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All Journal TEKNIK INFORMATIKA Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter JIK Jurnal Ilmu Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding SNATIKA Vol 01 (2011) Sinkron : Jurnal dan Penelitian Teknik Informatika Technomedia Journal Indonesian Journal of Electrical Engineering and Computer Science IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Loghat Arabi: Jurnal Bahasa Arab & Pendidikan Bahasa Arab International Journal of Cyber and IT Service Management (IJCITSM) Startupreneur Business Digital (SABDA Journal) Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Blockchain Frontier Technology (BFRONT) Journal of Current Research in Blockchain International Journal Research on Metaverse
Yusuf Abdurachman
UIN Syarif Hidayatullah Jakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Implementation of Data Mining with Naive Bayes Algorithm for Eligibility Classification of Basic Food Aid Recipients Yamato Shino; Yusuf Durachman; Nana Sutisna
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 2 No. 2 (2022): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v2i2.114

Abstract

One of the primary issues that the government of a nation concentrates on is poverty. The provision of precise and focused data on poverty is a crucial component of the Poverty Reduction Strategy. One technique for classifying data is Naïve Bayes. The aid manager will subsequently use the categorization findings to inform judgments about categorizing and determining who should get basic food assistance. Predictions for those who get basic food assistance fall into two categories: eligible and ineligible. Sample data from the hamlet of XYZ used as the basis for the forecast. In this study, a web-based application is used to construct and assess the Naïve Bayes method. The accuracy for 135 training data, 40 test data, and seven characteristics employed generates 86 percent accuracy, 85 percent recall, and 88 percent precision according to the assessment findings using the confusion matrix.
Co-Authors A'ang Subiyakto Abd. Rahman Ahlan Abd. Rahman Ahlan Rahman Ahlan Abdur Rohim Boy Berawi Abdur Rohim Boy Berawi Adrian Sean Bein Alauddin, Ilham Ali Djamhuri Amri Amri Anif Hanifa Setianingrum, Anif Hanifa Antonius Ivan, Antonius Arini Arini A’ang Subiyakto A’ang Subiyakto Subiyakto Bayu Waspodo Ditha Septiandani Elvi Fetrina Erwin Tobing, Erwin Evy Nurmiati Fitra Putri Oganda Fitroh Fitroh, Fitroh Harahap, Eka Purnama Himki, Arzo Huda, Muhammad Qomarul Husni Teja Sukmana Irma Yunita Isti Surjandari Prajitno, Isti Surjandari Kamal Yusuf Khodijah Hulliyah Kultsum, Lilik Ummi Lestari Santoso, Nuke Puji Lutfiya, Nur Aqilah M. Qamarul Huda Millah, Shofiyul Mira Kartiwi Mohammed Ali Berawi Muhamad Nur Gunawan Muhamad Yusup Nahdlah Nurul Murhum Nahry Nahry Nana Sutisna Nashikha, Ainun Perdana Miraj Pramono, Erry Sigit Rahman Raeyani Kalele Rahman, Abdul Wahab Abdul Ramadhan, Tarisya Shofiyul Millah Supardi Supardi Syopiansyah Jaya Putra Syopiansyah Jaya Putra Taqwa Hariguna Tarisya Ramadhan Teuku Yuri M. Zagloel Tuada, Linura Watini, Srie Wibowo, Wahyu Hendarto Yamato Shino Yelna Yuristiary