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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) IT JOURNAL RESEARCH AND DEVELOPMENT Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi
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STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Engineering Materials Science & Nanotechnology Physics

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Journal : IT JOURNAL RESEARCH AND DEVELOPMENT

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Geolocation Apps using A* Algorithm for Android Based Traders Istithoatun Kholishoh; Mardainis Mardainis; Susandri Susandri; Khusaeri Andesa
IT Journal Research and Development Vol. 6 No. 1 (2021)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2021.vol6(1).5398

Abstract

The development of technology has a positive effect on the trade sector, creating smartphones that can be utilized in all activities combined with the internet network. Activity that is currently growing is a mobile trader in the city of Pekanbaru. This development caused much competition, for example, in the Pekanbaru city area, especially in Sialangmunggu village. Traders around is difficult to find consumers because consumers do not have precise location and time information. Therefore, researchers aim to design and build applications by utilizing the functions of google maps and GPS (Global Positioning System) where the Algorithm to be applied is the A* algorithm whose function is to find the nearest location between buyers to mobile merchants, to accommodate data from mobile merchants where buyers can know the nearest position of the traveling merchant. Process analysis will be divided into running analysis that discusses the workings of the process of mobile traders and buyers in the field. Then the proposed system analysis of the analysis will be made by the author to maximize the process on the current analysis. By making analysis and design, the author will know the needs needed in the creation of the system. The result of using method A* is applied to displaying the merchant's route with the user, and the result can provide the fastest route to get to the trader. The use of method A* is also done to find the trader whose location is closest to the user's location, and the result can display the nearest trader.
New Student Drug Tests at College Using Principal Component Analysis Method Agnes Chrisnalia; Edwar Ali; Mardainis Mardainis; Rahmiati Rahmiati
IT Journal Research and Development Vol. 6 No. 2 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.7583

Abstract

Drugs are substances or illegal drugs that can endanger human life. Someone who consumes it in an inappropriate way will become dependent and even result in death. The physical characteristics of people who use drugs vary, but the more obvious characteristics are on the faces of drug users such as red eyes, stiff facial muscles, dark spots, pupils susceptible to light, sunken face shape, and dullness. The lack of physical characteristics of drug users due to similarities with other diseases makes it difficult for people to recognize them initially. However, for users whose face data has been tracked by the National Narcotics Agency, the facial data is stored in the dataset. This research was conducted with the aim of building a system that can detect and recognize prospective students whether they have ever been included in drug users recorded in the National Narcotics Agency dataset or not as one of the requirements for new student admissions to universities. The system built using the Principal Component Analysis method to process and extract images of the physical characteristics of drug users through the facial image data of drug users stored in the dataset. If the detected face has similarities with the characteristics in the dataset, it is necessary to suspect that the detected face is a drug user. The results of this study are the system is able to detect the faces of drug users using the Principal Component Analysis method with an accuracy of 90% and it is hoped that with this research the system can be one solution in helping universities as an identification effort to minimize drug use so that it can be an additional identification tool which strengthens someone detected using drugs.
Co-Authors -, Tashid Agnes Chrisnalia Agustin Agustriono, Agustriono Ambiyar, Ambiyar Arpi Nanda Putra Arpi Nanda Putra, Arpi Nanda Astarilla, Liya Edwar Ali Erianto, Zulhegi Erlinda, Susi Fitri Ayu Fransiskus Zoromi Hadi Asnal, Hadi Hamdani Hamdani Henda, Zupri Herianto Herianto Herwin Herwin, Herwin Irfansyah, Rahmat Istithoatun Kholishoh Jamaris, Muhamad Junadhi Junadhi Khusaeri Andesa Lusiana Lusiana M. Arifin M. Jamaris Melyanti, Rika Mursalim Mursalim Octadino Haryadi Rahmaddeni Rahmaddeni Rahman, Ardy Rahmiati Shandy Merdya Pratama Susandri, Susandri Susanti Susanti Susanti, Susanti Susi Erlinda Torkis Nasution Yansyah Saputra Wijaya Yoyon Efendi Yuda Irawan Zulhegi Erianto