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Journal : Jurnal Riset Informatika

ANALYSIS CLASSIFICATION SENTIMENT OF THE LARGE PRIEST OF FPI’S RETURN USING SVM CLASSIFICATION WITH OVERSAMPLING METHOD Zetta Nillawati Reyka Putri; Muhammad Muhajir
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1061.096 KB) | DOI: 10.34288/jri.v4i1.132

Abstract

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.
ANALYSIS OF DYNAMIC TIME WARPING IN THE DEVELOPMENT OF GROSS REGIONAL DOMESTIC PRODUCT YOGYAKARTA Inggrid Septa Narendra; Muhammad Muhajir
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (880.11 KB) | DOI: 10.34288/jri.v4i4.175

Abstract

Poverty in Indonesia has become a common thing that is still difficult to handle due to the presence of the covid virus outbreak attacks are causing the inability to buy and sell transactions, export and import goods and services, then the level of inequality increases. The tool measures the inequality level in an area seen from the Gini Ratio value. The Gini Ratio notes that the DI Yogyakarta province had the highest index value in Indonesia of 0,437 in September 2020. So this study aims to minimize the inequality in the DI Yogyakarta province by using the clustering method and Dynamic Location Quotient (DLQ). The clustering method with a hierarchical algorithm using the Dynamic Time Warping (DTW) distance and the DLQ method to predict regional economic sectors. Based on the result of the clustering analysis, there were 2 clusters, and the DLQ analysis obtained as many as 11 essential and 6 NPN-base sectors. Cluster 1 has 10 GRDP sectors with two industries that will become non-base sectors in the future, while cluster 2 has 7 GRDP sectors with three sectors it will become base sectors in the future.