Kemampuan Guru Memanfaatkan Lingkungan sebagai Sumber Belajar di Sekolah Dasar Negeri 29 Banda Aceh

Dalam mengajarkan materi yang berkaitan dengan lingkungan, guru dapat mengupayakan pemanfaatan lingkungan sebagai sumber belajar. Penelitian ini berupaya mengungkapkan bagaimanakah kemampuan guru dalam memanfaatkan lingkungan sebagai sumber belajar di Sekolah Dasar Negeri 29 Banda Aceh? Secara khusus penelitian ini bertujuan untuk mengetahui kemampuan guru dalam memanfaatkan lingkungan sebagai sumber belajar di Sekolah Dasar Negeri 29 Banda Aceh.Pendekatan yang dilakukan dalam penelitian ini adalah pendekatan kualitatif deskriptif yaitu penelitian yang berusaha mendeskripsikan suatu kejadian pada saat sekarang. Data penelitian ini bersumber dari wawancara dengan 6 orang guru SD Negeri 29 Banda Aceh yang terdiri dari 2 guru kelas IV, 2 guru kelas V, dan 2 guru kelas VI. Sumber data juga didapatkan dari hasil observasi lingkungan sekitar sekolah.Teknik pengumpulan data dilakukan dengan teknik wawancara dan observasi. Pada teknik wawancara peneliti menggunakan alat bantu perekam berupa media eletronik tape recorder. Dan pada observasi, peneliti bertindak sebagai pengamat penuh tanpa terlibat dalam interaksi belajar-mengajar. Teknik analisis data dalam penelitian ini: (1) reduksi data, (2) penyajian data, (3) serta menarik kesimpulan dan verifikasi.Berdasarkan hasil analisis data, temuan dari penelitian ini menunjukkan bahwa guru di Sekolah Dasar Negeri 29 Banda Aceh belum maksimal, karena hanya sebagian guru kelas yang memanfaatkan lingkungan didalam pembelajaran. Sedangkan sebagian guru lainnya tidak memanfaatkan lingkungan sebagai sumber belajar di dalam proses pembelajaran dan hanya menggunakan buku dan gambar visual sebagai sumber belajar

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods

Speed Effect to a Quarter Car ARX Model Based on System Identification

This paper presents the effect of car speeds on a quarter car passive suspension system model dynamics. The model is identified using system identification technique, in which the input-output data are collected by running a test car on an artificial road surface with two different speeds i.e., 10 km/h and 20 km/h. The quarter car passive suspension system dynamics is assumed to have an ARX model structure and identified using linear least-square estimation algorithm. The car vertical body acceleration, which is the output variable, is measured by installing an accelerometer sensor on the car body, above the suspension. On the other hand, the car shaft acceleration, which is the input variable, is measured by installing an accelerometer sensor at the lower arm of the car suspension. The best model for the 10 km/h car speed gives the output order () = 4, the input order () = 2, delay (d) = 1, the best fit = 90.65%, and the Akaike’s Final Prediction Error (FPE) = 5.315e-06. In contrast, the 20 km/h speed results in 4th output order (), 1stthe input order (), 1st delay (d), the best fit of 91.05%, and 7.503e-05Akaike’s FPE. These results show that the higher speed reduces the effect of the road surface to car dynamics, which is indicated by the order of the mode

An improved grey wolf optimizer with hyperbolic tangent updating mechanism for solving optimization problems

The original algorithm of Grey Wolf Optimizer (GWO) has a common problem which is too soon to trap in local optima. This paper presents the Improved Grey Wolf Optimizer (IGWO) by modifying the updating mechanism of the original GWO. The main idea of the new improvement is by introducing a nonlinear updating mechanism based on the hyperbolic tangent function to improve the efficiency of the exploration and the exploitation phase and to decrease the probability of trapping in local optima. The effectiveness of the new approach is evaluated on 30 well-known benchmark functions, and the results are compared with the original GWO. The preliminary findings show that the IGWO algorithm is able to obtain very competitive results in terms of objective functions minimization compared to original GWO algorithms

Speed effect to a Quarter car ARX model based on system identification

This paper presents the effect of car speeds on a quarter car passive suspension system model dynamics. The model is identified using system identification technique, in which the input-output data are collected by running a test car on an artificial road surface with two different speeds i.e., 10 km/h and 20 km/h. The quarter car passive suspension system dynamics is assumed to have an ARX model structure and identified using linear least-square estimation algorithm. The car vertical body acceleration, which is the output variable, is measured by installing an accelerometer sensor on the car body, above the suspension. On the other hand, the car shaft acceleration, which is the input variable, is measured by installing an accelerometer sensor at the lower arm of the car suspension. The best model for the 10 km/h car speed gives the output order (na) = 4, the input order (nb) = 2, delay (d) = 1, the best fit = 90.65%, and the Akaike's Final Prediction Error (FPE) = 5.315e-06. In contrast, the 20 km/h speed results in 4th output order (na), 1st the input order (nb), 1st delay (d), the best fit of 91.05%, and 7.503e-05 Akaike's FPE. These results show that the higher speed reduces the effect of the road surface to car dynamics, which is indicated by the order of the model

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size (CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods

Universal impulse noise suppression using extended efficient nonparametric switching median filter

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

ABSTRACTThe current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating the nonlinear position-updated mechanism of NSCA for enhanced exploration/exploitation competencies and proficiency of SED in maximizing stagnation avoidance within the local optima. Named the NSCA-SED algorithm, the applicability of the proposed method is assessed by scholastic adoption of a sixth-order numerical transfer function towards two independent high-order systems enclosing Double-Pendulum Overhead Crane and Flexible Manipulator. Experimentation results further suggested NSCA-SED as the superior alternative in terms of execution robustness and consistency excellence against other available optimization-based methods for tackling model order reduction. Exemplified simulations sequentially demonstrated considerable improvements by the employment of NSCA-SED over conventional SCA following respective enhanced proportions of 97.17%, 13.17% and 29.03% for Example 1, Example 2 and Example 3

Levy Flight Safe Experimentation Dynamics Algorithm for Data-Based PID Tuning of Flexible Joint Robot

This paper proposes the data-based PID controller of flexible joint robot based on Levy Flight Safe Experimentation Dynamics (LFSED) algorithm. The LFSED algorithm is an enhanced version of SED algorithm where the random perturbation of the updated tuning variable is based on Levy Flight function. By adopting the Levy Flight term to the updated equation of SED, it is expected that a more efficient searching can be performed than the uniform distribution random numbers. The effectiveness of the LFSED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the predefined control objective function. The simulation results showed that the data-based PID controller based on LFSED is able to produce better control accuracy than the conventional LFSED based method