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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Sahmanbanta Sinulingga
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Computer Science & IT

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PERANCANGAN SISTEM SIMULASI ANTRIAN KENDARAAN BERMOTOR PADA STASIUN PENGISIAN BAHAN-BAKAR UMUM (SPBU) MENGGUNAKAN METODE DISTRIBUSI EKSPONENSIAL STUDI KASUS : SPBU SUNSET ROAD Vero Wahyudi, Gustri; Sinulingga, Sahmanbanta; Firdaus, Fachrosi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Volume 1 No 2 - Nopember 2012
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Peningkatan jumlah kendaraan bermotor (roda empat keatas) yang signifikan membuatpihak SPBU harus bisa meningkatkan pelayanan terhadap customer, sehingga antrian panjangtidak terjadi disaat customer melakukan transaksi pembelian bahan bakar, akan tetapi efisiensidari segala aspek baik itu dari segi pompa (server), maupun operator tetap dijaga.Metode yang digunakan untuk menghitung waktu antar kedatangan dan waktu pelayanansetiap pelanggan atau pembeli yang dilayani oleh operator pada tiap pompa (server) adalahDistribusi Eksponensial karena distribusi ini lebih mendekati pendekatan yang lebih konstandaripada distribusi normal.Berdasarkan sistem simulasi yang dibangun diperoleh hasil bahwa dengan adanya 3pompa (server) pada SPBU Sunset Road peluang terjadinya antrian panjang bisa diminimalkan.
Pengenalan Wajah Menggunakan Two Dimensional Linear Discriminant Analysis Berbasis Optimasi Feature Fusion Strategy Sahmanbanta Sinulingga; Chastine Fatichah; Anny Yuniarti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 3 No 1 (2016): JATISI SEPTEMBER 2016
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.761 KB) | DOI: 10.35957/jatisi.v3i1.59

Abstract

The era of technology today,, research on biometric image is not common to do. One well researched biometric image is a face recognition (face recognition). Problems on the human face recognition is a diversity of features or shape between one another face to face. Therefore, the need for facial feature extraction and classification using a particular method so that the classification can be recognized correctly.In this study proposed feature extraction method that can overcome the problems of non-linear automatic data contained in the face image, called the Two Dimensional Linear Discriminant Analysis based on Feature Fusion Strategy (TDLDA-FFS). Not stopping on feature extraction, classification methods proposed also faces that can overcome the problems of the adaptive matrix which aims to study the benefit of weight on each - each input with the method Relevanced Generalized Learning Vector quantization (GRLVQ).This research integrates methods TDLDA-FFS and GRLVQ for face recognition. With the combination of both methods are proven to provide optimal results with a level of recognition accuracy ranged between 77.78% to 82.22% with a pilot using a databaseof facial images from the Institute of Business and Information Stikom Surabaya. While the test uses a database derived from YaleB Database achieve accuracy levels ranging from 88.89% to 94.44%.
Co-Authors Anny Yuniarti Chastine Fatichah Fachrosi Firdaus Gustri Vero Wahyudi