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Eko Fajar Cahyadi
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Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Institut Teknologi Telkom Purwokerto Jl. D. I. Panjaitan, No. 128, Purwokerto 53147, Indonesia
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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 14 Documents
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Search results for , issue "Vol 17 No 4 (2025): November" : 14 Documents clear
An EfficientNet and Dual Path Network Approach for Enhanced Brain Tumor Classification Andri Agustav Wirabudi; Lia Hafiza; Nurwan Reza Fachrurrozi; Agus Pratondo; Gagas Ezhar Rahmayadi
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1290

Abstract

Brain tumor classification is an essential step in medical image analysis, contributing to timely diagnosis and effective treatment planning. This study introduces a brain tumor classification model that integrates EfficientNet with Dual Path Networks (DPN) and a Multi-Head Self-Attention (MHSA) mechanism. The model is applied to classify three major types of brain tumors—glioma, meningioma, and pituitary—using MRI images. The integration of DPN allows the model to leverage both residual and dense connections for enhanced feature representation, while the MHSA module refines global and local contextual information. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 97.82%, sensitivity of 97.83%, specificity of 98.41%, precision of 98.34%, and F-score of 98.08%. These results indicate competitive performance compared to widely used architectures such as CNN, ResNet, and DenseNet, suggesting that the combined use of EfficientNet, DPN, and MHSA can provide a robust approach for brain tumor classification.
Design and Evaluation of a CMS-Integrated Academic Chatbot Using Gemini AI Bunga Laelatul Muna; Muhammad Lulu Latif Usman; Sudianto Sudianto; Bachtiar Herdianto
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1318

Abstract

Efficient academic services are a crucial component, and their implementation is the responsibility of higher education institutions. Telkom University Purwokerto (TUP) faces challenges in providing responsive academic services, especially in conventional online services. This research proposes the development and integration of an artificial intelligence-based academic chatbot, ‘Akif’, utilizing the Gemini 1.5 Flash model, which is linked to the institution’s Content Management System (CMS). This integration enables the retrieval of real-time information and automatic updates to the model. The tuning and evaluation process was conducted using the BLEU metric, with a value of 0.88 being reached, indicating a fairly good level of agreement between the generated answers and the reference. Although the results are promising, the system still faces limitations, particularly the risk of hallucination, which is a common challenge with generative models. Additionally, the use of BLEU as an initial evaluation metric overlooks aspects of semantic depth and user satisfaction. This research contributes a modular integration framework between generative AI and institutional systems, and highlights its potential and limitations in academic service automation.
Pendeteksian Varietas Biji Kopi: Studi Perbandingan pada Dataset USK-COFFEE dengan Model YOLO Imam Sayuti; Maulisa Oktiana; Kahlil Muchtar; Roslidar Roslidar; Khairun Saddami
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1333

Abstract

Abstract — Deep learning-based object detection has become an important technology in industrial automation, including the classification of coffee beans based on their quality and type. Coffee beans are one of the leading commodities with high economic value, especially in Indonesia. The manual process of sorting coffee beans is often inefficient and prone to errors. This research contributes to the development of artificial intelligence-based technology to improve the efficiency and accuracy of the coffee bean sorting process in the industry. The method was carried out to compare the performance of the YOLOv8 and YOLOv11 models in detecting multiclass coffee beans using interactive visualization through Streamlit. The model was trained using the USK-Coffee dataset which includes coffee bean classes such as Premium, Peaberry, Longberry, and Defect. The YOLOv8 and YOLOv11 models are fine-tuned and evaluated using metrics such as mean average precision (mAP), confusion matrix such as precision and recall. The results show that YOLOv8 excels in accuracy with a mAP@50 of 88.78%, compared to YOLOv11 with a mAP@50 of 88.44%. Streamlit-based interactive visualization has proven to be effective in displaying coffee bean detection results and making it easier for users to analyze model performance.
An IoT-based wireless monitoring system for the measurement of SO2 and NO2 concentrations emitted from a point source Sunarno - Sunarno; Purwanto - Purwanto; Edy - Wibowo
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1340

