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All Journal Data Science: Journal of Computing and Applied Informatics MATHunesa: Jurnal Ilmiah Matematika Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika Jurnal Riset Rumpun Ilmu Pendidikan (JURRIPEN) Journal of Innovative and Creativity Katalis Pendidikan: Jurnal Ilmu Pendidikan dan Matematika Jurnal Cendekia Ilmiah PESHUM Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam Interdisciplinary Explorations in Research Journal (IERJ)
Putri Maulidina Fadilah
Unknown Affiliation
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Education Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Penerapan Rancangan Acak Lengkap dalam Penilaian Cara Mengajar (Studi Kasus: Jurusan Matematika FMIPA Unimed) Dewi Lowisa Br Purba; Indah Febriani Sagala; Nagita Adella; Reski Augustian. S; Sudianto Manullang; Putri Maulidina Fadilah
Interdisciplinary Explorations in Research Journal Vol. 2 No. 3 (2024)
Publisher : PT. Sharia Journal and Education Center

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

Abstract

This research aims to evaluate the effectiveness of lecturers' teaching methods in the Mathematics Department, FMIPA, Medan State University (Unimed) using a Completely Randomized Design (RAL) approach. This method allows controlled testing of variability between groups of students, with a sample of 60 students selected at simple random. Data was collected through a questionnaire that measured student perceptions of teaching methods, followed by analysis using ANOVA. The research results show that the teaching methods applied have a significant influence on student learning outcomes, with differences between groups detected through statistical tests. This research provides recommendations for adopting more effective teaching methods in improving the quality of education in the academic environment.
Analisis Kesulitan Mahasiswa Program Studi Statistika Angkatan 2024 Universitas Negeri Medan Dalam Mengerjakan Tugas Mini Riset Aisyah Novianti; Elisabeth Putri Dayanti; Emily Theresia Silaen; Nia Rizkita Tambunan; Sudianto Manullang; Putri Maulidina Fadilah
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.1324

Abstract

Students of the Statistics Study Program often face difficulties in completing mini research projects, which are an essential part of project-based learning. This study aims to analyze the difficulties experienced by Statistics students at Universitas Negeri Medan (UNIMED), specifically the 2024 cohort, in conducting their mini research assignments. A descriptive quantitative approach was used. Data were collected through questionnaires and interviews, with 69 students selected using a simple random sampling technique. The questionnaire instrument was tested for validity and reliability. The results showed that all items were valid, and the reliability coefficient of 0.753 indicated that the instrument was consistent and reliable. The analysis revealed that students primarily struggled with understanding statistical concepts, processing data using statistical software, and writing structured research reports. These findings highlight the need for additional guidance in both methodological and technical aspects of mini research. This study is expected to serve as an evaluation reference for lecturers and study programs to improve the quality of research-based learning in higher education. Keywords: Learning Difficulties, Mini Research, Statistics Students, Validity, Reliability.
Spatial Clustering Analysis of Stunting in North Sumatra Based on Environmental Factors Using K-Means Algorithm Fanny Ramadhani; Dian Septiana; Sisti Nadia Amalia; Putri Maulidina Fadilah; Andy Satria
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i2-17179

Abstract

This research aims to analyze the spatial grouping of stunting events in North Sumatra based on environmental factors using the K-Means algorithm. The data used in this research includes the incidence of stunting, environmental factors (such as access to health services, living environment conditions, water use and sanitation), and spatial data (geographical coordinates). The data comes from Basic Health Research (RISKESDAS 2018, then processed and normalized. The elbow method and silhouette analysis are used to determine the optimal number of clusters, resulting in four different clusters. The application of the K-Means algorithm produces the following cluster characteristics: Cluster 1, with good environmental conditions and access to health services, shows low levels of stunting; Cluster 2, with moderate environmental conditions, shows moderate levels of stunting; Cluster 3, which is characterized by poor living conditions and limited access to health services, has levels high stunting; and Cluster 4, with varied environmental conditions but very limited access to health and sanitation services, also shows a high stunting rate. Validation using the Silhouette Coefficient produces an average score of 0.65 which indicates good clustering quality shows that environmental factors, access to health services, and sanitation conditions have a significant impact on the incidence of stunting. Based on these findings, policy and intervention recommendations are focused on Clusters 3 and 4, which have high stunting rates. The interventions carried out include increasing access and quality of nutrition, health services, sanitation conditions, economic empowerment, and health education.
PEMODELAN DAN PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PREDIKSI BANJIR: STUDI KASUS KOTA MEDAN Putri Maulidina Fadilah; Putri Harliana; Br Rambe, Imelda Wardani; Nasution, Alvi Sahrin
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p235 - 242

