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All Journal JISA (Jurnal Informatika dan Sains) Jurnal REP (Riset Ekonomi Pembangunan)
Negara, Julius Galih Prima
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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INTEGRATED FOOD SECURITY MANAGEMENT USING SIPEKAN (FOOD SECURITY MONITORING INFORMATION SYSTEM) Sasana, Hadi; Purbaningrum, Catarina Wahyu Dyah; Rohmah, Siti; Afriyanti, Afriyanti; Rochmadi, Imsak; Suprapto, Agus; Bharata, Risma Wira; Negara, Julius Galih Prima
Jurnal REP (Riset Ekonomi Pembangunan) Vol. 10 No. 1 (2025): April 2025
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/rep.v10i1.2107

Abstract

The study aims to investigate an integrated food security management using the Food Security Monitoring Information System, called “SIPEKAN” (Sistem Informasi Pemantauan Ketahanan Pangan) in Gunungkidul Regency, Yogjakarta, Indonesia. This study is a research and development-type of research (Research and Development) with the development model presented by Plomp (2013). The target groups are the community, farmers, agricultural extension workers, the agricultural service, the trade service, and the local government of Gunungkidul Regency. This research is expected to provide an integrated food security management equipped by the Food Security Monitoring Information System, called "SIPEKAN", which can be one answers to ensure food security in Gunungkidul Regency. By using SIPEKAN, it is projected that the community/farmers will easily sell their agricultural products, agricultural field workers will easily input agricultural potential data in each assisted area, the agricultural office and trade office will easily monitor product availability in each area in Gunungkidul Regency, and the Gunungkidul Regency Government can also employ the monitoring results provided by information system as a basis for policy making.
Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia Anreaja, Laurenzius Julio; Harefa, Norma Nobuala; Negara, Julius Galih Prima; Pribyantara, Venantius Nathan Hermanu; Prasetyo, Agung Budi
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1267

Abstract

Opensea is an NFT buying and selling application-based platform that is booming in the community. One way to find out the public's perception of the Opensea application is by sentiment analysis, as done in this study. Data that is used is user review data for the Opensea application in the Indonesian play store. The sentiment analysis technique used is the Naïve Bayes Classifier and the Support Vector Machine (SVM) method. Both are used to compare public responses from sentiment analysis of reviewed data labeled as positive, negative, and neutral. Based on this study, it was found that the Naive Bayes algorithm gives the results that class precision is 87.31%, class recall is 71.02%, and accuracy is 89.81%. While the SVM algorithm gives the results that class precision is 94.23%, class recall 71.96%, and Accuracy 90.78%. It is concluded that the SVM algorithm has a better performance than the Naive Bayes algorithm.  
Co-Authors Afriyanti Afriyanti, Afriyanti Agung Budi Prasetyo Agus Suprapto Anreaja, Laurenzius Julio Bharata, Risma Wira Catarina Wahyu Dyah Purbaningrum, Catarina Wahyu Dyah Hadi Sasana Harefa, Norma Nobuala Imsak Rochmadi Pribyantara, Venantius Nathan Hermanu Siti Rohmah