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Rudy Adipranata
Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
Computer Science & IT

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Penerapan Inventory Control untuk Meningkatkan Cost Efficiency pada Perusahaan Distributor PT. Y Nicholas Billy; Yulia Yulia; Rudy Adipranata
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Abstract

PT. Y is a distributor company that sells water heaters and several water heater accessories. The problem is that all sales and stock bookkeeping is still done manually via excel separately - excel data is separated so that there are often missing excel. The stock check process is also done manually by counting the items one by one within a certain time so that it takes a long time. Too much stock because they also want to buy items at low prices is also a problem. From the existing problems, PT. Y requires an administrative system that helps summarize sales and purchase data as well as methods in the form of Economic Order Quantity and Reorder Points to help PT. Y in preparing stock so as not to buy too much stock With the Inventory Control System that will be applied to PT. Y will help with the problem of miscalculation of excel data. Better stock management and purchase of goods with good calculations will help cost efficiency at PT. Y
Sistem Presensi Mahasiswa Menggunakan Face Recognition Dengan Metode Facenet Pada Android Evelyn Evelyn; Rudy Adipranata; Kartika Gunadi
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

As of today, the student attendance system in the University of Petra uses a QR code system to do their daily attendance. This QR code system has some flaws that are often exploited by the students. Students usually exploit this system by asking their peers to login into their accounts and take the attendance for them by scanning the shared QR code. Implementing face recognition can be one of the means of prevention. This attendance system is an Android based system using Facenet model for the facial recognition system. Formulas L2Norm and Cosine Similarity is used for comparison means for the results of the face recognition system. Results show that cosine similarity is most optimal when using the 0.5f threshold with the score of 0.5104218 and accuracy with the score of 0.77162087. Meanwhile, L2Norm results show that it is most optimal when using the 8.0f threshold with the score of 5.8973804 and accuracy with the score of 5.8973804.
Co-Authors Evelyn Evelyn Kartika Gunadi Nicholas Billy Yulia Yulia