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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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Image based anthracnose and red-rust leaf disease detection using deep learning Rajashree Y. Patil; Sampada Gulvani; Vishal B. Waghmare; Irfan K. Mujawar
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.v20i6.24262

Abstract

Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Improving visual perception through technology: a comparative analysis of real-time visual aid systems Othmane Sebban; Ahmed Azough; Mohamed Lamrini
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.v23i2.26455

Abstract

Visually impaired individuals continue to face barriers in accessing reading and listening resources. To address these challenges, we present a comparative analysis of cutting-edge technological solutions designed to assist people with visual impairments by providing relevant feedback and effective support. Our study examines various models leveraging InceptionV3 and V4 architectures, long short-term memory (LSTM) and gated recurrent unit (GRU) decoders, and datasets such as Microsoft Common Objects in Context (MSCOCO) 2017. Additionally, we explore the integration of optical character recognition (OCR), translation tools, and image detection techniques, including scale-invariant feature transform (SIFT), speeded-up robust features (SURF), oriented FAST and rotated BRIEF (ORB), and binary robust invariant scalable keypoints (BRISK). Through this analysis, we highlight the advancements and potential of assistive technologies. To assess these solutions, we have implemented a rigorous benchmarking framework evaluating accuracy, usability, response time, robustness, and generalizability. Furthermore, we investigate mobile integration strategies for real-time practical applications. As part of this effort, we have developed a mobile application incorporating features such as automatic captioning, OCR based text recognition, translation, and text-to-audio conversion, enhancing the daily experiences of visually impaired users. Our research focuses on system efficiency, user accessibility, and potential improvements, paving the way for future innovations in assistive technology.
A Model to Investigate Performance of Orthogonal Frequency Code Division Multiplexing Nasaruddin Nasaruddin; Melinda Melinda; Ellsa Fitria Sari
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.v10i3.840

Abstract

Orthogonal Frequency Code Division Multiplexing (OFCDM) is an attractive multiple access scheme for high data rate application in fourth-generation (4G) wireless communication system. Several previous researches were mainly investigated the performance of OFCDM based on variable spreading factor and subcarrier allocation. However, there are also several system parameters may affected the performance of OFCDM. For that purpose, this paper developes a model to investigate the impact of several parameters on the performance system of OFCDM over Rayleigh Fading channel as a realistic channel in wireless communication system.The proposed model is then created in the form of computer simulation using MATLAB programming in order to show the impact of several parameters for OFCDM’s performance including number of carriers, size of symbol, symbol rate, bit rate, size of guard interval and spreading factor. The simulation results show that the higher number of carriers, larger size of symbol, higher symbol rate, higher bit rate and larger spreading factor are giving the better system’s performance in terms of Bit Error Rate (BER). However, the larger guard interval is giving the worst system’s performance.So all the parameters should be considered in the implementation of OFCDM for the 4G wireless communication system.
VIKOR analysis in determining creditworthiness M. Syaifuddin; Ganefri Ganefri; Sukardi Sukardi; Asyahri Hadi Nasyuha; Egi Afandi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 6: December 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i6.23305

Abstract

Nowadays people are increasingly inclined to buy motorized vehicles because in addition to a light down payment, finance companies also provide convenience for the public in purchasing motorized vehicles. Even though the income level of the people in Indonesia is still relatively low, with a low down payment, the people are not too concerned about it. Honda showrooms carry out various forms of promotion and marketing so that the vehicles they sell get a response from consumers so they want to buy them. However, in fact there are still many forms of promotion that are not appropriate for consumers, so that in the motor vehicle loan process there are often obstacles caused by various factors. This study aims to create an analyst that can later be applied to computer systems, so that it can be said that by testing the system based on existing criteria, it will provide a definite answer in determining the creditworthiness of motorcycles to consumers.
Recent systematic review on student performance prediction using backpropagation algorithms Edi Ismanto; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Januar Al Amien; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.21963

Abstract

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years
Transforming e-government projects by developing a RAF using Scrum integrated with CASE tool in Botswana Thapelo Monageng; Bukohwo Michael Esiefarienrhe
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.v24i2.27431

