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 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 Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

TEM Journal ◽  
2021 ◽  
pp. 1738-1744
Author(s):  
Joseph Teguh Santoso ◽  
Ni Luh Wiwik Sri Rahayu Ginantra ◽  
Muhammad Arifin ◽  
R Riinawati ◽  
Dadang Sudrajat ◽  
...  
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
T Test ◽  
Bayes Method ◽  
Average Accuracy ◽  
Difference Test ◽  
Naive Bayes Method ◽  
Better Than

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

scholarly journals Klasifikasi Masyarakat Wajib Pajak Di Kabupaten Simalungun Menggunakan Metode Naive Bayes

2019 ◽  
Vol 1 (1) ◽  
pp. 15-23
Author(s):  
Idris Hanafia Lubis ◽  
P Poningsih ◽  
Ilham Sahputra Saragih
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Tax Administration ◽  
Regional Government ◽  
Poor Knowledge ◽  
Administration System ◽  
Information Session ◽  
The Government ◽  
Regional Governments

Regional tax is a tax set by the regional government with regional regulations, the collection of which is carried out by the regional government and the subsequent results are used to finance the expenditure of regional governments in implementing governance and development in the region. Poor knowledge and understanding of taxes received by the community in paying taxes. The quality of good tax knowledge will greatly affect the smooth running of taxpayers in fulfilling their tax obligations. In this study, the method used in measuring the free or not of taxpayers in Simalungun District uses the method of using Naive Bayes. The parameters used are Modernization of Tax Administration System, Taxation Information Session, Taxation of Awareness, Tax Sanctions, Taxpayer Compliance. The data used in this study provides questionnaires to people who have a type of business in Simalungun District specifically in Java. It is expected that the results of this study can help the government specifically the Regional Revenue Service to understand the understanding and responsibility of the community in fulfilling their tax obligations.

Sentiment Analysis of Snack Review Using the Naïve Bayes Method

2020 ◽  
Vol 8 (3) ◽  
pp. 333
Author(s):  
I Gede Cahya Purnama Yasa ◽  
Ngurah Agus Sanjaya ER ◽  
Luh Arida Ayu Rahning Putri
Keyword(s):  
Sentiment Analysis ◽  
Fast Food ◽  
Naive Bayes ◽  
Food Products ◽  
Naïve Bayes ◽  
Level Of Service ◽  
Snack Food ◽  
Bayes Method ◽  
Bayes Methods

Fast food is a product that we often encounter in stores such as convenience stores. Ready-to-eat products can now be easily found by consumers. One of the reason is due to the expansion of minimarkets in areas that are easily reached, such as housing complexes, school areas, and offices. Sentiment analysis is used to determine whether an opinion or comment on a product has a positive or negative interest and can be used as a reference in improving service, or improving product quality. In this research, we study the sentiments of consumers towards snack food products as a reference to improve the level of service and quality of these products.. We classify the sentiment of a review on snack food products as positive and negative. To classify the sentiments we apply the Naïve Bayes and Multinomial Naïve Bayes methods. We compare the two methods to study the most effective and efficient method for classifying sentiments on reviews of snack food products. Keywords: Sentiment Analysis, TF-IDF, Naïve Bayes,Multinomial, Review, Snack, Preprocessing

scholarly journals Perancangan Sistem Klasifikasi Penyakit Jantung Mengunakan Naïve Bayes

2020 ◽  
Vol 6 (1) ◽  
pp. 75
Author(s):  
Mufti Ari Bianto ◽  
Kusrini Kusrini ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Heart Disease ◽  
Heart Attack ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Average Accuracy ◽  
Classification Of Diseases ◽  
Fold Cross Validation ◽  
Naive Bayes Method

Serangan Jantung adalah salah satu penyakit yang paling mematikan tercatat di dunia, terdapat jumlah kasus baru Penyakit Jantung sebanyak 43,32% serta jumlah kematian sebanyak 12,91%. Pada tahun 2013 jumlah penderita Penyakit Jantung di Indonesaia sejumlah 61.682 orang, pada umumnya jumlah penderita penyakit ini terus meningkat dikarenakan kurangnya pengetahuan atau informasi tentang penyakit jantung tersebut, oleh karena itu dibutuhkan sebuah sistem yang dapat memberikan informasi serta klasifikasi penyakit secara dini yang dapat digunakan untuk klasifikasi apabila seseorang ingin mengetahui informasi ataupun gejala awal serangan jantung. Metode naïve bayes merupakan salah satu metode yang digunakan untuk melakukan klasifikasi berdasarkan probabilitas atau kemungkinan dari data sebelumnya, selain pendekatannya sederhana metode tersebut juga dapat melakukan klasifikasi secara baik. Mekanisme pengujiannya yaitu membagi 303 data kedalam 5 subset yang akan divalidasi dengan 5-fold cross validation. Hasil akhir dari penelitian ini adalah penerapan sistem klasifikasi dengan menggunakan metode naïve bayes yang akan menghasilkan nilai rata-rata akurasi sebesar 90,61%, presisi sebesar 87,44 %, dan recall sebesar 87,95%. Kata Kunci — klasifikasi, penyakit jantung, naïve bayesClassifier Heart attack is one of the most deadly diseases recorded in the world, there are a number of new cases of heart disease as much as 43.32% and the number of deaths as much as 12.91%. In 2013 the number of sufferers of heart disease in Indonesia amounted to 61,682 people, in general the number of sufferers of this disease continues to increase due to lack of knowledge or information about heart disease, therefore we need a system that can provide information and classification of diseases early that can be used for classification if someone wants to find out information or early symptoms of a heart attack. Naïve Bayes method is one of the methods used to classify based on the probability or likelihood of previous data, in addition to a simple approach the method can also do a good classification. The testing mechanism is to divide 303 data into 5 subsets that will be validated by 5-fold cross validation. The final result of this study is the application of the classification system using the Naïve Bayes method which will produce an average accuracy value of 90.61%, a precision of 87.44%, and a recall of 87.95%. Keywords — classification, heart disease, naïve bayes

