Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter


  • Mohamad Noor Universitas Nusa Mandiri
  • Windu Gata Universitas Nusa Mandiri
  • Risnandar Risnandar Ministry of Social Affairs - Republic of Indonesia
  • Fakhrudin Fakhrudin Ministry of Social Affairs - Republic of Indonesia
  • Anisah Novitarani Ministry of Social Affairs - Republic of Indonesia




Sentiment Analysis, Program Sembako(BPNT) , Twitter


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


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How to Cite

Noor, M., Gata, W., Risnandar, R., Fakhrudin, F., & Novitarani, A. (2022). Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 223–234. https://doi.org/10.37385/jaets.v4i1.1006