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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 3,484 Documents
 260   261   262   263   264 
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
A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter Heri Pratikno; Mohd Zamri Ibrahim; Jusak Jusak
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.v20i3.23319

Abstract

Full ferning is the peak of the formation of a salt crystallization line pattern shaped like a fern tree in a woman’s saliva at the time of ovulation. The main problem in this study is how to detect the shape of the salivary ferning line patterns that are transparent, irregular and the surface lighting is uneven. This study aims to detect transparent and irregular lines on the salivary ferning surface using a comparison of 15 pre-trained convolutional neural network models. To detect fern-like lines on transparent and irregular layers, a pre-processing stage using the Frangi filter is required. The pre-trained convolutional neural network model is a promising framework with high precision and accuracy for detecting fern-like lines in salivary ferning. The results of this study using the fixed learning rate model ResNet50 showed the best performance with an error rate of 4.37% and an accuracy of 95.63%. Meanwhile, in implementing the automatic learning rate, ResNet18 achieved the best results with an error rate of 1.99% and an accuracy of 98.01%. The results of visual detection of fern-like lines in salivary ferning using a patch size of 34×34 pixels indicate that the ResNet34 model gave the best appearance
Enhancement process of AES: a lightweight cryptography algorithm-AES for constrained devices Hussein M. Mohammad; Alharith A. Abdullah
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.v20i3.23297

Abstract

The restricted devices have a small memory, simple processor, and limited power. To secure them, we need lightweight cryptography algorithms, taking into account the limited specifications. Lightweight cryptography (LWC) algorithms provide confidentiality and maintain information integrity for devices with limited resources. This paper improves and enhances advanced encryption standard (AES) algorithm by reducing algorithm computation power and improving cryptography performance from the point of resource constraint devices. The proposed algorithm is fast and lightweight, which is essential for securing all kinds of data. Besides, the use of mix column overhead is dispensing with, and the ciphertext is processed by the mathematical function (continued fraction) to compress the ciphertext and make it more confusing and also to increasing the data transfer speed. The proposed lightweight cryptography-AES (LWC-AES) algorithm highly suitable for the timely execution of encryption and decryption (such as when encrypt text has (45.1 KB) encryption execution time for AES was (294 ms), while in LWC-AES was (280 ms), as well as suitable for the memory size of the resource-constrained devices for all types of data, than the AES algorithm. The proposed algorithm tested for security analysis using the Avalanche Effect parameter, and this test showed acceptable and within required security results
Energy-efficient speed profile: an optimal approach with fixed running time An Thi Hoai Thu Anh; Nguyen Van Quyen
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.v20i3.19525

Abstract

Tracking the optimal speed profile in electric train operation has been proposed as an efficient and feasible solution for not only reducing energy consumption, but also no at costs to upgrading the existing railway systems. This paper focuses on finding the optimal speed profile based on Pontryagin’s maximum principle (PMP) while ensuring the fixed running time, and comparing energy saving levels in the cases of applying or not applying PMP. The way to determine the fixed running time also differs from works published is to calculate the total trip time equal to scheduled timetable exactly. Calculating accelerating time ta, coasting time tc, braking time tb via values of maximum speed vh, braking speed vbof optimal speed profile. The other hands, vh and vb are determined by solving nonlinear equations with constraint condition: the running time equal to the demand time. Simulation results with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that energy reduction for the entire route when PMP utilization is up to 8.7% and running time complied with scheduled timetables.
A performance of radio frequency and signal strength of LoRa with BME280 sensor Puput Dani Prasetyo Adi; Akio Kitagawa
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.v18i2.14843

Abstract

LoRa is a Radio Frequency module that can send packet data up to 3 km in FSPL.LoRa has 3 different Frequency Radios i.e, 915 MHz, 868 MHz, and 433 MHz. LoRa testing is based on different distances, BME280 provides Barometric Pressure, Temperature, and Humidity data. An analysis from the results of the Received Signal Strength to the distance (m) to the farthest point to prove and provide QoS data from LoRa 915 MHz. Sensor nodes are built using ADR and Automatic sleep mode algorithms. Communication systems between nodes are built dynamic sensor nodes in mesh networking. Monitoring signal transferring on the 915 MHz Frequency waveform is carried out using the Textronix Spectrum analyzer. Based on the BME280 Data packet transmission from the LoRa 915 MHz Transmitter to the Receiver Receiver at a distance of 100m is -84 dBm and at a distance of 500m is -107 dBm. The LoRa Internet Gateway has 2 types of settings, i.e, application, and gateway, this setting is to find out the location of the gateway with longitude and latitude. Furthermore, the gateway holds Sensor data from the End node, while the Application Server displays sensor data in the form of Graphics in realtime.
Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades Ahmed Lateef Khalaf; Mayasa M. Abdulrahman; Israa Ibraheem Al_Barazanchi; Jamal Fadhil Tawfeq; Poh Soon JosephNg; Ahmed Dheyaa Radhi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.24854

Abstract

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for real-time object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes.
Cost-effective long-range secure speech communication system for internet of things-enabled applications Samer Alabed; Mohammad Al-Rabayah; Bahaa Al-Sheikh; Lama Bou Farah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26884