Abstract

The digital transformation in Botswana has placed strong emphasis on e-government initiatives aimed at improving public service delivery. However, these projects continue to face low success rates due to challenges such as inadequate and reactive risk management practices, limited technical expertise, and fragmented implementation. This study proposes an integrated risk assessment framework (RAF) that combines Scrum methodology with computer-aided software engineering (CASE) tools that allows for the development of an automated, proactive, and iterative approach to risk management that is specific to the socioeconomic circumstance of Botswana. A quantitative survey was conducted with 32 project management specialists involved in e-government projects to assess their familiarity with agile methods and CASE tools, perceptions of traditional risk management approaches, and acceptance of the proposed model. The results revealed that 90.6% of respondents were familiar with Scrum, 78.1% had used CASE tools, and 81.25% supported the new framework, highlighting the urgent need for real-time risk tracking and continuous stakeholder engagement. The proposed e-government risk assessment framework (e-GRAF) model offers a flexible and adaptive solution to strengthen risk management processes, increase the success rate of e-government projects, and improve the quality and resilience of digital governance systems in Botswana.
Privacy and safety of narrowband internet of things devices Ali Abdollahi; Shohreh Behnam Arzandeh; Mohsen Sheibani
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.v22i4.26279

Abstract

Technology’s increasing role in everyday life has pushed the evolution of the internet of things (IoT), which now permeates industries like information technology, agribusiness, and transportation. Critical concerns in IoT security include platform diversity and issues with authentication and authorization. Critical vulnerabilities identified by researchers contain unencrypted communications, compromised interfaces, and compromised access control processes. A new solution, narrowband IoT (NB-IoT), has responded. Based on the cellular network, this technology is designed for improved security and efficiency, operating within the fourth-generation mobile networks and leveraging essential network components. The current study focuses on NB-IoT vulnerabilities, particularly in the radio segment, which is notably vulnerable. The research utilized the open-source tool OpenLTE and hardware like software-defined radio (SDR) in a setting with active NB-IoT sensors on an LTE network. This included deploying a test listening tool and a laboratory-based IMSI catcher to intercept active device communications in a testbed. The results highlight significant vulnerabilities: sensors were deactivated following simulated network attacks with rogue eNodeB and traffic area update (TAU) messages, revealing the technology’s susceptibility to connection failure.
An optimum design of high sensitivity PMMA-coated FBG sensor for temperature measurement Dedi Irawan; Khaikal Ramadhan; Toto Saktioto; Azwir Marwin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 2: April 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i2.22746

Abstract

Fiber Bragg grating (FBG) with silica material has limitations in measuring mechanical quantities such as strain and temperature, this happens because silica fibers are easy to break at higher transverse or axial strains. This deficiency can be overcome in several ways, one of which is by coating the silica FBG with a coating material made of metal or polymer. In this research, the FBG sensor has been designed by poly methyl methacrylate (PMMA)-coated FBG and silica. The finite element method (FEM) is used to analyze the electric field distribution on the surface of PMMA coated FBG with a coating thickness of 20 µm. Furthermore, the sensitivity of each coated FBG as a temperature sensor was measured in the range of 25 ℃ to 85 ℃ using coupled mode theory (CMT). From the design and analysis of coated FBG, it was found that FBG coated with PMMA material had the highest sensitivity of 395.73pm/℃. However, the FBG sensor coated with silica material has a sensitivity of 13.73 pm/℃. the shift obtained is also linear along with the temperature of 25 ℃ to 85 ℃.
Neuromarketing case study: recognition of sweet and sour taste in beverage products based on EEG signal features Yuri Pamungkas; Riva Satya Radiansyah; Padma Nyoman Crisnapati; Yamin Thwe
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.v23i3.26626

Abstract

Consumers’ acceptance of a food product hinges on its taste. Culinary practitioners typically conduct organoleptic tests to evaluate a food/beverage’s taste. Organoleptic tests have a subjective nature, making a clear description difficult. In this study, we suggest implementing a brain signal-based electroencephalogram (EEG) taste assessment system to evaluate consumer responses to the tastes of a drink, specifically sour and sweet. The system distinguishes flavors based on EEG data. These classifiers, including recurrent neural network (RNN), long-short term memory (LSTM), and gated recurrent unit (GRU), are utilized for the classification process. Total 35 participants’ EEG data were recorded for this study. Temporal (T3 and T4) and centro parietal (CP1 and CP2) channels are used for recording. EEG signal processing involves filtering, artefact elimination, and band decomposition into delta, theta, alpha, beta, and gamma frequencies. In the time domain of clean EEG data, mean absolute value, standard deviation, and variance are used for signal feature extraction. Several classifiers (RNN, LSTM, and GRU) will be fed with the signal feature values as input. An accuracy of 88.62% was achieved using LSTM in the classification. The RNN and GRU models achieved classification accuracies of 88.56% and 87.15% respectively.
A robust method for VR-based hand gesture recognition using density-based CNN Liliana Liliana; Ji-Hun Chae; Joon-Jae Lee; Byung-Gook Lee
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.v18i2.14747

Abstract

Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy.

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