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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 733 Documents
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PENGGUNAAN MIXTURE MODEL KERNEL-GENERALIZED PARETO DISTRIBUTION DAN D-VINE COPULA DALAM MENGANALISIS UKURAN PELANGGARAN DATA Dzikra, Fathiyyah Yolianda; Wilandari, Yuciana; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.392-402

Abstract

The research conducted on the 2015-2021 Data Breach Report in the U.S. Department of Health and Human Services is a study related to the estimation and modeling of the breach sizes each type of entity using the Kernel-Generalized Pareto Distribution Mixture Model method, as well as the estimation of the dependence of breach sizes between years with the D-Vine Copula. The D-Vine Copula can accommodate the complex dependencies demonstrated by data breach reports across all enterprise categories. Before researching with D-Vine Copula, we will first model and estimate breach size parameters for each type of entity using the Mixture Model Kernel-Generalized Pareto Distribution (GPD). The Mixture Model can accommodate large data breach sizes via GPD and also allows the use of non-parametric kernel distributions to model smaller data breach sizes. The data resulting from the logarithmic transformation of entity data in the Business Associate and Healthcare Provider types has a right short-tail with Weibull distribution, while the Health Plan category has a right heavy-tail with Frechet distribution. The three types of entity were estimated using the maximum likelihood Cross-Validation method. Dependency estimation with D-Vine Copula shows that the breach sizes between years measure has a positive dependency.
ANALISIS SENTIMEN KEBIJAKAN PENYELENGGARA SISTEM ELEKTRONIK LINGKUP PRIVAT MENGGUNAKAN PENALIZED LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Amalia, Nur Afnita; Utami, Iut Tri; Wilandari, Yuciana
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.560-569

Abstract

The implementation of the Electronic System Operator (ESO) regulation, which imposes blocking sanctions on several ESOs that do not register, has caused a variety of opinions from the public, especially on social media Twitter to raise the hashtag #BlokirKominfo. In this research, sentiment analysis was carry outed to determine the response of Twitter users to the implementation of ESO regulations by MoCI. Sentiment analysis is a textual information extraction process that classifies sentiment into positive and negative categories. The steps that are used including crawling data, text preprocessing, labeling, feature selection, term weighting with TF-IDF and classification using the Penalized Logistic Regression (PLR) with the L1 regularization and Support Vector Machine (SVM) with the RBF kernel. Sentiment classification in PLR is basically finding the optimal weight parameter. The idea of SVM sentiment classification is to find the best hyperplane to separate the data points. Evaluation of classification performance uses the accuracy value calculated through the confusion matrix. The highest percentage of accuracy in sentiment classification results using the PLR is 84,12% and SVM is 83,53%. It means that the PLR algorithm works better than the SVM algorithm in classifying public sentiment towards the implementation of ESO regulations on Twitter.
Clustering Kabupaten/Kota di Provinsi Papua Berdasarkan Produk Domestik Regional Bruto Menurut Lapangan Usaha Menggunakan Single Linkage dan K-Medoids Allo, Caecilia Bintang Girik
Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.13.1.111-120

Abstract

Gross Regional Domestic Product (GRDP) using the production approach represents the total value added from goods and services produced by different sectors within a specific region over a defined timeframe. There are 17 business sectors used to obtain the GRDP. The growth rate of GRDP in Papua is decrease in 2023. The growth rate is only 3,44%,  whereas the previous year it reached 4,11%. An analysis is needed to assist the government to enhance the GRDP in Papua. Clustering method can group districts/cities that have similar characteristics. The aims of this article is to determine the best method for clustering districts/cities in Papua using GRDP data. Single Method and K-Medoids is used in this article. Based on silhouette coefficients, Single Methods is better than K-Medoids to clustering districts/cities in Papua. Based on criteria of silhouette coefficients, number of clusters formed is three.

Page 74 of 74 | Total Record : 733

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