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          "name": {
            "given": "Muhammad Yusuf",
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      "abstract": "Sektor teknologi menghadapi kesenjangan operasional yang signifikan\r\ndalam pengelolaan kesejahteraan kesehatan mental tenaga kerja secara proaktif,\r\nyang disebabkan oleh stigma yang masih melekat terhadap upaya mencari\r\nperawatan. Penelitian ini bertujuan untuk memprediksi kecenderungan profesional\r\nteknologi dalam mencari dukungan kesehatan mental secara sukarela dengan\r\nmemanfaatkan teknik machine learning. Dengan menganalisis lebih dari 1.200\r\nrespons dari OSMI Mental Health in Tech Survey, penelitian ini memodelkan\r\npengaruh faktor demografis, manfaat di tempat kerja, serta aspek anonimitas\r\nterhadap perilaku pencarian perawatan. Tahapan prapemrosesan data dilakukan\r\nsecara komprehensif, termasuk penerapan metode SMOTE untuk mengatasi\r\nketidakseimbangan kelas serta evaluasi terhadap dua belas algoritma klasifikasi.\r\nHasil penelitian menunjukkan bahwa algoritma Random Forest menghasilkan\r\nkinerja terbaik dengan tingkat akurasi sebesar 83,07% dan nilai F1-score sebesar\r\n0,83, yang efektif dalam menangkap interaksi nonlinier pada data. Selain itu, model\r\nRandom Forest secara signifikan mengungguli model dasar Decision Tree tunggal\r\ndengan peningkatan kinerja sekitar 7,6%, sehingga menegaskan pentingnya\r\npenggunaan metode ensemble dalam mengurangi varians model. Temuan ini\r\nmenyoroti potensi analitik prediktif dalam membantu organisasi merancang\r\nintervensi kesehatan mental yang lebih terarah dan tetap menjaga privasi individu.",
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      "keywords": "Prediksi kesehatan mental, Machine learning, Analitik sumber daya\r\nmanusia, Random forest, Ketidakseimbangan kelas, SMOTE, Mental health prediction, HR analytics, Class imbalance.",
      "thesis_type": "bachelor",
      "institution": "Universitas AMIKOM Yogyakarta",
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      "department": "Fakultas Ilmu Komputer",
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      "thesis_name": "skripsi",
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      "title": "PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING UNTUK\r\nPREDIKSI KESEHATAN MENTAL DI SEKTOR TEKNOLOGI",
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