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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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A Miniaturized In Situ Tensile Platform under Microscope Zhichao Ma Zhichao Ma; Hongwei Zhao; Hu Huang Hu Huang; Kaiting Wang; Qinchao Li Qinchao Li; Xiaoqin Zhou; Xiaoli Hu Xiaoli Hu
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.v10i3.832

Abstract

Aiming at the mechanical testing of three-dimensional specimens with feature size of centimeter level, a miniaturized tensile platform, which presents compatibility with scanning electron microscope (SEM) and metallographic microscope, was designed and built. The platform could accurately evaluate the parameters such as elastic modulus, elongation and yield limit, etc. The calibration experiments of load sensor and displacement sensor showed the two kinds of sensors had high linearity. Testing of transmission error and modal parameters showed that the platform presented good following behaviors and separation of resonance region. Comparison tests based on stress-strain curve were carried out between the self-made platform and the commercial tensile instrument (Instron) to verify the feasibility of the platform. Furthermore, the in situ tensile experiment under metallographic microscope was carried out on a kind of manganese steel.
Deep learning approach to DDoS attack with imbalanced data at the application layer Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Januar Al Amien; Edi Ismanto
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.v21i5.24857

Abstract

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
Rectangular module for large scale solar simulator based on high-powered LEDs array Napat Watjanatepin; Patcharanan Sritanauthaikorn
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.v20i2.23308

Abstract

The article describes a large scale of rectangular light source design comprised of six different types of high-power light emitting diodes (LEDs). The new modular based on the LED solar simulator with the greatest size using the symmetrical LED positioning method. The experiment provided the irradiation of the solar simulator in the class AAA over 416 cm2. The rectangular LED module illustrated the uniform distribution of the irradiance across the sample plane area. It reached the class A of air mass 1.5 for global spectrum (AM1.5G) (1000 W/m2) covering the 400 nm to 1100 nm wavelength range. The proposed system offered a spectral match of 100%, the temporal instability equivalent to 0.611%, and a non-uniformity of irradiance less than 2%. When the proposed solar simulator was tested in solar cell characteristics under standard test conditions, it was found that the short circuit current error between the sample solar cell under our solar simulator and the standard solar simulator was less than 0.538%. This proposed design is, therefore, an interesting design that can be applied easily and economically further for large scale solar simulators with its modular system.
Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing Fatima Zohra Cherhabil; Sonia Sabrina Bendib; Maamar Sedrati; Chahrazad Adouane; Sifeddine Benflis
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.v24i2.27293

Abstract

Offering a promising solution for latency-sensitive and resource-constrained internet of things (IoT) applications, mobile edge computing (MEC) extends cloud capabilities to the network edge. However, the decentralized nature of edge resources, coupled with stringent latency requirements and IoT energy constraints, presents significant challenges for efficient task offloading. Integrating IoT with MEC and software-defined networking (SDN) can meet the growing demands for low latency and energy-aware resource management. This paper proposes a hybrid evolutionary algorithm combining whale optimization algorithm (WOA) and particle swarm optimization (PSO) with crossover, mutation, and Lévy flight operators (CML) to balance exploration and exploitation. The algorithm minimizes a weighted sum function (energy 35%, delay 35%, and monetary cost 30%) for joint task offloading and resource allocation in SDN-enabled MEC environments. The proposed approach is evaluated against six well-known metaheuristics, analyzing performance across various metrics including scalability with up to 100 users. Experimental results, validated by non-parametric statistical tests, demonstrate that the proposed algorithm achieves statistically significant improvements in convergence speed, solution quality, and scalability, making it a robust and promising candidate for real-time MEC task scheduling.
The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis Yahaya Zakariyau Bala; Pathiah Abdul Samat; Khaironi Yatim Sharif; Noridayu Manshor
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.v22i4.25916

Abstract

This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a specific focus on the F1 score metric as evaluation criterion. Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). The choice of these algorithms reflects their prevalence in software defect prediction literature and their diversity. Through rigorous experimentation and analysis, the investigation unveils compelling evidence affirming the superiority of RF over its counterparts. The F1 score utilized as evaluation metric, capturing the delicate balance between precision and recall, essential in defect prediction scenarios. The nuanced examination of algorithmic efficacy provides practical insights for developers and practitioners navigating the challenges of cross-project defect prediction. By leveraging the rich and diverse AEEEEM dataset, this study ensures a comprehensive exploration of algorithmic influences across varied software projects. The findings not only contribute to the academic discourse on defect prediction but also offer practical guidance for real-world application, emphasizing the pivotal role of RF as a tool in enhancing predictive accuracy and reliability.
An evolutionary optimization method for selecting features for speech emotion recognition Kesava Rao Bagadi; Chandra Mohan Reddy Sivappagari
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.v21i1.24261

