PUTRO, JOKO HANDOYO IMAN (2025) PENGEMBANGAN APLIKASI PREDIKSI PRESTASI AKADEMIK SISWA SMP BERBASIS WEB MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Education is the main pillar in the development of a nation, where student academic achievement is a very important indicator in assessing the success of the learning process. The low achievement of Indonesian students according to the PISA report shows the need for innovative solutions. This research is appropriate to develop a web-based junior high school student academic achievement prediction application using the K-Nearest Neighbors (KNN) algorithm. The case study was conducted at SMPN 2 Boja, using student data such as academic grades, attendance and discipline, learning patterns and motivation, and extracurricular activities. The data is processed through cleaning, data transformation, normalization and encoding, dataset sharing. For the prediction process, the system uses Laravel and Rubix machine learning library. Evaluation is carried out using the black box testing method with Katalon Studio tools and teacher assessment based on system usability evaluation and prediction evaluation. The results of the teacher's assessment from the prediction evaluation aspect get a score of 4.8 which means very good, while for the system evaluation it gets a score of 4.4 which means good, this shows that the system runs well, produces predictions precisely and quickly, and helps in supporting the learning process at school. Keywords: Academic Achievement Prediction, K-Nearest Neighbor, Machine Learning, Laravel, SMPN 2 Boja Pendidikan merupakan pilar utama dalam pembangunan suatu bangsa, dimana prestasi akademik siswa adalah indikator yang sangat penting dalam menilai keberhasilan proses pembelajaran. Rendahnya pencapaian siswa Indonesia menurut laporan PISA menunjukkan adanya kebutuhan solusi inovatif. Penelitian ini tepat untuk mengembangkan aplikasi prediksi prestasi akademik siswa SMP berbasis web menggunakan algoritma K-Nearest Neighbors (KNN). Penelitian kasus dilaksanakan di SMPN 2 Boja, menggunakan data siswa seperti nilai akademik, kehadiran dan kedisiplinan, pola belajar dan motivasi, serta ekstrakurikuler. Data diproses melalui proses pembersihan, transformasi data, normalisasi serta encoding, pembagian dataset. Untuk proses prediksi, sistem menggunakan laravel dan library rubix machine learning. Evaluasi dilakukan dengan metode black box testing dengan tools katalon studio serta penilaian guru berdasarkan evaluasi kegunaan sistem dan evaluasi prediksi. Hasilnya dari penilaian guru dari aspek evaluasi prediksi mendapatkan nilai 4.8 yang artinya sangat baik, sedangkan untuk evaluasi sistem mendapatkan nilai 4.4 yang artinya baik, ini menunjukkan bahwa sistem berjalan dengan baik, menghasilkan prediksi dengan tepat dan cepat, dan membantu dalam menudukung proses pembelajaran di sekolah. Kata kunci: Prediksi Prestasi Akademik, K-Nearest Neighbor, Machine Learning, Laravel, SMPN 2 Boja
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