Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Tensor: Pure and Applied Mathematics Journal

Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke Dorteus Lodewyik Rahakbauw; Adya Afriananda; Henry W. M. Patty
Tensor: Pure and Applied Mathematics Journal Vol 3 No 1 (2022): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol3iss1pp11-22

Abstract

Stroke is a neurological function disorder caused by disruption of blood flow in the brain that arises suddenly and acutely within a few seconds or more precisely within a few hours that lasts more than 24 hours with symptoms or signs according to the affected area. Early detection of stroke usually takes a long time. With advances in technology, stroke can be prevented by detecting the risk early so that it can be treated quickly and increase the chances of recovery. The discussion of this research is about early detection of stroke risk by comparing using fuzzy logic Sugeno method and Mamdani method and using patient data at Dr. Hospital. H. Isaac Umarella. By using input variables in the form of: blood pressure, age, LDL, and blood sugar levels. Based on the results obtained from the calculation of Error with Mean Absolute Percentage Error (MAPE), the level of truth of the calculation of the Sugeno method is 87%, while the truth level of the Mamdani method is 85% so that it can be said that both methods get good results but Sugeno's fuzzy logic is superior with a value of small MAPE. In conclusion, fuzzy logic with the Sugeno method can be used in early detection of stroke risk.
Kajian Grup Galois Isomorfis dengan Grup Alternating A5 Henry Willyam Michel Patty; Fandy Sanudin; Francis Yunito Rumlawang; Dyana Patty
Tensor: Pure and Applied Mathematics Journal Vol 3 No 1 (2022): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol3iss1pp49-56

Abstract

Pemodelan Sistem Antrian Pelayanan BPJS (Badan Penyelenggara Jaminan Sosial) Menggunakan Petri Net dan Aljabar Max-Plus Simbolon, Yohana L.; Rumlawang, Francis Y.; Dahoklory, Novita; Patty, Henry W. M.; Taihuttu, Pranaya D. M.; Wattimena, Abraham Z. Wattimena
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss2pp75-86

Abstract

Hospitals are one of the health facilities that serve patients with various types of services, including BPJS patients. Like other hospitals, the queue system is a challenge in service management, especially in outpatient services. The imbalance between the number of patients coming and the service capacity can cause long waiting times. In this study, outpatient queue modeling was carried out at Leimena General Hospital, Ambon, using Petri Net to describe the service flow, and Max-Plus algebraic analysis was applied to estimate patient waiting times more accurately. The simulation results showed that increasing the number of resources, such as adding registration counters and doctors in the laboratory, was able to significantly reduce patient waiting times at various stages of service, especially in the pharmacy. This modeling shows that the Petri Net and Max-Plus approaches are not only effective in mapping the queue system, but can also be used as a basis for decision making in optimizing hospital services. This study is expected to be a reference for hospitals in improving service efficiency and for further researchers to develop more complex models by considering additional relevant variables.