scholarly journals KLASIFIKASI DAN ANALISIS DATASET KOMENTAR VIDEO YOUTUBE MENGGUNAKAN SUPERVISED LEARNING

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 86-90
Author(s):  
Finki Dona ◽  
Ihdalhubbi Maulida ◽  
Bayu Nugraha
Keyword(s):  
Supervised Learning ◽  
Naive Bayes ◽  
Naïve Bayes

Interaksi di dalam sosial media dapat di lihat dari komentar-komentar sebagai umpan balik dari setiap kegiatan yang ada di media sosial, mulai dari status yang berupa teks, gambar maupun video. Dari berbagai respon pada kolom komentar tersebut diperoleh sebuah informasi dari data yang tidak terstruktur sehingga perlu adanya suatu teknik untuk mendefinisikan nilai informasi Fokus dalam penelitian ini adalah untuk memverifikasi kebenaran dan menggali nilai informasi yang terstruktur sehingga dapat menggambarkan kejadian dan topik yang terhubung dari komentar-komentar yang ada di dalam video youtube yang menjadi objek penelitian ini. Dari hasil pengujian di atas dapat dilihat nilai performa dari hasil pengujian menggunakan metode Naïve Bayes mendapatkan akurasi sebesar 57,50%, sedangkan dengan menggunakan metode KNN mendapatkan akurasi 88.06%..

scholarly journals Klasifikasi Metagenom dengan Metode Naïve Bayes Classifier

2017 ◽  
Vol 3 (1) ◽  
pp. 9
Author(s):  
Dian Kartika Utami ◽  
Wisnu Ananta Kusuma ◽  
Agus Buono
Keyword(s):  
Supervised Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Studi metagenom merupakan langkah penting pada pengelompokan taksonomi. Pengelompokan pada metagenom dapat dilakukan dengan menggunakan metode binning. Binning diperlukan untuk mengelompokkan contigs yang dimiliki oleh masing-masing kelompok spesies filogenetik. Pada penelitian ini, binning dilakukan dengan menggunakan pendekatan komposisi berdasarkan supervised learning (pembelajaran dengan contoh). Metode supervised learning yang digunakan yaitu Naïve Bayes Classifier. Adapun metode yang digunakan untuk ekstraksi ciri adalah dengan melakukan perhitungan frekuensi k-mer. Klasifikasi pada metagenom dilakukan berdasarkan tingkat takson genus. Dari proses klasifikasi yang dilakukan, akurasi yang diperoleh dengan menggunakan fragmen pendek (400 bp) adalah 49.34 % untuk ekstraksi ciri 3-mer dan 53.95 % untuk ekstrasi ciri 4-mer. Sementara itu, untuk fragmen panjang (10 kbp), akurasi mengalami peningkatan yaitu 82.23 % untuk ekstraksi ciri 3-mer dan 85.89 % untuk esktraski ciri 4-mer. Dari hasil tersebut dapat disimpulkan bahwa akurasi semakin tinggi seiring dengan semakin panjangnya ukuran fragmen. Selain itu, penelitian ini juga menyimpulkan bahwa metode ekstrasi ciri yang memberikan hasil paling maksimal adalah dengan menggunakan ekstraksi ciri 4-mer.<br /><br />Kata Kunci: metagenom, k-mer, Naïve Bayes Classifier, binning, klasifikasi

scholarly journals Identification of Violent Response with Stretch Sensor Data from a Smart-Jacket using Naïve Bayes Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 5265-5270
Keyword(s):  
Supervised Learning ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Pressure Sensors ◽  
Naïve Bayes ◽  
Sensor Data ◽  
Body Movements ◽  
Bayes Algorithm ◽  
Do So

In this paper, a smart-jacket using stretch sensors, pressure sensors was built for purpose of generating body-movements data and in order to record different kinds of signals and the distribution of the same on the jacket. Every degree of motion, when exercised, generates voltage changes in the stretch sensors as it is its property to do so. This data is collected in a flora chip set, which is Arduino based. The collected data is processed, pruned and filtered for outliers. This paper concerns with a supervised learning algorithm called Naive Bayes, which is applied over independent datasets, meaning one set of observation has no direct relations to each other. The placement of sensor are on the shoulders and elbows and the responses from each are independent of each other. Using Naive Bayes, the date has been classified for the violent response and the normal action.

