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KERNEL Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika
Article . 2020 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Algoritma SVM untuk Memprediksi Pengunjung Wisata Musium di Jakarta

Authors: Nur Nafi'iyah;

Algoritma SVM untuk Memprediksi Pengunjung Wisata Musium di Jakarta

Abstract

Berbagai macam tempat wisata yang ada di Jakarta menjadi tujuan berlibur atau bermain, mulai dari wisata alam, mall, bioskop, taman hiburan, atau musium. Setiap individu mempunyai aktivitas dan rutinitas bermacam-macam, sehingga membutuhkan hiburan dan waktu untuk melepaskan kejenuhan. Dari website data.jakarta.go.id didapatkan dataset kunjungan wisata musium baik dari wisatawan Indonesia maupun luar Indonesia. Dari dataset tersebut dapat dimanfaatkan untuk diolah dan digali informasinya. Menggali dan mengolah dataset adalah suatu kegiatan data mining, yaitu menerapkan suatu algoritma untuk menggali pengetahuan. Algoritma SVM digunakan untuk memprediksi kunjungan wisata musium di Jakarta, di mana terdapat variabel tempat destinasi, bulan, jenis pengunjung dan jumlah pengunjung. Tempat destinasi ada 10 jenis wisata, dan jenis pengunjung ada 2, yaitu wisatawan dalam negeri dan luar negeri. Di mana hasil prediksi dari SVM pada data 222 baris pengunjung wisata musium di Jakarta jelek. Dibuktikan dari nilai selisih data nyata dengan hasil prediksi sangat tinggi, dan nilai errornya sangat tinggi 2838303,5.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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