Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp

Saputra, Irwansyah; Rosiyadi, Didi

doi:10.30998/faktorexacta.v12i2.4181

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…-K-Nearest Neighbors dan Support Vector Machines (SVM): SVM adalah algoritma yang mencoba mencari hyperplane yang memisahkan dua kelas secara optimal. SVM dapat lebih baik dalam kasus dengan dataset yang tidak terlalu besar, sedangkan KNN dapat lebih sesuai untuk klasifikasi instance individu tanpa proses pelatihan yang rumit [16]. Adapun langka-langka dari algoritma K-Nearest Neighbor sebagai berikut [6]:…”

Section: Metode Yang Digunakan a Klassen Typologyunclassified

Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Ngemba,

Raivandy,

Hendra

et al. 2023

JTMI

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Agriculture is a highly robust sector in Indonesia. This is evidenced in Central Sulawesi Province, where the gross domestic product (GDP) from the agricultural sector, based on constant prices from 2018 to 2021, continues to experience growth. Such conditions suggest that commodities in the agricultural sector have the potential to become export products, enabling a greater economic boost for the region. Before engaging in exports, it is necessary to identify which commodities have potential. One way to determine this is by applying Klassentypology. To simplify the process, it can be implemented in machine learning using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is chosen because this algorithm can handle data containing noise and has good adaptability when given new data. In this research, two machine learning models were developed. The first model is used to classify whether a commodity is advancing or lagging, while the second model is used to classify commodities that grow rapidly and slowly. The highest accuracy obtained from the first model is 96.23%. Meanwhile, the highest accuracy from the second model is 93.49%.

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Section: Metode Yang Digunakan a Klassen Typologyunclassified

Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Ngemba,

Raivandy,

Hendra

et al. 2023

JTMI

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“…The level of suitability of the information provided by the system and the information required by the user is also called precision, while the success rate of the system in finding information is recall [17]. Equation 2 and equation 3 are used to calculate precision and recall.…”

Section: F Classification Model Evaluationmentioning

confidence: 99%

Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Peryanto¹,

Yudhana

Umar

2022

khif

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Flowers are among the raw materials in many industries including the pharmaceuticals and cosmetics. Manual classification of flowers requires expert judgment of a botanist and can be time consuming and inconsistent. The ability to classify flowers using computers and technology is the right solution to solve this problem. There are two algorithms that are popular in image classification, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM). CNN is one of deep neural network classification algorithms while SVM is one of machine learning algorithm. This research was an effort to determine the best performer of the two methods in flower image classification. Our observation suggests that CNN outperform SVM in flower image classification. CNN gives an accuracy of 91.6%, precision of 91.6%, recall of 91.6% and F1 Score of 91.6%.

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“…The accuracy value is a calculation process to determine how accurate the correct prediction results are from the total data. The accuracy value can be calculated using equation (9). (9) The precision value is a calculation process to determine the ratio of t he prediction of a true positive class to the number of data that is predicted to be positive.…”

Section: Confusion Matrixmentioning

confidence: 99%

“…Research related to the comparison of the Support Vector Machine, K -Nearest Neighbor, and Naïve Bayes classification methods on the level of bully behavior on the Whatsapp application shows the Support Vector Machine method gets the best accuracy value of 81.58% [9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cyberbullying Level using Support Vector Machine Method

Purnayasa,

Suarjaya,

Dharmaadi

2022

JIM

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Internet users in Indonesia is increasing in every year. The increase caused by several factors, such as the increasingly even distribution of internet infrastructure in Indonesia. The internet has a positive impact such as facilitating communication between individuals, while the negative impact of the internet is intimidation to someone or known as cyberbullying. Cyberbullying has a huge impact on mental health person, causing victim to be angry, depressed, and anxious. This research aims to measure the level of cyberbullying in Indonesia on Twitter using TF-IDF and Support Vector Machine. Classification in this study is classified into two classes, namely cyberbullying and non-cyberbullying. Twitter data used in this study were 3,344,782 tweets that resulted in a cyberbullying classification level of 34.59% and a non-cyberbullying classification level of 65.41%. The best accuracy value obtained is 85%.

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Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp

Cited by 7 publications

References 1 publication

Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Analysis of Cyberbullying Level using Support Vector Machine Method

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