Fuzzy-DSM for Determining Public Transporter based on Geographical Condition
Ditdit Nugeraha Utama1, Dwi Adji Prasetyo2, Finka Aninditha Anggari3, Firas Abdullah Bar4, Muhammad Fascal Bhaskara5
1Ditdit Nugeraha Utama*, Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
2Dwi Adji Prasetyo, Information System Department, UIN Syarif Hidayatullah, Jakarta, Indonesia.
3Finka Aninditha Anggari, Information System Department, UIN Syarif Hidayatullah, Jakarta, Indonesia.
4Firas Abdullah Bar, Information System Department, UIN Syarif Hidayatullah, Jakarta, Indonesia.
5Muhammad Fascal Bhaskara, Information System Department, UIN Syarif Hidayatullah, Jakarta, Indonesia
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 1673-1676 | Volume-9 Issue-5, March 2020. | Retrieval Number: D1460029420/2020©BEIESP | DOI: 10.35940/ijitee.D1460.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Numerous types of public transporter in one region (in Indonesia) should be examined. The paper presents a decision support model (DSM) for examine the properness of public transporter implemented in one region (district) in Indonesia. Main method operated technically in this study is fuzzy logic with rule base as an engine core. Three types of public transporter examined are ojek, metromini, and angkot; respectively, in Indonesia they are public motorcycle, public mini bus, and public family vehicle. Four geographical parameters considered in the model are road traffic condition, traffic road length, rainfall, and congestion point. The model was finally able to recommend the most proper public transporter implemented in seven districts.
Keywords: Decision Support Model, Fuzzy Logic, Public Transporter.
Scope of the Article: Fuzzy logics