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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
 298   299   300   301   302 
Object detection and tracking with decoupled DeepSORT based on αβ filter Lakhdar Djelloul Mazouz; Abdessamad Kaddour Trea; Tarek Amiour; Abdelaziz Ouamri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27500

Abstract

With the rapid growth of the population, the demand for autonomous video surveillance systems has substantially increased. Recently, artificial intelligence has played a key role in the development of these systems. In this paper, we present an enhanced autonomous system for object detection and tracking in video streams, tailored for transportation and video surveillance applications. The system comprises two main stages: detection stage; this stage employs you only look once (YOLO)v8m, trained on the KITTI dataset, and is configured to detect only pedestrians and cars. The model achieves an average precision of 97.3% and 87.1% for cars and pedestrians classes respectively, resulting a final mean average precision (mAP) of 92.2%. Tracking stage; the tracking component utilizes the DeepSORT algorithm, which originally incorporates a Kalman filter for motion prediction and performs data association using cosine and Mahalanobis distances to maintain consistent object identifiers across frames. To improve tracking performance, we introduce two key modifications to the original DeepSORT: architecture modification and Kalman filter replacement. The tracking tests are carried out on KITTI and MOTChallenge Benchmarks. The final order tracking accuracy (HOTA) scores achieve 77.645 and 54.019 for Cars and Pedestrians classes respectively in the KITTI-Benchmark and 45.436 for the Pedestrians class in the MOTChallenge-Benchmark.
Optimized multi correlation-based feature selection in software defect prediction Muhammad Nabil Muyassar Rahman; Radityo Adi Nugroho; Mohammad Reza Faisal; Friska Abadi; Rudy Herteno
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.v22i3.25793

Abstract

In software defect prediction, noisy attributes and high-dimensional data remain to be a critical challenge. This paper introduces a novel approach known as multi correlation-based feature selection (MCFS), which seeks to address these challenges. MCFS integrates two feature selection techniques, namely correlation-based feature selection (CFS) and correlation matrixbased feature selection (CMFS), intending to reduce data dimensionality and eliminate noisy attributes. To accomplish this, CFS and CMFS are applied independently to filter the datasets, and a weighted average of their outcomes is computed to determine the optimal feature selection. This approach not only reduces data dimensionality but also mitigates the impact of noisy attributes. To further enhance predictive performance, this paper leverages the particle swarm optimization (PSO) algorithm as a feature selection mechanism, specifically targeting improvements in the area under the curve (AUC). The evaluation of the proposed method is conducted on 12 benchmark datasets sourced from the NASA metrics data program (MDP) corpus, renowned for their noisy attributes, high dimensionality, and imbalanced class records. The research findings demonstrate that MCFS outperforms CFS and CMFS, yielding an average AUC value of 0.891, thereby emphasizing it is efficacy in advancing classification performance in the context of software defect prediction using k-nearest neighbors (KNN) classification.
A proposal model using deep learning model integrated with knowledge graph for monitoring human behavior in forest protection Van Hai Pham; Quoc Hung Nguyen; Thanh Trung Le; Thi Xuan Dao Nguyen; Thi Thuy Kieu Phan
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.v20i6.24087

Abstract

In conventional monitoring of human behavior in forest protection, deep learning approaches can be detected human behavior significantly since thousands of visitors’ forest protection is abnormal and normal behaviors coming to national or rural forests. This paper has presented a new approach using a deep learning model integrated with a knowledge graph for the surveillance monitoring system to be activated to confirm human behavior in a real-time video together with its tracking human profile. To confirm the proposed model, the proposed model has been tested with data sets through case studies with real-time video of a forest. The proposed model provides a novel approach using face recognition with its behavioral surveillance of the human profile integrated with the knowledge graph. Experimental results show that the proposed model has demonstrated the model’s effectiveness.
K-Means clustering interpretation using recency, frequency, and monetary factor for retail customers segmentation Agung Nugraha; Yutika Amelia Effendi; Nicholas Nicholas; Zejin Tao; Mokh Afifuddin; Nania Nuzulita
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.v23i2.26044

Abstract

Efforts to retain customers represent a crucial customer relationship management (CRM) strategy in every business, offering the potential to enhance profits, particularly for small and medium enterprises (SMEs). In the context of this study, which focuses on the transaction dataset of retailers in a developing market, Indonesia, the emphasis has predominantly been on customer attraction rather than the implementation of customer retention strategies. The primary objective of this research was to scrutinize customer transaction data within the dataset. The K-Means clustering (KMC) method, integrated with recency, frequency, and monetary (RFM) attributes, was employed to classify customers and formulate effective strategies for customer retention. Conducted through a descriptive research method with a quantitative approach, the study involved sequential stages of data preprocessing and RFM analysis for comprehensive data analysis. The outcomes revealed the identification of 5 distinct clusters with associated strategies based on the RFM scores obtained. These strategies, tailored to each cluster, serve as valuable insights in industrial and innovation for marketing and business strategic teams, offering practical approaches to customer retention that can lead to increased benefits for SMEs.
Water Inrush Characteristics with Roadway Excavation Approaching to Fault Shuren Wang; Hu Wang Hu Wang
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.v10i3.830

