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All Journal RETORIKA: Jurnal Ilmu Bahasa Primary: Jurnal Pendidikan Guru Sekolah Dasar Musawa : Jurnal Studi Gender dan Islam Jurnal PAJAR (Pendidikan dan Pengajaran) Pendas : Jurnah Ilmiah Pendidikan Dasar DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Ilmiah Bina Edukasi Jurnal Kehutanan Papuasia (Journal of Papuasia Forestry) Jurnal Sains dan Teknologi Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Jurnal Biogenerasi Cassowary Journal Idea of History Jurnal Life Birth SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Jurnal Sosiohumaniora Kodepena (JSK) Bedelau: Journal of Education and Learning Khazanah Intelektual Jurnal Pendidikan AURA (Anak Usia Raudhatul Atfhal) Jurnal Pembelajaran dan Pengajaran Pendidikan Dasar (JP3D) Journal of Technomaterial Physics Kognitif: Jurnal Riset HOTS Pendidikan Matematika Journal of Language Education, Linguistics, and Culture Journal of Artificial Intelligence and Engineering Applications (JAIEA) Sajak: Jurnal Penelitian dan Pengabdian Sastra, Bahasa, dan Pendidikan Jurnal Smart Hukum (JSH) Jurnal Adijaya Multidisiplin Kartika: Jurnal Studi Keislaman Jurnal DIALEKTOLOGI Al-Arfa: Journal of Sharia, Islamic Economics and Law Dirasah: Jurnal Pendidikan Islam Cemara Education and Science Periodicals of Occupational Safety and Health International Journal of Educational Best Practices Jurnal Penelitian Sains dan Kesehatan Avicenna Jurnal Pengabdian Masyarakat Teknologi Laboratorium Medik Borneo PROCEEDING INTERNATIONAL BUSINESS AND ECONOMICS CONFERENCE (IBEC) Jurnal Lentera Edukasi Hawa : Jurnal Pemberdayaan Dan Pengabdian Masyarakat (HAWAJPPM) Pedagogi: Jurnal Pendidikan Dasar Sustainable Applied Modification Evidence Community
Erni
Jurusan Fisika, Universitas Sriwijaya, Sumatera Selatan, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Comparison of Random Forest and K-Nearest Neighbors in Heart Disease Prediction Erni; Alfarobi, Ibnu; Wawan Kurniawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1942

Abstract

Heart disease is one of the leading causes of death worldwide, with a death toll reaching 17.9 million cases annually according to the World Health Organization (WHO) and a prevalence of 1.5% in Indonesia. This high mortality rate demonstrates the importance of early detection and accurate prediction to prevent more serious complications. The development of artificial intelligence technology, particularly machine learning, offers a new approach in the medical field through the ability to analyze clinical data quickly and efficiently. This study was conducted to compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in predicting heart disease using a clinical dataset from Kaggle containing 20 samples and 9 attributes related to the patient's physiological condition. The parameter optimization process in both algorithms was carried out using grid search techniques with cross-validation to obtain the best model that can perform optimally on a limited dataset. Performance evaluation was carried out using accuracy, recall, and precision metrics to comprehensively measure the quality of the model predictions. The results of the study showed that the Random Forest algorithm provided superior performance with an accuracy of 0.75, a recall of 0.88, and a precision of 0.86, compared to KNN which only achieved an accuracy of 0.50, a recall of 0.67, and a precision of 0.67. These findings indicate that Random Forest is more effective in identifying the presence of heart disease, especially in terms of sensitivity to positive cases and prediction consistency. Thus, Random Forest has the potential to be a more appropriate algorithm for implementation in machine learning-based clinical decision support systems, to support the process of diagnosing heart disease more accurately and efficiently.
PENGARUH MODEL PROJECT-BASED LEARNING TERHADAP HASIL BELAJAR PESERTA DIDIK KELAS V SEKOLAH DASAR Yurma Vadelta; Erni; Niken Yuni Astiti; Fadhilah Khairani
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 01 (2026): Volume 11 No. 01 Maret 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.43248

Abstract

The problem of low science learning outcomes at SD Negeri 01 Bukit Kemuning in science learning for grade V is the main focus of this study. This study aims to determine the effect of the Project-Based Learning Model on the Learning Outcomes of Grade V Students in the Science Subject of Elementary Schools. This study uses a quantitative approach with a quasi-experimental design method with a nonequivalent control group design. Determination of the research sample uses a non-probability sampling technique with a purposive sampling technique. The population in this study amounted to 75 students with the samples used being students in grades V A and V B. Data were collected using test and non-test techniques in the form of observation and documentation. Data were analyzed using a simple Linear Regression Test. The results of this study indicate that there is a significant influence on the application of the Project-Based Learning model on the learning outcomes of grade V students in the science subject of elementary school.
Preliminary Analysis of Machine Learning Performance and the Effect of Outliers in Daily Rainfall Classification in Jambi City Naufal, Muhammad Risyad; Erni; Marathur Rodhiyah
Journal of Technomaterial Physics Vol. 8 No. 1 (2026): Journal of Technomaterial Physics
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jotp.v8i1.24702

