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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi (JIKOMTI)
Siti Herawati Fransiska Dewi
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Computer Science & IT

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Penerapan Algoritma Klasifikasi untuk Deteksi Dini Penyakit Jantung Koroner Berdasarkan Gejala Klinis Setiawan, Dita; Ali Muhammad; Siti Herawati Fransiska Dewi
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.706

Abstract

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).
Implementasi Metode Additive Ratio Assessment (Aras) Untuk Seleksi Penerimaan Beasiswa Siti Herawati Fransiska Dewi; Maulana, Imron Rizki; Setiawan, Dita
JIKOMTI : Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi Vol. 2 No. 1 (2025): JIKOMTI: Mei 2025
Publisher : Universitas Sains Indonesia Publishing

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Abstract

Seleksi penerimaan beasiswa membutuhkan metode yang objektif dan efisien. Penelitian ini mengimplementasikan metode Additive Ratio Assessment (ARAS) dalam proses seleksi beasiswa berbagai sekolah di Kota Jambi. ARAS digunakan untuk mengolah data multi-kriteria seperti prestasi akademik, penghasilan orang tua, jumlah tanggungan, dan keaktifan siswa. Hasil menunjukkan bahwa ARAS mampu memberikan rekomendasi yang tepat dan mempermudah pengambilan keputusan secara transparan. Sistem ini diharapkan meningkatkan akurasi dan efisiensi seleksi beasiswa di tingkat sekolah dasar.
Co-Authors Ali Muhammad Maulana, Imron Rizki Setiawan, Dita