Afaan Oromo News Text Categorization using Decision Tree Classifier and Support Vector Machine: A Machine Learning Approach

2017 ◽  
Vol 47 (1) ◽  
pp. 1-12
Author(s):  
Kamal Mohammed Jimalo ◽  
◽  
Ramesh Babu P ◽  
Yaregal Assabie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Text Categorization ◽  
Support Vector ◽  
Learning Approach ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Machine Learning Approach

scholarly journals Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

scholarly journals Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

2021 ◽  
Vol 10 (9) ◽  
pp. 67-72
Author(s):  
Talasila Bhanuteja ◽  
◽  
Kilaru Venkata Narendra Kumar ◽  
Kolli Sai Poornachand ◽  
Chennupati Ashish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Local Area ◽  
Learning Approach ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

Driver Recognition on Segway

2012 ◽  
Vol 3 (1) ◽  
pp. 76-88
Author(s):  
Hiroshi Sato ◽  
Julien Rossignol
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Approach ◽  
Statistical Machine Learning ◽  
Recognition Method ◽  
Human Behaviors ◽  
Machine Learning Approach ◽  
User Friendly

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors can make more user-friendly machines. In this paper, the authors propose the driver recognition method from their record of manipulations using support vector machine. The authors demonstrate the efficiency of the authors’ method using the Segway. The performance of the recognition is quite good especially when the authors introduce the pre-process with FFT.

A Support-Vector-Machine Method for Precisely Evaluating Planar Straightness Error

2012 ◽  
Vol 220-223 ◽  
pp. 1611-1614
Author(s):  
Jiang Meng
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Evaluation Method ◽  
Support Vector ◽  
Learning Approach ◽  
Support Vector Regression Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Learning Approach ◽  
Straightness Error

To present a precise and efficient algorithm to solve planar straightness error problems, a machine-learning approach to evaluate planar straightness error was presented in this paper. According to the similarity between the least envelope zone and the support vector regression model, the SVR-based method was developed to solve the problem of straightness error. The evaluation method was compared to some existing techniques. According to the results from three datasets, it is shown that the SVR-based method can provide precise and exact values of planar straightness error.

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