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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika dan Teknik Elektro Terapan JUTIK : Jurnal Teknologi Informasi dan Komputer JURIKOM (Jurnal Riset Komputer) Jurnal Kridatama Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Dananjaya, Md Wira Putra
Universitas Pendidikan Nasional
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Model Machine Learning yang Dioptimalkan untuk Prediksi Penyakit Jantung Menggunakan R Shiny Amritha, Yadhurani Dewi; Candrawengi, Ni Luh Putu Ika; Dananjaya, Md Wira Putra; Dayanti, Made Ari Riska
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi (In Progress)
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.1994

Abstract

Heart disease continues to be a major contributor to global mortality, highlighting the critical importance of early detection in enhancing patient outcomes. The increasing availability of structured clinical datasets has enabled the application of intelligent systems for risk prediction and diagnostic support. In this paper, the effectiveness of three supervised learning algo- rithms—Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT)—is evaluated for the task of heart disease prediction. This investigation is based on the Heart Failure Prediction dataset sourced from the Kaggle platform. The training process for each model involved a 10-fold cross- validation, with its hyperparameters later being tuned using grid search optimization. Model efficacy was measured against standard classification benchmarks, including accuracy, sensitivity, specificity, and the area under the ROC curve (AUC). The Random Forest model emerged as the most effective, demon- strating superior performance with an AUC of 0.9517, sensitivity of 81.18%, and specificity of 90.44%. To facilitate clinical use, this model was subsequently integrated into a user-friendly web tool built with the R Shiny framework. The interface allows users to input patient-level clinical data and obtain real-time predictions, along with visualizations of feature importance and risk probability. This implementation bridges the gap between algorithm development and practical application, offering a user- friendly decision support tool for early heart disease screening. The findings affirm that machine learning models, when properly tuned and validated, can serve as effective and interpretable tools in clinical decision-making. This work contributes to the advancement of e-health and the integration of AI-driven models into medical workflows
Klasifikasi Kualitas Tanah Berdasarkan Kandungan pH, Kelembapan, dan Suhu Menggunakan Algoritma K-Nearest Neighbors Md Wira Putra Dananjaya; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Putu Gita Pujayanti
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i4.4049

Abstract

This study aims to analyze soil quality using the K-Nearest Neighbors (KNN) algorithm based on environmental parameters such as temperature, humidity, pH, and nutrient content (N, P, K). The dataset used consists of 660 entries covering 22 different classes describing soil types with varying characteristics. The KNN model was applied to classify soil quality, and the results were evaluated using the Confusion Matrix and Classification Report. The accuracy of the model obtained was around 61%, indicating potential improvements in the classification of some more difficult soil classes. The model performed better on certain classes such as kidney beans, chickpeas, and grapes, but was less than optimal on other classes such as watermelon and pomegranate. These results indicate class alignment in the dataset that affects model performance. This study contributes to the application of machine learning algorithms in agriculture, especially for soil quality monitoring. In the future, this study opens up opportunities for further improvements by using parameter optimization techniques and other more complex algorithms. Thus, the results of this study can be used as a basis for developing intelligent systems for more effective and efficient soil management.
Co-Authors Alicia Alfia Asokawati Amritha, Yadhurani Dewi Candrawengi, Ni Luh Putu Ika Darmaastawan, Kadek Dayanti, Made Ari Riska Gede Humaswara Prathama Gede Indrawan Gusti Agung Ayu Putri I Gusti Ngurah Darma Paramartha Ni Luh Putu Ika Candrawengi Pujayanti, Putu Gita Putu Gita Pujayanti Putu Gita Pujayanti Putu Gita Pujayanti Putu Wira Buana Sariyasa . Yadhurani Dewi Amritha