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ENERGI LAPLACIAN SKEW PADA DIGRAF Fitria Dewi Puspitasari; Bayu Surarso
Jurnal Matematika Vol 1, No 1 (2012): jurnal matematika
Publisher : MATEMATIKA FSM, UNDIP

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Abstract

Digraph G is a pairs of set (V,Γ) , with V(G) is set of vertices G , and Γ(G) is set of arc G . Graph G can be representated in to matrix adjacencyS(G) , from matrix S(G) be obtained eigenvalues of graph G . The sum of the absolute values of its eigenvalues is energy skew of digraph G . From digraph G be obtained DG=diag(d1,d2,d3,…,dn) the diagonal matrix with the vertex degrees d1,d2,d3,…,dn of v1,v2,v3,…,vn . Then LG=DG-S(G) is called the laplacian matrix of digraph G . The sum of the quadrate values of each eigenvalues is energy laplacian skew. In this final project will explain about the concept of the skew laplacian energy of a simple, conected digraph G . Also find the minimal value of this energy in the class of all connnected digraphs on n≥2 vertices.
PELABELAN SUPER GRACEFUL – SISI PADA GRAF KUBUS HIPER UNTUK Destian Dwi Asyani; bayu surarso
Jurnal Matematika Vol 2, No 1 (2013): JURNAL MATEMATIKA
Publisher : MATEMATIKA FSM, UNDIP

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Abstract

ABSTRAKMisalkan  merupakan suatu graf sederhana, berhingga dan tak berarah dengan  dan  . Jika  dan  bilangan asli maka  dan  didefinisikan untuk  genap maka , untuk  ganjil maka , untuk  genap maka  dan untuk  ganjil maka . Sebuah graf  adalah graf  jika terdapat pemetaan injektif sedemikian sehingga  yang didefinisikan  oleh  adalah pemetaan surjektif. Graf kubus hiper  dan graf kubus hiper  adalah bukan graf . Hubungan nilai  dan  sehingga graf kubus hiper  merupakan graf   adalah jika maka  , jika maka  dan jika maka  and  .Kata Kunci : pelabelan , graf kubus hiper.
Penentuan Prioritas Perbaikan Jalan Berbasis Metode Analytic Network Process Sebagai Komponen Menuju Kota Cerdas Wahyul Amien Syafei; Kusnadi Kusnadi; Bayu Surarso
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 6, No 2 (2016): Volume 6 Nomor 2 Tahun 2016
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1073.086 KB) | DOI: 10.21456/vol6iss2pp105-113

Abstract

Smart city become a demand for people to live in more comfort and easier. Knowledge is needed in every decision support system in smart city. This paper presents a research in determining the priority order of road handling based on level of service by using Analytic Network Process which is implemented into decision support systems as a component toward smart city. Variables used refers to the Highway Capacity Manual Indonesia based on the volume of traffic and the road characteristic data. Analytic Network Process is used because of its advantages in conducting multi-criteria assessment on the basis of the subjective judgment of decision makers and can combine quantitative and qualitative data. From the results of the validation test between the system output and outcome data field issued by Dishubinkom Cirebon, the accuracy of the test results of 10 streets in the city of Cirebon with validation test Spearman Rank correlation is equal to 0.867. The results showed Analytic Network Process can be implemented and an appropriate solution in determining the road handling priority.
IMU-Based Early Warning System for Driver Drowsiness Detection via Head Movement Analysis Nurnaningsih, Desi; Kuswowo Adi; Bayu Surarso
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.45271

Abstract

The high incidence of road accidents caused by human error—accounting for approximately 69.7% of all motor vehicle accidents in Indonesia—demonstrates the urgent need for an effective driver monitoring system. One critical factor contributing to human error is driver drowsiness, which can be observed through behavioral indicators such as abrupt changes in head position. This study aims to develop a real-time early warning system for detecting driver drowsiness based on head movement patterns using a wearable device equipped with the MPU-6050 GY-521 accelerometer sensor. The system monitors acceleration on the X, Y, and Z axes and identifies drowsiness when simultaneous changes exceed predefined thresholds. A drowsiness event is characterized by a rapid head displacement, occurring within approximately 18–20 milliseconds. The thresholds applied for detection are 1.0g for the X axis, 3.5g for the Y axis, and 0.5g for the Z axis. In ten test scenarios simulating drowsy head movements, the system successfully identified seven instances, resulting in a detection accuracy of 70%. The novelty of this approach lies in its lightweight, non-intrusive design and its ability to function independently of lighting conditions, making it a practical solution for real-time driver safety enhancement.