Depression Detection with DM v1

2021 ◽  
Author(s):  
Umme Marzia Haque
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Supervised Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Models ◽  
Target Variable ◽  
Correlated Variables ◽  
Low Correlation ◽  
Depression Detection

The study has used data from YMM. The Yes/No variables that had a low correlation with target variable have been removed. To extract the most relevant features , the high correlated variables with the target variable , the Boruta method was used in conjunction with a Random Forest( RF) Classifier. To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool (TPOTclassifier) has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been employed in the depression identification step.has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) were employed in the depression identification step.

Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text

2012 ◽  
pp. 33-44
Author(s):  
Yanliang Qi ◽  
Min Song ◽  
Suk-Chung Yoon ◽  
Lori deVersterre
Keyword(s):  
Information System ◽  
Supervised Learning ◽  
Full Text ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Phrase Extraction ◽  
Learning Techniques ◽  
Key Phrase Extraction ◽  
And Performance ◽  
Key Phrases

Key-phrase extraction plays a useful a role in research areas of Information Systems (IS) like digital libraries. Short metadata like key phrases are beneficial for searchers to understand the concepts found in the documents. This paper evaluates the effectiveness of different supervised learning techniques on biomedical full-text: Sequential Minimal Optimization (SMO) and K-Nearest Neighbor, both of which could be embedded inside an information system for document search. The authors use these techniques to extract key phrases from PubMed and evaluate the performance of these systems using the holdout validation method. This paper compares different classifier techniques and performance differences between the full-text and it’s abstract. Compared with the authors’ previous work, which investigated the performance of Naïve Bayes, Linear Regression and SVM(reg1/2), this paper finds that SVMreg-1 performs best in key-phrase extraction for full-text, whereas Naïve Bayes performs best for abstracts. These techniques should be considered for use in information system search functionality. Additional research issues also are identified.

Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text

2011 ◽  
Vol 7 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Yanliang Qi ◽  
Min Song ◽  
Suk-Chung Yoon ◽  
Lori deVersterre
Keyword(s):  
Information System ◽  
Supervised Learning ◽  
Full Text ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Research Issues ◽  
Phrase Extraction ◽  
Learning Techniques ◽  
Key Phrase Extraction ◽  
Key Phrases

Key-phrase extraction plays a useful a role in research areas of Information Systems (IS) like digital libraries. Short metadata like key phrases are beneficial for searchers to understand the concepts found in the documents. This paper evaluates the effectiveness of different supervised learning techniques on biomedical full-text: Sequential Minimal Optimization (SMO) and K-Nearest Neighbor, both of which could be embedded inside an information system for document search. The authors use these techniques to extract key phrases from PubMed and evaluate the performance of these systems using the holdout validation method. This paper compares different classifier techniques and performance differences between the full-text and it’s abstract. Compared with the authors’ previous work, which investigated the performance of Naïve Bayes, Linear Regression and SVM(reg1/2), this paper finds that SVMreg-1 performs best in key-phrase extraction for full-text, whereas Naïve Bayes performs best for abstracts. These techniques should be considered for use in information system search functionality. Additional research issues also are identified.

Depression Detection Algorithm v1

2021 ◽  
Author(s):  
Umme Marzia Haque
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Supervised Learning ◽  
Naive Bayes ◽  
Detection Algorithm ◽  
Naïve Bayes ◽  
Learning Models ◽  
Target Variable ◽  
Correlated Variables ◽  
Depression Detection

The study has used data from YMM. The Yes/No variables that had a low correlation with target variable have been removed. To extract the most relevant features , the high correlated variables with the target variable , the Boruta method was used in conjunction with a Random Forest( RF) Classifier. To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool (TPOTclassifier) has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been employed in the depression identification step.has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) were employed in the depression identification step.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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

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