Optimasi Lampu Lalu Lintas Simpang Tabuik Kota Pariaman Menggunakan Graf Fuzzy Berbasis FIS Tipe Mamdani
Abstract
Poor traffic light regulation is one of the causes of congestion. One of the factors contributing to congestion is inadequate traffic light control, especially at the Tabuik Kota Pariaman intersection. Fuzzy graph is a part of mathematical science that combines graph theory with fuzzy logic. Fuzzy graphs can be used as a method to solve congestion problems related to traffic light durations. This research aims to determine the duration of green light based on queue length. The data collected includes primary data on traffic duration, road width, and the number of vehicles passing through each leg of the intersection. The research results show that by using a fuzzy graph based on the Mamdani fuzzy inference system, the overall average at the Tabuik intersection, the total duration of green light obtained is 80 seconds, which is a decrease of 8.75% from the initial condition, while the total duration of red light obtained is 404 seconds, which is an increase of 1.76% from the initial condition.
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Sugiyanto, Manajemen Proyek Kontruksi dan Teknik Pengendalian Proyek, Surabaya: Cipta Media Nusantara, 2021.
D. P. Utomo, “Permintaan Perjalanan Angkutan Umum Massal Kota Surabaya,” M.P.I, pp. 73-82, 2013.
H. Prasetyo, “Implementasi Algoritma Logika Fuzzy untuk Sistem Pengaturan Lampu Lalu Lintas menggunakan Mikrokontroler,” Techno, pp. 1-8, 2014.
Risdiyanto, Rekayasa dan Manajemen Lalu Lintas: Teori dan Aplikasi, Yogyakarta: PT Leutika Nouvalitera, 2014.
R. Munir, Matematika Diskrit, Bandung: Informatika, 2010.
A. P. Kurniawan, “Aplikasi Graf Fuzzy dan Aljabar Max-Plus untuk Pengaturan Lampu Lalu Lintas di Simpang Empat Beran,” Jurnal Matematika, vol. 6, pp. 72-86, 2017.
H. P. Sri Kusumadewi, Aplikasi Logika Fuzzy Untuk Pendukung Keputusan, Yogyakarta: Graha Ilmu, 2004.
Z. Zurzaq, “Prediksi Awal Ramadhan menggunakan Metode Fuzzy Tsukamoto,” ARITMATIKA: Jurnal Riset Pendidikan Matematika, vol. 1, pp. 88-95, 2020.
M. Akhoondzadeh, “Developing a Fuzzy Inference System Based on Multi-Sensor Data to Predict Powerful Earthquake Parameters,” remote sensing, pp. 1-16, 2022.
Yulmaini, Logika Fuzzy, Yogyakarta: Penerbit ANDI, 2018.
S. N. Sivanandam, Introduction to Fuzzy Logic using MATLAB, India: Springer, 2007.
V. Barod, “Comparison of Mamdani and Sugeno Type Fuzzy Inference System on Enrollment Datasets,” International Journal of Engineering Sciences & Research Technology, pp. 704-712, 2016.
M. I. Mujahidin, “Hubungan Tundaan dan Panjang Antrian Terhadap Konsumsi Bahan Bakar Akibat Penyempitan Jalan (Bottleneck) pada Pembangunan Flyover Palur,” e-Jurnal MATRIKS TEKNIK SIPIL, pp. 649-656, 2014.
S. Kusumadewi, “Fuzzy Multi-Criteria Decision Making,” Media Informatika, vol. 3, pp. 25-38, 2005.
A. Setiawan, Logika Fuzzy dengan Matlab, Denpasar: Jayapangus Press, 2018.
D. A. Puryono, “Metode Fuzzy Inferensi System Mamdani untuk menentukan Bantuan Modal Usaha Bagi UMKM Ramah Lingkungan,” STIMIKA, vol. I, pp. 1-6, 2014.
D. Abdullah, “Aplikasi Pewarnaan Titik pada Graf untuk Optimalisasi Durasi Lampu Lalu Lintas di Simpang Jalan Jemursari Kota Surabaya,” MATHunesa, vol. 10, no. 02, pp. 289-298, 2022.
DOI: http://dx.doi.org/10.24036/unpjomath.v8i3.15001