Abstract

Banjir merupakan salah satu bencana hidrometeorologis yang sering terjadi di Indonesia, termasuk di Kota Medan, dan menimbulkan kerugian sosial ekonomi yang signifikan. Penelitian ini bertujuan untuk mengembangkan dan membandingkan model prediksi kejadian banjir menggunakan tiga algoritma machine learning, yaitu Random Forest (RF), Support Vector Machine (SVM), dan Decision Tree (CART). Data yang digunakan merupakan data hidrometeorologis Kota Medan periode 2021–2024 yang diperoleh dari BMKG dan Geoportal BNPB, meliputi variabel curah hujan (RR), suhu (TN, TX, TAVG), kelembapan (RH_AVG), kecepatan angin (FF_X), arah angin (DDD_X), serta jumlah kecamatan dan rumah terdampak. Tahapan penelitian meliputi pra-pemrosesan data, pemodelan, serta evaluasi menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC. Hasil penelitian menunjukkan bahwa Random Forest memiliki kinerja terbaik dengan akurasi sebesar 86,24% dan AUC sebesar 0,851, yang menunjukkan kemampuan prediksi yang sangat baik. Analisis feature importance menunjukkan bahwa curah hujan (RR) merupakan faktor paling berpengaruh terhadap kejadian banjir, diikuti oleh temperatur minimum (TN) dan temperatur rata-rata (TAVG). Metode ROSE terbukti efektif dalam mengatasi ketidakseimbangan kelas dengan meningkatkan recall tanpa mengorbankan akurasi. Secara keseluruhan, hasil penelitian ini menunjukkan bahwa algoritma Random Forest lebih andal dan stabil dibandingkan SVM dan Decision Tree dalam konteks analisis data hidrometeorologis, serta berpotensi menjadi dasar pengembangan sistem peringatan dini banjir berbasis kecerdasan buatan di masa depan. Kata Kunci: Prediksi Banjir, Random Forest, Support Vector Machine, Decision Tree, ROSE.
Co-Authors Achmad Fajri Romadhoni Aisyah Novianti Andy Satria Angelica Carolina Tambunan Aulia Putri Br. Manurung Ayu Widya Sari Br Rambe, Imelda Wardani Cahaya Destia Regar Carles Syahputra Hulu Christine Simatupang Clara Jocelyn Harefa Dewi Lowisa Br Purba Diah Ayu Lestari Dian Septiana Dilla Nurfadiah Dimas Arza Nugraha Ega Ananda Br Sembiring Elisabeth Putri Dayanti Emily Theresia Silaen Febi Hijriana Femi Ezrani Siantur Fikri Agustin Hanafi Irsyad Pulungan Indah Febriani Sagala Ira Selvia Ritonga Irvina Abelia Harahap Ishak Munawar Kasih Simbolon Lirana Sapriani Gulo M. Fajar Alif M.E. Ernawati Siregar Manullang, Sudianto Nagita Adella Naomi Febrina Sitompul Nasution, Alvi Sahrin Nia Rizkita Tambunan Nova Oktavia Sitinjak Nur Maulidya Rizky Nursani Salsabillah Akza Raihan Riyadi Siregar Ramadhani, Fanny Reski Augustian. S Rifka Annaria Sibuea Rini Sartika Risa Rozzaqi Rambe Rotua Evelyn Tesalonika Sinambela Septia Cahaya Sari Sipayung Sisti Nadia Amalia Thanaya Lovry Lastiar Tia Ramadani Tiara Febriyanti Br Panjaitan Trinita Melyana Hutagalung Widya Narti Lubis Yasmin Khairunisa