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

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 4 No. 1 (2022) · hlm. 223–234

DOI
10.37385/jaets.v4i1.1006
ISSN
2715-6079, 2715-6087
Terbit
2022-10-03
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

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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  • Universitas Nusa Mandiri
  • Indonesian Institute of Sciences
  • Universitas Bina Sarana Informatika
  • Telkom University
  • Universitas Halu Oleo
  • Mercu Buana University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Basri BasriUniversitas Halu Oleo2
Rian ArdiantoAfiliasi tidak diketahui1
Tri RivanieUniversitas Nusa Mandiri1
Yuris AlkhalifiUniversitas Nusa Mandiri1
Fitra Septia NugrahaUniversitas Nusa Mandiri1
Iwan Muhammad ErwinIndonesian Institute of Sciences1
Esa PrakarsaIndonesian Institute of Sciences1
Bambang SugiartoIndonesian Institute of Sciences1
Eko Arif RiyantoAfiliasi tidak diketahui1
Tri JuninisviantyAfiliasi tidak diketahui1
Doddy Ferdian NasutionAfiliasi tidak diketahui1
Ridwansyah RidwansyahUniversitas Bina Sarana Informatika1
Jajang Jaya PurnamaUniversitas Bina Sarana Informatika1
Hendri Mahmud NawawiUniversitas Bina Sarana Informatika1
Susy RosyidaAfiliasi tidak diketahui1
Elly FirasariAfiliasi tidak diketahui1
Umi KhultsumAfiliasi tidak diketahui1
Monikka Nur WinnartoAfiliasi tidak diketahui1
Rousyati RousyatiUniversitas Nusa Mandiri1
Dany PratmantoUniversitas Bina Sarana Informatika1
Nia Kusuma WardhaniMercu Buana University1
Ema RachmawatiTelkom University1
Ahmad Habib FitriansyahTelkom University1
Arya PangestuTelkom University1
Bedy PurnamaTelkom University1
Sungkowo Edy MulyonoAfiliasi tidak diketahui1
budi wiyoto baktiyonoAfiliasi tidak diketahui1
Musleh HarryAfiliasi tidak diketahui1
Ahmad WahidiAfiliasi tidak diketahui1
Nur JannaniAfiliasi tidak diketahui1
Meisy FajariAfiliasi tidak diketahui1
Tata SukayatAfiliasi tidak diketahui1
Shaifurrahman ShaifurrahmanAfiliasi tidak diketahui1
M. Yusuf WibisonoAfiliasi tidak diketahui1
Nase NaseAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter</dc:title>
	<dc:creator xml:lang="en">Noor, Mohamad</dc:creator>
	<dc:creator xml:lang="en">Gata, Windu</dc:creator>
	<dc:creator xml:lang="en">Risnandar, Risnandar</dc:creator>
	<dc:creator xml:lang="en">Fakhrudin, Fakhrudin</dc:creator>
	<dc:creator xml:lang="en">Novitarani, Anisah</dc:creator>
	<dc:subject xml:lang="en">Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en">Program Sembako(BPNT) </dc:subject>
	<dc:subject xml:lang="en">Twitter</dc:subject>
	<dc:description xml:lang="en">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</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2022-10-03</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/1006</dc:identifier>
	<dc:identifier>10.37385/jaets.v4i1.1006</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS); 223-234</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v4i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/1006/723</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Journal of Applied Engineering and Technological Science (JAETS)</dc:rights>
</oai_dc:dc>