scholarly journals IDENTIFIKASI JENIS OBAT BERDASARKAN GAMBAR LOGO PADA KEMASAN MENGGUNAKAN METODE NAIVE BAYES

2016 ◽  
Vol 3 (2) ◽  
pp. 125
Author(s):  
Surya Rahayuda
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Naive Bayes ◽  
Modern Medicine ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Different Types ◽  
The Government ◽  
Numeric Data

<p><em>There many types of drugs have been approved by the government and circulating in the community, but many people don’t know. In this study, I want to create an application that can identify the type of drug based on the logo on the packaging. I’m using 4 different types of modern medicine and 3 types of herbal medicine, total there will be as many as 7 different logo that will be used. Pictures will be entered into the application, then detected the edges of the image using the Edge Detection, to get the shape of the logo image, after it is extracted using methods GLCM, extraction will produce output in the form of numbers, the numeric data is then classified using Naïve Bayes classification and will get the results in the form of the type of drug. From the experiments it was found that the resulting level of accuracy is quite high, there are 3 categories of types of drugs that have a high accuracy on Obat Bebas, Obat Bebas Terbatas and Obat Keras. From the results of these trials concluded that the Naïve Bayes method can be used to mengkalsifikasi types of drugs is based on the logo on the packaging of drugs</em>.</p><p><strong><em>Keywords: </em></strong><em>logo, drug, image processing, edge detection, GLCM, naïve bayes</em></p><p><em>Terdapat banyak jenis obat telah disetujui oleh pemerintah dan beredar di masyarakat, namun banyak masyarakat tidak mengetahuinya. Pada penelitian ini saya ingin membuat suatu aplikasi yang dapat mengindentifikasi jenis obat berdasarkan logo pada kemasan. Saya menggunakan 4 jenis obat moderen dan 3 jenis obat herbal, total akan terdapat sebanyak 7 macam logo yang akan digunakan. Gambar akan diinputkan ke dalam aplikasi, kemudian dideteksi tepian gambarnya menggunakan metode Edge Detection, untuk mendapatkan bentuk dari gambar logo, setelah itu diekstraksi menggunakan metode GLCM, hasil ekstraksi akan menghasilkan output berupa angka, data angka ini kemudian diklasifikasikan menggunakan metode Naïve Bayes dan akan mendapatkan hasil klasifikasi berupa jenis obat. Dari percobaan yang dilakukan didapatkan bahwa tingkat akurasi yang dihasilkan cukup tinggi, terdapat 3 buah kategori jenis obat yang memiliki akurasi yang tinggi yaitu pada jenis Obat Bebas, Obat Bebas Terbatas dan Obat Keras. Dari hasil percobaan tersebut disimpulkan bahwa metode Naïve Bayes dapat digunakan untuk mengkalsifikasi jenis obat berdasarkan logo pada kemasan obat.</em> <em></em></p><p><strong><em>Kata kunci: </em></strong><em>logo, obat, image processing, edge detection, GLCM, naïve bayes</em></p>

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

scholarly journals TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

2022 ◽  
Vol 5 (1) ◽  
pp. 116-123
Author(s):  
Yola Tri Handika ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Detection System ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Major Influence ◽  
Bayes Method ◽  
Training Stage ◽  
Political Conditions ◽  
Naive Bayes Method

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.

scholarly journals PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal)

2021 ◽  
Vol 10 (1) ◽  
pp. 11-20
Author(s):  
Reza Dwi Fitriani ◽  
Hasbi Yasin ◽  
Tarno Tarno
Keyword(s):  
Family Planning ◽  
Naive Bayes ◽  
Population Control ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The Family ◽  
The Government ◽  
Efficient Learning ◽  
Naive Bayes Method

