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All Journal Jurnal Energi Dan Manufaktur KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 JISI UMJ (Jurnal Integrasi Sistem Industri UMJ) Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Paradharma: Jurnal Aplikasi IPTEK
I Dewa Made Krishna Muku
STMIK STIKOM Indonesia
Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : SINTECH (Science and Information Technology) Journal

 1 
Support Vector Machine For Hoax Detection Ni Wayan Sumartini Saraswati; I Putu Krisna Suarendra Putra; I Dewa Made Krishna Muku; Gede Dana Pramitha
SINTECH (Science and Information Technology) Journal Vol. 6 No. 2 (2023): SINTECH Journal Edition Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i2.1366

Abstract

Along with the development of information technology, news media has also developed by presenting information online Along with the rapid development of online news, the spread of fake news information (hoaxes) is also increasing rapidly and widely. Hoax news is often spread intentionally for various purposes. Generally, hoax news aims to direct the reader's perception to believe in a bad perception of an event, character or even a company. The motivation is to invite readers to believe something that is not true with the aim of benefiting the news disseminator is something dangerous. This research aims to detect English-language hoaxes by applying the Support vector machine (SVM) algorithm. In this study, the data used are two data sources, namely English news datasets from Kaggle and English news taken from BBC. The results of this study show that the application of the SVM algorithm turns out to get good performance because the model is able to classify hoax news with an accuracy of 99.4% on Kaggle data while on the BBC news dataset the model gets an accuracy of 98.9%. This research also shows that the SVM method is proven to have good generalization properties. Where it is able to identify test data that is completely different from the training data.
Recognizing Hotel Visitors Preferences Based on Service Consumption Level Using K-Means Method Saraswati, Ni Wayan Sumartini; Bisena, I Kadek Agus; Muku, I Dewa Made Krishna; Krisna, Gede Gana Eka
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1443

Abstract

Consumer segmentation is an old issue that remains interesting to study today, given the magnitude of the benefits obtained when consumers can be segmented properly. Marketing cost efficiency is one of the great benefits of this process. Likewise, the effectiveness of marketing activities to maintain customer retention. It is because companies can better identify consumers. Based on the hotel service consumption level, this research could identify consumer clusters based on hotel consumer preferences. Thus, hotel management could target specific types of service promotion better and on target. This research built a hotel visitor clustering model using the K-Means Clustering method to determine customer segments based on the level and type of hotel service consumption. The K-Means model was built based on hotel visitor consumption data for each type of service. Furthermore, the hotel visitor clusters formed were identified by their characteristics. Four consumer clusters were obtained based on the silhouette score analysis, which described the characteristics of consumers in each cluster.
Co-Authors Alvin Limawan Susanto Aniek Suryanti Kusuma, Aniek Suryanti Atmaja, Ketut Jaya Baehaqi Christina Purnama Yanti Dewi Natalia, Sang Ayu Made Krisna Dirgayusari, Ayu Manik Eddy Hartono Gede Surya Mahendra Hadi Santosa I Gede Adi Sudi Anggara I Gede Andika I Gusti Ketut Sukadana I Kadek Agus Bisena I Ketut Setiawan I Made Dwi Budiana Penindra I Nyoman Agus Suarya Putra I Nyoman Jayanegara I Putu Dedy Sandana I Putu Krisna Suarendra Putra I Wayan Dharma Suryawan I WAYAN SUDIARSA Indra Pratistha Juniartini, Ni Komang Tri Kadek Budi Sandika Ketut Laksmi Maswari Krisna, Gede Gana Eka MADE WAHYU ADHIPUTRA Melinia Hutari Natalia, Sang Ayu Made Krisna Dewi NI LUH PUTU AGETANIA . NI LUH PUTU MERY MARLINDA Ni Made Lisma Martarini Ni Made Mila Rosa Desmayani* Ni Wayan Mirah Senja Pertiwi Ni Wayan Wardani Nirwana, Ni Kade Ayu Pramita, Dewa Ayu Kadek Pramitha, Gede Dana Putu Ananda Sitarasmi PUTU SUGIARTAWAN Saraswati, Ni Wayan Sumartini Sari, Ni Luh Pangestu Widya Waas, Devi Valentino Wardani, Ni Wayan Willdahlia, Ayu Gede