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All Journal Prosiding Seminar Nasional Official Statistics J-Icon : Jurnal Komputer dan Informatika
Jesica Nauli Br. Siringo Ringo
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Aplikasi Dynamic Factor Model untuk Nowcasting Pertumbuhan Ekonomi Regional Menggunakan Data Google Trends di Indonesia Jesica Nauli Br. Siringo Ringo; Anugerah Karta Monika
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.511 KB) | DOI: 10.34123/semnasoffstat.v2021i1.806

Abstract

Economic activity data is urgently needed to take various policies, but the data publication is experiencing delays. Gross Domestic Regional Product (GDRP) will be released within five weeks after the quarter ends. Nowcasting is an attempt to provide this data. Nowcasting is a method of forecasting the current period using higher frequency variables. Google Trends is high frequency data that is available in real time. This study aims to nowcast GDRP growth using Google Trends data. The nowcasting method used in this study is Dynamic Factor Model (DFM). Nowcasting results show that the model is able to capture the recent downturn in economic activity since the COVID-19 pandemic. The evaluation of the models between two data ranges shows that DFM is better in the data range that does not include periods of economic shock.
Perbandingan Metode Klasifikasi Multiclass untuk Pemetaan Zona Risiko COVID-19 di Pulau Jawa Jesica Nauli Br. Siringo Ringo; Wahyu Joko Mursalin; Nisrina Citra Nurfadilah; Dwiky Rachmat Ramadhan; Wa Ode Zuhayeni Madjida
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 1 (2021): Maret 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i1.3602

Abstract

Various attempts are needed to control the increment of COVID-19 cases in Indonesia, especially Java Island. One of the effective attempt to do this is through the preventive act by providing news about a region. Indonesia, through Satgas Penanganan COVID-19, has built a risk zone of district/city as a warning system for the public and the substance of policy making for government in region level. The risk zone is built by three kinds of indicator using a conventional technique named score weighting. By considering the importance of the risk zone for policy making in the government, this study aims to build a risk zone classification model for districts / cities in Java using several data mining classification techniques and determine the best classification model based on evaluation results. This study uses several classification technique on the purpose of comparation. These techniques are naive Bayes, decision tree, k-nearest-neighbor, and neural network. Before entering the modeling stage, data is being adjustedat the preprocessing stage where missing value and imbalanced data problems are identifies. These problems is being overcome by doing data imputation and oversampling techniques. The result of this study indicates that k-nearest-neighbor is the best model compared to other three models. This result is based on the evaluation measures of the four models where the k-NN model has the highest accuracy value, the macro average value for sensitivity, specivicity, and F1-Measure compared to other models.
Co-Authors Anugerah Karta Monika Dwiky Rachmat Ramadhan Nisrina Citra Nurfadilah Wa Ode Zuhayeni Madjida Wahyu Joko Mursalin