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Optimization of Linear Quadratic Tracking (LQT) Weight Matrices Using Simulated Annealing Applied on Planar Arm Model Dinita Rahmalia; Teguh Herlambang; Sigit Pancahayani; Khozin Mu’tamar
SPECTA Journal of Technology Vol. 4 No. 3 (2020): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1123.239 KB) | DOI: 10.35718/specta.v4i3.193

Abstract

Optimal controls have been applied in this time. One of simple optimal control which will be analyzed in this research is planar arm model dynamic. The planar arm model dynamic consists of joint angles consisting of shoulder joint and elbow joint, angle velocities, and joint torquest due to passive muscle forces. There are control inputs from six muscles in the system. In this research, from planar arm model, it will be designed optimal control using Linear Quadratic Tracking (LQT). The objective function of planar arm model is we will minimimize two angles consisting of shoulder joint and elbow joint. In LQT, the value of performance index depends on the weight matrices so that we should optimize the weight matrices. In this research, the optimization of weight matrices in planar arm model will be applied by Simulated Annealing. The Simulated Annealing method is based on the simulation of thermal annealing of critically heated solids. Based on simulation results, Simulated Annealing can optimize the weight matrices in LQT so that it results optimal performance index with angle as state solution can follow the reference and we also obtain optimal controls from six muscle forces applied.
Multi-Objective Portfolio Optimization Using Hybrid Ant Colony Optimization and Compromise Programming Rahmalia, Dinita; Husenti, Nadya
Jurnal Teknik Industri Vol. 25 No. 2 (2024): August
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/JTIUMM.Vol25.No2.131-144

Abstract

The increasing complexity of stock trading requires effective portfolio management to optimize returns while minimizing risks. Portfolio selection is critical in determining the most suitable combination of stocks, aiming to maximize expected returns and minimize risk within a given investment limit. This study constructs a mathematical model for portfolio optimization using six different stocks, incorporating constraints such as expected return, risk, and available investment. Given the multi-objective nature of the problem, a hybrid approach is proposed, combining Compromise Programming (CP), Nadir Compromise Programming (NCP), and Ant Colony Optimization (ACO) to address both minimization and maximization objectives. The ACO algorithm is applied to minimize deviation variables, which serve as the fitness function in the optimization process. The results demonstrate the effectiveness of the hybrid method in selecting portfolios that achieve minimal deviation, providing an optimal balance between risk and return. This research offers valuable insights for investors by illustrating the trade-offs between risk and reward in stock selection, contributing to more informed decision-making in portfolio management.
Penerapan Algoritma ID3 dan Algoritma C4.5 Untuk Klasifikasi Penerima BPNT Sholikhah, Minhatin Nisaatus; Rahmalia, Dinita; Pradana, Mohammad Syaiful
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 9 No 2 (2023): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v9i2.6111

Abstract

Non-Cash Food Assistance (BPNT) is social food assistance in the form of non-cash from the government which is given to Beneficiary Families (KPM) every month through an electronic account mechanism which is used only to buy food at traders or e-warongs. One of the difficulties that the government sometimes faces in distributing BPNT is that the distribution process is uneven and not on target. Therefore, it is necessary to carry out further analysis using a mathematical approach, so that we can determine the feasibility of a BPNT recipient prediction problem. Through the results of the data collection analysis, it can be seen whether residents are eligible to receive BPNT or not. Based on existing problems, a classification method is used to predict the eligibility of BPNT beneficiaries using two methods, namely the ID3 algorithm and the C4.5 algorithm. The ID3 algorithm produces an accuracy value of 90%, precision of 100%, and recall of 83.33%. The C4.5 algorithm produces an accuracy value of 80%, precision of 100%, and recall of 80%. The AUC/ROC value of the ID3 algorithm is 0.500, the classification is diagnosed in the AUC/ROC curve as failure or failure in classification. The C4.5 algorithm has an AUC/ROC value of 0.800, meaning that the classification is included in good classification. In this way, it can be concluded that the C4.5 algorithm has better results compared to the ID3 algorithm
Teknik Penalti pada Optimisasi Berkendala Menggunakan Particle Swarm Optimization Rahmalia, Dinita
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 3 No 1: Maret
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v3i1.1071

