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 Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain

2020 ◽  
Vol 1 (1) ◽  
pp. 19-26
Author(s):  
Rakhmi Khalida ◽  
Siti Setiawati
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Traffic Violations ◽  
The Government ◽  
Bayes Algorithm ◽  
User Friendly

Abstract   The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impression. Twitter social media is one of the objects to express opinions because it is user friendly, updated topics, and openly accesses tweets. Opinions on Twitter are collected, then the preprocessing stage is performed, then the selection of information gain features helps reduce noise caused by irrelevant labels, the next step is the classification of sentiments with the Naïve Bayes algorithm and finally polarity sentiments. This research resulted in an accuracy of 41.82%, a precision of 50.51% and a recall of 45.45%.   Keywords: Sentiment analysis, E-ticketing, Information Gain, Naive Bayes   Abstrak   Pemerintah Indonesia melakukan langkah perubahan untuk memperbaiki sistem pelayanan publik dalam pelanggaran berlalu-lintas yaitu dengan menerapkan sistem e-Tilang. Sistem ini menjadi solusi mendisiplinkan para pengendara kendaraan bermotor dari banyaknya melakukan pelanggaran berlalu-lintas. Keberadaan e-Tilang juga menjadi solusi mencegah kenakalan penegak hukum dari pungutan liar, istilah damai ditempat, hingga akuntabilitas uang denda. Dalam penelitian ini melakukan analisis sentimen tentang sistem e-Tilang atau opinion mining untuk mengelompokan ragam komentar masyarakat yang memberikan kesan positif, negatif atau netral. Media sosial Twitter menjadi salah satu objek untuk menyampaikan opini karena user friendly, topik ter-update, dan terbuka mengakses tweet. Opini pada twitter dikumpulkan, lalu dilakukan tahapan preprocessing, selanjutnya dengan seleksi fitur information gain membantu mengurangi noise yang disebabkan oleh label-label yang tidak relevan, tahap selanjutnya adalah klasifikasi sentimen dengan algoritma Naïve Bayes dan terakhir sentimen polarity. Penelitian ini menghasilkan accuracy 41,82%, presisi 50,51% dan recall 45,45%.   Kata kunci: Analisis sentimen, E-Tilang, Information Gain, Naive Bayes

scholarly journals OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

2019 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Emha Taufiq Luthfi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Education Institution ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Education Development ◽  
Main Requirement ◽  
Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy. 

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 Estimation System For Late Payment Of School Tuition Fees

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Siti Rokhmah ◽  
Isnawati Muslihah
Keyword(s):  
Naive Bayes ◽  
Information Gain ◽  
Educational Institution ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Tuition Fees ◽  
Bayes Method ◽  
Estimation System ◽  
Naive Bayes Method

The Surakarta Al-Islam Vocational School is a private educational institution that requires all students to pay school tuition fees. Education is an obligation for all Indonesian citizens. The cost of education is one of the most important input components in implementing education. Because cost is the main requirement in achieving educational goals. SPP School is a routine school fee that is carried out every month. Based on last year's School Admin report, many students were late in paying school tuition fees, around 60%. This is a very big problem because the income of school funds comes from school tuition. The purpose of this research is that the researcher will build a prediction system using the best classification method, which is to compare the accuracy level of the Naïve Bayes method with the K-K-Nearest Neighbor method. Because both methods can make class classifications right or late, in paying school fees. processing using dapodic data for 2017/2018 as many as 236 data. In improving accuracy, the researcher also applies feature selection with Information Gain, which is useful for selecting optimal parameters. System testing is carried out using the Confusion Matrix method. The final results of this study indicate that the Naïve Bayes Method + Information Gain Method produces the highest accuracy, namely 95% compared to the Naïve Bayes method alone, namely 85% and the K-NN method, namely 81%.

scholarly journals Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes

2020 ◽  
Vol 4 (3) ◽  
pp. 117
Author(s):  
Hardian Oktavianto ◽  
Rahman Puji Handri
Keyword(s):  
Breast Cancer ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Average Percentage ◽  
Average Value ◽  
Treatment Measures ◽  
Bayes Algorithm ◽  
Health Organization

Breast cancer is one of the highest causes of death among women, this disease ranks second cause of death after lung cancer. According to the world health organization, 1 million women get a diagnosis of breast cancer every year and half of them die, in general this is due to early treatment and slow treatment resulting in new cancers being detected after entering the final stage. In the field of health and medicine, machine learning-based classification has been carried out to help doctors and health professionals in classifying the types of cancer, to determine which treatment measures should be performed. In this study breast cancer classification will be carried out using the Naive Bayes algorithm to group the types of cancer. The dataset used is from the Wisconsin breast cancer database. The results of this study are the ability of the Naive Bayes algorithm for the classification of breast cancer produces a good value, where the average percentage of correctly classified data reaches 96.9% and the average percentage of data is classified as incorrect only 3.1%. While the level of effectiveness of classification with naive bayes is high, where the average value of precision and recall is around 0.96. The highest precision and recall values are when the test data uses a percentage split of 40% with the respective values reaching 0.974 and 0.973.

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