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All Journal Elementaria: Journal of Educational Research Jurnal Cermatika
Ahmad Arisman
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Religion Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Research And Developing Mathematics Knowledge Child Development Perspectives, 2022 Torang Siregar; Ahmad Arisman; Iskandarsyah; Risky Ardian; Awal Harahap
Elementaria: Journal of Educational Research Vol. 1 No. 2 (2023): Character Education and Learning Research
Publisher : Penerbit Hellow Pustaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61166/elm.v1i2.11

Abstract

Proficiency in mathematics is critical to success academically, economically, and in life. Greater success in math is related to entering and completing college, earning more in adulthood, and making more optimal decisions concerning health. Knowledge of math begins to develop at a young age, and this early knowledge matters: Knowledge of math at or before school entry predicts outcomes in math and reading across primary and secondary school. More than one children struggle to learn math. For example, only 60% of fourth-grade and 55% of eighth-grade students in the United States performed at or above proficiency in math on the 2020 National Assessment of Educational Progress, and proficiency rates were even lower for African-American and Hispanic children and for children from low-income homes. More than one students do not master challenging math content. Developing strong knowledge about mathematics is important for success academically, economically, and in life, but more than one children fail to become proficient in math. Research on the developmental relations between conceptual and procedural knowledge of math provides insights into the development of knowledge about math. First, competency in math requires children to develop conceptual knowledge, procedural knowledge, and procedural flexibility. Second, conceptual and procedural knowledge often develop in a bidirectional, iterative fashion, with improvements in one type of knowledge supporting improvements in the other, as well as procedural flexibility. Third, learning techniques such as comparing, explaining, and exploring promote more than one type of knowledge about math, indicating that each is an important learning process. Researchers need to develop and validate measurement tools, devise more comprehensive theories of math development, and bridge more between research and educational practice.
STUDI KASUS SMA N 1 SINUNUKAN : IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENERIMA BEASISWA PROGRAM INDONESIA PINTAR (PIP) Torang Siregar; Riski Ardian; Ahmad Arisman; Iskandarsyah
JURNAL CERMATIKA Vol. 4 No. 1 (2024): Jurnal Cermatika Vol 4 No 1 April 2024
Publisher : Program Studi Matematika Universitas Graha Nusantara Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64168/cermatika.v4i1.1324

Abstract

Abstrak Program Indonesia Pintar (PIP) merupakan salah satu kebijakan pemerintah yang diharapkan dapat meningkatkan aksesibilitas dan pemerataan pendidikan di Indonesia, namun dalam pelaksanaannya pemberian beasiswa dari program ini masih dijumpai banyak kasus yang kurang tepat sasaran. Salah satu permasalahannya adalah masih ditemukan siswa penerima bantuan pendidikan yang berasal dari keluarga yang mampu, sedangkan siswa yang kurang mampu justru tidak mendapatkan bantuan. Sehingga diperlukan suatu sistem klasifikasi berbasis web yang dapat mengklasifikasikan siswa layak atau tidak untuk mendapatkan PIP. Penelitian ini mengimplementasikan algoritma -nearest neighbor untuk mengklasifikasikan siswa penerima beasiswa PIP. Penelitian ini menggunakan data siswa/i SMAN 1 Sinunukan, Mandailing Natal yang didapat melalui penyebaran angket sesuai dengan kriteria yang telah ditentukan. Data yang telah diperoleh kemudian dilakukan preprocessing data dengan menggunakan Label Encoder dan Normalisasi Min-Max. Data dibagi menjadi dua jenis yaitu data training dan data testing. -fold cross validation digunakan untuk menentukan nilai yang optimal. Hasil penelitian ini memperlihatkan tingkat akurasi yang dihasilkan berdasarkan hasil pengujian yang dilakukan untuk implentasi algoritma -nearest neighbor dalam klasifikasi kelayakan penerima beasiswa PIP yaitu sebesar 70% dengan nilai .
Co-Authors Awal Harahap Iskandarsyah Riski Ardian Risky Ardian Torang Siregar