Abstract

Rainfall is a crucial meteorological parameter that significantly affects various sectors, particularly in tropical regions such as Jambi City. However, daily rainfall data often contain outliers and imbalanced class distributions, which can degrade the performance of machine learning-based classification models. This study aims to conduct a preliminary analysis of the performance of several machine learning algorithms for daily rainfall classification in Jambi City by examining the effects of outlier removal. The algorithms evaluated include Support Vector Machine (RBF), K-Nearest Neighbor, Naive Bayes, Decision Tree, and Random Forest. Model performance was assessed using accuracy and macro F1-score metrics. The rainfall classes used in this study consist of four categories: no rain, light rain, moderate rain, and heavy rain. The results indicate that outlier removal improves the accuracy of all evaluated algorithms, with the most substantial improvement observed in the Decision Tree model with accuracy improved from 45.71% to 57.36% and macro F1-score from 28.99% to 38.78%. Overall, the implementation of outlier removal yields more balanced and representative rainfall classification results, potentially serving as a basis for future quantitative rainfall regression studies.
Co-Authors A. Wardah Muzfah Abdul Matin Bin Salman Abung, Miranda Adelia Sari Adhista Rizky Wulandari Agnes Ratna Saputri Ahsan Kamil, Muh. Aldo Irsyaf Putra Alfarobi, Ibnu Angel Cristina apriliana, syafa Ardhi Yudisthira Ari Sofia, Ari Armaini Rambe Arsyad, Kamaruddin Azizah, Richia Deha Bahri Kamal, Bahri Bakri, Marlina Basrawi Basrin benny Bunaya Aziz, Thariqh Cintia Dewi Cahyati Citra Lestari, Bunga Dahnilsyah Darmawati. R Dayu RikaPerdana Deddy S Razak Deliani, Ni Wayan Destini, Frida dewi, arlita ratna Dhea Anisya Putri Effendy Erlin Fadhilah Khairani Fadhilah Khairani, Fadhilah Fadly Azhar Fajar Meirani Farda_Nur Ariza Farida, Anif Ghea Goh, Maggie Golan Hasan, Golan gultom, feby justin Hafrizal Halimul Bahri Hamdana Hanike Monim Harfal, Zaldi Hasniah Hasniah Hermawan, Jody Setya Irsan Rahman, Irsan Irwadi, Didi Izzatika, Amrina Jimmi Copriady Jismulatif, Jismulatif Jumiati Jumiati Kafabillah, Zidnyfahma Karina Rita Yanisa Kilawati, Andi Marathur Rodhiyah Marhamah Marufi Meliana Melodya, Deslyn Muhammad Ilyas Muhammad Kaulan Karima Muhibbuddin Murlan Mutia Aksari Nabila Fitri, Indah nabilla, cantika Naufal, Muhammad Risyad Ni Kadek Indah Sari Niken Yuni Astiti Novianty, Rica Regina Novita Novitri Nurasia Nurhayati Tanjung, Nurhayati Nurmalinda Nurul Faida Nurul Mahmudah Nurwahidin , Muhammad Oktaviona Pairin Parwati, Ni Kadek Ponisri, Ponisri Putri Rosyada, Balqis Putri Wahyuni Putri Yuanita Putri, Melinda Antoni Putriayu, Ninda Raemon Ramadya Vintika Laras Rasma Rasna Sarira Ratna Arbandari Rian Hidayat Rosmida Marbun Rotua Aruan, Rumiri Roza Linda Saeni, Fajrianto Safitri Ramadani sanjaya, naufal hafizh Selvia Mardiani Shevira Selsibilla Siska Mega Diana Sita Salsabilla, Safira Siti Nuraini SITI NURJANAH Soeryana, Dara Sri Wahyuni Sulaiman Sumarno Sunardin Susanto, Zaenal Adi Syafrudin Raharjo tamamah, nazwa fadhliah Tenriwati Utami, Rinda Aulia Wardi Metro Wawan Kurniawan Widodo Hariyono Yosi Darmayanti Yudhistira Anggraini, Yudhistira Yulia Citra, Yulia Yuni Astiti, Niken Yurma Vadelta Yusri Zaldi Harfal Zulham