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Implementasi Model Pembelajaran Snowball Throwing Untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Mahasiswa Program Studi Teknik Informatika Faoziyah, Nina
Jurnal Ilmiah Mandala Education (JIME) Vol 9, No 3 (2023): Jurnal Ilmiah Mandala Edcation (Agustus)
Publisher : Lembaga Penelitian dan Pendidikan Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58258/jime.v9i3.5779

Abstract

Setelah tiga tahun berturut-turut nilai matakuliah kalkulus integral mengalami penurunan, maka diadakan pembelajaran yang melibatkan mahasiswa lebih aktif yaitu dengan model pembelajaran snowball throwing. Tujuan penelitian ini untuk mengetahui dan menganalisis kemampuan pemecahan masalah matematis mahasiswa yang mendapatkan model pembelajaran snowball throwing dengan pembelajaran langsung. Metode yang digunakan adalah kuasi eksperimen dengan populasi seluruh mahasiswa teknik informatika semester 2 di salah satu  Perguruan Tinggi Kota Tegal tahun ajaran 2022/2023, sedangkan sampel terdiri dari dua kelas yang diambil secara acak. Instrumen yang digunakan adalah soal kemampuan pemecahan masalah matematis tipe uraian. Peningkatan kemampuan pemecahan masalah matematis antara kelas eksperimen dan kelas kontrol, dapat diketahui dengan perhitungan gain. Hasil Uji gain kelas eksperimen adalah 0,56 dan kelas kontrol adalah 0,35. Kedua kelas berada pada kriteria sedang. Dari  penelitian ini dapat disimpulkan bahwa: peningkatan kemampuan pemecahan masalah matematis mahasiswa yang mendapatkan pembelajaran snowball throwing lebih baik dibandingkan dengan mahasiswa yang mendapatkan pembelajaran langsung.
IMPLEMENTASI MODEL PEMBELAJARAN SNOWBALL THROWING DALAM MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH MATEMATIKA SISWA Faoziyah, Nina
NUSRA : Jurnal Penelitian dan Ilmu Pendidikan Vol. 4 No. 4 (2023): NUSRA: Jurnal Penelitian dan Ilmu Pendidikan, November 2023
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/nusra.v4i4.1725

Abstract

This study aims to determine the difference in increasing students' mathematics problem-solving abilities that obtain Snowball Throwing learning and those who obtain Direct learning. The method that is used in research is the method of quasi-experiment with the population of the entire student class VII one of the SMP Negeri in the city of Bandung in the teachings of 2012/2013, while the sample has two classes of the population that are selected by the random class. Instruments used in the research is about the ability to solve problems of mathematics type of description. The test is used to identify students mistakes of how to measure of students in completing the issue. To determine the increasing of student’s ability to solve problems mathematically between classroom control and class experiment, it can be done by using the calculation of the gain. The gain test results in increasing the mathematics problem-solving ability of students in the experimental class are 0,55 and the control class is 0,44. The second classes were on the middle criteria. From  the research is obtained the conclusion that: the increasing of students ability to solve problems mathematically students who acquire learning by using learning Snowball Throwing is better than students who received teaching direct mathematics.
A Novel Privacy-Preserving Algorithm for Secure Data Sharing in Federated Learning Frameworks Dalimarta, Fahmy Ferdian; Faoziyah, Nina; Setiawan, Doni
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5385

Abstract

Federated Learning (FL) has emerged as a promising paradigm for the collaborative training of machine learning models across decentralized devices while preserving data privacy. However, ensuring data security and privacy during model updates remains a critical challenge, particularly in scenarios that involve sensitive data. This study proposes a novel Privacy-Preserving Algorithm (PPA-FL) designed to enhance data security and mitigate privacy leakage risks in FL frameworks. The algorithm integrates advanced encryption techniques, such as homomorphic encryption, with differential privacy to secure model updates without compromising the utility. Furthermore, it incorporates a dynamic noise-adjustment mechanism to adaptively balance privacy and model accuracy. Extensive experiments on benchmark datasets demonstrate that PPA-FL achieves a competitive trade-off between privacy protection and model performance compared to existing methods. The proposed approach is computationally efficient and scalable, making it suitable for real-world applications in healthcare, finance, and the IoT environment. This research contributes to advancing secure data-sharing practices in federated learning, fostering the broader adoption of privacy-preserving machine learning solutions.