Support Vector Machine Untuk Pengenalan Bentuk Manusia Menggunakan Kumpulan Fitur Yang Dioptimalkan

Wenda, Alex

doi:10.23887/jstundiksha.v11i1.44437

Cited by 3 publications

(3 citation statements)

References 16 publications

(24 reference statements)

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“…These near objects are called support vectors. It is the best hyperplane because it provides the most significant margin value [8]. The Support Vector Machine method has three approach models called kernels, including radial, linear, and sigmoid kernels [9].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Pratiwi,

Sumarlinda,

Nastiti

2023

IJIST-DAS

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Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru's Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Pratiwi,

Sumarlinda,

Nastiti

2023

IJIST-DAS

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“…Analysis of Variance merupakan teknik standar untuk mengukur signifikansi statistik dari suatu set variabel independen dalam memprediksi variabel dependen [14].…”

Section: Analysis Of Variance (Anova)unclassified

Penerapan Seleksi Fitur Analysis of Variance Pada Algoritma Random Forest Classifier Dalam Klasifikasi Nilai Mahasiswa

Thoriq,

Pranoto,

Faldi

2023

Explore. jurnal. sistem. inf. dan. telematika

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In educational institutions, such as universities, it is important to pay attention to the performance of students so that they can complete their studies on time. However, there are still issues where some students are unable to finish their studies on time, and some even decide to drop out or become inactive as students. This is evidenced by the decline in student grades in the Indonesian Language course from the 2020 to 2021 cohorts at UMKT. To address this problem, a method is needed to measure students' performance in completing their studies. This study aims to identify the attributes that influence the decline in student grades in the Indonesian Language course, as well as improve the accuracy of the Random Forest Classifier algorithm using ANOVA feature selection. The data used in this study consists of UMKT student data who took the Indonesian Language course from the 2020/2021 to 2021/2022 academic years. The data was obtained from the academic administration department (BAA) of UMKT and the General Basic Course Unit (MKDU) of UMKT, with a total of 1028 data points. The data analysis process was conducted using the 5-Fold Cross Validation method. The results of the study indicate that attributes such as Progress, % Course completed, Assignment 1, and Assignment 2 have a significant influence on the decline in student grades in the Indonesian Language course. Furthermore, the use of ANOVA feature selection in the Random Forest Classifier algorithm improved its performance, with the accuracy increasing from 85.65% to 87.31%.

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“…Support Vector Machine (SVM) telah muncul sebagai sebuah teknik yang kuat untuk tujuan umum pengenalan pola (Wenda, 2022). Teknik ini telah diterapkan pada masalah -masalah regresi dan klasifikasi dengan kinerja yang sangat bagus (Altawaier & Tiun, 2016;Chen et al, 2015;Kremer et al, 2014;Qian et al, 2015) serta digunakan untuk mencari teks pertambangan dengan akurasi tinggi seperti pada penelitian yang dilakukan oleh Ira Zulfa dan Edi Winarko (Zulfa & Winarko, 2017).…”

Section: Pendahuluanunclassified

Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine

Hermawan,

Jowensen,

Junaedi

et al. 2023

j. sains. teknologi.

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Setiap tahun, jumlah orang yang menggunakan media sosial bertambah seiring dengan jumlah orang yang menggunakan internet. Peningkatan tersebut diiringi dengan meningkatnya informasi pada internet yang tentunya informasi tersebut mempunyai nilai jika dilakukan analisa. Untuk menganalisa data dalam jumlah besar dapat menggunakan teknik text mining. Text mining mampu memproses untuk memperoleh informasi berkualitas tinggi dari teks. Text mining juga dapat digunakan untuk menganalisa informasi seperti sentimen dari sebuah kalimat dengan sangat cepat untuk memudahkan dalam mendapatkan informasi yang berkualitas. Informasi diproses berasal dari media sosial berbasis text yaitu twitter yang mana pengambilan data dilakukan dengan bantuan Application Programming Interface dan menggunakan kata kunci berupa sebuah kata atau hashtag. Kalimat tersebut akan dilakukan proses text mining dengan menggunakan algoritma Support Vector machine untuk menghasilkan klasifikasi dari sentimen suatu kalimat ke dalam sentiment positif, netral atau negatif. Tingkat akurasi yang dihasilkan oleh proses ini adalah sebesar 73% berdasarkan data sentimen yang dimiliki. Tingkat akurasi dalam melakukan text mining sangat dipengarui pada proses Pre-Processing karena terdapat banyak kata perlu dilakukan pengelolahan lebih lanjut.

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Support Vector Machine Untuk Pengenalan Bentuk Manusia Menggunakan Kumpulan Fitur Yang Dioptimalkan

Cited by 3 publications

References 16 publications

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Penerapan Seleksi Fitur Analysis of Variance Pada Algoritma Random Forest Classifier Dalam Klasifikasi Nilai Mahasiswa

Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine

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