Komparasi Algoritma Klasifikasi Dan Penerapan Ner pada Analisis Sentimen Bencana Alam Banjir
DOI:
https://doi.org/10.22441/incomtech.v14i2.22962Kata Kunci:
Naive bayes, Random forest, Linier regression, Support vector machine, Decission tree, KNN, Named Entity Recognition (NER),Abstrak
Bencana alam akhir-akhir ini terjadi di Indonesia. Layanan jejaring sosial twitter memberi ruang bagi masyarakat untuk menanggapi terkait bencana yang disebabkan oleh fakor alam, namun tanggapan yang diberikan oleh masyarakat belum diklasifikasikan. Pada penelitian ini, analisis sentimen dilakukan terhadap tweet yang mengklasifikasikannya ke dalam kategori katastropik dan non-katastropik menggunakan metode klasifikasi komparatif dan pemodelan lain yang disebut Entity Recognition Modeling (NER). Penelitian ini menggunakan 6 algoritma klasifikasi, yaitu multinominal naive bayes, random forest, linier regression, support vector machine, decission tree dan KNN. Untuk data training dan data testing diambil dengan metode random sampling dengan presentase data training 80% dan data testing 20%. Pemodelan NER dilakukan dengan Spacy untuk mendapatkan LOCATION, ORGANIZATION, PERSON, QUANTITY, TIME. Setelah dilakukan pemodelan NER dengan spacy, dilanjutkan pengukuran accuracy, precision, recall, f1-score. Support. Perhitungan precission, recall, dan f-measure mendapatkan nilai terbaik yaitu 80%, 100%, dan 89% untuk metode NER Sedangkan untuk hasil prediksi bencana dan non bencana didapatkan hasil yaitu 81,60%, 82% dan 82%.
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