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All Journal Journal of Computer Networks, Architecture and High Performance Computing COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi
Rahmadan, Ilham
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mechanical Engineering Transportation

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Pengembangan Aplikasi Kas Adiwiyata di SDN Banjarsari III Berbasis Web Menggunakan Framework CodeIgniter untuk Meningkatkan Pengelolaan Sampah Faid, Mochammad; Rahmadan, Ilham; Firmandani, Ahmad Muzakki Eko Fitra
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 3, No 1 (2022): Pengembangan Aplikasi Berbasis Mobile untuk Layanan Publik: Studi Implementasi d
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1227.826 KB) | DOI: 10.33650/coreai.v3i1.4210

Abstract

Sampai saat ini masalah sampah masih menjadi permasalahan yang cukup serius bagi keberlangsungan hidup manusia terutama di Indonesia, berdasarkan data kementrian lingkungan hidup dan kehutanan, tercatat pada akhir tahun 2021 terdapat total 68,5 Juta ton sampah yang ada Di Indonesia. Beberapa kegiatan sudah dilakukan pemerintah melalui Gerakan pengelolaan Sampah, namun faktanya hal tersebut masih belum cukup untuk mengatasi permasalahan yang ada. Dalam hal ini peran masyarakat sangat penting dalam upaya pengelolaan sampah seperti mengumpulkan dan mendaur ulang samah. Dalam hal ini tentunya dibutuhkan pembukuan terkait hasil penjualan sampah agar semua pengeluaran dan pemasukan bisa dirinci dengan jelas, namun realita yang ada di lapangan terkait dengan pembukuan secara manual akan menimbulkan banyak masalah diantaranya perekapan hasil omset membutuhkan waktu yang lama dan juga rawan keliru, dengan menggunakan hardcopy berupa buku kas akan rawan untuk hilang atau rusak, tabungan anggota berupa sampa susah untuk dimonitoring. Berdasarkan pemaparan latar belakang diatas, peneliti tertarik untuk membuat sebuah sistem untuk mempermudah jalannya program pengelolaan sampah dengan judul " Upaya Meningkatkan Pengelolaan Sampah dengan Membuat Sistem KAS Adiwiyata di SDN 3 BANJARSARI Berbasis WEB dengan Framework Codeigniter.
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.
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 Firmandani, Ahmad Muzakki Eko Fitra Jasri , Moh. Mochammad Faid Shudiq, Wali Ja'far