; Ludia Ni’matuzzahroh
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12537

Abstract

One approach that is often used by researchers to determine the form of the relationship pattern between the response variables and predictor variables in regression analysis, namely the nonparametric approach, where the approach is used when the shape of the regression curve is assumed to be unknown. The truncated spline is a polynomial model in nonparametric regression that has segmented properties, where these properties provide better flexibility than ordinary polynomial models and are able to handle data whose behavior changes in certain sub-intervals due to the knot points in it. This study aims to apply a family of spline truncated polynomial models to nonparametric regression in the case of automotive data. The estimation method used is Ordinary Least Square (OLS). The number of knot points tested is 1 to 4-knot points with a degree of p=1,2,3. Based on the results of the analysis, the best model that produces the smallest GCV value is the nonparametric spline truncated quadratic regression model with 4 knots, which produces a GCV value of 522.27 and a coefficient of determination of 79.77%.
Peramalan Penjualan Helm dengan Metode ARIMA (Studi Kasus Bagus Store) Ida Bagus Bayu Mahayana; Indrawan Mulyadi; Siti Soraya
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12469

Abstract

Peramalan adalah kegiatan memperkirakan apa yang akan terjadi pada masa yang akan datang dengan memanfaatkan informasi yang ada pada masa itu, untuk menimbang kegiatan di masa yang akan datang. Metode yang digunakan adalah metode ARIMA (Autoregressive Integrated Moving Average) untuk menghasilkan peramalan yang cukup baik dibandingkan dengan metode-metode lainnya.Tujuan Penelitian ini adalah untuk mengetahui hasil peramalan penjualan helm pada toko Bagus Store untuk masa yang akan datang. Penyajian Data Setelah melakukan penelitian dan pengambilan data yang dilakukan secara primer pada toko Bagus Store. Dalam penelitian ini peneliti melakukan peramalan dengan metode ARIMA (Autoregreted Intergrated Moving Average) untuk data penjualan dari 21 September 2021 sampai 21 Desember. Dengan menggunakan aplikasi Minitab untuk melakukan perhitungan. Diantara semua model peneliti menemukan 3 model ARIMA yaitu ARIMA (1,0,1), ARIMA (1,0,0) dan ARIMA (0,0,1). Diantara 3 model tersebut model ARIMA (1,0,1) adalah model yang paling tepat dikarenakan hasi P valuenya lebih kecil dari 0,5.
Klasifikasi Hasil Seleksi Kompotensi Dasar CPNS Menggunakan Metode Decision Tree Ravensky T. Silangen; Muhammad Yahya Matdoan
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.12353

Abstract

Civil Servants (PNS) are one of the jobs that are of interest to various groups of people in Indonesia. The need for qualified and competitive human resources in this era of globalization requires the government to be more serious in recruiting prospective civil servants so that the realization of good service and organizational needs for existing position qualifications can be met. The implementation of the 2021 civil servant candidate selection at Pattimura University is carried out based on the regulations of the State Civil Service Agency with several stages of selection, one of which is the Basic Competence Selection with a predetermined value standard. This study aims to classify the test results of Candidates for Civil Servants at Pattimura University. The data used in this study is secondary data obtained from the State Civil Service Agency in 2021. The method used in this study is the Decision Tree method. The results show that there are 4 classes (classification) with an Accuracy value of 75%, Classification Error of 25%, Kappa of 0.947, Recall of 97.14%, and Precision of 93.94%.
Analisis Faktor PDRB Menurut Pengeluaran Yang Mempengaruhi Laju Pertumbuhan Ekonomi Provinsi Sulawesi Selatan Khaidarsyah Khaidarsyah; Isma Muthahharah
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.14189

Abstract

Factor analysis can be used to track the Gross Regional Domestic Product sector that affects the rate of economic growth. Based on data obtained from the Central Statistics Agency of South Sulawesi Province, there was a significant increase from 2010 to 2021, for example, household consumption expenditure in 2010 was 100 and in 2021 it rose to 167 and that applies to all components of expenditure. This type of research uses a quantitative research approach. The data used is secular data obtained from the BPS publication of South Sulawesi Province, where the object of research is Gross Regional Domestic Product by expenditure with 8 sectors for the last 12 years, namely from 2010-2021. All expenditure component variables are eligible for factor analysis because they have met the data adequacy test and the data freedom test, and only 1 factor is formed. The expenditure component in GRDP has a very strong relationship because it has a value > 0.5 which can be said that the Expenditure Component consists of Household Consumption, LNPRT Consumption, Government Expenditure, Gross Fixed Capital Formation, Changes in Inventory, Foreign Exports, Foreign Imports, Inter-regional Net Exports affect the economic growth rate of South Sulawesi Province. The most dominant variable is Gross Fixed Capital Formation because it has a greater correlation value among other variables.
Efficient Auxiliary Information Based Exponentially Weighted Moving Coefficient of Variation Control Chart using Hybrid Estimator : An Application to Monitor NPK Fertilizer Muhammad Alifian Nuriman; Endro Setyo Cahyono
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.14158

