Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Media Statistika Pattimura International Journal of Mathematics (PIJMath)
Mayawi, Mayawi
Unknown Affiliation
Decision Sciences, Operations Research & Management Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PARAMETER INDEPENDENT FUZZY WEIGHTED k-NEAREST NEIGHBOR Mayawi, Mayawi; Subanar, Subanar
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.162-172

Abstract

Parameter Independent Fuzzy Weighted k-Nearest Neighbor (PIFWkNN) as a classification technique developed by combining Success History based Parameter Adaptive Differential Evolution (SHADE) with Fuzzy k-Nearest Neighbor (FkNN), where this PIFWkNN does not state the optimization of weights and k values as two separate problems, but they’re combined into one and solved simultaneously by the SHADE algorithm. The steps for implementing the PIFWkNN method are explained, followed by its application to 10 different datasets, and then the accuracy is calculated. To see the consistency of the goodness of the classification of this method, the accuracy results are compared with the accuracy of the k-Nearest Neighbor (kNN), FkNN, and Weighted k-Nearest Neighbor (WkNN). The results show that the average accuracy of PIFWkNN, kNN, FkNN, and WkNN is 75.76%, 68.52%, 71.40% and 66.22% so PIFWkNN is higher than the three methods. Using the Wilcoxon Sign Rank (WSR) test also concluded that with a 95% confidence shows that every hypothesis had significant differences. Furthermore, it descriptively shows that the average rank of PIFWkNN is higher than the other. Thus, the PIFWkNN has higher accuracy than the kNN, FkNN, and WkNN.
Ordinal Logistic Regression Analysis of Factors that Affecting the Blood Sugar Levels Diabetes Mellitus Patients Mayawi, Mayawi; Nurhayati, Nurhayati; Talib, Taufan; Bustan, Ariestha W; Laamena, Novita S
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp33-42

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh faktor-faktor risiko terhadap kadar gula darah pada penderita diabetes mellitus menggunakan analisis regresi logistik ordinal. Faktor-faktor risiko yang dijadikan variabel bebas adalah usia, jenis kelamin, Indeks Massa Tubuh (IMT), tekanan darah, Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL), Thyrocalcitonin Hormone (TCH) dan Loss Trigliserida(LTG). Data yang digunakan dalam penelitian ini diperoleh dari https://hastie.su.domains/Papers/LARS/diabetes.data. Jumlah sampel yang diambil sebanyak 100 responden yang telah terdiagnosis diabetes mellitus. Hasil penelitian menunjukkan bahwa faktor-faktor risiko seperti usia, Indeks Massa Tubuh (IMT), Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL) dan jenis serum Thyrocalcitonin Hormone (TCH) berpengaruh signifikan terhadap kadar gula darah pada penderita diabetes mellitus. Model logit terbaik untuk regresi logistic ordinal adalah Logit 1 yaitu g(x_1 )= -2.721-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8 dan Logit 2 yaitu g(x_2 )= -8.571-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8. Disimpulkan bahwa analisis regresi logistik ordinal dapat digunakan untuk mengidentifikasi faktor-faktor yang mempengaruhi kadar gula darah pada penderita diabetes mellitus dan membantu pengembangan strategi pengelolaan dan intervensi yang lebih efektif
Co-Authors Bustan, Ariestha W Laamena, Novita S Nurhayati Nurhayati Subanar . Taufan Talib