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All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS)
Saeful Anwar
STMIK IKMI Cirebon
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Other

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Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

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Klasifikasi Penentuan Tingkat Penyakit Demam Berdarah dengan menggunakan Algoritma Naïve Bayes (Studi Kasus Puskesmas Nagreg) Saeful Anwar; Revita Lestari Faujiah; Tuti Hartati; Edi Tohidi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10299

Abstract

The rapid development of science and technology, especially in the field of information technology, can give rise to new innovations for presenting and managing information to meet information needs. The role of technology in the health and medical fields has helped a lot in helping the human spirit and has shown its importance. Dengue Hemorrhagic Fever (DHF) is a disease that occurs in children and adults with the main symptoms of fever, muscle and joint pain, which usually gets worse after the first two days. DHF is a public health problem in Indonesia where the number of sufferers tends to increase and its spread causes bleeding. Dengue fever is characterized by sudden high fever lasting 2-7 days without a clear cause accompanied by manifestations such as petechiae, epistaxis sometimes accompanied by vomiting of blood, diarrhea, decreased consciousness, tendency to cause shock and death. The Naïve Bayes algorithm is a form of data classification using probability and statistical methods. The algorithm uses Bayes' theorem and assumes that all attributes are independent or not interdependent given the values of the class variables. Another definition says that Naïve Bayes is a classification using probability and statistical methods discovered by the British scientist Thomas Bayes, namely predicting future opportunities based on previous experience. Proceeding to the final stage, the final stage or step is to see the level of accuracy or how well the classification of the model we are using is.
Klasifikasi Algoritma KNN dalam menentukan Penerima Bantuan Langsung Tunai Ryan Hamonangan; Risa Komala Sari; Saeful Anwar; Tuti Hartati
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10298

Abstract

Poverty is a condition where an individual or a group experiences economic incapacity to meet their basic needs, and this study involves broader aspects than just expenditures. The focus of this research is on Ciherang and Ciaro Villages, located in the Nagreg Subdistrict, Bandung Regency, which are areas with significant levels of poverty. This research responds to poverty issues by utilizing the K-Nearest Neighbor (KNN) Algorithm in the data mining classification process. KNN considers the proximity of a new object to its nearest neighbors, and as a supervised learning algorithm, KNN requires target information or classes in the analyzed dataset. The aim of this research is to provide information regarding the classification of recipients of Direct Cash Assistance (BLT) in Ciherang and Ciaro Villages. The research results present data on criteria for determining whether recipients are considered eligible or ineligible for BLT, with an accuracy rate reaching 81.56%. Additionally, the performance of this algorithm is demonstrated through true recall values for both eligible and ineligible recipients, with recall for true ineligible recipients at 88.43%, recall for true eligible recipients at 74.80%, precision for eligible recipients at 86.79%, and precision for ineligible recipients at 77.54%. These findings provide a basis for more accurate decision-making in determining BLT recipients in both villages. This can contribute to the design of more targeted and effective social policies in reducing the impact of poverty, providing in-depth insights into the characteristics of BLT recipients, and demonstrating the relevance and efficiency of the KNN algorithm in addressing complex social issues.
Co-Authors Abdul Aziz Abdul Robi Padri Ade Irma Purnamasari Afif Maulana Sidik Ahmad Faqih Ahmad Faqih Ali Ali Arif Rinaldi Dikananda Basysyar, Fadhil Muhammad Cep Lukman Rohmat Chaidir Ali Dian Ade Kurnia Dias Bayu Saputra Dilita Pramasmawari Lita Dimas Aji Dita Rizki Amalia Edi Tohidi Gifthera Dwilestari Gifthera Dwilestari Heliyanti Susana Ibnu Alfian Iin Kanda Muhamad Ishak Khamim Surya Hadi Kusuma Al Atros Kokom Komariyah Martanto Martanto . Nana Suarna Nana Suarna Narasati, Riri Narasati Nining Rahaningsih Nisa Dieanwati Nuris Odi Nurdiawan Raditya Danar Dana Rahaditya Dasuki Rahmat Mauludin Revita Lestari Faujiah Risa Komala Sari Ruli Herdiana Ryan Hamonangan Sandy Eka Permana Siti Rini Sari sri wahyuni nengsih Tati Suprapti Topan Hadi Tuti Hartati Yudhistira Arie Wijaya