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All Journal Inferensi Community Development Journal: Jurnal Pengabdian Masyarakat Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat

Dwi Rantini

Universitas Airlangga

Author-ID : 5896955

Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Mathematics Public Health Social Sciences Other

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Quantile Regression Neural Network Model For Forecasting Consumer Price Index In Indonesia Dwi Rantini; Made Ayu Dwi Octavanny; Rumaisa Kruba; Heri Kuswanto; Kartika Fithriasari
Inferensi Vol 1, No 1 (2018): Inferensi
Publisher : Department of Statistics ITS

The main purpose of time series analysis is to obtain the forecasting result from an observation for future values. Quantile Regression Neural Network is a statistical method that can model data with non-homogeneous variance with artificial neural network approach that can capture nonlinear patterns in the data. Real data that allegedly have such characteristics is Consumer Price Index (CPI). CPI forecasting is important to assess price changes associated with cost of living as well as identifying periods of inflation or deflation. The purpose of this research is to compare several method of forecasting CPI in Indonesia. The data used in this study during January 2007 until April 2018 period. QRNN method will be compared with Neural Network with RMSE evaluation criteria. The result is QRNN is the best method for forecasting CPI with RMSE 0.95.

Predicting Popularity of Movie Using Support Vector Machines Dwi Rantini; Rosyida Inas; Santi Wulan Purnami
Inferensi Vol 2, No 1 (2019): Inferensi
Publisher : Department of Statistics ITS

There are many movies performed, from low until high rating, which is the movie maybe popular or not popular. If many people watched that movie maybe it is popular, in other hand if a movie is watched by a little person so that movie can called as not popular movie. Popularity of movie can determined by several factors, such as likes, ratings, comments, etc. To determine popular or not popular of movie based on features, will use two classification methods that is logistic regression and Support Vector Machine (SVM). In this research, the data are Conventional and Social Media Movies Dataset 2014 and 2015. To get the best model and without ignoring the principle of parsimony, will do feature selection. The selected features are genre, sentiment, likes, and comments. That features will be used to classify the popularity of movies. This research used two classification methods namely logistic regression and Support Vector Machine (SVM). When used logistic regression, the accuracy is 77.29%, while used SVM the accuracy is 83.78%. Based on the accuracy of both methods, it is found that SVM gives the highest accuracy for CSM dataset. The highest accuracy is obtained from the SVM method with non-stratified holdout training-testing strategy.

Co-Authors Adam Maurizio Winata Chandrawati Putri Wulandari Fikri Arif Abdillah Fithriasari, Kartika Heri Kuswanto Made Ayu Dwi Octavanny Mohammad Ghani Ratih Ardiati Ningrum Rosyida Inas Rumaisa Kruba Santi Wulan Purnami

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