Abstract

A new communication framework has been developed that allows voice transmission over long distances for internet of things (IoT) applications such as healthcare, smart cities, and remote monitoring in the least costly way and most secure manner. The system is based on long range (LoRa) technology and takes advantage of its spread spectrum technique, to provide long range transmission without the high-power requirements. The main limitation is LoRa’s bandwidth with a maximum throughput of 22 kbps for data. This presents a challenge for voice transmission communications. To address this shortened bandwidth issue, researchers developed an innovative compression solution that compresses voice data to less than 8 kbps to fit into LoRa’s capabilities. The compression allows for real practical voice communications and possibly can provide even greater distance than an uncompressed voice transmission update. The voice communications transmissions have cryptographic protection in place to protect the transmitted voice messages from unauthorized access.
Study the effect of nickel and aluminium doped ZnO photoanode in DSSC Nur Syafiqah Nadiah Mohd Alias; Faiz Arith; Nur Syamimi Noorasid; Hafez Sarkawi; Ahmad Nizamuddin Muhammad Mustafa; Mohd Muzafar Ismail; Mohd Khanapiah Nor
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.v20i5.23013

Abstract

Dye sensitized solar cells (DSSC) is one of the promising candidates which are efficient, low-cost, and clean hybrid molecular solar cell devices. Zinc oxide (ZnO) has been widely used as the phoanode in DSSC due to its excellent charge conduction mechanism, yet still suffers from poor cell efficiency. In this study, aluminium doped ZnO (ZnO:Al) and Ni doped ZnO (ZnO:Ni) were studied as photoanode material in DSSC using solar cell capacitance simulator (SCAPS) simulation, and the electrolyte liquid considered a single solid p-type layer as hole transporting materials. Both studied photoanodes have demonstrated better cell performance than pure ZnO photoanode due to the small amount of aluminium (Al) and nikel (Ni) impurities added have enhanced the physiochemical properties of ZnO films. A power conversion efficiency (PCE) of 3.96% was obtained at 3 mol% ZnO:Al photoanode with optimized key parameters. These simulation results proved an opportunity to improve the performance of the DSSCs via doping engineering into the ZnO photonaode.
Adaptive trading system for sustainable forex markets Joni Fat; Parwadi Moengin; Pudji Astuti; Sally Cahyati
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.v24i4.27479

Abstract

This study presents a sustainable and ethically aligned algorithmic trading system for the Euro/United States Dollar (EURUSD) currency pair, integrating reinforcement learning (RL) with a Sugeno-type fuzzy inference mechanism. The framework emphasizes responsible AI principles by combining adaptability and interpretability to support transparent and explainable financial decision-making. Historical EURUSD M15 data from 2020 to 2023 were used for training, while 2024 data served for out-of sample testing. The system employs EMA50-based state classification, tabular state–action–reward–state–action or SARSA learning, and a fuzzy logic layer comprising 27 expert-defined rules. During backtesting, the agent executed 785 trades, achieving a net profit of USD 61.85, a profit factor of 1.04, and a balanced win–loss ratio. Risk-adjusted analysis showed moderate resilience (sharpe ratio = 0.53) and a maximum drawdown of 59.74%. The model demonstrated strong equity stability (ESI = 0.9672) and sensitivity to macroeconomic events identified through cumulative sum (CUSUM) analysis. While the system maintained capital preservation and interpretability, responsiveness under volatile conditions requires improvement. Future work will focus on adaptive exit logic, volatility-aware reward mechanisms, and regime-sensitive policy optimization. This study contributes to advancing sustainable, transparent, and risk-aware artificial intelligence (AI) frameworks in algorithmic trading.
An integration of quantum systems using BB84 for enhanced security in aeroponic smart farming Christy Atika Sari; Purwanto Purwanto; Eko Hari Rachmawanto; Abdul Syukur
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26450

Abstract

Modern aeroponic systems leverage internet of things (IoT) technology for automated control of climate, lighting, and nutrient delivery, rendering them susceptible to unauthorized access and network attacks. Such disruptions can lead to financial losses and impair agricultural productivity by altering essential growth conditions. To mitigate these risks, robust security measures including encryption and firewalls are essential, alongside continuous monitoring and updates to combat evolving threats. Addressing cyber threats in urban aeroponic systems, implementing quantum encryption emerges as a promising solution. Quantum key distribution (QKD) ensures highly secure encryption keys using quantum states that change upon eavesdropping, thereby thwarting intrusion attempts effectively. Integrating quantum encryption in aeroponic control systems safeguards data integrity and operational continuity against cyber threats, bolstering urban agriculture resilience. Our findings demonstrate the efficacy of quantum BB84 protocol integrated with API for Eve’s security. Quantum bit error rate (QBER) measurements revealed minimal interference (0.015) for Alice and Bob, contrasting with higher initial QBER (up to 1.0) for Eve, indicative of intrusion attempts. Histogram analysis further underscored quantum security’s effectiveness in identifying and mitigating breaches. For future research, enhancing quantum encryption protocols and integrating advanced detection mechanisms will be essential.

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