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All Journal CogITo Smart Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) The Indonesian Journal of Computer Science
Septia, Rapindra
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Evaluation of Data Mining in Heart Failure Disease Classfication: Afiatuddin, Nurfadlan; Rahmaddeni, Rahmaddeni; Pratiwi, Fitri; Septia, Rapindra; Hendrawan, Heri
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.726.460-473

Abstract

This study evaluates the effectiveness of data mining algorithms in heart failure disease classification. Various algorithms, including Random Forest, Decision Tree C4.5, Gradient Boosted Machine (GBM), and XGBoost, were applied to a heart failure dataset. The dataset was collected from multiple sources and preprocessed to address imbalances using the SMOTE (Synthetic Minority Over-sampling Technique) technique. The results indicate that employing SMOTE and parameter optimization through grid search significantly enhances the performance of these algorithms. XGBoost and GBM demonstrated superior accuracy, precision, and recall in both balanced and imbalanced data scenarios. In balanced data scenarios, XGBoost achieved an accuracy of 98.75% with an error rate of 1.25%, while GBM achieved an accuracy of 98.60% with an error rate of 1.40%. The study confirms that appropriate data preprocessing and parameter optimization are crucial for improving the accuracy of medical data analysis. These findings suggest that XGBoost and GBM are highly effective for heart disease prediction, supporting early diagnosis and timely medical intervention. Future research should explore alternative preprocessing techniques and additional algorithms to further improve prediction outcomes.
Heart Failure Disease Classification Using Random Forest Algorithm with Grid Search Cross Validation Technique Septia, Rapindra; Junadhi; Susi Erlinda; Wirta Agustin
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4765

Abstract

Heart failure is one of the leading causes of death worldwide and requires early detection to reduce the risk of serious complications. However, the imbalance in medical data poses a challenge in developing accurate prediction models. This study developed a heart failure classification model using the Random Forest algorithm, optimized with Grid Search Cross Validation to find the best combination of hyperparameters. The dataset consisted of 300 observations with 12 medical features and 1 target feature. Data preprocessing included outlier removal using the Interquartile Range (IQR) and Winsorize methods. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, resulting in a more balanced training data distribution. The dataset was split into 80% training and 20% testing data using stratified sampling to maintain class proportions. The model was evaluated using accuracy, precision, recall, and F1-score metrics, with results showing 90% accuracy, 0.94 precision for class 0, 0.80 precision for class 1, 0.91 recall for class 0, and 0.86 recall for class 1. The model was implemented in a Streamlit-based application, allowing users to input health parameters and receive interactive predictions. This study demonstrates that optimizing the Random Forest algorithm with Grid Search Cross Validation can improve heart failure classification performance, providing a practical solution for supporting heart failure classification. Keywords: Heart Failure Classification, Random Forest, Hyperparameter Optimization, SMOTE, Model Evaluation.
Komparasi Multiple Linear Regression dan Decision Tree dalam Memprediksi Penetasan Penyu Jenis Chelonioidea Sp di Pulau Mangkai Agustriono; Septia, Rapindra; Rahmaddeni
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6844

Abstract

Pulau Mangkai yang terletak di Kabupaten Kepulauan Anambas Provinsi Kepulauan Riau. Secara geografis Pulau Mangkai terletak pada titik koordinat 03005’32’ LU dan 105035’00”BT dengan luas + 2,27 km. Pantai bagian utara di Pulau Mangkai merupakan tempat peneluran penyu. Konservasi salah satu langkah untuk menekan menurunnya populasi penyu, pengelolaan kawasan yang terintegrasi dengan tetap mempertimbangkan ekologi dan ekosistem serta mengkolaborasikan keberadaan spesies yang terancam punah, masyarakat di sekitar kawasan melalui mekanisme ekowisata minat khusus. Peran serta Machine Learnging dipelukan dalam menganalisis lama penetasan telur penyu pada pengelolaan kawasan konservasi. Tujuannya adalah untuk mencari algoritma yang terbaik dalam memprediksi lama waktu yang dibutuhkan dalam penetasan telur penyu untuk melihat ketersediaan relokasi sarang penyu dan menjadi paket wisata Turtle Watching. Pemodelan algoritma Multiple Linear Regression diperoleh nilai RMSE 3,96387 pada data training dan 4,95446 pada data testing, sementara pada Algoritma Decission Tree nilai RMSE pada data training 4,29728 dan 4,82765 pada data testing. Pengujian pada data aktual untuk model prediksi pada algoritma Multiple Linear Regression dan algoritma Decission Tree dengan kedalaman sarang = 47, jumlah telur = 173 butir, jenis penyu = sisik, tanggal bertelur = 27 April 2022. Algoritma Multiple Linear Regression memprediksi telur akan menetas selama 47,99 dibulatkan menjadi 48 hari, sementara Algoritma Decission Tree memprediksi telur akan menetas selama 48 hari.
Co-Authors Afiatuddin, Nurfadlan Agustriono fitri pratiwi, fitri Hendrawan, Heri Junadhi Junadhi Rahmaddeni Rahmaddeni SUSI ERLINDA Wirta Agustin