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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 69 Documents
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Penerapan Metode Analisis Regresi Linier Pada Faktor-Faktor Penguasaan Kosa Kata Bahasa Inggris Mahasiswa Fauzan Hari Sudding Sally; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm85

Abstract

This research aims to determine whether social media and students’ motivation to learn significantly affect students’ English vocabulary mastery using regression analysis. The findings indicate that students’ motivation to know has a significant effect on students' English vocabulary mastery. The coefficient of determination obtained is 0.301, which means that the motivation to learn variable can explain the vocabulary mastery variable by 30.1%. In comparison, the remaining 69.9% is explained by other variables not included in this research. However, there is no significant relationship was found between the use of social media by the students and their English vocabulary mastery
Penerapan Radial Basis Function Neural Network dalam Mengklasifikasikan Kab/Kota di Provinsi Sulawesi Selatan Berdasarkan Indeks Kesejahteraan Rakyat Jiran Julita; Sudarmin Sudarmin; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm95

Abstract

Jiran Julita, 2023. Penerapan Radial Basis Function Neural Network dalam Mengklasifikasikan Kab/Kota di Provinsi Sulawesi Selatan Berdasarkan Indeks Kesejahteraan Rakyat. Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar. (Dibimbing oleh Sudarmin dan Zulkifli Rais). Klasifikasi merupakan cara pengelompokan benda berdasarkan ciri-ciri yang dimiliki oleh objek klasifikasi. Metode yang digunakan dalam penelitian ini yaitu radial basis function neural network (RBFNN) yang merupakan salah satu arsitektur ANN yang popular digunakan dalam klasifikasi. Penelitian ini bertujuan untuk melihat klasifikasi dari Kab/Kota di Provinsi Sulawesi Selatan berdasarkan indeks kesejahteraan rakyat menggunakan RBFNN. Adapun data yang digunakan berjumlah 24 data dengan 10 variabel. Pada penelitian ini metode K-Means diaplikasikan untuk mengelompokan Kab/Kota di Provinsi Sulawesi Selatan berdasarkan indeks kesejahteraan rakyat dengan validasi cluster menggunakan Davies Boulding Index, hasil klasifikasi dari penelitian ini diperoleh 4 cluster terbaik berdasarkan indeks kesejahteraan rakyat Kab/Kota di Provinsi Sulawesi Selatan dengan perfoma klasifikasi dengan hasil accuracy 90%, precision 75%, recall 100% dan F-Measure 85%.
Analisis Regresi Nonparametrik Spline Truncated untuk Menganalisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Sulawesi Selatan Devi Carolin Wongkar; Ruliana Ruliana; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm101

Abstract

The nonparametric regression analysis is a regression model used to determine the relationship between response variable and independent variables with unknown regression curve shapes. In the nonparametric approach, one of the frequently used estimators is the spline truncated. Spline truncated model is a segmented polynomial truncation model. The advantage of this model is that it is flexible because it has knot points that can show changes in data patterns. The unemployment rate in South Sulawesi Province in 2021 reached 5.72% and became the province with the second highest unemployment rate on Sulawesi Island. Therefore, spline truncated nonparametric regression modelling will be carried out in the case of unemployment rate with each of the factors that are thought to be influential because the regression curve is found not to form a certain pattern. Based on the analysis results, the best truncated spline nonparametric regression model was obtained using three knot points and obtained the minimum GCV value of 0.38 with a coefficient of determination (R2) value of 89%. Factors that have a significant effect on the unemployment rate in South Sulawesi are mean years of schooling (x1) and labour force participation rate (x2).
Analisis Support Vector Regression (SVR) untuk meramalkan Indeks Kualitas Udara di Kota Makassar Rahmat, Rahmat Wahyudi; Annas, Suwardi; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm107

Abstract

Polusi udara merupakan salah satu permasalahan yang belum terselesaikan sampai saat ini terutama di kota besar di Indonesia. Kondisi ini tentu sangat mengkhawatirkan mengingat polutan yang dikeluarkan oleh kendaraan bermotor seperti karbon monoksida (CO), partikulat matter (PM), nitrogen oksida ( ), sulfur dioksida ), dan karbon dioksida ( ) sangat berbahaya bagi kesehatan manusia. Oleh karena itu perlu dilakukan penelitian untuk mengetahui peramalan indeks kualitas udara dimasa mendatang. Maka pada penelitian ini digunakan metode SVR untuk meramalkan indeks kualitas udara di Kota Makassar. SVR merupakan pengembangan Support Vector Machine (SVM) untuk kasus regresi. Dalam penelitian ini metode SVR digunakan dengan kernel terbaik sebagai bantuan penyelesaian masalah non-linier, metode Min – Max Normalization untuk normalisasi data, pembagian data training dan data testing yang digunakan yakni 80%:20%, pemilihan model terbaik dengan Grid Search Optimization. Hasil peramalan yang didapatkan bahwa kelima variabel indeks kualitas udara di kota makassar tergolong baik dengan nilai RMSE yaitu Partikulat (PM10) 0,12352, Sulfur Dioksida ( ) 0,11502, Ozon ( ) 0,13561, Nitrogen dioksida ( ) 0,11380, Karbon Monoksida (CO) 0,00699 artinya kemampuan model dapat mengikuti pola data dengan baik.
Analisis Regresi Data Panel pada Angka Partisipasi Murni Jenjang Pendidikan SMP Sederajat di Provinsi Jawa Barat pada Tahun 2018-2021 Karunia Rahayu Ayu; Muhammad Kasim Aidid; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm113

