Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM

Rabbani, Salsabila; Safitri, Dea; Rahmadhani, Nadila; Sani, Al Amin Fadillah; Anam, M. Khairul

doi:10.57152/malcom.v3i2.897

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Partitioning a dataset using split data is one of the many factors that affect the performance of classification models in machine learning algorithms [33]. The process of separating test data and training data is known as split data [34]. Training and testing data can be separated using k-fold cross-validation and holdout validation techniques.…”

Section: Split Datamentioning

confidence: 99%

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Rian Oktafiani,

Arief Hermawan,

Donny Avianto

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

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Section: Split Datamentioning

confidence: 99%

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Rian Oktafiani,

Arief Hermawan,

Donny Avianto

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Banyak penelitian terdahulu yang berkaitan dengan sentimen analisis menggunakan metode dari Support Vector Machine [28], [29]. Penelitian terdahulu dilakukan oleh Siti Sumayah menggunakan Support Vector Machine dengan hasil akurasi 81% dari 2504 data [6].…”

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Komparasi Algoritma Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Metaverse

Novianti,

Shiddieq,

Roji

et al. 2024

MALCOM

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Metaverse telah mencuri perhatian dunia karena kemampuannya untuk menggabungkan dunia nyata dan dunia virtual. Minat terhadap metaverse semakin meningkat seiring dengan dampak pandemi COVID-19 dan proyek pembangunan Ibu Kota Nusantara (IKN), serta pertumbuhan penggunaan platform digital. Perbincangan isu metaverse semakin meroket naik setelah perusahaan Facebook merubah namanya menjadi Meta. Studi ini bertujuan untuk membandingkan akurasi tertinggi antara metode algoritma Naïve Bayes dengan SVM dalam menganalisis respons masyarakat terhadap metaverse. Studi ini menggunakan metode sentiment analisis. Penggunaan dua algoritma menjadi keterbaruan penelitian. Studi kali ini menggunakan data yang diambil dari Twitter (x) dan disimulasikan menggunakan sentiment analisis dari algoritma SVM dan algoritma Naïve Bayes. Berdasarkan penelitian, ditemukan bahwa akurasi algoritma SVM mencapai 90,32% presisi sebesar 0,90 dan recall sebesar 0,86, sedangkan algoritma Naïve Bayes mencapai 84,23% presisi sebesar 0,87 dan recall sebesar 084. Dengan adanya penelitian ini dapat memberikan wawasan terhadap tren metaverse, serta membandingkan hasil akurasi tertinggi antara dua algoritma.

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Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM

Cited by 2 publications

References 19 publications

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Komparasi Algoritma Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Metaverse

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