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Isran K. Hasan
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Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
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JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.34312/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
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Search results for , issue "Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics" : 5 Documents clear
PEMODELAN JUMLAH TITIK PANAS DI PROVINSI KALIMANTAN TIMUR DENGAN METODE SINGULAR SPECTRUM ANALYSIS KUKUH WAHYU HIDAYAT; SRI WAHYUNINGSIH; YUKI NOVIA NASUTION
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7287

Abstract

Singular Spectrum Analysis (SSA) merupakan metode analisis runtun waktu dengan pendekatan nonparametrik, di mana metode ini tidak memerlukan beberapa asumsi. SSA cukup powerfull terutama untuk menangani data runtun waktu berpola musiman. Jumlah titik panas di Provinsi Kalimantan Timur memiliki unsur pola musiman berdasarkan beberapa penelitian yang telah dilakukan. Tujuan penelitian ini adalah menentukan model SSA terbaik yang digunakan untuk meramalkan data jumlah titik panas di Provinsi Kalimantan Timur. SSA terbagi menjadi dua tahap dasar yang saling berkaitan, yaitu tahap dekomposisi dan rekonstruksi. Pola musiman pada data dapat diketahui menggunakan analisis periodogram. Berdasarkan hasil analisis diperoleh model SSA, yaitu terdiri dari model peramalan untuk komponen tren dan model peramalan untuk komponen musiman. Tahap berikutnya dilakukan peramalan berdasarkan model yang diperoleh untuk bulan Februari 2020 hingga Januari 2021. Jumlah titik panas pada rentang bulan Februari 2020 hingga bulan Januari 2021 akan terjadi peningkatan dan penurunan titik panas secara drastis. Jumlah titik panas tertinggi akan terjadi pada bulan April tahun 2020, yaitu sebesar 1.840 titik panas.
PERBANDINGAN REGRESI NONPARAMETRIK KERNEL DAN B-SPLINES PADA PEMODELAN RATA-RATA LAMA SEKOLAH DAN PENGELUARAN PERKAPITA DI INDONESIA SEPTIE WULANDARY; DRAJAT INDRA PURNAMA
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7501

Abstract

Analisis regresi merupakan salah satu alat statistik yang banyak digunakan untuk mengetahui hubungan antara dua variabel acak atau lebih. Metode penaksiran model regresi terbagi atas regresi parametrik dan nonparametrik. Penelitian ini bertujuan menganalisis pola hubungan pengeluaran perkapita terhadap rata-rata lama sekolah di Indonesia tahun 2018 melalui perbandingan regresi nonparametrik, yaitu regresi kernel dan spline. Regresi kernel yang digunakan adalah regresi kernel dengan metode penaksir Nadaraya-Watson (NWE), sedangkan regresi spline yang digunakan adalah B-Splines. Berdasarkan nilai Generalized Cross Validation (GCV) yang minimum dari model regresi B-Splines, digunakan model dengan degree 2. Perbandingan model terbaik antara model NWE dan B-Splines dilakukan berdasarkan nilai RMSE terkecil dan kurva yang dihasilkan. Pada penelitian ini, model yang terbaik adalah model B-Splines karena memiliki RMSE 0,705, lebih kecil dibandingkan NWE dengan RMSE 1,854. Selain itu, regresi B-Splines memiliki kurva yang halus dan mengikuti sebaran data dibandingkan kurva NWE.
PENGUJIAN HIPOTESIS SIMULTAN MODEL REGRESI NONPARAMETRIK SPLINE TRUNCATED DALAM PEMODELAN KASUS EKONOMI ANDREA TRI RIAN DANI; NARITA YURI ADRIANINGSIH; ALIFTA AINURROCHMAH
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7755

Abstract

The pattern in a relationship between the response variable and the predictor variable can be known and some cannot be known. In determining the unknown pattern of relationships, nonparametric regression approaches can be used. The nonparametric regression approach is very flexible. One of the most frequently used nonparametric regression approaches is the truncated spline. Truncated splines are polynomial pieces that are segmented and continuous. The purpose of this study is to obtain the best estimator model in the Gini Ratio case against the variables suspected of influencing it, then perform simultaneous hypothesis testing on the nonparametric regression model. The criteria for the goodness of the model use the GCV and R2 values. In the case modeling of the District / City Gini Ratio in East Java Province using a nonparametric regression approach, it was found that the truncated spline estimator with 3 knots points gave quite good results. This is indicated by the coefficient of determination of the truncated spline estimator, which is 84.76%. Based on the results of simultaneous testing, it was found that the open unemployment rate, the percentage of poor people and the rate of economic growth simultaneously had an influence on the Gini Ratio.
PENGGUNAAN SELF ORGANIZING MAP DALAM PENGELOMPOKAN TINGKAT KESEJAHTERAAN MASYARAKAT IRWAN IRWAN; ASTRI YUNI HASHARI; HISYAM IHSAN; AHMAD ZAKI
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7266

Abstract

Self Organizing Map (SOM) is one of the topology forms of Unsupervised Neural Network where in the learning process does not require output target. Clusters in this research consist of one or more regency/city areas that have certain characteristics based on the variables. Each cluster had to be validated by using the Davies Bouldin Index value to get the best cluster formation from the SOM algorithm learning process. The best cluster model is the cluster model that has the smallest Davies Bouldin Index value. This research used 30 variables that refer to the key statistics of South Sulawesi Province People's Prosperity in 2018 by BPS of South Sulawesi Province. In this research, four cluster formation models were formed which began by forming 2 cluster model to form 5 cluster. Based on the Davies Bouldin Index value, it was found that the  5 cluster model have minimum value of 0.17.
PENERAPAN METODE EXPONENTIAL MOVING AVERAGE PADA PERAMALAN PENGGUNAAN AIR DI PDAM KOTA GORONTALO WA SALMI; ISMAIL DJAKARIA; RESMAWAN RESMAWAN
Jambura Journal of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v1i2.7152

Abstract

Facing the dry season, it is probable that there is a lack of water or excess distribution at one point during distribution to every house that uses PDAM water every day. This will result in community instability in using water and inaccurate users. Therefore, forecasting of the amount of water used in PDAM Kota Gorontalo for the next period. The method used to forecast is the Exponential Moving Average method. Criteria in determining the best method is based on the value of Mean Absolute Deviation and Mean Absolute Percentage Error. After forecasting each smoothing constant is compared, the best model. in predicting the amount of water use in PDAM Kota Gorontalo is an Exponential Moving Average with a smoothing constant of 0.15 because it has the smallest MAD and MAPE values.

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