Abstract

Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID’19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes.
Adulterated beef detection with redundant gas sensor using optimized convolutional neural network Ardani Cesario Zuhri; Agus Widodo; Mario Ardhany; Danny Mokhammad Gandana; Galang Ilman Islami; Galuh Prihantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26490

Abstract

Various types of research have been developed to detect beef adulteration, but the accuracy and reliability of these results still require improvement. This study proposes designing a highly precise redundant electronic nose system using an optimized convolutional neural network (CNN) method to detect adulterated beef mixed with pork. As baselines, other classifiers are also utilized, namely the decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). Several data preprocessing methods are employed to increase prediction accuracy, namely feature selection, principal component analysis (PCA), and time series smoothing. The weight of each data sample was 100 g with 15 classes of pork and beef mixing ratios of 0%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% pork. With the single-layer sensor configuration, the average CNN classification success rates were 97.15%, 96.29%, and 99.64% for layers 1, 2, and 3, respectively. In addition, from the combination of the three layers, a prediction results of 99.72% was obtained. Thus, a redundant gas sensor array configuration can improve the classification results. In addition, the relatively high accuracy of the optimized CNN provides a convincing alternative for identifying possible beef adulteration.
Face recognition based on curvelets, invariant moments features and SVM Mohammed Talal Ghazal; Karam Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14106

Abstract

Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production.
Internet of things system for lime planting in Maha Sarakham community Tharach Arreerard; Woraphapa Arreerard; Naphattanon Thongpan; Niraj Ruangsan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 1: February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i1.23287

Abstract

The objectives of this research were: i) to study components of the internet of things (IoT) system to support community lime planting (CLP); in Maha Sarakham Province; ii) to develop IoT-CLP; and iii) to transfer IoT-CLP to the target community in Maha Sarakham Province. The research tools were: i) IoT-CLP system suitability assessment form; ii) IoT-CLP efficacy evaluation form; iii) a questionnaire to assess the effectiveness of the community lime planting; and iv) a survey of community satisfaction towards IoT-CLP. The research results showed that i) components of IoTCLP consist of: (1.1) data receivers: soil moisture, temperature, soil pH, fertilizer/nutrient; (1.2) control units: water supply, water spraying, soil pH control, and processing, fertilizer/nutrients; (1.3) operating systems: water supply, water spraying, soil pH communication and distribution system of fertilizer/soil nutrients water; (1.4) mobile application; ii) the effectiveness of IoT-CLP was evaluated at the highest level; and iii) the results of lime cultivation using IoT-CLP showed a higher yield than the traditional practices in terms of the amount of lime, size, and weight. The community was satisfied with IoT-CLP at the highest level.
Statistical analysis for the pitch of mask-wearing Arabic speech Hasan M. Kadhim; Alaa H. Ahmed; Saif A. Abdulhussien
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.22071

Abstract

The study is a comparison between the statistical properties of pitch (F0) for mask-wearing speech and unmasked. The speakers are Arab, of different ages and genders. A robust algorithm for pitch tracking (RAPT) is used for estimating F0. The subjective tests denote that masked speech is attenuated, and noisy-background speech has fewer F0 candidates. Using objective tests, 60% of female and male F0s do not change when wearing masks. The remaining 40% of speech F0s change (the percentage gross error), by an approximately 20% increase and 20% decrease. The percentage classification error is about 10%. The F0 changes in females younger than 12 years old are fewer compared with similarly-aged males. The F0 changes of females older than 12 years old were approximately equal compared with similarly-aged males. An average of F0 (M) is used for each speech to divide its F0 band (50-500) Hz into two bands, lower-band (LB) (50-M) Hz and upper-band (UB) (M-500) Hz. The attributes of the two bands have been statistically analyzed. The F0 classification error (CE) for females is higher than for males, but the gross error (GE) for males is higher than for females. The F0 change values are directly proportional to the probability of F0 change.

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