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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer)
Hidayana, Reymond Syahputra
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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IDENTIFIKASI POLA TIDUR GENERASI Z (GEN-Z) MENGGUNAKAN ALGORITMA KLUSTERISASI K-MEANS Annisa, Riski; Rahayuningsih, Panny Agustia; Anna, Anna; Hidayana, Reymond Syahputra; Ramadhani, Zulfikar Ismaya
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1740

Abstract

Generation Z faces great challenges in maintaining healthy sleep patterns due to lifestyle changes and high exposure to technology. This study aims to identify the sleep patterns of Generation Z as well as the main factors that affect their sleep quality using the K-Means algorithm. Data was collected from 300 participants through an online questionnaire that included variables such as sleep duration, difficulty sleeping, caffeine consumption, electronic device use, physical activity, and freshness after waking up. With a clustering approach, the results of the study showed that there were three main patterns: irregular sleep patterns (45%), healthy sleep patterns (35%), and poor sleep patterns (20%). The cluster with healthy sleep patterns had an average of 7-8 hours of sleep, high physical activity, and low caffeine consumption, while irregular sleep patterns were less dominated by the use of electronic devices before bedtime, high caffeine consumption, and low physical activity. These findings highlight the importance of lifestyle management in improving the sleep quality of Generation Z and provide a basis for the development of more effective interventions. This study concludes that data-based clustering is a useful method to understand the sleep patterns of a particular population in more depth.
Sistem Prediksi Risiko Penyakit Jantung Berbasis Machine Learning dan Framework Streamlit Hidayana, Reymond Syahputra; Regina, Fransiska; Rendi, Rendi; Annisa, Riski
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10158

Abstract

Abstrak - Penelitian ini menggunakan algoritma pembelajaran mesin untuk membangun sistem yang dapat memprediksi risiko penyakit jantung. Dalam dataset Cleveland Heart Disease, tiga algoritma Logistic Regression, XGBoost, dan Naive Bayes digunakan dengan pembagian data uji dan latih sebesar 80:20. Pembersihan data, pemisahan fitur dan target, pelatihan model, dan evaluasi menggunakan metrik akurasi, presisi, recall, f1-score, dan AUC dilakukan. Hasil pengujian menunjukkan bahwa Logistic Regression adalah yang terbaik dengan skor akurasi, presisi, recall, dan f1-score sebesar 0,90, dan AUC sebesar 0,94. Selanjutnya, model terbaik diterapkan pada sistem prediksi berbasis web yang menggunakan framework Streamlit. Selain data pengguna, sistem dapat menampilkan risiko penyakit jantung secara informatif. Berdasarkan hasil penelitian, model Logistic Regression dapat digunakan sebagai alat bantu awal dalam mendeteksi risiko penyakit jantung secara efektif.Kata kunci : Prediksi Penyakit Jantung; Machine Learning; Logistic Regression; Klasifikasi; Streamlit; Abstract - This study employs machine learning algorithms to develop a system capable of predicting the risk of heart disease. Using the Cleveland Heart Disease dataset, three algorithms—Logistic Regression, XGBoost, and Naive Bayes—were applied with an 80:20 train-test split. Data cleaning, feature–target separation, model training, and evaluation using accuracy, precision, recall, f1-score, and AUC metrics were conducted. The results indicate that Logistic Regression performs the best, achieving accuracy, precision, recall, and f1-score values of 0.90, and an AUC of 0.94. The best-performing model was then deployed in a web-based prediction system using the Streamlit framework. In addition to user input, the system provides an informative display of heart disease risk. Based on the findings, the Logistic Regression model can serve as an effective preliminary tool for detecting heart disease risk.Keywords: Heart Disease Prediction; Machine Learning; Logistic Regression; Classification; Streamlit;
Co-Authors Anna Anna, Anna Panny Agustia Rahayuningsih Ramadhani, Zulfikar Ismaya Regina, Fransiska Rendi Rendi, Rendi Riski Annisa