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Nizirwan Anwar
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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,452 Documents
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Chroma consistency and luminous efficacy for a WLED using remote phosphor configuration with a tri-layer design Phuc Dang Huu; Phan Xuan Le
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.24080

Abstract

The light quality of white-light-emitting diodes (WLEDs), an essential element for the improvement of WLEDs performance, can now be estimated by the angular color uniformity (ACU). In this study, a single micro-patterned layer is used to compare the variations between the traditional remote phosphor (RP) layer and the remote phosphor layer (single remote micro-patterned phosphor film (RMPP) layer). Furthermore, we investigate the application of a novel triple remote phosphor layer to improve the ACU in RP down-light lamps. Besides, the optical efficiency of the layers as well as the distribution for the angular correlated color temperature (ACCT) were also measured experimentally. According to the findings, the dual-RMPP-layer structure can achieve better chromatic uniformity with just 441 K of correlated color temperature (CCT) variance. Meanwhile, the single RMPP layer shows an ACCT deviation of 556 K and RP film structure of 1390 K. The simulation incorporating e finite-difference time-domain (FDTD) as well as the approach of ray-tracing ensures an increase in ACU. Furthermore, compare to the traditional RP layer, the single and dual RMPP layers configuration result in respective luminous efficiency ameliorations of 6.68% and 4.69%. The scattering principle and combining influence from the micro-molded layer may explain the enhancement in ACU as well as lumen.
Comparative evaluation of classical and machine learning methods for medical image enhancement Md. Mehedi Hasan; Sujon Chandra Sutradhar; Zannatul Ferdushie; Rabeya Basri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27700

Abstract

Medical imaging is critical for diagnostic accuracy, yet raw images often suffer from noise and low contrast. This study provides a comparative evaluation of classical methods, namely the Laplace transform (LT), Sobel operator (SO), and histogram equalization (HE), against a data-driven convolutional neural network (CNN) using the musculoskeletal radiographs (MURA) and Human Metapneumovirus (HMPV) lung computed tomography (CT) datasets. While quantitative analysis shows that HE and SO significantly outperform other methods in isolated contrast enhancement and edge definition, they often introduce artifacts. In contrast, the CNN based approach demonstrates superior detail preservation and entropy, offering a more balanced and adaptive solution for diverse diagnostic requirements. Our findings statistically validate that although classical operators remain highly effective for specific boundary detection tasks, machine learning (ML) frameworks provide the most robust performance for cross-modality image enhancement, bridging the gap between raw data acquisition and clinical interpretation.
Adaptive diving depth control system for the drifting autonomous underwater vehicle Viktor Ivel; Yuliya Gerasimova; Sayat Moldakhmetov; Makhabbat Krivolapova
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25926

Abstract

This article considers the system for controlling the diving depth of a drifting autonomous underwater vehicle (DAUV), which navigates underwater under the influence of sea currents in order to collect scientific information. The paper solves the problem of identifying non-stationary hydrodynamic parameters of the DAUV with the aim of adaptive adjustment of the DAUV control algorithm to increase the accuracy of bringing the DAUV to a given depth and minimizing the consumption of electricity consumed by power actuators. The solution to the problem is based on the use of parametric identification apparatus and adaptive control principles. The high quality of the DAUV diving depth control is achieved through the use of the method of adaptive adjustment of the parameters of the DAUV program model. The use of parametric identification of the hydrodynamic parameters of the DAUV made it possible to quickly adjust the corrective link in the control chain of the executing mechanism of the DAUV. The developed computer models and a set of semi-realistic tests made it possible to choose the most acceptable identification algorithm and configure the software implementation of the DAUV diving depth control law.
A study on PMSM drive systems fed by multi-level inverter using linear quadratic regulator control for electric vehicle applications Vo Thanh Ha; Pham Thi Giang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24432

Abstract

This paper presents research on a permanent magnet synchronous motor (PMSM) by a 5-level T-type inverter using linear quadratic regulator (LQR) control for speed control. The space vector modulation (SVM) structure for the 5-level T-type inverter to improve the control quality of the PMSM drive system is presented. This paper aims to successfully design a state feedback controller to control motor speed in a permanent magnet synchronous motor drive system. Simulink results are performed with different speed ranges and the same torque load. These results will focus on evaluating quality when the PMSM drive operates at all speeds. MATLAB/Simulink proves the results.
Development smart eyeglasses for visually impaired people based on you only look once Hassan Salam Abdul-Ameer; Hassan Jaleel Hassan; Salma Hameedi Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.22457

Abstract

Visually impaired people are facing many problems in their life. One of these problems is how they can find the objects in their indoor environment. This research was presented to assists visually impaired people in finding the objects in office. Object detection is a method used to detect the objects in images and videos. Many algorithms used for object detection such as convolutional neural network (CNN) and you only look once (YOLO). The proposed method was YOLO which outperforms the other algorithms such as CNN. In CNN the algorithm splits the image into regions. These regions sequentially enters the neural network for object detection and recognition so CNN does not deal with all the regions at the same time but YOLO looks the entire image then it produces the bounding boxes with convolutional network and the probabilities of these boxes, this makes YOLO faster than other algorithms. Open source computer vision (OpenCV) used to capture frames by using camera. Then YOLO used to detect and recognize the objects in each frame. Finally, the sound in Arabic language was generated to tell the visually impaired people about the objects. The proposed system can detect 6 objects and achieve an accuracy of 99%.
Deep learning-based power amplifier linearization in OFDM systems with unknown channel state information Meryem Mamia Benosman; Mohammed Yassine Bendimerad; Fethi Tarik Bendimerad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27236

