scholarly journals Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

2017 ◽  
Vol 24 (5) ◽  
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Support Vector ◽  
Construction Costs

PERBANDINGAN METODE PREDIKSI SUPPORT VECTOR MACHINE DAN LINEAR REGRESSION MENGGUNAKAN BACKWARD ELIMINATION PADA PRODUKSI MINYAK KELAPA

2019 ◽  
Vol 4 (2) ◽  
pp. 104-107
Author(s):  
Andi Bode
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Support Vector ◽  
Backward Elimination

Pohon kelapa banyak dimanfaatkan oleh manusia, sehingga tumbuhan ini dianggap tumbuhan serbaguna, salah satunya minyak kelapa yang dihasilkan oleh buah pohon kelapa. Produksi jumlah minyak kelapa menjadi bagian penting disetiap perusahaan yang bergerak di bidang produksi dengan tujuan mencapai target hasil produksi. Namaun Produksi minyak setiap hari mengalami perubahan fluktuatif. Perusahaan sangat memerlukan prediksi jumlah produksi. Penelitian ini bermaksud membandingakn metode support vector machine dan linear regression mengunakan fitur seleksi backward elimination berdasarkan data time series Sales Order. Hasil penelitian pada dataset sales order dengan menggunakan metode Support Vector Machine (SVM) didapatkan RMSE 0,127, dengan menggunakan metode SVM dan Backward Elimination (BE) didapatkan RMSE 0,115, dengan metode Linear Regression (LR) didapatkan RMSE 0,118 dan dengan menggunakan metode LR dan Backward Elimination didapatkan RMSE 0,118.  Dari hasil perbandingan tersebut dapat disimpulkan bahwa kinerja SVM menggunakan Backward Elimination lebih baik dibanding SVM, LR dan LR menggunakan Backward Elimination

Image Texture Classification Based on LS-SVM

2012 ◽  
Vol 182-183 ◽  
pp. 869-872
Author(s):  
Yan Ling Zhao ◽  
Xiao Shi Zheng ◽  
Guang Qi Liu ◽  
Na Li
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Least Squares ◽  
Texture Classification ◽  
Energy Ratio ◽  
Image Texture ◽  
Support Vector ◽  
Good Ability ◽  
Non Linear

LS-SVM (Least Squares Support Vector Machine) is simple and has a good ability of non-linear regression. As inputs of LS-SVM, DC-Energy-Ratio and Deviation of image samples are extracted first. Output of LS-SVM is the current texture classification. The results show that LS-SVM classifies images accurately by training the proposed two features.

scholarly journals Komparasi Algoritma Kasifikasi dengan Pendekatan Level Data Untuk Menangani Data Kelas Tidak Seimbang

2017 ◽  
Vol 3 (1) ◽  
pp. 1-6
Author(s):  
Ahmad Ilham
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Level Data ◽  
Under Sampling

Masalah data kelas tidak seimbang memiliki efek buruk pada ketepatan prediksi data. Untuk menangani masalah ini, telah banyak penelitian sebelumnya menggunakan algoritma klasifikasi menangani masalah data kelas tidak seimbang. Pada penelitian ini akan menyajikan teknik under-sampling dan over-sampling untuk menangani data kelas tidak seimbang. Teknik ini akan digunakan pada tingkat preprocessing untuk menyeimbangkan kondisi kelas pada data. Hasil eksperimen menunjukkan neural network (NN) lebih unggul dari decision tree (DT), linear regression (LR), naïve bayes (NB) dan support vector machine (SVM).

scholarly journals Tối ưu hóa ước tính mức tiêu thụ năng lượng trong các tòa nhà dựa trên các thuật toán trí tuệ nhân tạo

2020 ◽  
Vol 14 (1V) ◽  
pp. 35-45
Author(s):  
Trần Đức Học ◽  
Lê Tấn Tài
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Ensemble Model ◽  
Chi Square ◽  
Chi Squared ◽  
Classification And Regression ◽  
Automatic Interaction Detector

Mô phỏng và dự báo năng lượng tiêu thụ đóng vai trò quan trọng trong việc thiết lập chính sách năng lượng và đưa ra quyết định theo hướng phát triển bền vững. Nghiên cứu này sử dụng phương pháp kỹ thuật thống kê và công cụ trí tuệ nhân tạo bao gồm mạng nơ-ron thần kinh (ANNs – Artificial neutral networks), máy hỗ trợ véc tơ (SVM – Support vector machine), cây phân loại và hồi quy (CART - Classification and regression trees), hồi quy tuyến tính (LR - Linear regression), hồi quy tuyến tính tổng quát (GENLIN - Generalized linear regression), tự động phát hiện tương tác Chi-squared (CHAID - Chi-square automatic interaction detector) và mô hình tổng hợp (Ensemble model) để dự đoán mức tiêu thụ năng lượng trong các căn hộ tòa nhà chung cư. Bộ dữ liệu để xây dựng mô hình gồm 200 mẫu được khảo sát ở nhiều chung cư tại TP. Hồ Chí Minh. Mô hình đơn có hiệu quả tốt nhất trong quá trình dự đoán là CART, trong khi đó mô hình được tổng hợp tốt nhất là CART + GENLIN. Từ khóa: ước tính; tòa nhà; tiêu thụ năng lượng; khai phá dữ liệu, trí tuệ nhân tạo.

scholarly journals IoT-based Estimation System for Microcystis aeruginosa Cyanobacteria in Laguna de Bay using an Arduino-controlled Spectrophotometric Device

2021 ◽  
Vol 325 ◽  
pp. 04007
Author(s):  
Lawrence D. Alejandrino ◽  
Jessica Joy D. Jocson ◽  
Micah Romina R. Mirarza ◽  
Ericson D. Dimaunahan ◽  
Ramon G Garcia ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Potable Water ◽  
Low Cost ◽  
High Accuracy ◽  
The Philippines ◽  
Support Vector ◽  
Svm Algorithm ◽  
Laguna De Bay ◽  
Estimation System

Laguna de Bay, the largest freshwater lake in the Philippines, provides livelihood to the fishermen and serves as a source of potable water to the locals. However, freshwater quality has degraded, whereas one of the main contributors are Cyanobacteria that produce cyanotoxins. Existing studies that uses a similar device are either too expensive or too bulky. The purpose of this study is to estimate the cyanobacteria concentration by using a low-cost 16-channel spectrophotometric device to determine the level of severity efficiently. Using Linear Regression, the dataset is modelled by the algorithm to estimate the number of cyanobacteria present on the water sample, while Support Vector Machine (SVM) algorithm for severity level classifier. This study achieved high accuracy in estimating the cyanobacteria using linear regression and classifying the level of severity by support vector machine.

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