Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of the Indonesian Mathematical Society Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan
Dwi Mifta Mahanani, Dwi Mifta
Unknown Affiliation
Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Karakterisasi Dekomposisi Modul yang Dibangun secara Hingga atas Daerah Valuasi Mahanani, Dwi Mifta; Waluyo, Pudji Astuti
Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Vol 1 No 1 (2017): Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai Islami )
Publisher : Mathematics Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (326.145 KB)

Abstract

Tulisan ini membahas tentang karakterisasi dekomposisi modul yang dibangun secara hingga atas daerah valuasi. Ada beberapa konsep yang diperlukan untuk mengetahui kapan modul yang dibangun secara hingga dapat didekomposisikan. Konsep-konsep tersebut mengenai deret komposisi dan dimensi Goldie. Oleh karena itu, akan dibahas pula deret komposisi dan dimensi Goldie dari modul yang dibangun secara hingga atas daerah valuasi. Akan ditunjukkan bahwa modul yang dibangun secara hingga atas daerah valuasi dapat didekomposisikan menjadi tambah langsung submodul-submodul siklis jika dan hanya jika panjang deret komposisi $RD$nya sama dengan dimensi Goldienya.
A Finitely Generated Module over a Valuation Domain Mahanani, Dwi Mifta
CAUCHY Vol 6, No 1 (2019): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.899 KB) | DOI: 10.18860/ca.v6i1.6798

Abstract

This article discusses about some properties which are equivalent between a finitely generated module over PID and a finitely generated module over a valuation domain. This can be done by considering a finitely generated module over a DVR. Although in general a PID is not a valuation domain or vice versa, these equivalence of some properties will be valid. It is because a DVR is a PID and a valuation domain at the same time. Those the equivalent properties in a finitely generated module over DVR are related with the decomposition of the module and the height of an element in that module.
Connectivity Indices of Coprime Graph of Generalized Quarternion Group Zahidah, Siti; Mahanani, Dwi Mifta; Oktaviana, Karine Lutfiah
Journal of the Indonesian Mathematical Society VOLUME 27 NUMBER 3 (November 2021)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.27.3.1043.285-296

Abstract

Generalized quarternion group (Q_(4n)) is a group of order $4n$ that is generated by two elements x and y with the properties x^{2n}=y^4=e and xy=yx^{-1}. The coprime graph of Q_{4n}, denoted by Omega_{Q_{4n}}, is a graph with the vertices are elements of Q_{4n} and the edges are generated by two elements that have coprime order. The first result of this paper presented that Omega_{Q_{4n}} is a tripartite graph for n is odd and Omega_{Q_{4n}} is a star graph for n is even. The second one presented the connectivity indices of Omega_{Q_{4n}}. Connectivity indices of a graph is a research area in mathematics that popularly applied in chemistry. There are six indices that are presented in this paper, those are first Zagreb index, second Zagreb index, Wiener index, hyper-Wiener index, Harary index, and Szeged index.Generalized quaternion group (Q4n) is a group of order 4n that is generated by two elements x and y with the properties x 2n = y 4 = e and xy = yx−1 . The coprime graph of Q4n, denoted by ΩQ4n , is a graph with the vertices are elements of Q4n and the edges are formed by two elements that have coprime order. The first result of this paper presents that ΩQ4n is a tripartite graph for n is an odd prime and ΩQ4n is a star graph for n is a power of 2. The second one presents the connectivity indices of ΩQ4n . Connectivity indices of a graph is a research area in mathematics that popularly applied in chemistry. There are six indices that are presented in this paper, those are first Zagreb index, second Zagreb index, Wiener index, hyper-Wiener index, Harary index, and Szeged index.
Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization Anam, Syaiful; Purwanto, Imam Nurhadi; Mahanani, Dwi Mifta; Yusuf, Feby Indriana; Rasikhun, Hady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6307

Abstract

Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. The manual selection of hyperparameters requires a large amount of time and computational resources. Automatic HO is needed to avoid this problem. Several studies have shown that Bayesian Optimization (BO) works better than Grid Search (GS) or Random Search (RS). Based on these problems, this study proposes health risk classification using XGBoost with Bayesian Hyperparameters Optimization. The goal of this study is to reduce the time required to select the best XGBoost hyperparameters and improve the accuracy and generalization of XGBoost performance in health risk classification. The variables used were patient demographics and medical information, including age, blood pressure, cholesterol, and lifestyle variables. The experimental results show that the proposed approach outperforms other well-known ML techniques and the XGBoost method without HO. The average accuracy, precision, recall and f1-score produced by the proposed method are 0.926, 0.920, 0.928, and 0.923, respectively. However, improvements are needed to obtain a faster and more accurate method in the future.
HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION Anam, Syaiful; Putra, M. Rafael Andika; Fitriah, Zuraidah; Yanti, Indah; Hidayat, Noor; Mahanani, Dwi Mifta
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0797-0806

Abstract

The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users. There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard.
SOME FUNDAMENTAL PROPERTIES OF HEAPS Mahanani, Dwi Mifta; Ismiarti, Dewi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp1927-1932

Abstract

Heap is defined to be a non-empty set with ternary operation satisfying associativity, that is for every and satisfying Mal’cev identity, that is for all . There is a connection between heaps and groups. From a given heap, we can construct some groups and vice versa. The binary operation of groups can be built by choosing any fixed element of heap and is defined by =[x,e,y] for any . Otherwise, for given a binary operation of group , we can make a ternary operation defined by for every On heaps, there are some notions which are inspired by groups, such as sub-heaps, normal sub-heaps, quotient heaps, and heap morphisms. On this study, we will associate sub-heaps and corresponding subgroups and discuss some properties of heap morphisms.
Co-Authors Feby Indriana Yusuf Fitriah, Zuraidah Hady Rasikhun Imam Nurhadi Purwanto Indah Yanti Ismiarti, Dewi Noor Hidayat, Noor Oktaviana, Karine Lutfiah Pudji Astuti Waluyo, Pudji Astuti Putra, M. Rafael Andika Siti Zahidah Syaiful Anam