Abstract

Pure Enrollment Rate (APM) is the ratio of school-age children to the corresponding age population and is expressed as a percentage. Regression analysis is a statistical analysis method that aims to see the relationship between a dependent variable and one or more independent variables. Regression using panel data is called the panel data regression model. Panel data is a combination of time series and cross-section data. This study aims to determine the modeling of regression analysis with panel data regarding the Pure Participation Rate (APM) at the junior high school education level in West Java Province in 2018-2021 and to determine the factors that affect the level of Pure Participation Rate (APM) at the junior high school education level in West Java Province in 2018-2021. Based on the model selection carried out by conducting the Chow Test, Hausman Test, and Breucsh-Pagan Test, the best model is the Random Effect Model. From the random effect model, it is known that the factor or variable that has a very significant effect on the Pure Participation Rate (APM) of equivalent junior high schools in West Java province is the student-to-school ratio variable (X1).
IMPLEMENTASI ANALISIS REGRESI LOGISTIK DENGAN METODE MACHINE LEARNING UNTUK MENGKLASIFIKASI BERITA DI INDONESIA Fahmuddin S, Muhammad; Aidid, Muhammad Kasim; Nurliah, Muhammad Jabbar Taslim
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm116

Abstract

Perkembangan internet sangat pesat, internet menjadi sumber informasi yang mudah untuk diakses seperti halnya berita. Perkembangan ini selain membawa dampak yang positif tentu juga dampak yang negatif di dalamnya. Penelitian ini bertujuan untuk mengetahui hasil evaluasi dan tingkat akurasi klasifikasi berita di Indonesia dengan menggunakan analisis regresi logistik beserta metode supervised learning. Data yang digunakan diperoleh dari data.mendeley.com diantaranya berita dengan total berita 600. Setelah dilakukan preprocessing data, diperoleh jumlah kata dalam dataset sebanyak 104.020 kata. Setelah membagi dataset menjadi data latih sebanyak 80% atau 480 data dan data uji sebanyak 20% atau 120 data, diperoleh hasil akurasi dalam mengklasifikasi berita menggunakan analisis regresi logistik dengan metode supervised learning sebesar 78,3%.
APLIKASI FUNGSI TRANSFER MULTIVARIAT UNTUK PERAMALAN CURAH HUJAN DI KOTA MAKASSAR Idul Fitri Abdullah Abdullah; Ruliana Ruliana; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm117

Abstract

This study aims to determine the transfer function model and factors that significantly affect the level of rainfall in Makassar city. This study uses rainfall data as the output series and air humidity (X1), air temperature (X2) and wind speed (X3) as the input series. The data used is monthly data with a period of January 2013 - December 2022. The initial stage of modeling is done by determining the ARIMA model of each input series which is then used to calculate the identification of the transfer function model Based on the research obtained multivariate transfer function model X1 (b=3, r=0, s=0) X12(b=0, r=0, s=0) ARIMA (2,1,0)(1,1,0)12 with air humidity and air temperature being significant factors affecting rainfall in Makassar city.
Pemodelan Regresi Data Panel terhadap Determinan Indeks Kualitas Lingkungan Hidup (IKLH) Provinsi di Pulau Sulawesi Tahun 2011-2020 Nurhamidah Mursyidin; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm118

Abstract

PEMODELAN FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP ANGKA BUTA HURUF DI PROVINSI SULAWESI SELATAN DENGAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) Beddu Solo, Nurul Era Natasyah; Muhammad Nusrang; Zakiyah Mar'ah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm141

Abstract

Geographically Weighted Logistic Regression (GWLR) is the development of a logistic regression model applied to spatial data from non-stationary processes with categorical response variables. The high rate of illiteracy is one of the crucial problems in the field of education that has not been resolved to date. South Sulawesi is the 4th province with the highest percentage of illiteracy in Indonesia in 2022. This research aims to obtain the GWLR model and the factors that have a significant influence on the illiteracy rate in South Sulawesi in 2022. In this research, we compare three functions Kernel weightings are Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. Selection of the best model uses the smallest AIC value. The results of this research are that the GWLR model with the Adaptive Tricube Kernel weighting function is the best model in modeling cases of illiteracy in South Sulawesi in 2022 which is obtained based on the smallest AIC value and the factor that has a significant influence on the illiteracy rate is the Open Unemployment Rate (X1), percentage of poor population (X2), Elementary School Enrollment Rate (X3), and area with city status (X4).
PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA INDEKS HARGA SAHAM GABUNGAN (IHSG) TAHUN 2018 – 2023 Zakiyah Mar'ah; Ruliana, Ruliana; Magfirah Septiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm144

Abstract

Nonparametric regression is one of the methods used to estimate the pattern of the relationship between response variables and predictor variables where the shape of the regression curve is unknown and is generally assumed to be contained in an infinite dimensional function space and is a smooth function (Eubank, 1999). The MARS method is one method that uses a nonparametric regression approach and high-dimensional data. These namely data has a number of predictor variables of 3 ≤ k ≤ 20 and data samples of size 50 ≤ n ≤ 1000. This research discusses Multivariate Adaptive Regression Spline (MARS) Modeling on the Composite Stock Price Index (JCI) 2018 - 2023. MARS modeling is obtained from a combination of basis function (BF), maximum interaction (MI), and minimum observation (MO) based on the minimum Generalized Cross Validation (GCV) value. The results of this study were obtained from the combination value of BF = 16, MI = 1, and MO = 2 with GCV = 60710.98. The factors that affect the Jakarta Composite Index (JCI) are Inflation (X1), Rupiah to USD Exchange Rate (X3), and Money Supply (X4).

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