The Family Planning Program (KB) launched by the Government of Indonesia to address the problem of population control does not always produce the desired program results. In 2017, there were 7 users of the IUD contraceptive type of contraceptive who failed from 1,102 new IUD users in Kendal Regency so that the ratio of success and failure to the IUD KB program when compared to users of the new IUD KB is 0.64%: 99.36% . The ratio of success and failure of family planning programs which tend to be unbalanced makes it difficult to predict. One of the handling imbalanced data is oversampling, for example using Random Oversampling (ROS). Naive Bayes is used for classification because it’s easy and efficient learning model. The data in this study used 14 independent variables and 1 dependent variable. The results of this study indicate that the G-mean of Naive Bayes is less than 60%. The G-mean of ROS-Naive Bayes is 96.6%. It can be concluded that in this research, the ROS-Naive Bayes method is better than the Naive Bayes method for detecting the success status of IUD family planning in Kendal Regency. Keywords: Naive Bayes, Random Oversampling, G-mean

scholarly journals Penerapan Algoritma Naive Bayes pada Penentuan Kelayakan Calon Tenaga Kerja Indonesia

2019 ◽  
Vol 1 ◽  
pp. 771
Author(s):  
Winda Hana Purba ◽  
Poningsih Poningsih ◽  
Dedi Suhendro ◽  
Irfan Sudahri Damanik ◽  
Ilham Syahputra Saragih
Keyword(s):  
Unemployment Rate ◽  
Migrant Workers ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Level Of Education ◽  
High Unemployment ◽  
Low Level ◽  
High Unemployment Rate ◽  
The Government

Indonesian Manpower is a potential that is a huge potential for the progress of the country. However, the difficulty of employment and the high unemployment rate in Indonesia requires that some people seek perfect employment abroad, in order to improve economic levels. The lack of selection resulted in many problems in the workforce, the low level of education of prospective migrant workers resulted in them having an easy risk on other party tricks, non-violence, unpaid salaries and so on. In accordance with what has been surveyed, it turns out that the sending of these workers is actually not feasible, given the level of education, skills and abilities that are lacking for employment abroad. This study aims to facilitate the government or companies engaged in the field to channel selected workers using the Naive Bayes Method.

Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes

2017 ◽  
Vol 9 (1) ◽  
pp. 50-58
Author(s):  
Antonius Rachmat C ◽  
Yuan Lukito
Keyword(s):  
Mobile Application ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Accuracy Rate ◽  
Bayes Method ◽  
Public Figures ◽  
Index Terms ◽  
Bahasa Indonesia ◽  
The Web

Instagram is the most famous pictures and videos media sharing based on the web & mobile application. Instagram users can have picture posts that can be commented by their followers. Indonesian public figures such as actors, actresses, musicians use Instagram to promote their activities to their followers. Unfortunately, there are a lot of spam comments in Instagram that need special attention and have to be removed. This research grabs Instagram comments and builds the dataset from Indonesian public figures who have more than one million followers. By using preprocessing (tokenization, stop words removal, and stemming), TF-IDF weighting, and supervised learning, Naive Bayes method is used to detect spam comments in Indonesian. Naive Bayes produces 74,31% accuracy rate on unbalanced datasets and 77,25% accuracy rate on balanced datasets. This result shows that Naïve Bayes can be used to build an automatic Indonesian spam comments detector on Instagram with high accuracy rate. The novelty of this research is that Naive Bayes can be used to detect spam comment on our Indonesian Instagram comments dataset. Index Terms—Instagram, Naive Bayes, Indonesian spam comments, spam comments detection.

scholarly journals Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS)

Petir ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 131-144
Author(s):  
Mochamad Farid Rifai ◽  
Hendra Jatnika ◽  
Bowval Valentino
Keyword(s):  
Naive Bayes ◽  
System Development ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Pass Rates ◽  
Certification Program ◽  
Bayes Method ◽  
Probability And Statistics ◽  
Microsoft Office ◽  
Alternative Solutions

This research discusses prediction pass rates the certification microsoft office specialist 2013 version (word and excel) aimed to provide information concerning to pass rates and certification give alternative solutions to determine the program certificationi appropriate to chosen before test certification. Naive bayes used for the classification certification graduation where participants know what information pass and did not finish. Naive bayes is a classification with the probability and statistics to predict opportunities in the future based on the Provided before. In this study, system development CRISP-DM to use of the become more ordered and testing done with the BlackBox to test each function is on the application built. From the study, produce values probability of 0.001042 the accuracy of 99 %. These results, proving that naïve bayes method can be used to assist in a prediction graduation rates participants (word and excel), because it produces quite high accuracy. So participants were able to determine the certification program proper chosen before test certification.

