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All Journal MATHunesa: Jurnal Ilmiah Matematika Gunung Djati Conference Series
Intan Nur Khasanah
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BUDIDAYA BAYAM HORENSO (Spinacia oleracea L.) OLEH KELOMPOK TANI KATENZO DI PANGALENGAN, JAWA BARAT Intan Nur Khasanah; Esty Puri Utami
Gunung Djati Conference Series Vol. 49 (2025): RISMA: Riset Magang Mahasiswa Agroteknologi 2020
Publisher : UIN Sunan Gunung Djati Bandung

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Bayam horenso (Spinacia oleracea L.) atau lebih dikenal dengan bayam jepang merupakan salah satu jenis tanaman yang dimanfaatkan bagian daunnya. Permintaan pasar akan bayam horenso kian meningkat sehingga aspek budidayanyapun harus ditingkat demi memenuhi kebutuhan konsumen. Kegiatan pengumpulan informasi dilakukan saat Praktik Kerja Lapangan (PKL) pada 23 Januari sampai 24 Februari 2023 di kelompok tani Katenzo yang beralamatkan Kampung Cikole, Desa Margamukti, Kecamatan Pangalengan, Kabupaten Bandung, Jawa Barat. Tujuan dari catatan teknis ini adalah untuk memberikan informasi tentang teknik budidaya bayam Jepang di kelompok tani Katenzo. Metode yang digunakan dengan melakukan observasi, praktik lapangan, wawancara dan studi literatur. Hasil yang diperoleh menunjukkan bahwa budidaya bayam horenso yang dilakukan oleh kelompok tani Katenzo meliputi pengolahan lahan, penyemaian, penanaman, pemeliharaan, panen hingga pasca panen.
PENERAPAN K-NEAREST NEIGHBORS (K-NN) DALAM MENILAI POTENSI DROP OUT MAHASISWA: STUDI PADA ASPEK AKADEMIK, SOSIAL, DAN EKONOMI Vanya Tania; Intan Nur Khasanah; Syah Albani; Nur Azizah; Nurul Jihan
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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College is the highest educational institution that is important in preparing students to face global challenges. However, not a few students experience academic failure in completing their lectures (drop out). The high dropout rate can be minimized by making the right decisions to prevent students from dropping out. To help make these decisions, a technology such as Educational Data Mining (EDM) is needed to better understand student data patterns. One method in EDM that can be used is data classification with the concept of K-Nearest Neighbor (K-NN). In this research, K-NN works by analyzing the closeness of student data to predict the likelihood of a student graduating on time or at risk of dropout. The data used covers aspects of student life, such as academic, social, and economic aspects. Based on the results of the analysis, it can be concluded that the use of K-NN has good performance and is quite accurate but can be improved by adding resampling techniques to overcome class imbalances that may occur from the influence of large amounts of data. However, the K-NN approach can help universities design policies because it is simple to implement, making it easy to apply in the academic field. Keywords: Educational Data Mining (EDM), K-Nearest Neighbors (KNN), Drop Out, College.
Co-Authors Esty Puri Utami Nur Azizah Nurul Jihan Syah Albani Vanya Tania