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VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : http://dx.doi.org/10.35580/variansiunm26374
Core Subject : Economy, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2022)" : 5 Documents clear
Analisis Ridge Robust Penduga Generalized M (GM) Pada Pemodelan Kalibrasi Untuk Kadar Gula Darah Agung Tri Utomo; Erfiani Erfiani; Anwar Fitrianto
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (384.666 KB) | DOI: 10.35580/variansiunm14

Abstract

Calibration modeling is one of the methods used to analyze the relationship between different methods. The relationship is like the relationship between invasive and non-invasive blood sugar measurement. Problems that often arise in calibration modeling are multicollinearity and outliers. Multicollinearity problems can cause the regression confidence interval to widen, so that there is no statistically significant regression coefficient. Outliers cause statistical tests to deviate. The handling of these problems can be solved by robust ridge analysis. Ridge robust is a combined analysis of ridge regression and robust regression. Ridge regression is able to overcome the problem of multicollinearity and robust regression can overcome the problem of outliers. The estimator used is Generalized M (GM). This method will be applied to a calibration model that uses invasive and non-invasive blood sugar level data. The model used with Generalized M (GM) estimator robust regression using modulation clusters 50 to 90 in 2017 is better than the modulation group 50. up to 90 in 2019. The statistical values obtained are SSE of 0.910, RMSEadj of 0.114, and RMSEP of 0.030. Calibration models that have outliers and multicollinearity problems can be overcome by robust ridge regression. The feasibility value of the model obtained in the GM estimator robust regression is smaller than the MM estimator ridge robust regression in the calibration modeling for non-invasive blood sugar level data. That is, the best model that can be used is the robust ridge regression GM estimator.
Metode Subtractive Fuzzy C-Means (SFCM) dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kemiskinan Nurul Imania Kalla; Suwardi Annas; Muhammad Fahmuddin
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (868.759 KB) | DOI: 10.35580/variansiunm25

Abstract

The percentage of poor people in South Sulawesi Province has increased in the last 3 years. It is necessary to handle poverty that by adjusting to the poverty level of each district. It is necessary to have a description of socio-economic conditions by grouping regency/city based on poverty indicators. The purpose of this grouping is determining the poverty level of each district so that it can be used as material for evaluating poverty problems. The clustering method often used in research was Fuzzy C-Means (FCM). In the development, FCM was combined with the Subtractive Clustering (SC) method to obtain the hybrid Subtractive Fuzzy C-Means (SFCM) method. The SFCM method has the advantage of speed, iteration, resulting in a data partition that is more stable and accurate when it is compared to the previous method. On this research, the SFCM method was applied with 4 variables from the poverty indicator data for South Sulawesi Province in 2021. The results of the analysis show that based on the Partition Coefficient Index (PC), the percentage of poverty in South Sulawesi Province was divided into three clusters, namely the low, medium and high poverty percentage clusters. Clusters of low poverty percentage consist of Bulukumba, Bantaeng, Takalar, Gowa, Soppeng, Wajo, Sidenreng Rappang, East Luwu, Makassar City, Parepare City, and Palopo City. Clusters of medium poverty percentage consist of Sinjai, Maros, Barru, and Enrekang. Meanwhile, Clusters of high poverty percentage consist of the Selayar Islands, Jeneponto, Pangkajene and Islands, Bone, Pinrang, Luwu, Tana Toraja, North Luwu, and North Toraja
Regresi Data Panel dan Aplikasinya dalam Kinerja Keuangan terhadap Pertumbuhan Laba Perusahaan Idx Lq45 Bursa Efek Indonesia Nurul Madany; Ruliana Ruliana; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.985 KB) | DOI: 10.35580/variansiunm28

