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IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK MENGANALISIS SENTIMEN USER TWITTER

Ahmiyul, Aisyah Mutiah (Elfitra, 2021-12)
ANALISIS SENTIMEN TERHADAP PELAKSANAAN PEMBATASAN SOSIAL BERSKALA BESAR (PSBB) PADA MASA NEW NORMAL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

Handayani, Dwi (Elfitra, 2022-01)
KLASIFIKASI BERITA MENGGUNAKAN SUPPORT VECTOR MACHINE UNTUK MENGETAHUI JENIS KONTEN E-GOVERNMENT

Erlinda, Ella (2020-10)
ANALISA PERSEPSI MASYARAKAT TERHADAP DRAF KUHP 2019 MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Ramadhan, Rizki (2020-06)
ANALISIS SENTIMEN TERHADAP LAYANAN GOFOOD PADA MASA AWAL DAN PENURUNAN PANDEMI MENGGUNAKAN METODE NAÏVE BAYES

Sari, Intan Permata (Elfitra, 2022-07)
PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR PADA PROGRAM STUDI SISTEM INFORMASI UNIVERSITAS RIAU

Natasha, Syafira (Elfitra, 2022-04)
Event of the 2020 regional head election became controversial, because it was held during the Covid-19 that hit Indonesia. Although it was postponed, the General Elections Commission (KPU) has rescheduled the 2020 regional head elections until the end of the year. The KPU believes that the regional head election will still be carried out by prioritizing health protocols at every stage of the regional head election. Responding to two equally important conditions, comments appear on various social media such as Twitter where everyone is free to give positive and negative opinions. Public concerns arise if the 2020 simultaneous regional head elections are still held, the possibility of Covid-19 getting out of control. This regional head election will be an arena for the spread and transmission of Covid-19 on a large scale. The studied was conducted to obtain a general perception or picture of public sentiment towards the implementation of regional head elections during the Covid-19 using the Support Vector Machine (SVM) method. The stages in this research included data collection, data pre-processing, TF-IDF weighting, data sharing, classification used Support Vector Machine (SVM) and evaluation used a confusion matrix. Based on the resulted of the research used the confusion matrix, the highest accuracy value is 97%, the highest precision value is 96.8% and the recall is 99% with the distribution of tested data and training data 90%: 10%.

Sepdiansyah, Wahyu (Elfitra, 2022-07)

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