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All Journal JURNAL INFORMATIKA Jurnal Ilmiah Sinus
Anggita Risqi Nur Clarita
Universitas Pelita Bangsa
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Perbandingan Efektivitas Metode SAW dan AHP dalam Seleksi Penerima Beasiswa di LIPIA Jakarta Clarita, Anggita Risqi Nur; Fadhillah, Faizah Via; Nurhaliza, Zahra; Fatchan, Muhamad; Anshor, Abdul Halim
Jurnal Ilmiah SINUS Vol 23, No 1 (2025): Vol. 23 No. 1, Januari 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i1.877

Abstract

This research discusses the application of SAW and AHP methods in the selection process of scholarship recipients at LIPIA Jakarta. Given the complexity of assessing potential recipients, both methods are evaluated to determine which one provides the most accurate and efficient results. SAW and AHP methods are used to process the assessment of several criteria, such as Arabic Written Test, Arabic Oral Test, Diploma Score, Memorization and Good Behavior which have been given weights and scales according to their importance. The main objective of this research is to compare the effectiveness of the two methods in determining scholarship recipients who meet the criteria at LIPIA. The results of this comparison are expected to provide recommendations regarding the most suitable method to improve objectivity, accuracy, and efficiency in scholarship selection.
Model Rekomendasi Konten Edukasi Diabetes pada Instagram dengan Integrasi TF-IDF dan Cosine Similarity berbasis Natural Language Processing Anggita Risqi Nur Clarita; Muhamad Fatchan; Karina Imelda
JURNAL INFORMATIKA Vol 15, No 1 (2026): Jurnal Informatika
Publisher : Informatics Engineering Department, Dayanu Ikhsanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55340/jiu.v15i1.2719

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.
Co-Authors Abdul Halim Anshor Faizah Via Fadhillah Karina Imelda Muhamad Fatchan Nurhaliza, Zahra