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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
 270   271   272   273   274 
Classifying date fruits using the transfer learning model Alia Nadzirah Mohd Adnan; Nurul Amelina Nasharuddin
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.v22i4.25924

Abstract

Date palm trees originate in many tropical regions of the world and produce dates. Each variety can be differentiated through the shape, texture, size, and colour of the fruits. People have difficulties visualising and recognising the types of date fruits because they have many varieties and species. An Android-based mobile application is being proposed to help users quickly identify the dates based on their images and expand their knowledge of dates. The date fruit species classification mobile application categorises nine different varieties of date fruits, namely Ajwa, Medjool, Rutab, Nabtat Ali, Meneifi, Galaxy, Sugaey, Shaishe, and Sokari. The classification, which is based on a transfer learning technique from a pre-trained neural network, achieved a 94.2% accuracy rate. The mobile application features a user-friendly graphical interface that makes it easy to use and understand. Users can learn about different date fruit varieties and improve knowledge retention through a mini game. The application’s usability, usefulness, and interface design were confirmed through the user acceptance survey.
Design of 15 level reduced switches inverter topology using multicarrier sinusoidal pulse width modulation Selvabharathi Devadoss; Palanisamy Ramasamy; Amit Amit; Aditya Agarwal; Saptarshi Gupta
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.v21i1.24263

Abstract

In this proposed paper, multicarrier sinusoidal pulse width modulation (M-SPWM) method is implemented for design of 15 level reduced switches inverter topology. This inverter topology generates 15 level output-voltage with suitable switching pulse production using M-SPWM and altered level of voltages are attained with distinction of modulation index. The split inductor is used to diminish the harmonic content and flatted output current. This type of system which contains different range of different range of voltage supplies. As a result, this inverter reduces the difficulty in gating time calculation and there is no neutral point fluctuation issue. This paper illuminates the modes of switching and minimization of stress in voltage and harmonic diminution are examined. The grades of the projected multilevel inverter (MLI) system are verified using Matlab/Simulink and dsPIC controller respectively
Earthquake magnitude prediction based on radon cloud data near Grindulu fault, Indonesia using the statistical method Sunarno Sunarno; Thomas Oka Pratama; Faridah Faridah; Nugroho Ananto; Hermin Kartika Sari; Rony Wijaya; Memory Motivanisman Waruwu
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.v23i3.26494

Abstract

Earthquake prediction is one of the most challenging and vital tasks that demands new methodologies for improving the accuracy of predictions. The research aims to present how radon gas concentration fluctuations are associated with the prediction of earthquakes in the Eurasian-Indo-Australian Plates. The paper discusses a statistical method of forecasting earthquake magnitudes greater than M4.5 from real-time radon gas monitoring close to the Grindulu Fault, Pacitan, East Java, Indonesia. This developed model has had the least errors in the form of mean absolute error (MAE), 0.30; mean absolute percentage error (MAPE), 0.06; root mean square error (RMSE), 0.55; mean squared error (MSE), 0.30; symmetric mean absolute percentage error (SMAPE), 0.06; complex normalized mean absolute percentage error (cnMAPE), 0.97; error absolute average (EAA), 0.30; and error relative average (ERA), -0.11, showing great accuracy and uniformity in prediction. These observations support the model’s efficiency that may be adopted in earthquake early warning systems for better disaster preparedness. Predictive errors are reduced, and there is support for improved disaster management strategy, public safety education, and effective emergency response personnel training. This study can be used as a foothold for further advances in earthquake prediction methodologies and refinement of early warning systems.
Rogowski coil sensor in the digitization process to detect partial discharge Eka Putra Waldi; Asri Indah Lestari; Rudy Fernandez; Syaifa Mulyadi; Yoshinobu Murakami; Naohiro Hozumi
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.v18i2.14282

Abstract

This paper presents the construction of a Rogowski Coil sensor with an air core to detect partial discharge using a digital oscilloscope. Two types of sensors are used. The first is the primary sensor winding with back wire, and the second is without back wire, labeled BW and WBW, respectively. The numbers of primary-turn in the sensors are 5, 10, 20, and 40 turns. The performance of the sensors is tested using two types of tests. First, the wave response test with a fixed imitation partial discharge magnitude input is used to select the optimum sensitivity with the lower sampling rate, aims to select the peak or valley value as a magnitude partial discharge value. The second test is using an imitation partial discharge ramp to check the linearity of the sensors. The imitation of the partial discharge inputs is generated by a commercial charge calibrator. The wave response test results show an increase in the number of turns that corresponds to an increase of the sensor output for both sensors in a non-linear trend. In determining the sampling rate, the detection of magnitude in the valley is better than the peak. All sensors act linear toward the imitation partial discharge ramp either in BW or WBW conditions.
Least significant bit technology for hiding text data using video steganography Huda A. Ali; Alyaa J. Jalil; Marwah K. Hussein
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.v22i1.24029

