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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 473 Documents
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Integrating Gamification in Expert Systems: A Novel Approach for Stress Disorder Diagnosis in Digital Mental Health prasetyaningrum, putri taqwa; Ibrahim, Norshahila; Yuniasanti, Reny; Subagyo, Ibnu Rivansyah
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1324

Abstract

The increasing prevalence of stress disorders highlights the need for innovative, accessible, and engaging diagnostic tools in mental health services. This study presents the design and implementation of a gamified expert system for diagnosing stress disorders, integrating gamification elements to enhance user engagement and reduce stigma. The system employs the forward chaining method to deliver high-accuracy, rule-based diagnoses while incorporating features such as points, rewards, and leaderboards to motivate user interaction.The system's development followed a user-centered design approach to ensure an intuitive interface aligned with user needs. Evaluation results demonstrated a diagnostic accuracy rate of 92%, validated by mental health professionals, alongside significant improvements in user engagement metrics, including session frequency and duration. Qualitative feedback indicated that gamification effectively reduced stigma and increased motivation for mental health assessments.These findings suggest that gamified expert systems can bridge gaps in accessibility and engagement in mental health services. This research contributes to the advancement of digital health technologies by providing practical insights into integrating gamification into expert systems to foster proactive mental health management.
Autism Face Detection System using Single Shot Detector and ResNet50 Melinda, Melinda; Alfariz, Muhammad Fauzan; Yunidar, Yunidar; Ghimri, Agung Hilm; Oktiana, Maulisa; Miftahujjannah, Rizka; Basir, Nurlida; Acula, Donata D.
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Investigating Synthetic Traffic Generators for Zipf Distribution Simulation Accuracy Fahrianto, Feri; Arifin, Viva; Shofi, Imam Marzuki; Suseno, Hendra Bayu; Amrizal, Victor; Azhari, Muhamad; Pratiwi, Anggy Eka
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1359

Abstract

Accurate traffic generation is essential for realistic network simulations in systems such as Content Delivery Networks (CDNs), Information-Centric Networks (ICNs), and the Internet of Things (IoT). These environments handle various types of data traffic—ranging from web pages and videos to sensor data and software updates—making it critical to model traffic patterns effectively. A well-designed traffic generator enables researchers and engineers to simulate real-world workloads, test scalability, and evaluate the performance of caching, routing, and resource allocation strategies under realistic conditions. Each traffic class has unique characteristics, including object size distributions, access patterns, and temporal dynamics. Capturing these differences is key to producing meaningful simulation results. For instance, CDNs require simulation of content popularity and delivery latency, ICNs focus on content retrieval and caching efficiency, while IoT simulations demand modeling of device behavior and intermittent communication. To support such complex scenarios, a traffic generator must not only mimic real user behavior but also allow for flexible scaling, combination, and modification of traffic patterns. This paper presents a method for evaluating synthetic traffic generators by comparing their output to the statistical properties of the Zipf distribution. The focus is on assessing whether synthetic traffic accurately reflects the heavy-tailed nature of real-world traffic as modeled by Zipf’s law. By analyzing the frequency distribution of requests generated by the traffic model and comparing it to theoretical Zipf curves, the study provides insights into the fidelity of the traffic generator. We measure the discrepancy between the simulated network traffic and the theoretical model to evaluate the accuracy and realism of the traffic generation approach.
Fuzzy Logic-Based Aquaculture Climate Control System Design On A Fishpond Wicaksono, Damar; Mareta, Affix; Adiana, Beta Estri; Salim, D Jayus Nor
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1152

Abstract

One important key to determining water quality as a habitat for live fish is the climate of the water. A good water climate in aquaculture is needed by aquatic animals to survive, such as fish and other aquatic organisms. To maintain it, it can be controlled by the aeration rate and monitoring the amount of water by microcontroller. Monitoring water quality and maintaining the amount of water activity at a certain volume is necessary by farmers to get good harvests. Fish Farmers typically rely on irrigation and rain, which cause the aquatic climate to change. Therefore, this research proposed to design a system to maintain the water climate and amount of water so that they remain in accordance with the levels required by fish. In general, a water climate control system using Mamdani fuzzy logic control has been implemented, which is used to manipulate output equipment in the form of aeration speed based on water climate parameters as the input. This system will also automatically control to drain or refill water if needed by water pump. This system consists of materials, components, and sensors to get the data. A pond-type aquarium is used as a simple test. As a method to make this happen, fuzzy logic is used based on the available input and categorizes it into several criteria, then provides knowledge-based principles, inference mechanisms, and the defuzzification phase. The test results showed success in controlling the water climate quality and the maintain amount of water, with success rate at 88,89% and 91,67%. This shows that the proposed system has worked effectively to control climate water quality and maintain the amount of available water.
Performance Comparison of Three Modified Howland Current Source Circuits in Low-Cost Bioimpedance Analyzer for Bio-Phantom Signal Acquisition Firmansyah, Eka; Wicaksono, Ridwan
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1218

Abstract

Electrical impedance tomography (EIT) supports recent advancements in gastric monitoring for telehealth applications. The challenge in providing low-cost EIT is ensuring a stable current injection over diverse tissue conductivities within the abdominal cavity. Three current source circuits based on Howland's current source are investigated to be applied in a low-cost portable bioimpedance analyzer (p-BIA). Those are: a) triple amplifier Howland current source (TAHCS), b) mirrored Howland current source (MHCS), and c) composite mirrored Howland current source (CMHCS). Evaluation has been conducted based on signal-to-noise ratio (SNR), broadness of the frequency range, and output impedance over frequency value and stability. Due to its high SNR value (82.56 to 102.1 dB), broad (500 Hz to 500 kHz) operating frequency, and consistent output impedance over operating frequency (10 to 25 kΩ), CMHCS is a good option in realizing p-BIA in gastric monitoring based EIT.
Automated Component Detection for Quality PCB Using YOLO Algorithm with IoT Real-Time Streaming on Raspberry Pi Nugroho, Waluyo; Zahabiyah, Rifdah; Arifiant, Mada Jimmy Fonda; Afianto, Afianto
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1313

