Claim Missing Document
Check
Articles

Found 12 Documents
Search

Dampak Internet terhadap Produktivitas dan Kreativitas Pelajar di Era Digital Di SMA Neger 6 Tangerang Selatan Fito Wahyu Pratama, Fito; Muhajir, Abdullah; Nuno, Muhamad; Vembra, Aliano; Dharmawan, Aldo Lega; Fajri, Haris Al; Faqih, Muhamad Nurul; Sanjaya, Kennanda Ariel; Zaldi, Muhamad; Alhafidz , Dzikri; Manap, Nur Hanafi
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2024): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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

Abstract

Internet telah menjadi bagian tak terpisahkan dari kehidupan pelajar di era digital, memberikan dampak signifikan baik positif maupun negatif terhadap produktivitas dan kreativitas. Penelitian ini bertujuan untuk mengidentifikasi pengaruh penggunaan internet pada pelajar, termasuk manfaat seperti akses informasi dan pengembangan kreativitas, serta tantangan seperti gangguan konsentrasi dan potensi kecanduan. Metode yang digunakan adalah pendekatan deskriptif dengan observasi dan diskusi interaktif di SMAN 6 Tangerang Selatan. Hasil menunjukkan bahwa internet mampu meningkatkan kemampuan belajar dan kreativitas siswa jika digunakan secara bijak, namun juga berisiko menurunkan produktivitas jika penggunaannya tidak terkontrol. Kesimpulan dari penelitian ini adalah pentingnya literasi digital, manajemen waktu, dan peran aktif sekolah serta orang tua dalam mendukung penggunaan internet yang positif dan produktif.
Classification of Heart Disease Based on Clinical Data Using the K-Nearest Neighbor Method Muhajir, Abdullah; Cendra Harmon
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID655

Abstract

Heart disease is one of the leading causes of death worldwide; therefore, methods that can support early and accurate diagnosis are urgently needed. This study aims to classify heart disease based on patients’ clinical data using the K-Nearest Neighbor (KNN) method. The dataset used consists of patients’ clinical data, including attributes such as age, gender, blood pressure, cholesterol levels, maximum heart rate, and other medical attributes.The research stages include data preprocessing, transformation of categorical data into numerical form, data normalization using StandardScaler, and data splitting into training and testing sets with a ratio of 80% and 20%, respectively. The classification process is carried out using the K-Nearest Neighbor algorithm with a K value of 7. Model performance evaluation is conducted using a confusion matrix and evaluation metrics including precision, recall, f1-score, and accuracy.The results show that the KNN method is able to classify heart disease with an accuracy rate of 57%. The model demonstrates good performance on the majority class; however, its performance on the minority class remains low due to data imbalance and similarities in characteristics between classes. Therefore, the KNN method can be used as an initial approach for classifying heart disease based on clinical data, although further development is still required to improve model performance