scholarly journals PENERAPAN ALGORITMA NAIVE BAYES DALAM MENENTUKAN KONSENTRASI SISWA TERHADAP PROSES BELAJAR MENGAJAR DI SMP TAMAN ASUHAN

2021 ◽  
Vol 4 (2) ◽  
pp. 142-155
Author(s):  
Farhannah Silmi Az Zahra Farhannah ◽  
Solikhun Solikhun
Keyword(s):  
Learning Process ◽  
Teaching And Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Research Data ◽  
Bayes Method ◽  
A Value ◽  
The Government

The purpose of this study is to analyze whether students concentrate or not on the teaching and learning process at Pematangsiantar Park in SMP. To determine the concentration of students in the teaching and learning process, the Naive Bayes classification of data mining methods is used. Sources of research data were obtained using a questionnaire distributed to Pematangsiantar Park Middle School. So hopefully this research can help the government and the school in monitoring the concentration of students so that it can help in improving the quality and quality of schools. Based on that research that has been done,the writer uses the Naïve Bayes Method to predict student concentration resulting in a value of 95.31%, while the predicition of lack of concentration results in a value of 100.00%

scholarly journals Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes

2021 ◽  
pp. 221-226
Author(s):  
Fajri Karim ◽  
Gunadi Widi Nurcahyo ◽  
S Sumijan
Keyword(s):  
Expert System ◽  
Brain Damage ◽  
Programming Language ◽  
Blood Supply ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The Public ◽  
The Brain

Stroke is a disease caused by brain damage caused by disruption of the blood supply to the brain. At this time in general, people are still not very familiar with how this stroke disease or do not realize the symptoms that may have appeared from the start. People also tend to be hesitant to visit the hospital to check their symptoms and feel they are delaying further examinations. This is certainly a scourge that continues to make the number of strokes increase. In assisting the community in identifying stroke disease, an expert system is needed that is able to identify the type of stroke based on the symptoms felt. The data used in this study were obtained from Brain Hospital. Dr. Drs. M. Hatta Bukittinggi which was later developed into a website-based system using the PHP Framework Laravel programming language and MySQL as the database. The system is built based on the Naive Bayes method which is one of the Expert System methods that has a high accuracy value. The use of this system is expected to be able to provide knowledge to the public about the symptoms that might lead to what type of stroke the user might suffer, so that the user can use the results of the system as a reference to visit the hospital and immediately get more targeted help. This system can perform calculations that match the results of the doctor's diagnosis with an accuracy value of 100% in identifying the type of stroke from 10 data samples used.

scholarly journals Classification of Community Complaints Against Public Services on Twitter

2020 ◽  
Vol 1 (1) ◽  
pp. 7-10
Author(s):  
Muqorobin Muqorobin ◽  
Siti Rokhmah ◽  
Isnawati Muslihah ◽  
Nendy Akbar Rozaq Rais
Keyword(s):  
Public Service ◽  
Public Services ◽  
Naive Bayes ◽  
Text Processing ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The Government ◽  
Bayes Algorithm

Abstract— Information on public services is an important part of increasing community satisfaction with government policies. Complaints and Complaints of the community become mediators to improve public services according to community needs.Twitter is one of the most widely used social media in the community to post activities, experiences, and complaints about public services through the internet easily and realtime.The amount of information on Twitter is mixed between satisfaction and extensibility of public services, making it difficult for the government to make decisions in public policy. The role of Big Data can be a solution to classifying data to predict satisfaction or extensibility of public services with parameters: markets, transportation and hospitals.Data sources taken from Twitter are 700 data texts. The twitter classification of public service complaints is built using the Naïve Bayes Algorithm Method, because the algorithm can classify based on probability values. Text processing is done by filtering text and selecting text to be ordered.The results of this study indicate that the Naïve Bayes Method is able to properly classify public service complaints based on 3 parameters, transportation, markets and hospitals. System testing using 700 data obtained the best results accuracy value: 86%, and precision: 72%, recall 81% and f-measure: 83%.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

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