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Ansari Saleh Ahmar
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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 6 Documents
 1 
Search results for , issue "Vol. 5 No. 03 (2023)" : 6 Documents clear
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.
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%.
GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) IN MODELING THE RISK FACTORS OF PNEUMONIA DISEASE AMONG TODDLERS IN THE CENTRAL SULAWESI PROVINCE Mar'ah, Zakiyah; Rais, Zulkifli; Haris, A. Sulfiana
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/variansiunm151

Abstract

This research was conducted to map and model the number of Pneumonia cases in Central Sulawesi Province using the Geographically Weighted Negative Binomial Regression (GWNBR) approach. The data used were Pneumonia case data in Central Sulawesi Province obtained from the Health Publication of Central Sulawesi Province in 2021. The analysis results with the GWNBR method indicated that predictor variables significantly influencing the number of Pneumonia cases in each district/city of Central Sulawesi Province were Exclusive Breastfeeding Percentage (X1), Complete Basic Immunization Percentage (X2), Percentage of Toddlers Receiving Vitamin A (X3), and Percentage of Coverage of Toddler Services (X5). Meanwhile, the variable Low Birth Weight (X4) does not significantly affect the cases.
Statistical Downscaling Modeling with Time Lag Components for Forecasting Rainfall in Wet and Dry Seasons Meyliana, Sitti Masyitah; Mar'ah, Zakiyah; Hafid, Hardianti
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/variansiunm180

Abstract

Climate change in Indonesia often poses a serious threat to the agricultural sector. The impacts can include reduced agricultural productivity. In this context, rainfall variables are frequently used in research related to the impacts of climate change. In this study, precipitation data from the global circulation model (GCM) outputs are used as predictor variables and rainfall data from the Indramayu station are used as response variables in statistical downscaling modeling. The cross-correlation function between these variables plays an important role in statistical downscaling modeling. The cross-correlation function can enhance the correlation between predictor variables and response variables. Therefore, this research aims to compare the rainfall prediction results using initial GCM data (GCM) and GCM data with lag components (lagged GCM) determined based on the cross-correlation function. The methods used in statistical downscaling modeling are partial least squares regression (PLSR) and principal component regression (PCR). The modeling results using data from the period 1993-2020 show that the PLSR model on lagged GCM data is the best compared to other models (PLSR on GCM data, PCR on GCM data, and PCR on lagged GCM data). This model produces the highest coefficient of determination and the smallest RMSE value. Furthermore, the PLSR model on lagged GCM data can predict the 2008 rainfall data, following the actual rainfall pattern with the smallest RMSEP value. In general, modeling using lagged GCM data provides better rainfall prediction results compared to GCM data
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana, Ruliana; Mustika M, Sitti Sri
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/variansiunm193

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method
Metode Radial Basis Function Neural Network Untuk Klasifikasi Kab/Kota Tertinggal Di Provinsi Sulawesi Selatan Ruliana, Ruliana; Rais, Zulkifli; Mar'ah, Zakiyah; Hasnita, Hasnita
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/variansiunm197

Abstract

A disadvantaged area is an area that has the characteristics of tending to be left behind compared to other areas. Radial basis function neural networks are a part of Artificial Neural Networks, which use radial basis activation functions and are commonly used in classification cases. All districts/cities in South Sulawesi province have different characteristics from other districts/cities. Therefore, districts/cities are grouped into 2 groups to identify districts/cities that have characteristics that tend to be the same based on indicators of regional underdevelopment. The grouping results are then used as actual values ​​for classification using the RBFNN method, to determine the classification results and performance of the RBFNN method. In classifying districts/cities in South Sulawesi province based on indicators of regional underdevelopment using the radial basis function neural network method, an accuracy value of 91% was obtained using a comparison of 55% training data and 45% testing data and an f-measure value of 92% was obtained

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