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All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak AMMA : Jurnal Pengabdian Masyarakat
Edi Tohidi
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

 1 
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.
Penerapan Algoritma Naive Bayes pada Analisis Sentimen Ulasan Aplikasi Whoosh – Kereta Cepat Di Google Play Store Tuti Hartati; Rachmat Trikar Sohadi; Edi Tohidi; Edi Wahyudin
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.v6i1.10307

Abstract

Sentiment analysis of user reviews on mobile app distribution platforms is a complex and crucial issue, especially with the rapid growth of the number of users and the volume of reviews. This research focuses on the application of Naive Bayes Algorithm to analyze the sentiment of user reviews of WHOOSH app on Google Play Store. Naive Bayes algorithm was chosen due to its efficiency and easy implementation. Using a dataset of 500 cleaned and labeled reviews (positive or negative), the model was trained and achieved 81.25% accuracy. The high precision for the positive class (90%) demonstrates the model's ability to correctly identify positive reviews. Although the recall of the positive class is high (94%), the recall of the negative class still needs to be improved (64%). Overall, the Naive Bayes model is effective for classifying sentiment in WHOOSH user reviews, but needs to improve the accuracy and recall of negative classes.
Co-Authors Adi Kaswadi Agus Surip Anita Nur Kirana Eddiwan Dwiguna Edi Wahyudin Fadhil Muhammad Basysyar Fajrina Putri Salsabila Gifthera Dwilestari Hegarmanah Muhabatin Hilya Ashfia Nabila Ika Anikah Iqbal Agis Junizar Irfan Ali Lian Siliayani Muhammad Aji Pratama Muhammad Harun Al- Musa Nela Puji Rahayu Rachmat Trikar Sohadi Raena Agustin Laeliyah Revita Lestari Faujiah Saeful Anwar Shifa Dwi Oktaviani Siti Aisyah Tuti Hartati Uyu Nuraeni Yudhistira Arie Wijaya Yuniar Kartika