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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Pengabdian Informatika (JUPITA) Scientific Journal of Informatics
Wala, Jihan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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Implementasi K-Means Clustering pada Pengelompokan Pasien Penyakit Jantung Wala, Jihan; Herman, Herman; Umar, Rusydi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.205-216

Abstract

Heart disease is a prominent global health concern, necessitating early identification and patient grouping for effective management. This study employs the K-Means clustering algorithm with a medical dataset of 303 patients, encompassing various attributes. These include Age, Gender, Chest Pain Type, Blood Pressure, Serum Cholesterol Level, Fasting Blood Sugar, Resting Electrocardiographic Results, Maximum Heart Rate, Angina, ST Depression, and Slope of the ST Segment. The goal is to categorize patients into four clusters based on chest pain types, a crucial symptom indicating disease severity. The computation concludes after the sixth iteration, revealing Cluster 1 (27 patients), Cluster 2 (135 patients), Cluster 3 (15 patients), and Cluster 4 (126 patients). Collaborative analysis with medical experts highlights that Cluster 1, mainly comprising older males, exhibits high-risk indicators. While this grouping aids in personalized treatment strategy development, further clinical validation involving more experts and datasets is imperative for enhanced reliability.
PELATIHAN PENGENALAN DAMPAK POSITIF DAN NEGATIF DALAM PENGGUNAAN ARTIFICIAL INTELLIGENCE PADA BIDANG PENDIDIKAN Wala, Jihan; Nahdli, Muhammad Fahmi Mubarok; Ardiansyah, Ricy; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian Informatika Vol. 2 No. 4 (2024): JUPITA Volume 2 Nomor 4, Agustus 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Artificial Intelligence (AI) merupakan kecerdasan yang ditunjukan dengan suatu objek buatan. AI memiliki potensi untuk mengubah pendidikan dengan mempersonalisasi pengalaman belajar, menyediakan bimbingan belajar yang cerdas, mengintegrasikan teknologi yang mendalam, dan mengotomatiskan pembuatan konten. Dampak positif AI mencakup peningkatan personalisasi pembelajaran, penghematan waktu bagi tenaga pendidik, serta peningkatan aksesibilitas dan kualitas pendidikan. Dampak negatif penggunaan AI yaitu kurangnya sentuhan manusia, risiko ketergantungan pada teknologi, mengurangi kemampuan berpikir kritis dan pemecahan masalah secara mandiri. Oleh karena itu, diperlukan pelatihan yang bertujuan untuk mengedukasi siswa SMK 2 Al-Hikmah 1 Sirampog, Brebes, Jawa Tengah, tentang dampak penggunaan AI dalam pendidikan. Peningkatan pengetahuan siswa diukur melalui pre-test dan post-test. Kegiatan ini mencakup serangkaian sesi yang dirancang untuk memberikan pemahaman mendalam kepada peserta mengenai pengaruh teknologi AI melalui berbagai aktivitas interaktif, diskusi, dan presentasi dengan total peserta sebanyak 30 siswa. Hasil dari pengabdian ini menghasilkan peningkatan pada kategori pengetahuan "Sangat Paham" meningkat dari 50% menjadi 80%.
Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method Wala, Jihan; Herman, Herman; Umar, Rusydi; Suwanti, Suwanti
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14096

Abstract

Purpose: Heart disease is the leading cause of death throughout the world, especially in developing countries like Indonesia. Modern approaches for diagnosing and managing heart disease rely on machine learning due to the complexity of medical data. Among the biggest challenges in using machine learning is clustering heart disease patients. This study aims to develop a machine-learning model using K-means clustering to determine the severity and level of patient emergencies. The specific objective of the model is to find the optimal number of clusters using the Elbow Method. Methods/Study design/approach: Model development using a dataset from the Kaggle Repository consisting of 303 patient data. Each data point consists of the attributes age, gender, type of chest pain, blood pressure, serum cholesterol level, blood sugar, electrocardiography results, maximum heart rate, angina, ST depression, and segment slope ST. The combination of the K-means clustering algorithm and the Elbow Method is expected to find the optimal number of clusters in the developed model. Result/Findings: The results of building a machine learning model show that k-means clustering is quite effective in clustering heart disease patients. For the model built with 303 data points, the elbow method successfully identified the optimal number of clusters, resulting in two clusters (k=2), where the elbow point on the graph shows a significant decrease in the Sum of Squared Errors (SSE). Novelty/Originality/Value: This study combines the k-means clustering algorithm and the elbow method to determine the severity and level of patient emergencies. The clustering model produces specific risk clusters that help doctors determine more appropriate interventions.
Co-Authors Ardiansyah, Ricy Herman Herman Yuliansyah, Herman Nahdli, Muhammad Fahmi Mubarok Rusydi Umar Suwanti Suwanti Veithzal Rivai Zainal