Analisis Kinerja Support Vector Machine dalam Mengidentifikasi Komentar Perundungan pada Jejaring Sosial

Sitepu, Ade Clinton; Wanayumini, Wanayumini; Situmorang, Zakarias

doi:10.30865/mib.v5i2.2923

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…The SVM-L learner widget is used to process data using a linear SVM kernel. The linear kernel in the SVM method can be calculated by equation ( 1) [20]:…”

Section: Methodsmentioning

confidence: 99%

Analysis of SVM and Naive Bayes Algorithm in Classification of Nad Loans in Save and Loan Cooperatives

Riyadi¹,

Siregar²,

Margolang³

et al. 2022

JURTEKSI

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Non-performing loan (NPL) is a risk that credit unions must face and to avoid that, prospective debtors need to be surveyed. With previous loan data, support vector machine and naïve bayes can be used as classification methods to give a decision about NPL. We use a data set with 61 data and process the data with orange 3.30 application to see the difference between SVM using linear (SVM-L), polynomial (SVM-P), RBF (SVM-R) and sigmoid (SVM-S) kernel with naïve bayes. We use a cross validation technique with various folds to measure the classification results and a convusion matrix to measure the data training classification results. Naïve bayes scores the highest in terms of accuracy and SVM-R scores the highest in terms of F1, precision and recall. SVM-P scores the lowest in terms of accuracy, F1, precision and recall. Naïve bayes scores the highest in terms of proportion of predicted for true negative class and proportion of actual for true positive class. SVM-S scores the highest in terms of proportion of predicted for true positive class and proportion of actual for true negative class. SVM-P scores the lowest in both proportion of predicted and proportion of actual. Keywords: classification; naïve bayes; non-performing loan; support vector machine Abstrak: Kredit macet merupakan resiko yang sering dialami koperasi simpan pinjam, sehingga perlu dilakukan survei terhadap calon debitur agar kredit menjadi sehat. Dengan menggunakan data pemberian kredit sebelumnya, support vector machine dan naïve bayes digunakan sebagai metode klasifikasi untuk memberikan keputusan macet atau tidaknya kredit anggota koperasi Mutiara Sejahtera. Data set yang berjumlah 61 data diolah menggunakan aplikasi Orange 3.30 dan dilihat perbandingan antara metode SVM dengan kernel linear, polynomial, RBF dan sigomoid dengan metode naïve bayes. Cross validation dengan jumlah fold bervariasi digunakan sebagai nilai ukur klasifikasi dan convusion matrix digunakan sebagai nilai ukur klasifikasi data training. Hasil yang diperoleh adalah naïve bayes memiliki nilai accuracy tertinggi dan SVM kernel RBF memiliki nilai F1, precision dan recall tertinggi. SVM kernel polynomial memiliki nilai terendah untuk accuracy, F1, precision dan recall. Naïve bayes memiliki nilai tertinggi untuk proportion of predicted (PoP) kelas true negative dan proportion of actual (PoA) kelas true positive. SVM kernel sigmoid memiliki nilai tertinggi untuk PoP kelas true positive dan PoA kelas true negative. SVM kernel polynomial memiliki nilai terendah baik untuk PoP maupun PoA true negative dan kelas true positive. Kata kunci: klasifikasi; kredit macet; naive bayes; SVM

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“…The SVM-L learner widget is used to process data using a linear SVM kernel. The linear kernel in the SVM method can be calculated by equation ( 1) [20]:…”

Section: Methodsmentioning

confidence: 99%

Analysis of SVM and Naive Bayes Algorithm in Classification of Nad Loans in Save and Loan Cooperatives

Riyadi¹,

Siregar²,

Margolang³

et al. 2022

JURTEKSI

View full text Add to dashboard Cite

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“…Data mining is the process of observing large data and information, which has not previously been detected but can be understood [13]. Data mining methods involve the use of statistical, mathematical, and artificial intelligence techniques to analyze data and identify patterns that can be used to make better decisions, optimize business processes, or reveal new knowledge [14]. Data mining is often used in various fields, including business, health, finance, science and marketing [3].Data mining is a stage of data management that uses statistical techniques, artificial intelligence, machine learning to extract and identify useful information and important indicators from various databases such as Kaggle [15].…”

Section: Data Miningmentioning

confidence: 99%

Comparation of Decision Tree Algorithm, Naive Bayes, K-Nearest Neighbords on Spotify Music Genre

Hendri,

Nadha,

Basri

et al. 2024

IJATIS

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Comparison of Decision Tree, Naive Bayes, K-Nearest Neighbords Algorithm on Spotify Music Genre Decision Tree, Naive Bayes, K-Nearest Neighbords This research aims to compare three algorithms Decision Tree, Naive Bayes and K-Nearest Neighbors (K-NN) in classifying Spotify music genres using dataset from Kaggle. The results show that the Decision Tree algorithm produces an accuracy of 23%, Naive Bayes 17%, and K-Nearest Neighbors 19%. This research provides an overview of Spotify music listeners in choosing music genres. Based on research results, the Decision Tree algorithm has the highest accuracy in classifying Spotify music genres, with the Electric Dance Music (EDM) genre being the most popular among Spotify music fans, followed by rap, pop, r&b, Latin and rock. . Meanwhile, the Naive Bayes and K-Nearest Neighbors algorithms show lower accuracy.

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“…We use the accuracy, precision, and recall values to rank each model, using formulas as shown in equations (3) to (5) [30]. Where: TP (true positive) is the number of positive-class data predicted correctly; TN (true negative) is the total negativeclass data predicted correctly; FP (false positive) is the number of positive-class data incorrectly predicted; FN (false negative) is the number of negative-class data incorrectly predicted [31].…”

Section: E Evaluationmentioning

confidence: 99%

Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

Firmansyah¹,

Rosnelly

Wanayumini³

2023

ICoSTEC

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Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.

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Analisis Kinerja Support Vector Machine dalam Mengidentifikasi Komentar Perundungan pada Jejaring Sosial

Cited by 7 publications

References 11 publications

Analysis of SVM and Naive Bayes Algorithm in Classification of Nad Loans in Save and Loan Cooperatives

Analysis of SVM and Naive Bayes Algorithm in Classification of Nad Loans in Save and Loan Cooperatives

Comparation of Decision Tree Algorithm, Naive Bayes, K-Nearest Neighbords on Spotify Music Genre

Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

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