{
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      "keywords": "Penyakit Jantung, Transformer, PSO, Klasifikasi, Deep Learning, Heart Disease, Classification.",
      "title": "IMPLEMENTASI MODEL TRANSFORMER MENGGUNAKAN\r\nPARTICLE SWARM OPTIMIZATION (PSO) UNTUK PREDIKSI\r\nPENYAKIT JANTUNG BERDASARKAN PARAMETER KLINIS",
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          "name": {
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            "family": "Kabuchi",
            "given": "Avankha Barlian",
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      "date": "2026-05-11",
      "thesis_type": "bachelor",
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      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/33133",
      "type": "thesis",
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      "rev_number": 15,
      "abstract": "Penyakit jantung merupakan salah satu penyebab utama kematian di dunia \r\ndengan jumlah kasus yang terus meningkat akibat gaya hidup tidak sehat dan\r\nketerbatasan dalam proses diagnosis yang masih bergantung pada interpretasi\r\nsubjektif tenaga medis. Kondisi ini berdampak pada ketidakakuratan diagnosis serta\r\nketerlambatan dalam penanganan, sehingga diperlukan sistem pendukung\r\nkeputusan yang mampu melakukan deteksi dini secara cepat dan akurat berbasis\r\ndata klinis. Penelitian ini bertujuan untuk mengembangkan model Prediksi penyakit\r\njantung menggunakan arsitektur Transformer yang dioptimasi dengan algoritma\r\nParticle Swarm Optimization (PSO). Metode penelitian meliputi tahap\r\npengumpulan dataset dari repositori publik, preprocessing data, pembangunan\r\nmodel Transformer untuk data tabular, serta optimasi hyperparameter\r\nmenggunakan PSO untuk memperoleh parameter terbaik. Evaluasi model\r\ndilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil\r\npenelitian menunjukkan bahwa model Transformer yang dioptimasi menggunakan\r\nPSO mampu menghasilkan performa klasifikasi yang baik dengan akurasi tinggi\r\ndan keseimbangan pada metrik evaluasi lainnya. Selain itu, model mampu\r\nmengidentifikasi parameter klinis yang berpengaruh terhadap risiko penyakit\r\njantung. Penelitian ini berkontribusi dalam pengembangan sistem prediksi berbasis\r\nkecerdasan buatan yang dapat dimanfaatkan oleh tenaga medis sebagai alat bantu\r\ndiagnosis serta oleh masyarakat untuk deteksi dini penyakit jantung. Penelitian\r\nselanjutnya dapat mengembangkan model dengan dataset yang lebih besar dan\r\nmetode optimasi yang lebih kompleks.",
      "thesis_name": "skripsi",
      "department": "Fakultas Ilmu Komputer",
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