Abstract

Water inrush disaster is an important factor in restricting safe production of the coal mine. Taking the roadway in seam in Danhou Coal Mine, as the engineering background, according to the spatial relationship of the roadway, the impermeable layer, the fault and the loading conditions, the fault activation mechanical model under the roadway excavation disturbance was built, and the fault activation conditions, roadway water inrush criterion and water inrush three modes were put forward. A three-dimensional numerical calculation models were built by using FLAC3D. Through fluid-solid coupling calculation, the surrounding rock damage and failure, the water inrush channel formation, and the evolution process of water inrush of the roadway excavation approaching the fault were analyzed. Moreover, the displacement field, the stress field and the surrounding rock plastic failure characteristics of the roadway were revealed. Furthermore, under the conditions of different water pressure, impermeable rock thickness, fault displacement, and fault dip angles, the roadway water inrush modes and their evolution characteristics were comparatively analyzed.
A novel compact dual-band bandstop filter with enhanced rejection bands Ayyoub El Berbri; Hassna Agoumi; Seddik Bri; Youssef El Amraoui; Adil Saadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24825

Abstract

In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Leukocytes identification using augmentation and transfer learning based convolution neural network Mohammed Sabah Jarjees; Sinan Salim Mohammed Sheet; Bassam Tahseen Ahmed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.23163

Abstract

Most haematological diseases can be diagnosed using the morphological analysis of the microscopic blood image. The basic routine of the morphological analysis can be performed using the microscopic device which requires the skills and experiences of the haematologists. An inexperienced haematologist can lead to critical human errors. Therefore, this paper aims to propose an automated classification system used to classify different types of leukocytes based on the convolution neural network (CNN) algorithm. CNN has achieved robust performance in various fields especially in medical applications. A dataset of microscopic blood cells images of the conforming tags (basophil, eosinophil, erythroblast, lymphocyte, monocyte, neutrophil, and platelet) was used to train and test the proposed algorithm. The augmentation and deep transfer approaches were used to improve and enhance the performance of the CNN algorithm. The overall accuracy of the proposed classifier was 98% with Visual Geometry Group-19 (VGG-19). The obtained accuracy was higher than the state-of-art algorithms. To conclude that using the augmentation and deep transfer approaches with VGG-19 can obtain better classification results.
Performance assessment of an adaptive model predictive control with torque braking for lane changes Zulkarnain Zulkarnain; Irwin Bizzy; Armin Sofijan; Mohd Hatta Mohammed Ariff
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27167

Abstract

The growing demand for autonomous vehicles requires robust control systems that can maintain safety during complex maneuvers like lane changes. However, a significant research gap exists in developing controllers that effectively manage the combined challenges of steering and braking across diverse and unpredictable driving conditions, such as varying speeds and low-friction road surfaces. This research addresses this gap by proposing and evaluating an adaptive model predictive control (MPC) system integrated with a torque braking distribution strategy. The key advantage of our adaptive method is its ability to continuously update its internal model in real-time, allowing it to anticipate and respond to changing road friction and vehicle dynamics more effectively than a static controller. In simulations of a lane change maneuver across speeds of 10-25 m/s and road friction levels from 0.3 (icy) to 1 (dry asphalt), the proposed system demonstrated a substantial performance improvement. The proposed framework demonstrated a 52.8% average reduction in lateral tracking error and enhanced stability by reducing the yaw rate by up to 41.8% on low-friction surfaces, compared to a non-adaptive MPC baseline. These results quantitatively confirm that our framework’s synergistic coordination of steering and braking significantly enhances the safety, precision, and reliability of autonomous lane change maneuvers.
End-fire multibeam radial line slot array antennas Teddy Purnamirza; Junisbekov M. Shardarbekovich; Riza Adi Jaya; Imran Mohd Ibrahim; Kabanbayev A. Batyrbekovich; Depriwana Rahmi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25883

Abstract

This research proposed and verified a novel method in realizing end-fire radial line slot array (RLSA) antennas. This method involved the use of high beamsquint values in the design of slot pairs, which aimed to shift the antenna’s beam toward the end-fire direction. Furthermore, identical slot pairs were also placed in the antenna’s background to further squint the beam in the end-fire direction. By using this method, forty multibeam end-fire RLSA antennas were modeled and simulated to determine the most efficient model to be fabricated. The accuracy of the simulations was confirmed through measurements taken from the fabricated prototype, which demonstrate good agreement with the simulation results and confirm the validity of the proposed method. The result showed that it is possible to design four end-fire beam antennas with a gain of 8 dBi, directions of 0°, 90°, 180°, and 270° in the azimuth direction, and a beamwidth of about 20°. The antenna also showed low reflection and bandwidth of about 500 MHz, which is suitable for Wi-Fi applications.
A comparison of different support vector machine kernels for artificial speech detection Choon Beng Tan; Mohd Hanafi Ahmad Hijazi; Puteri Nor Ellyza Nohuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24259

Abstract

As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.

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