scholarly journals Komparasi Algoritma Nonparametrik untuk Klasifikasi Citra Wajah Berdasarkan Suku di Indonesia

2020 ◽  
Vol 6 (3) ◽  
pp. 337
Author(s):  
Seno Hartono ◽  
Anggi Perwitasari ◽  
Herry Sujaini
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Tool ◽  
Mining Tool

Klasifikasi merupakan metode data mining yang berfungsi untuk mengatur dan mengkategorikan data pada kelas yang berbeda-beda. Penelitian ini bertujuan untuk membandingkan dan menentukan algoritma nonparametrik terbaik dalam pengklasifikasian citra wajah. Dalam proses pengklasifikasian, penelitian ini menggunakan algoritma klasifikasi nonparametrik yaitu k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, dan AdaBoost Untuk mengklasifikasikan citra wajah penduduk Indonesia yang berasal dari suku Batak, Dayak, Jawa, Melayu, dan Tionghoa. Penelitian ini menggunakan Orange Data Mining Tool sebagai alat bantu untuk melakukan proses data mining. Dari hasil pengklasifikasian dengan menerapkan algoritma k-Nearest Neigbor, Support Vector Machine, Decision Tree, dan AdaBoost, SVM memberikan nilai akurasi yang lebih baik dibanding algoritma lainnya. Rata-rata nilai precision keempat algoritma tersebut berturut-turut adalah Support Vector Machine 37.5%, diikuti oleh algoritma k-Nearest Neighbor 31.55%, AdaBoost 30.25%, dan untuk Decision Tree 29.75%.

scholarly journals Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients Recovery

2020 ◽  
Author(s):  
L. J. Muhammad ◽  
Md. Milon Islam ◽  
Usman Sani Sharif ◽  
Safial Islam Ayon
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The World ◽  
Novel Coronavirus

Abstract Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotics medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients’ recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85 % which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Anifuddin Azis
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya.  CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN),  Random Forest, Backpropagation.

Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients

2018 ◽  
Vol 10 (2) ◽  
pp. 22-35 ◽  
Author(s):  
Reza Safdari ◽  
Peyman Rezaei-Hachesu ◽  
Marjan GhaziSaeedi ◽  
Taha Samad-Soltani ◽  
Maryam Zolnoori
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Classification Tree ◽  
Roc Curves ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Knowledge Based ◽  
Class 1 ◽  
Sensitivity Specificity

Medical data mining intends to solve real-world problems in the diagnosis and treatment of diseases. This process applies various techniques and algorithms which have different levels of accuracy and precision. The purpose of this article is to apply data mining techniques to the diagnosis of asthma. Sensitivity, specificity and accuracy of K-nearest neighbor, Support Vector Machine, naive Bayes, Artificial Neural Network, classification tree, CN2 algorithms, and related similar studies were evaluated. ROC curves were plotted to show the performance of the authors' approach. Support vector machine (SVM) algorithms achieved the highest accuracy at 98.59% with a sensitivity of 98.59% and a specificity of 98.61% for class 1. Other algorithms had a range of accuracy greater than 87%. The results show that the authors can accurately diagnose asthma approximately 98% of the time based on demographics and clinical data. The study also has a higher sensitivity when compared to expert and knowledge-based systems.

scholarly journals Prediction Of Plastic Degrading Microbes

2021 ◽  
Author(s):  
Hemalatha N ◽  
Akhil Wilson ◽  
Akhil Thankachan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Random Forest Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Plastic Pollution ◽  
Forest Model

Plastic pollution is one of the challenging problems in the environment. But a life without plastic we cannot imagine. This paper deals with the prediction of plastic degrading microbes using Machine Learning. Here we have used Decision Tree, Random Forest, Support vector Machine and K Nearest Neighbor algorithms in order to predict the plastic degrading microbes. Among the four classifiers, Random Forest model gave the best accuracy of 99.1%.

scholarly journals KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER

2019 ◽  
Vol 15 (2) ◽  
pp. 267-274
Author(s):  
Tati Mardiana ◽  
Hafiz Syahreva ◽  
Tuslaela Tuslaela
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif.  Penelitian ini menggunakan data realtime dari  tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil  pengujian  dengan  confusion  matrix  diperoleh  nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine  sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine  dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.  

scholarly journals Klasifikasi Jenis Pemeliharaan dan Perawatan Container Crane menggunakan Algoritma Machine Learning

MATICS ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 21-27
Author(s):  
Via Ardianto Nugroho ◽  
Derry Pramono Adi ◽  
Achmad Teguh Wibowo ◽  
MY Teguh Sulistyono ◽  
Agustinus Bimo Gumelar
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Container Crane ◽  
Model Tree

Pada industri jasa pelayanan peti kemas, Terminal Nilam merupakan pelanggan dari PT. BIMA, yang secara khusus bergerak dibidang jasa perbaikan dan perawatan alat berat. Terminal ini menjadi sentral tempat untuk melakukan aktifitas bongkar muat peti kemas domestik yang memiliki empat buah container crane untuk melayani dua kapal. Proses perawatan alat berat seperti container crane yang selama ini beroperasi, agaknya kurang memperhatikan data pengelompokkan atau klasifikasi jenis perawatan yang dibutuhkan oleh alat berat tersebut. Di kemudian hari, alat berat dapat menunjukkan kinerja yang tidak maksimal bahkan dapat berujung pada kecelakaan kerja. Selain itu, kelalaian perawatan container crane juga dapat menyebabkan pembengkakan biaya perawatan lanjut. Target produksi bongkar muat dapat berkurang dan juga keterlambatan jadwal kapal sandar sangat mungkin terjadi. Metode pembelajaran menggunakan mesin atau biasa disebut dengan Machine Learning (ML), dengan mudah dapat melenyapkan kemungkinan-kemungkinan tersebut. ML dalam penelitian ini, kami rancang agar bekerja dengan mengidentifikasi lalu mengelompokkan jenis perawatan container crane yang sesuai, yaitu ringan atau berat. Metode ML yang pilih untuk digunakan dalam penelitian ini yaitu Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, Logistic Regression, J48, dan Decision Tree. Penelitian ini menunjukkan keberhasilan ML model tree dalam melakukan pembelajaran jenis data perawatan container crane (numerik dan kategoris), dengan J48 menunjukkan performa terbaik dengan nilai akurasi dan nilai ROC-AUC mencapai 99,1%. Pertimbangan klasifikasi kami lakukan dengan mengacu kepada tanggal terakhir perawatan, hour meter, breakdown, shutdown, dan sparepart.

A Business Classifier to Detect Readability Metrics on Software Games and Their Types

2013 ◽  
Vol 4 (4) ◽  
pp. 47-57
Author(s):  
Yahya M. Tashtoush ◽  
Derar Darwish ◽  
Motasim Albdarneh ◽  
Izzat M. Alsmadi ◽  
Khalid Alkhatib
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Software Metrics ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobile Environments ◽  
New Approach ◽  
Data Mining Tool ◽  
Mining Tool

Readability metric is considered to be one of the most important factors that may affect games business in terms of evaluating games' quality in general and usability in particular. As games may go through many evolutions and developed by many developers, code readability can significantly impact the time and resources required to build, update or maintain such games. This paper introduces a new approach to detect readability for games built in Java or C++ for desktop and mobile environments. Based on data mining techniques, an approach for predicting the type of the game is proposed based on readability and some other software metrics or attributes. Another classifier is built to predict software readability in games applications based on several collected features. These classifiers are built using machine learning algorithms (J48 decision tree, support vector machine, SVM and Naive Bayes, NB) that are available in WEKA data mining tool.

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