The intensive use of social media among students poses a risk of triggering Fear of Missing Out (FoMO), which negatively affects mental health and learning focus. This study aims to develop a classification model to detect FoMO tendencies among students at SMAN 11 Kabupaten Tangerang. A quantitative approach was used, employing the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The analyzed variables include gender, duration of social media use, access frequency, desire to stay updated, and its impact on productivity. Data were collected from 244 respondents and processed through pre-processing, modeling, and evaluation stages. Validation results show that KNN achieved the highest accuracy at 94.69%, while Naïve Bayes reached 93.06%. These findings indicate that KNN is more effective in detecting FoMO tendencies based on numerical data and has the potential to support early intervention efforts in educational settings.Keywords: Fear of Missing Out; K-Nearest Neighbor; Social Media; Classification; Naive Bayes AbstrakPenggunaan media sosial secara intensif di kalangan pelajar berisiko memunculkan gejala Fear ofaMissing Out (FoMO), yang berdampak negatif pada kesehatan mental dan fokus belajar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi kecenderungan FoMO pada pelajar SMAN 11 Kabupaten Tangerang. Metode yang digunakan adalah pendekatan kuantitatif dengan algoritma K-NearestiNeighbor (KNN) dan NaïveiBayes. Variabel yang dianalisis meliputi jenis kelamin, durasi penggunaan media sosial, frekuensi akses, keinginan untuk tetap update, dan pengaruh terhadap produktivitas. Data dikumpulkan dari 244 responden dan diproses melalui pre-processing, modeling, dan evaluasi. Hasil validasi menunjukkan bahwa KNN menghasilkan akurasi tertinggi sebesar 94,69%, sementara Naïve Bayes mencapai 93,06%. Temuan ini menunjukkan bahwa KNN lebih efektif untuk mendeteksi kecenderungan FoMO berbasis data numerik dan berpotensi mendukung pengembangan intervensi dini dalam konteks pendidikan.Kata kunci: Fear of Missing Out; K-Nearest Neighbor; Media Sosial; Klasifikasi; Naive Bayes