Abstract

Due to the rapid development of information technology, the process of exchanging information over the web has become simple and fast. Nevertheless, the transmission of information will be at risk due to the nature of the internet, as anyone can access and change the data by hacking it. Therefore, it is necessary for protecting any personal information from being accessed by unlawful individuals. It became necessary to use encryption and steganography techniques for protecting data. Steganography is a manner used for covering hidden information in several other media while preserving the form of the original data and leaving no evidence of hidden data, whereas, encryption changes the original information into unclear or ambiguous information, which is called scrambled. The paper presents a method of video steganography as a powerful and effective tool for data steganography. We used a video as a spreading medium and then used it for concealment of an audio message and a document (in a pdf file) in a method that makes the information invisible using the least significant bit (LSB) method.
A sink based data gathering technique by using clustering for wireless sensor networks Yousra Abdul Alsahib S. Aldeen; Haider Mohammed Abdulhadi
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.v20i4.22181

Abstract

New advance and integrated technologies have changed the traditional systems and convert these systems into more intelligent, feasible and cost effective systems. In all data communications domain, data gathering is one of the significant task performed by using any techniques, tools and devices. Wireless sensor networks (WSN) also gained popularity in various fields where the sensor node sensed the information by using sink or gateway nodes and further send to central units for decision making. With passage of time, these networks have faced complexities where most of the existing techniques have suffered with load balancing, complex processes, overhead and energy consumption issues. Firstly, this paper provides detail comparison of existing data gathering techniques adopted for WSN and then provides their performance analysis. After comparison, this paper proposes a novel data gathering techniques called a sink based data gathering techniques (ASDG) to collect the data from the sensor nodes and further send for decision making. Experimental results show that proposed techniques is better than existing techniques and provide more efficient data delivery ratio with more network lifetime. The results also indicated that when using the proposed technique, the no of dead sensor nodes are less as compared to the existing ones at different rounds.
Real-time classification of Pasaman oranges using Mamdani fuzzy inference system and ESP32 microcontroller Fahmi Fitrio Fauzi; Ifmalinda Ifmalinda; Azrifirwan Azrifirwan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27407

Abstract

Manual classification of Pasaman oranges based on visual assessment of size and color often produces inconsistent results due to human subjectivity. This study develops an automatic classification system using a Mamdani fuzzy inference system (FIS) implemented on an ESP32 microcontroller. Fruit diameter is measured using a high-frequency sound wave ranging module (HC-SR04) ultrasonic sensor, while surface color is detected using a TAOS color sensor 3200 (TCS3200) color sensor. The obtained data are processed through fuzzification, inference, and defuzzification to classify oranges into three quality grades (A, B, and C). System performance evaluation shows strong agreement between the developed system and matrix laboratory (MATLAB) simulation, with a coefficient of determination (R²) value of 0.9855, indicating reliable and consistent classification performance for automated agricultural grading applications.
Enhancing melanoma skin cancer classification through data augmentation Mohammed M’hamedi; Mohammed Merzoug; Mourad Hadjila; Amina Bekkouche
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26106

Abstract

Skin cancer is a dangerous and prevalent cancer illness. It is the abnormal growth of cells in the outermost of the skin. Currently, it has received tremendous attention, highlighting an urgent need to address this worldwide public health crisis. The purpose of this study is to propose a convolutional neural network (CNN) to help dermatology physicians in the inspection, identification, and diagnosis of skin cancer. More precisely, we offer an automated method that leverages deep learning techniques to categorize binary categories of skin lesions. Our technique enlarges skin cancer by utilizing data pre-processing and augmentation to address the imbalanced class problem. Subsequently, fine-tuning is conducted on the pre-trained models visual geometry group (VGG-19) and MobileNetV2 to extract and classify the image features using transfer learning. The model is tested on the society for imaging informatics in medicine international skin imaging collaboration (SIIM-ISIC) 2020 dataset and achieved an accuracy of 95.16%, sensitivity of 90.83%, specificity of 99.2%, area under curve (AUC) of 97.57%, and precision of 99.06%. The proposed model based on MobileNetV2 outperforms the other techniques.
Airflow vibration of diaphragmatic breathing: model and demonstration using optical biosensor Toto Saktioto; Defrianto Defrianto; Nurfi Hikma; Yan Soerbakti; Syamsudhuha Syamsudhuha; Dedi Irawan; Okfalisa Okfalisa; Bambang Widiyatmoko; Dwi Hanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.23613

Abstract

Optical fiber is increasingly popular and appreciated as a modern sensor technology in various sectors, one of which is for medical functions. This study was conducted to detect human diaphragmatic breathing flow using theoretical and experimental approaches. Initially, the lung model was formed using the finite element method and the Navier-Stokes equation by applying the principles of momentum and continuity. Furthermore, fiber Bragg grating (FBG) and single mode fiber (SMF) were experimentally designed with sinusoidal patterned macro-scale bending as a stretch sensor in a breathing belt applied to the diaphragm. The simulation model shows the airflow velocity increases up to 4 m/s when it flows into smaller branches. While the experimental results show that the largest power loss occurs at a buffer diameter of 0.8 cm. The power loss detected in SMF is a maximum of -0.18 dBm during inhalation and a minimum of -0.28 dBm during expiration. However, FBG bending is superior with high sensitivity.
Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers Madhura Kalbhor; Swati Vijay Shinde; Hemant Jude
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.v20i5.22440

Abstract

Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper, an investigative study for the classification of cytology images is proposed. The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.

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