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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA MIND (Multimedia Artificial Intelligent Networking Database) Journal
ARIF, SITI NOVIANTI NURAINI
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Evaluasi Algoritma Pembelajaran Terbimbing terhadap Dataset Penyakit Jantung yang telah Dilakukan Oversampling MASRURIYAH, ANIS FITRI NUR; NOVITA, HILDA YULIA; SUKMAWATI, CICI EMILIA; ARIF, SITI NOVIANTI NURAINI; RAMADHAN, ANGGA RAMDA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 8, No 2 (2023): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v8i2.242-253

Abstract

AbstrakPenyakit jantung mengalami peningkatan setiap tahunnya dan menjadi penyebab kematian tertinggi di Indonesia, terutama pada usia produktif. Pola makan yang tidak seimbang dan gaya hidup tidak sehat menjadi faktor penyebab prevalensi penyakit jantung yang tinggi. Bidang ilmu kedokteran mulai beradaptasi dan mengandalkan model prediksi otomatis berbasis komputer untuk diagnosis secara tepat dan akurat. Data tentang penyakit jantung seringkali memiliki ketidakseimbangan, yaitu jumlah data pada kelas minoritas lebih kecil daripada kelas mayoritas. Oleh karena itu, teknik oversampling seperti SMOTE dan ADASYN digunakan untuk menangani masalah ini. Hasil dari penelitian ini Algoritma Random Forest Classifier menjadi model perbandingan terbaik dengan akurasi sekitar 90,71%. Penerapan teknik oversampling SMOTE + Random Forest, akurasi dapat meningkat hingga sekitar 94,54% dengan kurva ROC sebesar 98,4%. Model diagnosa yang akurat dapat menjadi media bagi tenaga medis untuk mengambil langkah pencegahan yang tepat dan meningkatkan kualitas perawatan pasien.Kata kunci: ADASYN, Klasifikasi, Pohon Keputusan, Regresi, SMOTEAbstractHeart disease is rapidly increasing in Indonesia and has become the primary cause of death, particularly among those in their productive years. The prevalence of heart disease is due to unhealthy lifestyle choices and an imbalanced diet. The medical field is relying more heavily on computer-based automatic prediction models to ensure precise and accurate diagnoses. However, data on heart disease is frequently imbalanced, with fewer cases in the minority class. To resolve this issue, oversampling techniques such as SMOTE and ADASYN have been implemented. The study demonstrates that the Random Forest Classifier Algorithm is the most effective comparison model, with an accuracy rate of approximately 90.71%. By implementing the SMOTE + Random Forest oversampling technique, the accuracy rate increased to around 94.54%, with a ROC curve of 98.4%. A highly accurate diagnostic model is essential for enabling medical personnel to take appropriate preventive measures and enhance the quality of patient care.Keywords: ADASYN, Classification, Decision Tree, Regresi, SMOTE
Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM) Arif, Siti Novianti Nuraini; Siregar, Amril Mutoi; Faisal, Sutan; Juwita, Ayu Ratna
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7844

Abstract

Cardiovascular disease (CVD) is a general term for disorders related to the heart, coronary arteries, and blood vessels. These diseases are most commonly caused by blocked blood vessels, either due to fat buildup or internal bleeding. According to the WHO, each year, cardiovascular diseases account for 32% of all deaths, which translates to about 17.9 million people annually. The numerous factors causing CVD make it challenging for doctors to diagnose patients who are at low or higher risk of heart attacks. A machine learning model is needed for the early recognition of heart attack symptoms. Supervised learning models such as KNN and SVM were used in previous studies without feature selection, with datasets obtained from Kaggle. PCA was applied to reduce data dimensions to smaller variables. With the use of confusion matrix and ROC curve evaluations, the accuracy results of the previous KNN model improved from 83.6% to 90.16%. The SVM model also saw an accuracy increase from 85.7% to 86.88%. The use of PCA feature selection demonstrated an improvement in accuracy in the study. The KNN model, with a higher accuracy rate of 90.16%, is better for classifying individuals as normal or diagnosed with a heart attack.
Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM) Arif, Siti Novianti Nuraini; Siregar, Amril Mutoi; Faisal, Sutan; Juwita, Ayu Ratna
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7844

Abstract

Cardiovascular disease (CVD) is a general term for disorders related to the heart, coronary arteries, and blood vessels. These diseases are most commonly caused by blocked blood vessels, either due to fat buildup or internal bleeding. According to the WHO, each year, cardiovascular diseases account for 32% of all deaths, which translates to about 17.9 million people annually. The numerous factors causing CVD make it challenging for doctors to diagnose patients who are at low or higher risk of heart attacks. A machine learning model is needed for the early recognition of heart attack symptoms. Supervised learning models such as KNN and SVM were used in previous studies without feature selection, with datasets obtained from Kaggle. PCA was applied to reduce data dimensions to smaller variables. With the use of confusion matrix and ROC curve evaluations, the accuracy results of the previous KNN model improved from 83.6% to 90.16%. The SVM model also saw an accuracy increase from 85.7% to 86.88%. The use of PCA feature selection demonstrated an improvement in accuracy in the study. The KNN model, with a higher accuracy rate of 90.16%, is better for classifying individuals as normal or diagnosed with a heart attack.
Co-Authors Faisal, Sutan Fitri Nur Masruriyah, Anis Juwita, Ayu Ratna Novita, Hilda Yulia RAMADHAN, ANGGA RAMDA Siregar, Amril Mutoi Sukmawati, Cici Emilia