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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Anisah Novitarani
Ministry of Social Affairs - Republic of Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter Mohamad Noor; Windu Gata; Risnandar Risnandar; Fakhrudin Fakhrudin; Anisah Novitarani
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (669.652 KB) | DOI: 10.37385/jaets.v4i1.1006

Abstract

Food Assistance Program (Program Sembako) is a development of the Non-Cash Food Assistance (BPNT) program which has been implemented by the Ministry of Social Affairs since 2017, namely of food assistance in the form of non-cash from the government which is given to Beneficiary Families (KPM) every month through an electronic account mechanism that is used only to buy food in food traders/e-warong in collaboration with banks. Twitter social media has now become one of the places to disseminate information about the Program Sembako/BPNT. This case study uses text mining techniques with the support vector machine (SVM), Naïve Bayes (NB) and K-Nearest Neighbor (k-NN) methods which aims to classify public sentiment towards the Program Sembako/BPNT on Twitter. The dataset used is tweets in Indonesian with the keywords “BPNT” and “Kartu Sembako” with a total dataset of 1,094 tweets. Text mining, transformation, tokenize, stemming and classification, etc. A useful technique for constructing sentiment classification and analysis. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. The results obtained by optimization using Particle Swam Optimization (PSO) using the support vector machine (SVM) algorithm and the accuracy value obtained is 78.02%, with a precision value of 78.73%, a recall value of 82.16%, and an AUC of 0.848
Co-Authors Fakhrudin Fakhrudin Mohamad Noor Risnandar Risnandar Windu Gata