Abstract

Optimisasi berkendala adalah proses pencarian nilai optimum dengan memenuhi berbagai kendala. Optimisasi berkendala dapat diselesaikan dengan metode eksak maupun heurisitik. Dalam penelitian ini, akan digunakan metode heuristik seperti Particle Swarm Optimization (PSO). PSO adalah metode optimisasi yang terinspirasi dari perilaku populasi ikan atau unggas dalam mencari sumber makanan.PSO dapat digunakan pada optimisasi dengan kendala. Namun dalam update posisi partikel, supaya optimisasi memenuhi kendala, partikel akan dikenakan nilai penalti jika tidak memenuhi kendala. Simulasi diberikan pada dua model optimisasi.Hasil simulasi menunjukkan teknik penalti dapat menemukan pendekatan solusi optimal pada optimisasi berkendala.
ESTIMATION OF THIRD FINGER MOTION USING ENSEMBLE KALMAN FILTER Herlambang, Teguh; Nurhadi, Hendro; Muhith, Abdul; Rahmalia, Dinita; Tomasouw, Berny Pebo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (460.243 KB) | DOI: 10.30598/barekengvol16iss3pp1079-1086

Abstract

Post-stroke is a stage a patient undergoes if the patient has had a previous stroke. Stroke is a big and serious problem. As the second most common cause of disability of people at age of over 60 years. For patients having experienced a stroke, rehabilitation is a way to make them able to do activities of daily living as before. Stroke Rehabilitation is a comprehensive medical management and rehabilitation (in medical, emotional, social, and vocational aspects) concerning disabilities caused by stroke through a neuro-rehabilitation approach with the aim of optimizing recovery. The finger prosthetic arm robot is one of the results of the health technology development to help accelerate the rehabilitation process specifically for finger movements. One of the efforts to develop a finger robot is to estimate the movement of the fingers, in this case the finger size used is taken from those of Javanese people in Indonesia as the data to be simulated. In this paper is an estimation of the finger motion,particularly that of the third finger of the right hand, conducted using the Ensemble Kalman Filter (EnKF) method. The simulation results produced the third finger motion estimates with an accuracy of around 92% - 99%.
DIVING MOTION ESTIMATION OF REMOTELY OPERATED VEHICLE USING ENSEMBLE KALMAN FILTER AND H-INFINITY Herlambang, Teguh; Suryowinoto, Andy; Adrianto, Dian; Rahmalia, Dinita; Nurhadi, Hendro
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.202 KB) | DOI: 10.30598/barekengvol17iss1pp0095-0100

Abstract

ROV (Remotely Operated Vehicle) is a product of technological development, functioning to perform tasks in the water. Big tasks such as coral reef exploration, oil refineries, underwater monitoring, and sea accident rescue are carried out by such technology. ROV or unmanned submarines have 6 degrees of freedom, but for diving it requires only 3 movements, that is, surge, heave, and pitch motions. In its operation, the ROV requires a navigation system in the form of estimation of the ROV position under diving conditions. In this study, two methods were used to estimate the ROV position under diving conditions, that is, the H-infinity method and the Ensemble Kalman Filter (EnKF). Both methods proved reliable on other platforms. The simulation results in this study showed that the EnKF method was more accurate than the H-Infinity method. The H-Infinity method had an accuracy of around 87%, while the EnKF method reached an accurate of 99 %.
Pengaruh Korelasi Data pada Peramalan Suhu Udara Menggunakan Backpropagation Neural Network Rahmalia, Dinita; Aini, Nur
Zeta - Math Journal Vol 4 No 1 (2018): Mei
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.33 KB) | DOI: 10.31102/zeta.2018.4.1.1-6

Abstract

Air temperature forecasting is important role in agriculture, flight, trading and so on. The method used for forecasting is Neural Network (NN). NN works as human neural system. One of NN type used for forecasting is Backpropagation where Backpropagation model is there are hidden layers between input and output. Due to forecasting result depends on data correlation, then this research will explain about the effect of data correlation on air temperature forecasting. To obtain forecasting result, Backpropagation algorithm will be used. Simulations are applied in three dataset with different structures. Based on simulation results, data which have strong correlation can result better forecasting based on smaller Mean Square Error (MSE).