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3,491 to 3,500 of 3,511 Results
mape.m
Mar 8, 2022 - ws prediction
Plain Text - 81 B - MD5: ecf164e1dc8664c3ad87c4c5f273eaed
rmse.m
Mar 8, 2022 - ws prediction
Plain Text - 75 B - MD5: 47030da10de536afae7c3793bd0f76b8
rsquare.m
Mar 8, 2022 - ws prediction
Plain Text - 1004 B - MD5: 9d570ff60416b1dd0ac10051ee5f194c
svm_berhasil.m
Mar 8, 2022 - ws prediction
Plain Text - 3.7 KB - MD5: 0da59d2a3b94a63008ae60cce4ee3bcb
Information Security Assessment on Court Tracking Information System: A Case Study from Mataram District Court
Mar 7, 2022 - CANDIWAN CANDIWAN Dataverse
CANDIWAN, CANDIWAN, 2022, "Information Security Assessment on Court Tracking Information System: A Case Study from Mataram District Court", https://doi.org/10.34820/FK2/I9VOJC, Telkom University Dataverse, V1
This data has relation with our paper : Information Security Assessment On Court Tracking Information System: A Case Study from Mataram District Court
Work Paper Gap ISO 27001 2013 and Maturity(DipulihkanOtomatis).xlsx
MS Excel Spreadsheet - 9.0 MB - MD5: bfe0d3381c9fa2e31fb3d43cd0c8c138
Measurement of maturity level based on ISO/IEC 27001:2013 in Mataram District Court
Beauty Product Review
Mar 6, 2022 - Mahendra's Dataset
Purbolaksono, Mahendra Dwifebri; Adiwijaya; Said Al Faraby, 2022, "Beauty Product Review", https://doi.org/10.34820/FK2/NAZYE1, Telkom University Dataverse, V1, UNF:6:3WcQ72ieFP5SlXmF9IPVZA== [fileUNF]
This dataset contains the Review of Beauty Product in the Bahasa Indonesia text representation. Each text in the dataset has been categorized into Price, Packaging, Product, and Aroma. Also, each category has been classified into Positive, Neutral, and Negative.
produk_kecantikan.tab
Mar 6, 2022 - Beauty Product Review
Tab-Delimited - 1.5 MB - MD5: 25321ae57c005ccdeaf802486f0af7d2
Indonesian Online News Comment: Abusive Text Identification
Mar 5, 2022 - Ade Romadhony Dataverse
Romadhony, Ade, 2022, "Indonesian Online News Comment: Abusive Text Identification", https://doi.org/10.34820/FK2/DQEVRR, Telkom University Dataverse, V1, UNF:6:FL7AmAWefBkzMld2oMk8RA== [fileUNF]
This dataset consists of comments that are in some of the top news stories in 2019. comments obtained from the kompas, kaskus, and detik. The labeling process is carried out by 10 people and each comment was labeled by 3 annotators. Each comment is labeled with: 1: 'not abusive'...
Abusive Language Detection on Indonesian Online News Comments Dataset .tab
Tab-Delimited - 419.6 KB - MD5: 029c665b5533182a66e2944cb4a11cf2
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