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Indriati, Rini
University of Nusantara PGRI Kediri
Computer Science & IT Education

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DESAIN DATABASE UNTUK OPTIMALISASI SISTEM PREDIKSI TRANSAKSI PENJUALAN Sucipto, Sucipto; Indriati, Rini; Hariawaan, Fitra Bagoes
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 2, No 2 (2017)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (218.177 KB) | DOI: 10.29100/jipi.v2i2.367

Abstract

The advance of informational technology is expected to be a tool which helps data tabulating process.  Management and usage of the data systematically can produce detailed information. The important of data management is able to enhance the quality of information that is processed. Data management is started from the storage system of data namely database design. In the database design, the thing that should be heeded is the relation of each table inside of it. The relation among the table shows the linkages data that will be processed to be essential information. In this research, the focused discussion is optimalizing a table for further tabulating towards a marketing informational system database.  The database that will be optimalized will be used to marketing prediction tabulation. In tabulating stage, data prediction in database will be exported into two databases namely PostgreSQL and MariaDB. The data used for examining amount 476619 marketing data is taken from marketing transaction among 2015-2017. This research data is tabulated using join table that produce 374 data. The data is the information that will be used for marketing prediction. Based on rapidity examination of marketing data tabulation by using PostSQL indicates slight difference around 0.26 seconds faster than MariaDB. The research result of database optimalizing design which has been made shows that PostgreSQL is better in data tabulating rapidity than MariaDB
Hoax Detection at Social Media With Text Mining Clarification System-Based Sucipto, Sucipto; Tammam, Aditya Gusti; Indriati, Rini
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 3, No 2 (2018)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (186.002 KB) | DOI: 10.29100/jipi.v3i2.837

Abstract

Hoax is a current issue that is troubling the public and causes riot in various fields, ranging from politics, culture, security and order, to economics. This problem cannot be separated from the impact of rapid use of social media. As a result, every day there are thousands of information spread on social media, which is not necessarily valid, so that people are potentially exposed to hoax on social media. The hoax detection system in this study was designed with an Unsupervised Learning approach so that it did not require data training. The system is built using the Text Rank algorithm for keyword extraction and the Cosine Similarity algorithm to calculate the level of document similarity. The keyword extraction results will be used to search for content related to input from users using the search engine, then calculate the similarity value. If the related content tends to come from trusted media, then the content is potentially factual. Likewise, if the related content tends to be published by unreliable media, then there is the potential for hoax. The hoax detection system has been tested using confusion matrix, from 20 news content data consisting of 10 correct issues and 10 wrong issues. Then the system produces a classification with details of 13 issues including wrong and 7 issues including true, then the number of classifications that match the original label are 15 issues. Based on the results of the classification, an accuracy value of 75% was obtained.
Co-Authors Hariawaan, Fitra Bagoes Sucipto Sucipto Tammam, Aditya Gusti