Abstract

This paper presents an end-to-end deep learning-based approach for orthogonal frequency-division multiplexing (OFDM) communication systems impaired by nonlinear power amplifiers (PAs) and channel fading. The PA nonlinearity is modeled using the modified Rapp model, and simulations are performed on a 64-subcarrier OFDM system with a cyclic prefix (CP) of 8 and 16-quadrature amplitude modulation (16-QAM). The proposed autoencoder-based OFDM–PA (AE-OFDM-PA) system jointly optimizes the transmitter and receiver through end-to-end learning, enabling simultaneous compensation of both PA nonlinearities and channel distortions without requiring explicit channel state information (CSI) estimation. Instead, the model leverages embedded pilot sequences to learn the implicit CSI representation directly from data, allowing the receiver to correct amplitude and phase distortions adaptively. Simulation results demonstrate that AE-OFDM-PA significantly outperforms conventional OFDM and OFDM-PA systems, achieving over 70× block error rate (BLER) improvement compared with the uncompensated OFDM-PA system at an input back-off (IBO) of 3 dB. Furthermore, the proposed method achieves approximately 11.5 dB adjacent channel leakage ratio (ACLR) improvement over the classical memory polynomial digital predistortion (DPD) technique, while slightly reducing the peak-to-average power ratio (PAPR). Overall, AE-OFDM-PA provides a robust, spectrally efficient, and low-complexity solution for nonlinear and fading environments with unknown or varying CSI.
A good result of brain tumor classification based on simple convolutional neural network architecture Eko Hari Rachmawanto; Christy Atika Sari; Folasade Olubusola Isinkaye
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25863

Abstract

Brain tumor disease has become a topic of research whether it is in the case of segmentation or classification. For the case of classification, the types of brain tumors that are grouped generally consist of high-grade glioma (HGG) and low-grade glioma (LGG) tumors. In this research we are doing, we propose a method for classifying 2 types of tumors, namely HGG and LGG, using the convolutional neural network (CNN) algorithm which is trained and will be tested against the 2018 and 2019 brain tumor segmentation (BRATS) datasets which have 4 modalities, namely fluid-attenuated inversion recovery (FLAIR), T1, T1ce, and T2 totaling 2048 images. The CNN algorithm was chosen because it can directly receive input in the form of a magnetic resonance image (MRI) with the feature extraction process as well as the classification algorithm. By forming a simple CNN algorithm architecture with only 3 convolutional layers which have an input layer in the form of a full MRI image with dimensions of 240×240×3, we obtained a relatively high accuracy result of 94.14%, it can even be said to be better than similar methods but with more complicated architecture.
Various control methods of permanent magnet synchronous motor drives in electric vehicle: a technical review Marulasiddappa Hallikeri Basappa; Pushparajesh Viswanathan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.24236

Abstract

Day by day use of internal combustion engines (ICE) compared to electric vehicles (EV) is deteriorating because mainly of pollution and their less fuel availability. In the present scenario, an electric vehicle plays a major role in place of an ICE vehicle. So that performance of EV can be improved by proper selection of electric motor. Initially, EV prefers induction motors for traction purposes, but complexity in controlling induction motor, permanent magnet synchronous motor (PMSM) presently used in EV by most of the electric vehicle manufacturers due to its advantages. This paper reviews on various control methods for PMSM used in EV. Various control methods are being used for EV applications. Initially, conventional direct torque control (DTC) technique being used in controlling electric motors but it has a drawback of high torque and flux ripples. Hence, intelligent controllers are predominantly using in controlling PMSM drives.
Human–robot collaboration with mixed reality for interactive and safe workspaces Sanghun Nam
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26275

Abstract

Realizing seamless collaboration between humans and robots in shared workspaces requires advanced systems that can ensure safety and efficiency while considering the inherent unpredictability of human movement. This paper proposes a system that integrates mixed reality (MR) and robotics through a unified coordinate system to facilitate real-time interaction and collaboration. By leveraging a MR interface, human collaborators can visualize and interact with the projected paths of the robotic arms, thereby enhancing both spatial awareness and task coordination. The proposed system adapts the robot’s movement path dynamically using the Voronoi diagram algorithm to modify trajectories in response to the detection of a human hand within a predefined caution zone. This mechanism reduces the risk of collisions, which ensures safer collaborative environments. The proposed system’s ability to exchange motion information between the operator and the robot supports real-time adjustments and promotes an intuitive and efficient collaborative experience. Our findings suggest that integrating MR technology in human–robot collaboration systems can improve safety protocols and operational fluidity dramatically, thereby representing a significant step forward in the development of safe, efficient, and effective interactive robot systems.
A New Image Segmentation Algorithm and Its Application in Lettuce Object Segmentation Jun Sun Jun Sun; Yan Wang Yan Wang; Xiaohong Wu Xiaohong Wu; Xiaodong Zhang; Hongyan Gao
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.837

Abstract

Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity.

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