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%

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 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%.

Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

2021 ◽  
Author(s):  
Sulthan Rafif ◽  
Pramana Yoga Saputra ◽  
Moch Zawaruddin Abdullah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Research Fields ◽  
Naive Bayes Method

scholarly journals Penggunaan Metode Kooperatif Picture And Picture Guna Meningkatkan Kualitas Pembelajaran

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Suryani Suryani
Keyword(s):  
Social Sciences ◽  
Learning Process ◽  
Teaching And Learning ◽  
Student Learning Outcomes ◽  
A Value ◽  
Level Of Activity ◽  
Quality Of Learning ◽  
Cooperative Methods

In the teaching and learning process of Social Sciences, many problems are carried out by the teacher. One of them is some students still rely on their teacher's activities in learning, so the role of students is still very lacking. Even thought, the teacher always involving students to play an active in learning process. In contrary, students assume that the subject of Social Sciences is very boring. So that this causes a low quality of learning in Social Sciences, especially in  SMP Negeri 21 Pontianak. This cases has tickled the writer to take the theme of research on picture and picture cooperative methods to improve the quality of learning and increase the interest and activity of students in teaching and learning activities in Social Sciences.As for the techniques used of data collection is through observation by teachers and collaborators. The research involved two cycles of 42 students. While the data analysis used description with percentage techniques. Furthermore the level of activity of students and teachers is expressed in the category of "very good", "good" or "moderate", while the success of using picture and picture cooperative methods is stated as "successful", "less successful", or "unsuccessful".The results of this reseacrh with picture and picture cooperative methods have been successful, which is showing indicated by an increase in student activity of learning because it has reached the specified criteria is 81% of students are actively involved, while teacher activities reach 86.53%, as well as student learning outcomes with a value of 78.57, where this shows that it is exceeding the KKM value of 72, with a percentage of completeness of 76%, which has exceeded the criteria of 75%.

scholarly journals Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain

2020 ◽  
Vol 7 (3) ◽  
pp. 599
Author(s):  
Arif Bijaksana Putra Negara ◽  
Hafiz Muhardi ◽  
Indira Melinda Putri
Keyword(s):  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Review Section ◽  
User Reviews ◽  
Average Value

<p class="Abstrak">Zaman sekarang tren masyarakat untuk memesan tiket pesawat sudah melalui situs-situs <em>booking</em> <em>online</em>. Pegipegi.com merupakan salah satu <em>website</em> yang menyediakan pemesanan tiket dan menyediakan fitur ulasan bagi pengunjung untuk menyampaikan opini. Pengunjung lain yang membaca ulasan-ulasan tersebut dapat memperoleh gambaran secara lebih objektif mengenai maskapai penerbangan. Ulasan pengguna yang terdapat pada website pegipegi.com saat ini sudah sangat banyak sehingga hal ini menyulitkan dan memakan waktu untuk membaca secara keseluruhan. Oleh karena itu dirancang analisis sentimen guna membantu mengklasifikasi ulasan kedalam kategori positif atau negatif sehingga dapat memberikan rekomendasi maskapai penerbangan berdasarkan jumlah kategori ulasan. Metode yang diterapkan untuk klasifikasi sentimen adalah Naïve Bayes dengan seleksi fitur <em>Information Gain</em>. Adapun tujuan dari penelitian ini adalah mengetahui pengaruh dari pemilihan fitur <em>Information Gain</em> terhadap akurasi klasifikasi dan membuktikan bahwa metode Naïve Bayes dengan <em>Information Gain</em> dapat digunakan untuk klasifikasi analisis sentimen. Hasil pengujian yang telah dilakukan menunjukkan bahwa nilai rata-rata akurasi, <em>precision</em>, <em>recall</em> setelah penambahan <em>Information Gain</em> menunjukkan hasil yang lebih baik sebesar 0,865 jika dibandingkan sebelum penambahan information gain yakni sebesar 0,81.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Nowadays people tend to order airplane tickets through online booking sites. Pegipegi.com is a website that provides ticket reservations and a review section for visitors to express their opinions. Other visitors who read the reviews can get a more objective picture of airlines. The user reviews contained on the pegipegi.com website are currently very large so this makes it difficult and time consuming to read in its entirety. Therefore sentiment analysis is designed to help classify reviews into positive or negative categories so that they can provide airline recommendations based on the number of review categories. The method applied for sentiment classification is Naïve Bayes with the Information Gain feature selection. The purpose of this study was to determine the effect of selecting the Information Gain feature on classification accuracy and prove that the Naïve Bayes method with Information Gain can be used for the classification of sentiment analysis. The results of the tests that have been done show that the average value of accuracy, precision, recall after adding Information Gain shows better results of 0.865 compared to the addition of information gain which is equal to 0.81</em>.</em></p>

scholarly journals Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

2021 ◽  
Vol 5 (1) ◽  
pp. 123-131
Author(s):  
Ni Luh Putu Merawati Putu ◽  
Ahmad Zuli Amrullah ◽  
Ismarmiaty
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Bayes Method ◽  
A Value ◽  
Negative Class ◽  
Naive Bayes Method ◽  
Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.  

Meningkatkan kualitas proses belajar mengajar melalui fungsi pengawasan dalam manajemen mutu terpadu di Wilayah Kepengawasan Gugus Sekolah 05 Kecamatan Magersari Kota Mojokerto

2018 ◽  
Vol 1 (2) ◽  
pp. 21
Author(s):  
Sunarija Sunarijah
Keyword(s):  
Quality Management ◽  
Performance Improvement ◽  
Learning Process ◽  
Teaching And Learning ◽  
Teacher Performance ◽  
Quality Of Education ◽  
Design Activity ◽  
Quality Of Teaching ◽  
The Government

Building the quality of education continues to be done, both by the government and the school as education providers. In an effort to meet the needs and demands of society on the quality of education and simultaneously as a response to the changes of life very quickly in the era of globalization. For that reason researchers need research on improving the quality of teaching and learning process through supervisory function in the integrated quality management in the District Kepengawasan Cluster 05 District Magersari Mojokerto City. This research uses action research for three cycles. Each cycle consists of one meeting each of which consists of four stages: design, activity and observation of reflection and revision. The target of this research is Master of Kepengawasan District School Cluster 05 Magersari Subdistrict Mojokerto City 15 people. The data obtained in the form of the results of supervision assessment of teaching and learning process. The success of the learning process in cycle III shows that the cycle can be stopped because it has met the target of> 70%. This is evident from the average increase in teacher performance per cycle. The conclusions of this research are: The quality of teaching and learning process through supervisory function in integrated quality management in Kepengawasan Area School Cluster 05 Magersari Sub-district Mojokerto City has increased, it can be seen from the average of teacher performance improvement from 65,87 percent in cycle I, to 76.67 in Cycle II and increased to 82.86 percent in Cycle III.

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