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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sistem Komputer dan Informatika (JSON)
Dedi Pramana
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau Dedi Pramana; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 3, No 2 (2021): Desember 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3570

Abstract

Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China.  This virus continues to grow until it spreads to various countries in the world.  As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19.  Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients.  Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital.  This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm.  The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%.  The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.
Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors Dedi Pramana; M Afdal; Mustakim Mustakim; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6523

Abstract

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau Dedi Pramana; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 2 (2021): Desember 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3570

Abstract

Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China.  This virus continues to grow until it spreads to various countries in the world.  As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19.  Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients.  Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital.  This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm.  The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%.  The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.
Co-Authors Inggih Permana M Afdal Mustakim Mustakim