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 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>

scholarly journals Penerapan Metode Naive Bayes Dalam Pemilihan Kualitas Jenis Rumput Taman CV. Rumput Kita Landscape

2018 ◽  
Vol 9 (2) ◽  
pp. 162-171
Author(s):  
Sri Rahayu ◽  
Anita Sindar RMS
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Air Humidity ◽  
Bayes Method ◽  
Market Prices ◽  
Decision Making System ◽  
Quality Ranking ◽  
Selection Of

Penataan taman yang menarik, sejuk dan indah memerlukan budget yang tinggi.  Dari beragam jenis rumput, umumnya Rumput Mini ditanam untuk mempercantik rumah atau bangunan. Para pengelola jasa taman menentukan kualitas rumput dari pengalaman sehari-hari. Ini menunjukkan belum adanya pemanfaatan sistem komputer dalam pemilihan jenis rumput taman yang berkualitas, menyebabkan terjadi kesalahan dalam menentukan kualitas rumput terbaik. Dalam permasalahan ini metode Naïve Bayes digunakan sebagai Sistem Pengambil Keputusan (SPK). Naïve bayes merupakan metode pengklasifikasian ada tidaknya ciri tertentu dari sebuah kelas. Empat kriteria pemilihan kualitas jenis rumput taman yaitu suhu udara, curah hujan, kelembapan udara dan harga pasar. Hasil perangkingan dari R1, R2, R3, R4, R5, R6, R7 menunjukkan R6: Rumput Golf= 0.4705882353;  R7: Rumput Swiss= 0.4705882353 merupakan rumput yang memiliki Kualitas Baik.   Kata Kunci: Pemilihan Rumput, Kualitas, Ranking, Naïve Bayes   Abstract An attractive, cool and beautiful garden arrangement requires a high budget. Of the various types of grass, generally Mini Grass is planted to beautify your home or building. The managers of garden services determine the quality of grass from everyday experience. This shows that there is no use of computer systems in the selection of quality garden grass types, causing errors in determining the best quality of grass. In this problem the Naïve Bayes method is used as a Decision Making System (SPK). Naïve Bayes is a method of classifying the presence or absence of certain characteristics of a class. Four criteria for selecting the quality of garden grass types are air temperature, rainfall, air humidity and market prices. The ranking results of R1, R2, R3, R4, R5, R6, R7 indicate R6: Golf Grass = 0.4705882353; R7: Swiss grass = 0.4705882353 is a grass that has good quality.    Keywords: Selection Of Grass, Quality, Ranking, Naïve Bayes

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.

scholarly journals User Loyalty Prediction Using Naive Bayes Method in "Udatari" an art Performance Marketplace

2021 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Ngurah Agus Sanjaya ER ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
K Value ◽  
Customer Lifetime ◽  
Average Accuracy ◽  
Silhouette Index ◽  
The Right ◽  
Past Experiences ◽  
Naive Bayes Method

Udatari is the first traditional dance platform in Indonesia which provides information about traditional events such as, dance tutorials, group dancer and dance attributes. The tight competition in the startup world, requires Udatari as a new startup to manage application users optimally. Knowing loyal users will help startups determine the right marketing strategy. In this study, the method used for clustering is the K-Means method where this method seeks to classify existing data into several groups provided that the data in one group have the same characteristics as each other. The model used for the clustering process is RFM, namely recency, frequency and monetary. The purpose of this clustering is to get the segmentation of users who have different Customer Lifetime Value. The second method for conducting classification is the Naïve Bayes method, where this method predicts future opportunities based on past experiences. The purpose of this classification is to predict new users into the user segmentation obtained from the clustering results. From the results of this study, the optimum k value for K-Means are 3 clusters with the largest CLV value in the second cluster where testing on this method uses the Silhouette Index. Furthermore, for the test results of the Naïve Bayes method, the average accuracy value is 97.44% where the accuracy of each class is 92.31% for cluster 0 (first cluster), 100% for the second cluster and 100% for the third cluster. Keywords: K-Means, Naïve Bayes, Loyalty, Segmentation, RFM

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