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All Journal Competitor : Jurnal Pendidikan Kepelatihan Olahraga Bulletin of Electrical Engineering and Informatics SINTEK JURNAL: Jurnal Ilmiah Teknik Mesin Jurnal Teknik Mesin Info-Teknik Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Biology Teaching and Learning JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Pengabdian Al-Ikhlas Diklat Review : Jurnal manajemen pendidikan dan pelatihan Jurnal Engine: Energi, Manufaktur, dan Material Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Applied Science in Civil Engineering Al-Ulum: Jurnal Pendidikan Islam Jurnal Ilmiah Wahana Pendidikan Nusantara: Jurnal Pendidikan Indonesia JIPMukjt:Jurnal Ilmu Pendidikan Muhammadiyah Kramat Djati Al-Ulum: Jurnal Pendidikan Islam Jurnal Locus Penelitian dan Pengabdian AGROTEKBIS: Jurnal Ilmu Pertanian Bhinneka Multidisiplin Journal Jurnal Penelitian Sains dan Kesehatan Avicenna SENMEA Prosiding SeNTIK STI&K Dinamika Pendidikan Al-Bahtsu: Jurnal Penelitian dan Pendidikan Islam Jurnal Kajian Ekonomi dan Perbankan Syariah Jurna Pendidikan Manajemen Transportasi Custodia: Journal of Legal, Political, and Humanistic Inquiry Innovasi : Jurnal Inovasi Pendidikan
Rendi Rendi, Rendi
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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;
ANALISIS SUMBER BELAJAR DALAM PEMBELAJARAN IPA DI SDI NURUL HIDAYAH rendi, Rendi
INNOVASI : JURNAL INOVASI PENDIDIKAN Vol. 11 No. 2 (2025): AGUSTUS (JURNAL INOVASI PENDIDIKAN)
Publisher : Education Reserach Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64540/zpm5v185

Abstract

This study aims to analyze learning resources in science education at SDI Nurul Hidayah. The research uses a qualitative approach with data collection techniques in the form of in-depth interviews and direct observations. The results show that teachers at SDI Nurul Hidayah utilize various types of learning resources, including place-based learning resources, material-based learning resources, people-based learning resources, book-based learning resources, and event-based learning resources. Based on the observations, each type of learning resource has its own advantages and challenges. For instance, place-based learning resources such as the school yard and garden are often limited by weather conditions, time constraints, and insufficient logistics. Material-based learning resources, such as teaching aids, models, and natural objects, face limitations in terms of quantity and availability. The findings indicate that in order to achieve the optimal learning outcomes in science education, teachers need to manage the learning process effectively and engagingly. Therefore, it can be concluded that proper science education supports the teaching and learning process in elementary schools, although optimizing resources, increasing the number and availability of teaching aids, and improving teachers' skills are areas that still require enhancement
Co-Authors Adawiyah , Rodiyatul Afifah, Rida Nurul Ahsin Shidqi Aidil Shafwan Akhmad Qashlim, Akhmad Alwan , Noufal Anam, Moh Nurul Arafat, Arafat Ari Wibowo Arief, Saifullah Arifin, Jainal Asiyah, Asiyah Assulamy, Hafif Assyifa , Widya Nur Bantun, Suharsono Boy Laksmana Dedek, Rahmad Devi Fitrianah Dwi Husniyati Fahmi Mauliansyah, Syarif Fauzi, M. Ismul GESTY ERNESTIVITA Ghani, Bima Gunawan Gunawan Hartadi, Budi Hasbiadi Hasbiadi Herwin Herwin, Herwin Hestiana, Sry Hidayana, Reymond Syahputra Himawan, Ferdiansyah Himawan, Salamet Nur Husnaini Amiroh Irawan, Heri Irfansyah, Muhammad Iryanto Iryanto Jayanti Yusmah Sari Karhani, Muna Karmini Karmini Karyati Karyati Khayya, Fadhilatul Kurnia Adi Cahyanto Kusuma, Adhi La Ode Liaumin Azim Liana, Risma Lumbanraja, Maha Martabar Mangatas Luthfan Tri Mukti Mardiana Mardiana Masitah Masnama, Masnama Meri, Meri MMSI Irfan ,S. Kom Muhammad Firman Muhammad Iqbal Muhammad Khairil Mujiburrahman Mujiburrahman Munthe, Irwan Salomo Mustafa Mustamiin, Muhamad Nazha, Amelia Nengsih, Hijrana Nisa Solihat, Ayu Nur Asikin Nurfitriani Nurfitriani, Nurfitriani Nurlaili Nurlaili Pauzan, Muhammad Priawan, Diva Hadi Rahmat Hidayat Rahmawati, Rahmawati Raihani, Sherly Ramedlon, Ramedlon Rasmiati Rasyid Regina, Fransiska Reno Firdaus Ridho Abdillah Rifai, Mesrawati Riski Annisa Rita Eka Izzaty, Rita Eka Robertus Masyhuri Robieth Sohiburoyyan Rohman , Siti Rosita Rosita Sabilla, Della Salmawati Salmawati, Salmawati Selmi, Selmi Setiawan, Muhammad Arief Siti Asiah Sohiburroyan, Robieth St.Nurfatul Jannah Sukardi Sukardi Sulaeman Sulaeman Sulmi, Sulmi Syahrizan, Wan Dedy Wanci, Risman Wati, Vera Yuliana, Rosnita Yupande, Putra Yusran, Nadilah Zulfandi, Zulfandi