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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND
RAHMI HG, IZZATI
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ANALISIS SURVIVAL UNTUK PARAMETER SKALA DARI DISTRIBUSI WEIBULL MENGGUNAKAN MLE DAN METODE BAYESIAN Yanuar, Ferra; Wulandari, Sisca; Rahmi HG, Izzati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.091 KB) | DOI: 10.30598/barekengvol15iss1pp147-156

Abstract

Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.
PERBANDINGAN METODE FUZZY TIME SERIES MARKOV CHAIN DAN FUZZY TIME SERIES CHENG DALAM MERAMALKAN NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA SERIKAT (AS) HIDAYATULLAH, M.PIO; YOZZA, HAZMIRA; RAHMI HG, IZZATI
Jurnal Matematika UNAND Vol. 12 No. 2 (2023)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.12.2.121-134.2023

Abstract

Nilai tukar mata uang atau yang sering disebut dengan kurs merupakanharga satu unit mata uang asing dalam mata uang domestik atau dapatjuga dikatakan harga mata uang domestik terhadap mata uang asing.Nilai tukar rupiah terhadap dolar Amerika Serikat memainkan peranan sentraldalam perdagangan internasional, karena nilai tukar rupiah terhadap dolarAmerika Serikat memungkinkan seseorang untuk membandingkan harga-hargasegenap barang dan jasa yang dihasilkan berbagai negara. Pertumbuhan nilaitukar mata uang yang stabil menunjukkan bahwa negara tersebut memilikikondisi perekonomian yang stabil. Oleh sebab itu perlu dilakukan peramalannilai tukar rupiah terhadap dolar Amerika Serikat untuk beberapa waktu yangakan datang sebagai dasar pengambilan keputusan bagi pemerintah. Beberapametode peramalan yang dapat dilakukan untuk meramalkan data time seriesnilai tukar rupiah terhadap dolar Amerika Serikat adalah metode fuzzy timeseries markov chain dan fuzzy time series Cheng. Kedua metode ini akan ditentukanhasil peramalannya kemudian dibandingkan tingkat akurasinya menggunakanMSE, MAE, dan MAPE sehingga diperoleh metode peramalan yangpaling tepat untuk meramalkan nilai tukar rupiah terhadap dolar AmerikaSerikat. Pada penelitian ini diperoleh metode terbaik untuk meramalkan nilaitukar rupiah terhadap dolar Amerika Serikat adalah metode fuzzy time seriesmarkov chain.
Comparison Between SARIMA Model and Artificial Neural Network On Forecasting Foreign Tourist in Batam City Rasyid, Fadila; DEVIANTO, DODI; RAHMI HG, IZZATI
Jurnal Matematika UNAND Vol. 12 No. 3 (2023)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.12.3.282-290.2023

Abstract

Batam City is one of the tourist attractions in Indonesia with the number of foreign tourist arrivals increasing every year. As one of the impacts of increasing the number of foreign tourist visits, the provincial government must improve the existing facilities in the tourism area, both in quality and quantity. In order for these facilities to be adequate to serve foreign tourists visiting Batam City in the future, it is estimated that the number of tourist visits to Batam City in the future is expected. This study aims to model foreign tourist arrivals using the SARIMA method and Neural Networks and compare the accuracy of the two methods with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The best SARIMA model for data on the number of foreign tourist arrivals to Batam City is SARIMA (2, 1, 0)(1, 1, 0)12 with MSE = 2,672,774,359 and MAPE = 21,4487%. The Neural Network Model is ˆy = max(0, 0.03208266 + 0.48310924V1 +...+ 0.46732363V8) with MSE = 171.279.990 and MAPE = 7.1404%. Thus, modeling with Artificial Neural Networks in these cases provides a better model than SARIMA in modeling data on the number of tourist visits to Batam City.
Co-Authors Devianto, Dodi Hazmira Yozza HIDAYATULLAH, M.PIO Rasyid, Fadila wulandari, sisca Yanuar, Ferra