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      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32294",
      "keywords": "Analisis Sentimen, Klasifikasi Teks, Multinomial Naive Bayes,\r\nComplement Naive Bayes, Google Play Store, Sentiment Analysis, Text Classification.",
      "rev_number": 16,
      "status_changed": "2026-07-22 02:37:42",
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      "department": "Fakultas Ilmu Komputer",
      "date": "2026-02-19",
      "corp_creators": [
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      "creators": [
        {
          "name": {
            "given": "Nabil Rafii Setya",
            "lineage": null,
            "honourific": null,
            "family": "Nugraha"
          },
          "nim": "21.11.4475"
        }
      ],
      "abstract": "Aplikasi Roblox merupakan salah satu platform permainan daring yang\r\nmemiliki jumlah pengguna sangat besar dan aktif, sehingga ulasan pengguna di\r\nGoogle Play Store menjadi sumber informasi penting untuk mengetahui tingkat\r\nkepuasan, keluhan, serta persepsi pengguna terhadap kualitas aplikasi. Namun,\r\nbanyaknya ulasan yang bersifat bebas dan tidak terstruktur menyebabkan\r\npengembang maupun pihak terkait kesulitan dalam menganalisis opini pengguna\r\nsecara manual, yang dapat berdampak pada lambatnya pengambilan keputusan\r\ndalam peningkatan kualitas layanan dan pengalaman pengguna. Oleh karena itu,\r\npenelitian ini bertujuan untuk melakukan analisis sentimen dan klasifikasi ulasan\r\npengguna aplikasi Roblox di Google Play Store menggunakan algoritma\r\nMultinomial Naive Bayes dan Complement Naive Bayes. Metode penelitian yang\r\ndigunakan meliputi pengumpulan data ulasan, tahap praproses teks seperti case\r\nfolding, tokenizing, stopword removal, dan stemming, kemudian dilanjutkan\r\ndengan pembobotan kata menggunakan TF-IDF serta proses pelatihan dan\r\npengujian model klasifikasi. Hasil penelitian menunjukkan bahwa kedua algoritma\r\nmampu mengklasifikasikan sentimen ulasan pengguna ke dalam kategori positif,\r\nnegatif dan netral dengan performa yang baik, di mana algoritma Complement\r\nNaive Bayes menunjukkan hasil yang lebih optimal dalam menangani\r\nketidakseimbangan data dibandingkan Multinomial Naive Bayes. Penelitian ini\r\ndiharapkan dapat dimanfaatkan oleh pengembang aplikasi, peneliti, dan pihak\r\nterkait sebagai bahan evaluasi kualitas aplikasi serta sebagai referensi dalam\r\npengembangan sistem analisis sentimen berbasis pembelajaran mesin di masa\r\nmendatang.",
      "title": "ANALISIS SENTIMEN DAN KLASIFIKASI ULASAN\r\nPENGGUNA APLIKASI ROBLOX DI GOOGLE PLAY\r\nMENGGUNAKAN ALGORITMA NAIVE BAYES",
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