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All Journal Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Computer Science (CO-SCIENCE)
Sukmawati, Cici
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Computer Science & IT

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Algoritma Naïve Bayes, Random Forest dan Support Vector Machine untuk Klasifikasi Penyakit Sapi Sukmawati, Cici
Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Vol 9 No 1 (2024): Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi
Publisher : Teknik Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/technoxplore.v9i1.6480

Abstract

Berdasarkan dari data World Health Organization (WHO) menyebutkan bahwa Indonesia menjadi salah satu negara yang memiliki penduduk terbanyak di dunia dengan urutan keempat. Karena jumlah penduduk yang banyak, sehingga kebutuhan protein hewani meningkat dengan kesadaran pentingnya akan gizi. Salah satu sumber protein hewani yang bisa diperoleh dari daging sapi. Hal itu menyebabkan kesehatan sapi menjadi suatu hal yang berpengaruh untuk memenuhi kebutuhan gizi. Dalam menjaga kesehatan sapi, perlu adanya suatu pengamatan terhadap gejala-gejala penyakit pada sapi. Demikian hal itu, teknik data mining dapat berfungsi untuk mengatasi solusi terkait mengklasifikasikan penyakit pada bidang teknik informatika. Penelitian ini menggunakan metode Teknik Data Mining dengan 3 percobaan. Adapun percobaan pertama menggunakan algoritma Naive bayes dengan akurasi 0.8, presisi 0.8 dan recall 0.8. Sedangkan percobaan ke 2 menggunakan algoritma Random Forest menghasilkan akurasi sebesar 0.8, presisi 0.8 dan recall 0.8. Kemudian, untuk percobaan ke 3 menggunakan algoritma Support Vector Machine menghasilkan akurasi 0.8, presisi 0.6 dan recall 0.8
Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Co-Authors Arif, Siti Budi Dermawan, Budi Masruriyah, Anis Novita, Hilda Ramadhan, Angga