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Stability in Time-Delay Systems: Quiet Standing Case Study Fitri Yakub; Akira Kojima; Yasuchika Mori
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

The analysis of linear time-delay systems has attracted much interest in the literature over last five decade. Two types of stability conditions, namely delay-independent which results guarantee stability for arbitrarily large delays and delay-dependent, results take into account the maximum delay that can be tolerated by the system and, thus, are more useful in applications. The stability in general for linear time-delay systems, can be checked exactly only by eigenvalue considerations. When reasonable chosen with intentional delays, case study effects on time-delay of ankle torque on the stability of quiet standing, it can be used to stabilize and improve the close-loop response of these systems.

Stability in Time-Delay Systems: Quiet Standing Case Study Fitri Yakub; Akira Kojima; Yasuchika Mori
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v2i1.264

The analysis of linear time-delay systems has attracted much interest in the literature over last five decade. Two types of stability conditions, namely delay-independent which results guarantee stability for arbitrarily large delays and delay-dependent, results take into account the maximum delay that can be tolerated by the system and, thus, are more useful in applications. The stability in general for linear time-delay systems, can be checked exactly only by eigenvalue considerations. When reasonable chosen with intentional delays, case study effects on time-delay of ankle torque on the stability of quiet standing, it can be used to stabilize and improve the close-loop response of these systems.

Stability in Time-Delay Systems: Quiet Standing Case Study Fitri Yakub; Akira Kojima; Yasuchika Mori
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

The analysis of linear time-delay systems has attracted much interest in the literature over last five decade. Two types of stability conditions, namely delay-independent which results guarantee stability for arbitrarily large delays and delay-dependent, results take into account the maximum delay that can be tolerated by the system and, thus, are more useful in applications. The stability in general for linear time-delay systems, can be checked exactly only by eigenvalue considerations. When reasonable chosen with intentional delays, case study effects on time-delay of ankle torque on the stability of quiet standing, it can be used to stabilize and improve the close-loop response of these systems.

Hospital quality classification based on quality indicator data during the COVID-19 pandemic Nurhaida, Ida; Dhamanti, Inge; Ayumi, Vina; Yakub, Fitri; Tjahjono, Benny
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4365-4375

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.

RHO–LSTM-based optimal scheduling at the motorcycle battery swapping station under battery heterogeneity Fauziah, Nisa Evi; Romdlony, Muhammad Zakiyullah; Muharam, Aam; Yakub, Fitri
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1172

This research proposes a mechanism that enables the battery swapping station (BSS) to provide battery swap services for multiple types of batteries, termed battery heterogeneity, utilized in electric motorcycles. The number of batteries for each type is established. The battery charging cost is calculated in real time, and the station's profit is maximized by optimizing battery swap scheduling. The issues are modeled as a mixed-integer non-linear problem (MINLP), then linearized as a mixed-integer linear problem (MILP), using the grid electricity price from the real-time pricing mechanism to calculate the battery's charging/discharging cost. Swap scheduling is optimized using the rolling horizon optimization (RHO) approach, which takes into account a variety of constraints. These constraints include battery type, battery SoC, arrival time of the electric motorcycle, grid electricity pricing at time t, and battery power utilization. The long-short term memory (LSTM) predicts the electric motorcycles' arrival time at t+1 based on prior data. The results show that optimization scheduling generates a higher overall profit per day than unscheduled operation. Profit by the RHO-LSTM method is 23.77 % greater than by the RHO-Polynomial method and 0.26 % greater than by unscheduled operation. Furthermore, the number of batteries provided by the RHO-LSTM method is 40 % greater than by the RHO-polynomial method.

Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic Muljono, Muljono; Haryanto, Hanny; Andono, Pulung Nurtantio; Nugroho, Raden Arief; Yakub, Fitri; Sukmono, Indriyo K.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3702-3714

Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.

Localization and mapping of autonomous wheel mobile robot using Google cartographer Hidayati, Qory; Setyawan, Novendra; Faruq, Amrul; Irfan, Muhammad; Kasan, Nur; Yakub, Fitri
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp322-331

COVID-19 has become a world concern because of the spread and number of cases that have befallen the world. Medical workers are the first exposed group because they have direct contact with patients. So, a vehicle is needed to replace tasks such as logistics, delivery, and patient waste transportation. An autonomous wheeled mobile robot (AWMR) is a wheeled robot capable of moving freely from one place to another. AWMR is required to have good navigation and trajectory control skills. The purpose of this study is to develop an AWMR navigation system model based on the simultaneous localization and mapping (SLAM) algorithm, accurately in a dynamic environment. With this research, developing a good navigation and trajectory method for AWMR, in the future, it can be applied to produce an AWMR platform for multipurpose. This research was conducted in two stages of development. The first year is the research that is currently being carried out, focused on sensor modeling, designing SLAM-based navigation models, and making navigation system testbeds. This research produces a trajectory navigation and control system that can be implemented on an AWMR platform for the purposes of logistics, transportation, and patient waste in hospitals.

Prediction of flood-affected areas based on geographic information system data using machine learning Faruq, Amrul; Syafaah, Lailis; Irfan, Muhammad; Abdullah, Shahrum Shah; Mohd Hussein, Shamsul Faisal; Yakub, Fitri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4675-4683

Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models, including random forest (RF) and support vector machine (SVM), to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.

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