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All Journal JURNAL PIJAR Jurnal DIDIKA: Wahana Ilmiah Pendidikan Dasar Jurnal Agrotek UMMat JPMI (Jurnal Pendidikan Matematika Indonesia) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Indonesian Physical Review Variance : Journal of Statistics and Its Applications Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Jurnal Statistika dan Matematika (Statmat) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Educational Research and Social Sciences (IJERSC) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application TEACHING : Jurnal Inovasi Keguruan dan Ilmu Pendidikan SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA STRATEGY : Jurnal Inovasi Strategi dan Model Pembelajaran Jurnal Pijar MIPA Jurnal Penelitian, Pengembangan Pembelajaran dan Teknologi (JP3T) Journal of applied statistics and data mining
Ristu Haiban Hirzi, Ristu
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Journal : JAMBURA JOURNAL OF PROBABILITY AND STATISTICS

 1 
Prediksi Jumlah Wisatawan Menggunakan Metode Random Forest, Single Exponential Smoothing dan Double Exponential Smoothing di Provinsi NTB Ristu Haiban Hirzi; Umam Hidayaturrohman; Kertanah Kertanah; M. Hadiyan Amaly; Rody Satriawan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.17088

Abstract

The aim of study is to forecast global tourist visits and compare the forecasting methods to determine the best method using random forest, single exponential smoothing and double exponential smoothing, respectively. These methods are applied in global tourist visit data in West Nusa Tenggara Province. Random forest, single exponential smoothing and double exponential smoothing are familiar methods and are frequently utilized in forecasting. In addition, the three methods have great accuracy for time series data, such as data of global tourist visits. The data used in this study is data of global tourist visits from 2014 to 2021 in West Nusa Tenggara province. Applying the random forest, single exponential smoothing and double exponential smoothing methods in forecasting, the result shows that double exponential smoothing method is the best, based on the smallest value of Mean Absolute Percentage Error (MAPE) of 325.759. The forecasting result found out that tourist visits will increase from previous time, starting from August, 2021 to July, 2021 with an estimated 847 to 1045 lives
PERBANDINGAN METODE ANN BACKPROPAGATION DAN ARMA UNTUK PERAMALAN INFLASI DI INDONESIA Amaly, M. Hadiyan; Hirzi, Ristu Haiban; Basirun, Basirun
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15440

Abstract

A country's development progress can be measured by good economic growth. If economic growth experiences rapid growth, it will usually trigger price increases. The occurrence of an uncontrolled increase in the price of goods or services for the needs of the community can cause inflation. inflation rate for a country is an inflation rate that has a low and stable value. One alternative is to provide an overview of the inflation in Indonesia by using forecasting analysis techniques. In this study, inflation forecasting analysis in Indonesia was carried out using the ANN Backpropagation and ARMA methods. The purpose of this research is to compare the performance results of the two methods and look at the best method for forecasting results. Based on the results of the analysis with the ANN Backpropagation method, the best network architecture model was ANN(7-4-1) using an epoch value of 400 and a learning rate of 0,1 with a value of MSE = 0,0112 and RMSE = 0,1065. While the results of the analysis using the ARMA method, the best model was obtained, namely ARMA(2,0,1) with the value MSE = 0,0648 and RMSE = 0,2545. So that the most optimal method used to predict inflation for the next period is the ANN Backpropagation method because it has a smaller error value. From this model, the results of forecasting inflation rates for the months of May to December 2022 are also obtained with a range of 0,01% to 0,5%. 
Co-Authors Abdul Rahim Abdullah Abdullah Abdullah Abdullah, Abdullah Agustifa Zea Tazliqoh Amaly, M. Hadiyan Aryani Novianti, Baiq ASEP SAEFUDDIN Astutu, Siti Hariati Atria Ayu Septiani Ayu Septiani, Ayu Basirun, Basirun Bidayah, Nikmatul Chandrawati, Chandrawati Chintyana, Alissa DEDY EKA HASPANI Dinda Ayu Lara Saky EKA OKTAVIANI Fahrurrozi Fahrurrozi Hadian Mandala Putra Hanifa Prahastami Pambayun Hanipar Mahyulis Sastriana Hapipi Hapipi Harista Almiatus Soleha Jatiatul Muhsinah Kertanah Kertanah Kertanah, Kertanah Khaerus Syahidi Lukmanul Hakim M. Hadiyan Amaly Muanah, Muanah Muhammad Gazali Muhammad Malthuf Muhammad Malthuf Nawwarun Uyun Fikriah Nazri, Muhammad NENY ENDRIANA Pratama, Ridho Yudha Putri Septiana Rizky Rahadi, Irwan Ramli, Tri Juliantin Rodi Satriawan Rody Satriawan Sabar Sabar Sapiruddin, Sapiruddin Siti Hariati Hastuti Sri Supiyati, Sri Sripatmi Sripatmi Sripatmi Sripatmi Umam Hidayaturrohman WIBOWO, JANU ARLIN Wiwit Pura Nurmayanti Wiwit Purwa Nurmayanti Zaotul Wardi Zuhut Ramdani