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All Journal Journal of Intelligent Decision Support System (IDSS) Journal of Computer Networks, Architecture and High Performance Computing Journal of Electrical Engineering and Computer (JEECOM) Insand Comtech : Information Science and Computer Technology Journal COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JAR'S (Journal of Advanced Research in Informatics)
Shudiq, Wali Ja'far
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Mechanical Engineering Transportation

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Prediksi Kelayakan Mahasiswa sebagai Penerima Beasiswa Bank Indonesia pada Tahap Seleksi Administrasi di Universitas Nurul Jadid Menggunakan Algoritma K Nearest Neighbor Permatasari, Uky Oktavia Risti; Shudiq, Wali Ja'far; Jasri, Moh
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 1 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i1.8425

Abstract

Beasiswa ialah jenis bantuan keuangan yang diberikan kepada mahasiswa untuk membantu mereka membayar biaya pendidikan. Tahap seleksi administrasi merupakan langkah awal dalam menilai kelayakan mahasiswa. Proses seleksi administrasi jika dilakukan secara manual dapat memakan waktu dan sumber daya, serta keputusan manual dapat rentan terhadap subjektivitas, dan perbedaan penilaian antar panitia seleksi. Oleh karena itu dibutuhkan Prediksi yang akurat untuk dapat mengoptimalkan penggunaan sumber daya, mengetahui faktor utama dan faktor pendukung untuk membantu pihak terkait dalam menentukan kelayakan mahasiswa untuk dinyatakan lolos seleksi administrasi secara lebih mendalam. Tujuan penelitian ini ialah meminimalkan adanya pengaruh keputusan yang bersifat subjektivitas serta meminimalisir adanya human erorr. Penelitian ini mengusulkan Prediksi Kelayakan Mahasiswa Algoritma K Nearest Neighbor (KNN). Perhitungan jarak yang digunakan dalam penelitian ini ialah Euclidean distance yang dimana digunakan untuk mengukur seberapa mirip data yang akan di prediksi dan data latih yang ada. Implementasi algoritma ini menggunakan python di google colab. Dataset yang digunakan dalam penelitian ini ialah sebanyak 350 record data, dengan membagi 75% sebagai data training, dan 25% sebagai data testing. Hasil dari penelitian ini menunjukkan bahwa Algoritma K Nearest Neighbor (KNN) mampu menjadi model prediksi kelayakan yang baik, ditunjukkan dengan nilai akurasi sebesar 93%.   
Increasing Student Interest in Learning through the Implementation of the K-Nearest Neighbor Algorithm in Classifying Learning Preferences at SMAN 1 Kraksaan Jasri , Moh.; Rahmadan, Ilham; Shudiq, Wali Ja'far
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4526

Abstract

This research examines the effectiveness of implementing the K-Nearest Neighbor (KNN) algorithm in classifying student learning preferences and its impact on increasing interest in learning at SMAN 1 Kraksaan. The main aim of the research is to optimize learning methods through personalization based on individual student preferences. The study involved 560 students of SMAN 1 Kraksaan, with data including variables of age, gender, academic grades, daily study time, attendance and participation in class. The KNN algorithm is used to classify students' learning preferences into visual, auditory, kinesthetic, and reading/writing categories. The learning method is then adjusted based on the results of this classification. The results show that the KNN algorithm is able to classify student learning preferences with an accuracy of 80.36%. After implementing personalized learning methods, there was a significant increase in students' interest in learning, with an average increase of 1.76 points on a 10-point scale. Paired t-test analysis showed a statistically significant difference between interest in learning before and after intervention (p < 0.0001). This research concludes that the implementation of the KNN algorithm in classifying learning preferences can help increase students' interest in learning effectively. These findings emphasize the importance of personalization in education and demonstrate the potential of integrating machine learning in the pedagogical process to improve learning outcomes.
Co-Authors Abu Tholib ANDI WIJAYA Andi, Moh syaiful Bambang Bambang Eko Purnomo Fawaid, Izzah Diyanah Fila, Nining Guterres, Juvinal Ximenes Hairani Hairani Hasyim, Fuadz Inda Arifin, Nur Hatima Jasri , Moh. Jasri, Moh Khairi, Ahmad Khotimah, Putri Dwi Chusnul Maghfirah, Lidya Erika Permatasari, Uky Oktavia Risti Rahmadan, Ilham Ratri Enggar Pawening Sholehah, Aisyatus Sukron, Moh wahyuni wahyuni