Abstract

Regression is a statistical analysis that shows the relationship between one bound changer and one or more free changers. In the development of regression analysis, which can not only be observed at one time but can be observed in several time periods known as panel data regression. In conducting the regression analysis of panel data, there are three tests carried out to select a fixed model, namely the chow test, the Hausman test, and the pagan breucsh test. This study aims to see the influence of free variables, namely roa, roe, and npm on bound variables, namely company profit growth. The implementation of the method is carried out in the case of data on the financial performance of the LQ45 company and the growth of the company's profit LQ45. The result of the panel data regression modeling, namely the fixed effect model, is that financial performance has a significant effect on the company's profit growth whereas in the financial performance indicators the roa variable has a positive and significant influence and has a presentation that explains the free variables, namely roa, roe, and npm, on profit growth of the remaining 21% explained by other variables.
REGRESI PANEL SPASIAL UNTUK PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI KABUPATEN/KOTA SE-KALIMANTAN Muh. Gunadil Ukra; Muhammad Nusrang; Bobby Poerwanto
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.811 KB) | DOI: 10.35580/variansiunm34

Abstract

MUH. GUNADIL UKRA 2022. Regresi Panel Spasial Untuk Pemodelan Indeks Pembangunan Manusia Di Kabupaten/Kota Se-Kalimantan Tahun 2017-2021, Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, (dibimbing oleh Muhammad Nusrang dan Bobby Poerwanto). Penelitian ini membahas tentang Indeks Pembangunan Manusia di Kabupaten/Kota Se-Kalimantan dengan membandingkan model regresi panel spasial SAR-FE dan SEM-FE. Pembangunan manusia merupakan faktor penting dalam meningkatkan kesejahteraan penduduk. United Nations Development Programme (UNDP) yang juga menyatakan bahwa manusia yang bermartabat adalah manusia yang dapat menikmati umur panjang, sehat, dan menjalankan kehidupan yang produktif. Untuk melihat faktor apa saja yang berpengaruh terhadap IPM di Kabupaten/Kota Se-Kalimantan dan model apa yang cocok untuk menggambarkan IPM di Kabupaten/Kota Se-Kalimantan tahun 2017-2021. Maka dari itu dilakukan analisis regresi panel spasial dengan menggunakan Dimensi IPM yaitu umur panjang dan hidup sehat, pengetahuan dan standar hidup layak. Analisis regresi data panel adalah analisis regresi dengan struktur data merupakan data panel dengan data cross section dan time series. Pengujian asumsi dengan kenormalan galat, multikolinieritas dan autokorelasi spasial perlu dilakukan sebagai syarat menentukan model regresi panel spasial pengaruh tetap. Selanjutnya dilakukan uji kebaikan model dengan menggunakan R-Square yang di mana dari nilai SAR-FE sebesar 0,9997669 dan nilai SEM-FE sebesar 0,9997541. Dengan kesimpulan nilai tertinggi merupakan model terbaik, SAR-FE merupakan model yang paling baik digunakan dalam memodelkan IPM di Kabupaten/Kota Se-Kalimantan karena memiliki nilai lebih besar dibandingkan dengan SEM-FE. Pengaruh kedekatan spasial dipengaruhi oleh rata-rata IPM di Kabupaten/Kota lain yang berdekatan. Rata-rata lama sekolah merupakan faktor yang paling berpengaruh terhadap IPM di Kabupaten/Kota Se-Kalimantan tahun 2017-2021. Hasil penelitian dapat dijadikan informasi dan evaluasi bagi pemerintah untuk memperhatikan perubahan IPM setiap Kabupaten/Kota terutama di Kabupaten/Kota terutama di Kabupaten/Kota yang berdekatan.
Penerapan Metode Singular Spectrum Analysis dalam Peramalan Jumlah Produksi Beras di Kabupaten Gowa Rezki Amalia Idrus; Ruliana Ruliana; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.022 KB) | DOI: 10.35580/variansiunm40

Abstract

Total rice production in Gowa Regency from January 2018 to December 2020 has decreased which is not significant every month, so it is necessary to do a forecast to anticipate food shortages in the future. This study aims to determine the yield of rice production in Gowa Regency and to model data from October 2021 to September 2022 using the Singular Spectrum Analysis (SSA) method. Based on the results of the analysis, the MAPE value obtained is 6.32% so it can be said that forecasting using the SSA method is very accurate

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