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All Journal Jurnal Ilmiah Kursor INTEK: Jurnal Penelitian
Anggun Winursito
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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IMPLEMENTATION OF FACE RECOGNITION USING GEOMETRIC FEATURES EXTRACTION Risanuri Hidayat; Muhammad Oka Bagus Wibowo; Brama Yoga Satria; Anggun Winursito
Jurnal Ilmiah Kursor Vol 11 No 2 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i2.284

Abstract

The face is among the biometric objects used to recognize one’s identity. There are various face recognition system methods that can be applied, one of which is geometric features-based face recognition. Geometric features are unique features extraction of one’s facial components. These features are obtained by calculating the comparison values of the distance measurement between facial components served as a reference like eyes, nose, and mouth. This research implemented a face recognition system using the geometric features method on a significantly low-spec computer system. This implementation was carried out by building a system, installing it on a computer system, and then testing it using laptops or computer devices and the camera web. The face recognition system would process the facial input images, extract their geometric features, and match the results with the data stored in the database. The research results were a low-spec computer system that could recognize its users by providing real-time feedback in the form of users’ names with an accuracy of 98%.
Improving Speed Performance of Select Random Query in SQL Database Muhammad Nur Yasir Utomo; Alvian Bastian; Anggun Winursito
INTEK: Jurnal Penelitian Vol 7, No 1 (2020): April 2020
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (868.105 KB) | DOI: 10.31963/intek.v7i1.1536

Abstract

Select random is a query in a SQL database that can retrieve data randomly from a table. Select random is often used to present data in various applications such as websites, data mining and others. Unfortunately, ordinary select random query is inefficient in terms of processing time if used in large table. This paper, tries to solve this problem by proposing two optimized methods of select random query, namely the Small Percentage Order by Rand (SPO-Rand) and the Filtered Column Order by Rand (FCO-Rand). The two proposed methods are then compared in terms of processing speed with a standard Select Random query or Normal Order by Rand (NO-Rand). The scenario of the experiment is to collect five random data from several data sets, ranging from 10.000 to 200.000 data. Based on the results of experiments that have been conducted, the proposed FCO-Rand method obtained the best process speed with 0.074 seconds at 200.000 data, followed by SPO-Rand with 0.265 seconds. These result are much faster than the standard random select method (NO-Rand) which takes up to 7,035 seconds for the same task.
Co-Authors Alvian Bastian Brama Yoga Satria Muhammad Nur Yasir Utomo Muhammad Oka Bagus Wibowo Risanuri Hidayat