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

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 Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

2021 ◽  
Vol 3 (2) ◽  
pp. 107-113
Author(s):  
Kartarina Kartarina ◽  
Ni Ketut Sriwinarti ◽  
Ni luh Putu Juniarti
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Entry And Exit ◽  
Accuracy Rate ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Bayes Methods ◽  
Accuracy Test ◽  
Student Graduation ◽  
Selection Of

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

scholarly journals Penentuan Penerimaan Mahasiswa Baru Pascasarjana Menggunakan Simple Additive Weighting (SAW)

2019 ◽  
Vol 8 (2) ◽  
pp. 121-129
Author(s):  
Febri Hadi ◽  
Dodi Guswandi
Keyword(s):  
Decision Making ◽  
Graduate Students ◽  
Decision Making Process ◽  
Postgraduate Student ◽  
Prospective Students ◽  
Study Programs ◽  
Decision Making System ◽  
Simple Additive Weighting ◽  
Selection Of

The decision-making system for the selection of new postgraduate student admissions which is carried out manually requires 7 days to submit the decision results. The selection is very important, so that the quality of input (input) of prospective students can be maintained in accordance with established standards. Therefore we need a system that can help in the decision making process quickly, precisely, and accurately. The purpose of this study is to help postgraduate master's study programs in conducting the selection of prospective graduate students in accordance with their abilities and disciplines. The method used in data processing using the Simple Additive Weighting (SAW) method, is a method of weighting the sum of the criteria values ​​of each alternative. The results of the decision in the form of ranking the number of values, based on the passing grade value that has been set> 0.70 declared passed, or <0.70 declared not passed.

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 Fuzzy Analytical Hierarchy Process Method to Determine the Quality of Gemstones

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Mochammad Sobandi Dwi Putra ◽  
Septi Andryana ◽  
Fauziah ◽  
Aris Gunaryati
Keyword(s):  
Decision Making ◽  
Analytical Hierarchy Process ◽  
Fuzzy Analytical Hierarchy Process ◽  
Ahp Method ◽  
Ahp Model ◽  
Decision Making System ◽  
Hierarchy Process ◽  
Selection Of ◽  
Process Method

The selection of quality gemstones requires a special ability to select and assess the quality of gemstones to be traded. The diversity of types of gemstones and consumers becomes an obstacle in itself when the knowledge and ability of individuals to analyze the quality of gemstones is minimal. The decision-making method used is Fuzzy Analytical Hierarchy Process (F-AHP) method which is widely used in various sectors. F-AHP is easy to adapt to many decision issues; the research proposes a decision-making system using the F-AHP algorithm to analyze the quality of gemstones. The results obtained with the use of F-AHP model in the selection of quality gemstones show the highest quality of gemstones of all stones compared, Rubi 1, with a weight value of 0.152942.

scholarly journals A COMPARISON OF ACCURACY BETWEEN TWO METHODS: NAЇVE BAYES ALGORITHM AND DECISION TREE-J48 TO PREDICT THE STOCK PRICE OF PT ASTRA INTERNATIONAL Tbk USING DATA FROM INDONESIA STOCK EXCHANGE

2019 ◽  
Vol 7 (1) ◽  
pp. 1244-1258
Author(s):  
Joan Yuliana Hutapea ◽  
Yusran Timur Samuel ◽  
Heima Sitorus
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Stock Prices ◽  
Stock Price ◽  
Naive Bayes ◽  
Stock Exchange ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm

The ability to predict the stock prices is very important for market players, whether individual or organizational investors.  The market players needs to know how to predict, that will help them in their decision making process, whether to buy or to sell its shares, so that it can maximize profits and reduce potential losses due to mistakes in decision making.  In accordance to this, the authors conducted a study that aimed to analyze and to compare the accuracy of two (2) methods that is used to predict the stock prices, namely: the Naїve Bayes Method and the Decision Tree-J48 Method. The amount of data used in this study were 1,195 stock datas of PT Astra International Tbk, issued by the IDX, by the period of January 1, 2013 to November 30, 2017. This study uses 7 attributes, namely:  Previews, High, Low, Close, Volume, Value, and Frequency. By using the WEKA application the result shows that, the accuracy of the Naïve Bayes Method using 20% of testing data, is 92.0502%, the precision value is 0.920 and the value of recall is 0.961,  while the accuracy of the Decision Tree J-48 method, using 20% of testing data, is 98.7448%, with precision value of 0.989 and the value of recall of 0.997.   Through this results,  it can be concluded that the decision tree J-48 algorithm has a better accuracy results compared to the Naive Bayes algorithm in predicting the stock price of PT. Astra Internasional Tbk.

scholarly journals Sistem Pendukung Keputusan Pemilihan Jenis Bibit Unggul Kelapa Sawit Menggunakan Metode Analytic Network Process

2020 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Amelia Rizqi Utami ◽  
S Solikhun ◽  
I Irawan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Support System ◽  
Oil Palm ◽  
Analytic Network Process ◽  
Network Process ◽  
Decision Making System ◽  
Quality Of Research ◽  
Selection Of

With the Decision Support System can improve the quality of research to be made. For example, in the selection of superior quality oil palm seeds for the new land planting process. However, in the selection of oil palm seedlings not only in financial terms, but must be seen from a variety of criteria such as weather which is very influential in the process of selecting superior seeds of oil palm and others. If the oil palm seedlings to be selected have met the criteria, the oil palm seedlings will be a good supporting factor. The support system applied in this study is called the Analytic Network Process (ANP). The ANP method is a procedure which is used to make decisions with many interrelated criteria. by using the ANP method, it will produce priority value weighting on all elements contained in the decision making system.

Prediction of User Loyalty Using the Naive Bayes Method in the "Goprint" Online Printing Marketplace

2020 ◽  
Vol 8 (3) ◽  
pp. 227
Author(s):  
Gede Widiastawan ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Naive Bayes Method

Goprint is an Online Printing Marketplace that connects printing services with users who want to print documents quickly without the need to queue. In the span of time from April 2019 to September 2019 it was found that the number of Goprint users reached 407 users, 24 partners, and 256 orders. From transactions that have been carried out by users, not a few orders are often canceled due to ineffective Goprint features or poor partner performance. This causes Goprint users to feel dissatisfied with the services provided by the Goprint application. The Naive Bayes algorithm is one of the algorithms used for classification or grouping of data, but can also be used for decision making. With this algorithm and the problems that occur, the authors make a system to predict the loyalty of Goprint users to anticipate users who stop leaving Goprint because they are not satisfied or loyal users. The data used as training data is 20 and testing data is 10. From the test results it is found that the value of precision is 80%, 100% recall, and 90% accuracy.

scholarly journals Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students' Majors Prediction

2018 ◽  
pp. 104 ◽  
Author(s):  
Alfa Saleh ◽  
Fina Nasari
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
K Value ◽  
Student Data ◽  
Positive Step ◽  
Selection Of ◽  
Naive Bayes Method

The Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the field of interest. In previous research, Naive Bayes method has been tested to classify the student’s department based on the criteria that support the case study on Private Madrasah Aliyah PAB 6 Helvetia students and the accuracy of the test from 100 student data is 90%. in this study, the researcher developed a previously used method by applying an equal-width interval discretization that would transform numerical or continuous criteria into a categorical criteria with a predetermined k value, different k values ??would be tested to find the best accuracy value. from the 120-student data that have been tested, it is proved that the result of the classification of the application of equal-width interval discretization on the Naive Bayes method with the value of k = 8 is better and increased the accuracy value 91.7% to 93.3%.

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%

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