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      "abstract": "Popularitas game pada platform Roblox meningkat pesat seiring\r\nbertambahnya jumlah pengguna dan pengembang. Kondisi ini menyulitkan\r\npenentuan tingkat popularitas game secara objektif apabila hanya dilakukan secara\r\nmanual, terutama bagi pengembang baru dan pengguna dalam memilih game yang\r\nberkualitas. Oleh karena itu, diperlukan sistem klasifikasi otomatis yang mampu\r\nmengelompokkan popularitas game berdasarkan data aktivitas pengguna. \r\nPenelitian ini bertujuan untuk membangun sistem klasifikasi popularitas \r\ngame Roblox menggunakan metode machine learning dengan memanfaatkan data \r\naktivitas pengguna. Dua algoritma yang digunakan adalah K-Nearest Neighbor\r\n(KNN) dan Support Vector Machine (SVM). Tahapan penelitian meliputi prapemrosesan\r\n\r\ndata, pembagian data latih dan uji, pembangunan model, tuning\r\nparameter, serta evaluasi kinerja menggunakan metrik akurasi dan confusion\r\nmatrix. Model yang dihasilkan kemudian diimplementasikan ke dalam aplikasi\r\nberbasis Streamlit untuk pengujian prediksi secara interaktif. \r\nHasil penelitian menunjukkan bahwa model KNN memiliki performa lebih\r\nbaik dibandingkan SVM, dengan akurasi sebesar 97,53%, sedangkan SVM\r\nmencapai 95,43%. Sistem yang dikembangkan terbukti mampu mengklasifikasikan\r\npopularitas game Roblox ke dalam kategori High, Medium, dan Low secara efektif.\r\nPenelitian ini diharapkan dapat menjadi alat bantu bagi pengembang, pengguna,\r\ndan pihak platform dalam menganalisis popularitas game, serta dapat\r\ndikembangkan lebih lanjut dengan penambahan fitur atau penerapan metode yang\r\nlebih kompleks.",
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      "date": "2026-02-23",
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      "thesis_type": "bachelor",
      "dir": "disk0\/00\/03\/22\/00",
      "title": "KLASIFIKASI POPULARITAS GAME DI ROBLOX\r\nBERDASARKAN AKTIVITAS PENGGUNA MENGGUNAKAN\r\nALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN KNEAREST\r\nNEIGHBOR\r\n(KNN)",
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