Abstract

This paper presents the development of an automated component detection system for quality control in Printed Circuit Boards (PCBs) by integrating the YOLO object detection algorithm with Internet of Things (IoT) real-time streaming on a Raspberry Pi platform. The proposed system aims to address the challenges associated with traditional manual inspection methods, including time inefficiency, human error, and limited accuracy in detecting faulty components. The YOLO model, renowned for its high-speed and accurate object detection capabilities, was trained to identify various PCB components and deployed on a Raspberry Pi due to its affordability, portability, and low power consumption. To enable real-time remote monitoring and analysis, IoT capabilities were incorporated using the MQTT protocol, allowing seamless data transmission to remote servers or devices. The experimental results demonstrated the effectiveness of the proposed system, achiev-ing an average detection accuracy of 95%, making it a reliable solution for real-time quality assurance in PCB manufacturing. The novelty of this study lies in the innovative integration of the YOLO algorithm with IoT technology on a cost-efficient platform, providing a scalable and practical solution for automating PCB inspection processes. This approach not only enhances inspection efficiency but also reduces operational costs, offering significant value to the electronics manufacturing industry. Future work will focus on scaling thesystem for broader applications and improving the detection capabilities for more complex PCB designs.
Enhancing Disease Diagnosis Coding: A Deep Learning Approach with Bidirectional GRU For ICD-10 Classification Priwibowo, Aqge; Dewa, Chandra Kusuma; Luthfi, Ahmad
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1320

Abstract

The health insurance claim in hospitals involves selecting specific ICD-10 codes for primary diagnosis texts. With rising claim volumes, the need for faster, more accurate coding is critical. This study develops a deep learning model to classify diagnosis texts into relevant ICD-10 codes using 9,982 original medical records from a national referral hospital under the Indonesian Ministry of Health. The classification method employs a BiGRU layer architecture, known for its effectiveness in handling sequential data, such as diagnosis texts. BiGRU operates bidirectionally, enhancing the model’s ability to capture the context from both past and future sequences. In this architecture, the BiGRU layer serves as the classification layer, stacked above the BERT layer, which functions as the vector embedding layer, converting text into numerical representations for the model. The results of the study demonstrate a promising solution for codifying primary diagnosis texts, achieving a precision of 82.18% and a recall of 81.59%. Despite the strong performance of the model, further improvements are possible. Interestingly, the study also observed that the size of the class volume per ICD-10 code is not the only factor affecting classification performance, as some classes with smaller volumes exhibited better classification results. However, merging rare classes did not improve performance and even worsened it, suggesting that better ways to handle underrepresented classes are needed. Experiments with different embedding layers, such as IndoBERT and BioClinicalBERT, and hyperparameter tuning yielded minimal performance gains, suggesting the need for alternative optimization strategies.
Prototype Innovation of IOT Based Tissue Box Using Microcontroller ESP8266 and Infrared Sensor Rosita, Yesy Diah; Amaliah, Nuuraan Risqi; Putra, Andhika Cahyono; Syifa, Fikra Titan; Azizah, Nur
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1321

Abstract

Toilet papers in public restrooms are a form of care from facility managers to help maintain hygiene, particularly in sensitive areas. Typically, toilet paper is exposed outside the tissue box, making it a breeding ground for bacteria. The uncontrolled use of tissue allows users to take as much as they wish, indirectly accelerating deforestation. Additionally, information about tissue stock in toilet paper dispensers is needed by cleaning staff or Office Boy (OB) to know when to refill before it runs out. This study aims to develop an innovative tissue box prototype that incorporates minimization, optimization, and efficiency. The prototype is equipped with an infrared (IR) sensor as an input mechanism to dispense tissue, an ESP8266 module connected to the internet to help cleaning staff monitor the tissue stock and implement the tissue usage control system based on an adjustable time interval of n minutes. The prototype has been designed, tested, and proven functional.
Load-Shedding Optimization Using Hybrid Grey Wolf - Whale Algorithm to Improve The Isolated Distribution Networks Sujono, Sujono; Musafa, Akhmad
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1336

Abstract

The integration of distributed generation allows the distribution network to operate in either on-grid or off-grid mode. In off-grid mode, the power supply from the main grid is interrupted, and distributed generation becomes the main source of power to meet the load's power demand. The absence of power supply from the main grid reduces the grid's ability to meet load power demand. The load power demand is larger than the distributed generation capacity, causing a power deficit in the network. This paper studies strategies for restoring power balance through optimal load shedding, taking into account the presence of priority loads that require power demand to be maintained and met. The optimization objective is to maximize the remaining load with an optimal composition so that the power loss is minimal. The load-shedding optimization uses a hybrid Grey Wolf Algorithm and Whale Optimization Algorithm (GW-WOA). The performance of GW-WOA is tested by load shedding optimization on a 118-bus IEEE radial distribution system integrated with 12 units of DG. The network loading factor variation consists of 80%, 100%, and 140% of the base load. Regarding all loading factors, the GW-WOA hybrid algorithm is superior to the standard GWO and WOA. The GW-WOA hybrid algorithm can converge faster to obtain the global optimal solution to realize power balance, overcome power deficit, maximize remaining load, and minimize power loss in the network. The GW-WOA hybrid algorithm has improved the performance of load-shedding optimization in isolated distribution networks with global optimal results and shorter iterations.
Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Muttaqin, Ikram; Zakaria, Lailatul Qadri
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.

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