Abstract

In this era, manufacturing sectors should ensure the quality of their production process and products. They must reduce the variability that occurs in their operation. Coefficient variation control charts have become important statistical Process Control (SPC) tools for monitoring processes when the process mean linear function with the standard deviation. In recent years, auxiliary information-based-CV control charts using memory type structure have been investigated to enhance the sensitivity of control charts. Auxiliary information is selected when the variable remains stable during the monitoring period. Nevertheless, the AIB statistic is constructed based on lognormal transformation, and no research investigated the memory type CV chart using estimator of AIB-CV from the combination of ratio and regression form called hybrid form. This research proposes a hybrid auxiliary information-based exponentially weighted moving coefficient of variation (Hybrid AIB-EWMCV) control chart for detecting small to moderate shifts in the CV process. The Average Run Length (ARL) simulation shows that increasing the level of correlation and sample sizes enhances the detection ability of the control chart. Also, the proposed chart performs well than existing chart. A real dataset from fertilizer manufacturing is implemented to explain the condition of the process by using a Hybrid AIB-EWMCV control chart.
Analisis Risiko Gempabumi di Sumatera dengan Cauchy Cluster Process Yuniar Mega Kartikasari; Achmad Choiruddin
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.12307

Abstract

Sumatra is one of the prone areas in Indonesia to earthquakes. This condition is due to its geography which is traversed by active faults, subduction zones, and volcanoes. In this study, the distribution of earthquakes occurrences in Sumatra is modeled by considering the effects of spatial trends due to subduction zones, active faults, and volcanoes and also considering the cluster effects caused by mainshock and aftershock activities using the inhomogeneous Cauchy cluster process. In spatial trend modeling, there are indications that there is multicollinearity issue characterized by a high correlation among geographical factors, so this study considers ridge regularization to overcome this problem. The results of data exploration show that the earthquakes in Sumatra are not homogeneous and form clusters due to geological factors such as the presence of volcanoes, subduction zones, and active faults. Earthquake intensity modeling with ridge regularization produces an AIC value of -2280648 which is smaller than the model without regularization. The Cauchy cluster model by considering ridge regularization resulted in an estimated number of 63 mainshocks with a standard deviation of aftershocks around the mainshocks of 17.685 km. The closer a location to a fault, the risk of an earthquake occurring at that location increases by 1.6972 times. The closer a location to a subduction zone, the risk of an earthquake at that location increases by 1,25899 times, and the closer a location is to a volcano, the risk of an earthquake at that location increases by 1.55910 times.
Comparisons of Logistic Regression and Support Vector Machines in Classification of Echocardiogram Dataset Neni Alya Firdausanti; Ratih Ardiati Ningrum; Siti Qomariyah
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.14121

Abstract

Echocardiography is a test that uses sound waves to produce an image of our heart. This image is called an echocardiogram. This paper uses Echocardiogram Dataset, in which the problem is to classify from 7 features whether the patient will survive or not. In this study, the classification method is used to solve this problem. Some classification methods can be applied to classify category response variables, such as Logistic regression and Support Vector Machines (SVM). The method for predicting best accuracy used holdout and cross-validation. Before doing classification, some preprocessing procedures were applied to this dataset. The preprocessing procedures include missing value imputation using median imputation, outliers detection in univariate and multivariate procedures, and feature selection using the backward method. The result of classification in the analysis showed that SVM with unstratified holdout gave the best accuracy, that is 91.54%.
Pemodelan Faktor – Faktor yang Mempengaruhi Kasus Pneumonia pada Balita di Provinsi Jawa Barat dengan Metode Geographically Weighted Generalized Poisson Regression Vergilia Agam Saputri; Purhadi Purhadi
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.12619

Abstract

Acute infection of lung tissue that can be caused by various microorganisms, namely fungi, viruses, and bacteria is called pneumonia. Pneumonia is the leading cause of death in children worldwide. West Java is in the top three of the number of deaths due to pneumonia in children under five in Indonesia and ranks 1st in the number of pneumonia sufferers in children under five. In solving this case, it is necessary to model with spatial effects because it is necessary to pay attention to geographical conditions in West Java, namely the GWGPR method. The highest number of pneumonia cases, as many as 10818 cases, was in Cirebon Regency while the lowest number of cases was in Banjar City as many as 573 cases. The best modeling results from the minimum AICc criteria of 483.98 are using the GWGPR method with exposure that forms two groups of districts/cities based on variables that have a significant effect on cases of pneumonia in children under five in all districts/cities, namely the percentage of vitamin A administration and the percentage of clean-living behavior and healthy.
Analisis Model Cox Proportional Hazard dan Regresi Logistik sebagai Upaya Pencegahan Covid-19 di Kota Palopo Avini Avini; Krisna Wansi Patunduk; Sumarni Sumarni; Harbianti Harbianti; Ananda Pratiwi; Rahmat Hidayat
Inferensi Vol 5, No 2 (2022)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i2.14064

Abstract

This study focused on Covid-19 patients in Palopo City. This study aims to model the recovery time of Covid-19 patients in the city of Palopo. The variables used are factors that are thought to affect the survival period of Covid-19 patients. The instrument used in this study is secondary data obtained from the Palopo City Health Office. The data analysis used in this study is the Cox proportional hazard method to determine the relationship between the dependent variable and the independent variable and the Logistics Regression method, which is the analytical method used to see the relationship between nominal or ordinal predictor variables as well as intervals or ratios. The results of the research carried out are the Cox proportional hazard method analysis states that the variable fever symptom significantly affects the survival of Covid-19 patients in Palopo city with a significance level of 1 time greater than the other variables. Analysis of the logistic regression method states that the fever symptom variable has a significant effect on the survival time of Covid-19 patients in Palopo City. Furthermore, based on the results of the comparison of AIC values it is stated that the best model that can be used to model the survival rate of covid-19 patients in Palopo city is the cox model proportional hazard.

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