Analisis Sentimen Calon Kepala Daerah Maluku Utara dengan Metode CRISP-DM
DOI:
https://doi.org/10.32877/bt.v7i3.2285Keywords:
Analisis Sentimen, CRISP-DM, Media Sosial, Neural Network, Pemilihan Kepala Daerah
Abstract
Pemilihan kepala daerah merupakan salah satu wujud nyata dari demokrasi lokal yang memungkinkan masyarakat mengekspresikan aspirasi politiknya secara langsung. Namun, untuk memahami dinamika persepsi publik terhadap calon kepala daerah, dibutuhkan pendekatan analitis yang mampu menafsirkan opini masyarakat dalam skala luas dan waktu nyata. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap calon kepala daerah di Provinsi Maluku Utara dengan memanfaatkan data komentar dari media sosial YouTube dan TikTok. Subjek penelitian berupa komentar publik yang dikumpulkan melalui teknik crawling dengan kata kunci tertentu terkait nama calon dan isu politik lokal. Penelitian ini menggunakan pendekatan CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari enam tahapan: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan deployment. Proses analisis mencakup preprocessing teks seperti tokenizing, case folding, stop word removal, dan stemming. Komentar diklasifikasikan secara manual ke dalam tiga kategori sentimen: positif, negatif, dan netral. Model klasifikasi dibangun menggunakan algoritma Neural Network melalui platform Orange Data Mining. Hasil penelitian menunjukkan bahwa kandidat 3 memperoleh sentimen positif tertinggi, kandidat 1 mendapat sentimen negatif terbanyak, dan kandidat 5 mendominasi pada sentimen netral. Model menunjukkan kinerja tinggi dengan akurasi mencapai 97,2%, presisi 98,5%, dan recall 88,8%. Temuan ini menunjukkan bahwa kombinasi metode CRISP-DM dan pembelajaran mesin dapat memberikan wawasan strategis yang bermanfaat bagi pengambil kebijakan dan tim kampanye dalam memahami persepsi publik secara lebih komprehensif dan real-time. Penelitian ini dapat menjadi referensi bagi pemangku kepentingan dalam merancang strategi komunikasi politik yang lebih tepat sasaran.
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