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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 5 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2022)" : 5 Documents clear
APLIKASI MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI CURAH HUJAN DI KOTA MAKASSAR Muhammad Reski Mattalunru; Suwardi Annas; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.514 KB) | DOI: 10.35580/variansiunm2

Abstract

Analisis regresi nonparametrik merupakan metode alternatif ketika asumsi parametrik terlanggar. Kemampuan estimasi yang tinggi serta sifatnya yang fleksibel membuat regresi nonparametrik menjadi sebuah pemodelan masa kini dan masa mendatang. Memperhatikan gejala alam dewasa ini semakin hari semakin sulit untuk diduga. Musim hujan merupakan salah satu fenomena alam yang semakin hari semakin mengarah pada pola yang tidak menentu. Bulan yang biasanya telah menjadi penanda musim kemarau malah tiba-tiba terjadi curah hujan yang sangat deras bahkan mengakibatkan banyak kerugian. Maka dibutuhkan pemodelan untuk mengetahui faktor-faktor apa yang mempengaruhi curah hujan. Metode Multivariate Adaptive Regression Splines (MARS) merupakan salah satu metode pemodelan modern dengan kemampuan estimasi yang tinggi. Selain itu MARS memilki sifat yang fleksibel serta ketangguhan mengatasi data yang berdimensi tinggi yaitu data yang memiliki variabel bebas 3 ≤ x ≤ 20 dan ukuran data sampel 50 ≤ n ≤ 1000. Model MARS diperoleh dari kombinasi antara Basis Fungsi (BF), Maksimum Interaksi (MI) dan Minimum Observasi (MO) dengan Generalized Cross Validation (GCV) yang bernilai kecil. Pada penelitian ini banyaknya variabel bebas yang digunakan sebanyak 4 variabel. suhu udara, kelembaban udara, kecepatan angin, dan tekanan udara merupakan variabel bebas yang mempengaruhi curah hujan di Kota Makassar dengan tingkat kontribusi masing-masing sebesar 86,54%, 100%, 39,38% dan 54,68%. Kombinasi model terbaik MARS pada penelitian ini adalah BF=12, MI=1, dan MO=1 dengan GCV=31,14
ANALISIS BAYESIAN SURVIVAL WEIBULL UNTUK MENENTUKAN FAKTOR YANG MEMPENGARUHI LAJU KESEMBUHAN PASIEN RAWAT INAP KANKER SERVIKS DI RSDU KOTA MAKASSAR Nini Harnikayani Hasa; M Nadjib Bustan; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.876 KB) | DOI: 10.35580/variansiunm6

Abstract

Survival analysis is a statistical procedure for analyzing data where the response variable is the time until the occurrence of an event. In this study, Bayesian survival Weibull was used to determine the factors that influence the rate of recovery of cervical cancer inpatients. The data used in this study is cervical cancer inpatient data at the Makassar City Hospital for the 2017-2019 period. Based on the results of the analysis, it was found that a significant factor affecting the healing rate of cervical cancer inpatients was complications. Cervical cancer inpatients who experience complications tend to recover slower by 0.258 than patients who do not experience complications.
PEMODELAN LAJU INFLASI DENGAN MENGGUNAKAN REGRESI NON-LINEAR BERBASIS ALGORITMA GENETIKA (Kasus: Kota-Kota di Pulau Jawa) Wildan Mujahid; Muhammad Arif Tiro; Ruliana Ruliana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.978 KB) | DOI: 10.35580/variansiunm7

Abstract

This research is applied research that uses non-linear regression on the inflation rate data and the factors that are thought to influence it. By using the RESET Test, statistics are obtained, namely the RESET value = 3.7506 with P value = 0.04138, which means that the inflation data is appropriate to use non-linear regression. From the results of this study, it was found that the average inflation rate of 26 cities in Java was 22.08% with a standard deviation of 24.33%. From the results of this study it was also found that the consumer price index (X1), city/district minimum wages (X2), and regional gross domestic product (X3) are factors that affect the inflation rate with the best model with an RMSE value of 0.445.
ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN KERNEL RADIAL BASIS FUNCTION (RBF) UNTUK MEMPREDIKSI LAJU INFLASI DI INDONESIA Isnaeni R; Sudarmin Sudarmin; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.99 KB) | DOI: 10.35580/variansiunm13

Abstract

Inflation is one indicator that affects the economic growth of a country. As a developing country, Indonesia has an unstable inflation rate every year. Therefore, it is necessary to predict the inflation rate in the future to be useful for formulating future economic policies. SVR is a Support Vector Machine (SVM) development for regression cases. In the SVR method, the RBF kernel is used as an aid in solving non-linear problems, the Min-Max Normalization method for data normalization, distribution of training data and testing data, selecting the best model with Grid Search Optimization, then forecasting using the model obtained with parameter = 0,1, C = 1, and = 3. The forecasting results obtained were evaluated by looking at the RMSE value, the test value obtained was RMSE of 0.0020, which means the model's ability to follow the data pattern well
PENGGUNAAN METODE DOUBLE EXPONENTIAL SMOOTHING BROWN UNTUK MERAMALKAN KASUS POSITIF COVID-19 DI PROVINSI PAPUA Ratu Huriyah Ali; M. Nadjib Bustan; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.18 KB) | DOI: 10.35580/variansiunm39

Abstract

Forecasting is an activity to predict events that will occur in the future. The data used in this study is data on the addition of positive cases of COVID-19 per day in Papua Province from March 21, 2020 to November 25, 2020. The forecasting method used for data that has an element of trend is the double exponential smoothing brown method. The number of additional positive cases of COVID-19 which tends to increase is assumed to be a trend. In this study, the used is = 0.10 which is obtained based on the smallest SSE, MSE, and MAE values. Forecasting the addition of positive cases of COVID-19 in Papua Province for the next 7 days, namely November 26, 2020 to December 2, 2020, obtained additional positive cases of COVID-19 per day as many as 81, 82, 82, 83, 83, 84, and 84.

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