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All Journal Infotech Journal Indonesian Journal of Business Intelligence (IJUBI) BERNAS: Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Simposium Nasional Multidisiplin (SinaMu)
Abdurahman, Dede
Universitas Majalengka
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IMPLEMENTASI METODE K-NEARST NEIGHBOR (K-NN) MEMPREDIKSI NILAI AKADEMIK MAHASISWA BERDASARKAN INDEKS PRESTASI SEMESTER Dede Abdurahman; Suhendri; Deffy Susanti; Dede Didin
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.8066

Abstract

Perguruan tinggi, seperti Universitas Majalengka, memiliki peran penting dalam memberikan pendidikan akademik dan profesional. Untuk meningkatkan kualitas layanan akademik, Fakultas Teknik Universitas Majalengka merancang aplikasi berbasis web menggunakan metode K-Nearest Neighbor (KNN) untuk memprediksi perkembangan Indeks Prestasi (IP) mahasiswa. Dengan tujuan memantau dan membantu mahasiswa dalam merencanakan studi mereka, aplikasi ini menggabungkan teknologi informasi dan algoritma KNN. Hasil penelitian menunjukkan bahwa aplikasi ini berhasil memprediksi kemajuan IP mahasiswa secara akurat. Dosen pembimbing akademik dapat menggunakan prediksi ini untuk memberikan pendampingan kepada mahasiswa yang berpotensi mengalami penurunan nilai. Dengan pendekatan waterfall dalam pengembangan sistem aplikasi, serta implementasi metode KNN, aplikasi ini menjadi alat yang berharga dalam meningkatkan kualitas pendidikan di Fakultas Teknik Universitas Majalengka. Dengan demikian, aplikasi ini membawa manfaat bagi dosen, Fakultas Teknik, dan mahasiswa, membantu mereka dalam merencanakan strategi peningkatan prestasi akademik. Dengan pemanfaatan teknologi dan prediksi yang tepat, aplikasi ini mendukung pencapaian visi dan misi Fakultas Teknik untuk berperan aktif dalam pembangunan teknologi, ilmu pengetahuan, dan seni nasional.
ANALSISIS KEBERHASILAN LITERASI DA NUMERASI SISWA SEKOLAH DASAR PADA PROGRAM KAMPUS MENGAJAR ANGKATAN 5 DI KABUPATEN MAJALENGKA MENGGUNAKAN METODE NAÏVE BAYES Dede Abdurahman; Tri Ferga Prasetyo; Iid Muiz Awaludin
Indonesian Journal of Business Intelligence (IJUBI) Vol 7 No 1 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i1.4276

Abstract

The teaching campus provides collaboration between students and teachers in the development of learning which has an impact on strengthening literacy and numeracy. Results of the Program for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS) in 2015 Indonesia obtained a literacy score of 397 and a numeration of 386 ranked 62 out of 72 countries while in 2018 Indonesia obtained a literacy score of 371 and a numeration of 379 ranked 74 out of 79 countries. In Indonesia, parents pay little attention to their children's literacy and numeracy skills, so that the literacy and numeracy skills of students in Indonesia are relatively low. With the presence of teaching campus students it is hoped that they can become partners of teachers in developing literacy and numeracy learning models. To see the results of the success rate of teaching campus students in applying literacy and numeracy learning models, an analysis using the classification method is needed. Therefore, the authors make an analysis of the literacy and numeracy success of elementary school students in the class 5 teaching campus program using the Naïve Bayes Classifier method. Data classification techniques were carried out manually and by using the Rapidminer software, data classification was carried out by dividing into two categories in the form of literacy and numeracy results. The results of testing the training data and data testing for literacy get an accuracy value of 95.71%, 97.92% precision, 95.92% recall while for numeration get an accuracy value of 95.71%, 93.75% precision, 96.77% recall.
Co-Authors Ardi Mardiana Awaludin, Iid Muiz Budiman Budiman Dadan Zaliluddin Dede Didin Deffy Susanti Deffy Susanti Dony Susandi Fhaisal Anwar Harun Sujadi Hilman Prayitno Iid Muiz Awaludin Muhamad Ade Kurnia Muhamad Dendi Purwanto Nunu Nurdiana Puput Putriani Suhendri Suhendri Tantri Wayuni Tri Ferga Prasetyo Winda Nur Hamidah Yuliana Purnama Citra