Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter
DOI:
https://doi.org/10.37385/jaets.v4i1.1006Keywords:
Sentiment Analysis, Program Sembako(BPNT) , TwitterAbstract
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
Downloads
References
Ahmad, F., Tang, X. W., Qiu, J. N., Wróblewski, P., Ahmad, M., & Jamil, I. (2022). Prediction of slope stability using Tree Augmented Naive-Bayes classifier: Modeling and performance evaluation. Math. Biosci. Eng, 19, 4526-4546.
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
Cubillos, M., Wøhlk, S., & Wulff, J. N. (2022). A bi-objective k-nearest-neighbors-based imputation method for multilevel data. Expert Systems with Applications, 117298.
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022). Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nature Biotechnology, 40(2), 245-253.
Fan, Y., Wang, P., Heidari, A. A., Chen, H., & Mafarja, M. (2022). Random reselection particle swarm optimization for optimal design of solar photovoltaic modules. Energy, 239, 121865.
Gallego, A. J., Rico-Juan, J. R., & Valero-Mas, J. J. (2022). Efficient k-nearest neighbor search based on clustering and adaptive k values. Pattern Recognition, 122, 108356.
Hertina, H., Nurwahid, M., Haswir, H., Sayuti, H., Darwis, A., Rahman, M., ... & Hamzah, M. L. (2021). Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception. Bulletin of Electrical Engineering and Informatics, 10(4), 2231-2236.
Kania, I. (2022). Evaluation of the Non-Cash Food Assistance Program in Sadang Village, Sucinaraja District, Garut Regency. ijd-demos, 4(2).
Mansour, N. A., Saleh, A. I., Badawy, M., & Ali, H. A. (2022). Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy. Journal of ambient intelligence and humanized computing, 13(1), 41-73.
Marcec, R., & Likic, R. (2022). Using twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate Medical Journal, 98(1161), 544-550.
Sabanci, K., Aslan, M. F., Ropelewska, E., & Unlersen, M. F. (2022). A convolutional neural network?based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine. Journal of Food Process Engineering, 45(6), e13955.
Safitri, N. R. Y., & Rodiyah, I. (2022). Implementation of the Non-Cash Food Assistance Program in Sidoarjo Regency. Indonesian Journal of Public Policy Review, 20, 10-21070.
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022). Particle swarm optimization: A comprehensive survey. IEEE Access.
Vu, D. H. (2022). Privacy-preserving Naive Bayes classification in semi-fully distributed data model. Computers & Security, 115, 102630.
Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6), 4335-4385.
Zhou, J., Zhu, S., Qiu, Y., Armaghani, D. J., Zhou, A., & Yong, W. (2022). Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm. Acta Geotechnica, 17(4), 1343-1366.
Zhou, W., Jiang, H., Cheng, Y., Pei, L., & Ding, S. (2022). Predicting seasonal patterns of energy production: a grey seasonal trend least squares support vector machine. Expert Systems with Applications, 118874.
Zimbra, D., Abbasi, A., Zeng, D., & Chen, H. (2018). The state-of-the-art in Twitter sentiment analysis: A review and benchmark evaluation. ACM Transactions on Management Information Systems (TMIS), 9(2), 1-29.
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
