Model Rekomendasi Konten Edukasi Diabetes pada Instagram dengan Integrasi TF-IDF dan Cosine Similarity berbasis Natural Language Processing
Abstract
Meningkatnya prevalensi Diabetes Melitus menuntut penguatan literasi kesehatan digital mandiri. Namun, mayoritas edukasi di Instagram bersifat generalis dan belum tersegmentasi sesuai karakteristik medis pasien. Penelitian ini bertujuan mengembangkan model rekomendasi konten edukasi diabetes personal menggunakan pendekatan Content-Based Filtering. Berbeda dari model pencocokan kata konvensional terdahulu yang gagal menangani tingginya tumpang tindih istilah medis pada teks non-formal, kebaruan penelitian ini terletak pada integrasi knowledge-base terminologi medis hierarkis (Tipe 1, Tipe 2, Umum) yang diselaraskan bersama praktisi kesehatan untuk memandu akurasi pembobotan. Metode yang digunakan meliputi Term Frequency-Inverse Document Frequency (TF-IDF) untuk representasi fitur dan Cosine Similarity untuk mengukur kemiripan leksikal antar-vektor teks. Hasil evaluasi terhadap 75 data uji menunjukkan capaian akurasi klasifikasi back-end sebesar 84% (Interval Kepercayaan 95%: [75,70%, 92,30%]), dengan kualitas urutan pemeringkatan rekomendasi terarah pada nilai rata-rata Precision at 3 (P@3) sebesar 88%. Analisis data empiris mengonfirmasi kesenjangan di lapangan di mana 72,45% konten didominasi materi Umum. Kesimpulannya, model ini menunjukkan potensi performa baik dalam meminimalkan bias prediksi pada dataset terkait, meski masih memiliki keterbatasan overlap kata kunci pada kategori Tipe 2 (Recall 0,76). Pengembangan ke depan memerlukan arsitektur multimodal untuk memproses informasi dari media visual.
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DOI: https://doi.org/10.55340/jiu.v15i1.2719
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