Abstract

Air pollution monitoring and assessment is a challenging problem from local to global scales with consequences on human health and other resources. Furthermore, monitoring of air pollution is often difficult to attain due to the high cost of research especially using the conventional bulky monitoring samplers. Therefore, the purpose of this study is to develop a cheap, portable, and efficient NO2 and SO2 pollution monitoring system using IoT technology. The experimentation consists of two major stages vis-a-vis system design and field implementation. The system design is mainly the use of SO2, NO2, and meteorology sensors attached to a microprocessor which transfers processed data to the cloud repository. The monitoring system developed in this study showed good stability, but the quality of the communication network was in the unsatisfactory category hence its baud rate could be improved in the future. This system was successfully deployed to measure the concentration of SO2 and NO2 emitted from combustions of sawdust, coal powder in an incinerator. In addition, NO2 from the exhaust of a 96 kWh-1 diesel-powered electric generator engine was also monitored. The results showed high variations in SO2 concentrations were obtained during combustion of sawdust and coal powder, though with only 25 % violation of the national reference-quality standard even with different weights. For the exhaust gas of the generator set engine, the average concentration of NO2 detected was 6.68 ppm which was lower than the QSA (Quality Standard of Ambient air) value of 79.9 ppm. Further works are needed to demonstrate the use of this technology in monitoring SO2 and NO2 emitted from other sources such as industrial, vehicular, solid waste combustions, and biogenic emissions
Optimization of Limit Switch Usage as a Motion Boundary Detection System in a Dolly System Dea Purbawati; Parama Diptya Widayaka
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1349

Abstract

Efficient container handling is a critical aspect of global trade, as container terminals serve as the primary hubs connecting maritime transport and inland logistics networks. The increasing demand for faster and safer container operations has encouraged the adoption of the double chassis (dolly) system, which significantly accelerates loading and unloading activities. However, this system is also prone to operational risks. Extreme maneuvers performed by truck operators can cause collisions between the dolly triangle and the chassis frame, potentially resulting in structural damage, reduced equipment lifespan, and a higher risk of accidents. To address this challenge, this study proposes the optimization of limit switches as a motion boundary detection system in the dolly mechanism to enhance both safety and operational efficiency. The research combines experimental testing with mathematical model-based simulations, employing trigonometric analysis to determine the safe maneuvering angles of the dolly system. The proposed system integrates limit switches on the towing hitch to detect critical angles and provide early warnings through two alarm levels. The first alarm is activated at angles less than 40° as an initial safety signal, while the second alarm is triggered at angles less than 50° to prevent severe collisions. The results demonstrate that the proposed system effectively minimizes collision risks with a relatively low error rate, particularly at smaller maneuvering angles. The main contribution of this study lies in presenting a practical and low-cost safety mechanism that integrates simple sensor technology with mathematical modeling, offering an innovative solution to support safer and more efficient container terminal logistics.
Enhancing IoT Data Security: AES Encryption for Protecting Data in Transit- A Case Study in Smart Agriculture Imam Asrowardi; Septafiansyah Dwi Putra; Eko Subyantoro; Bita Parga Zen
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1361

Abstract

The integration of IoT in smart agriculture facilitates real-time environmental monitoring and efficient farming operations. However, the sensitive nature of data in transit presents significant security challenges, primarily due to threats like data interception and unauthorized access. This study explores the implementation of AES-128 encryption as a solution to secure data transmission within a smart agriculture IoT system. Utilizing NodeMCU microcontrollers with DHT22 sensors, this research investigates the encryption and decryption of environmental data (temperature and humidity) through the MQTT protocol, with Node-RED providing data visualization. Experimental results indicate that AES-128 encryption adds minimal overhead, maintaining real-time system performance while safeguarding data integrity and confidentiality. This encryption framework not only reinforces secure data transmission but also enhances decision-making reliability in agricultural management, underscoring the practical benefits of data security in IoT-based smart farming.
Early Warning Safety System Development for Electric Vehicle Batteries to Prevent Fires and Accidents: Implementation in Urban Public Transportation Dedid Cahya Happyanto; Jelia Anita; Akhmad Hendriawan
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1383

Abstract

The increasing adoption of electric vehicles (EVs) in urban public transportation has raised significant safety concerns, particularly regarding thermal runaway incidents that may lead to catastrophic fires. Existing battery monitoring systems often provide inadequate warning times and lack predictive capabilities to mitigate failures before they reach critical conditions. This study proposes an intelligent early warning system for EV battery safety in public transportation fleets by employing predictive analytics. The system integrates a distributed Internet of Things (IoT) sensor network that monitors temperature, voltage, current, and gas emissions, combined with machine learning algorithms—specifically, Random Forest and Support Vector Machine—to analyze battery performance patterns. The proposed architecture incorporates edge computing for real-time data processing and cloud infrastructure for centralised fleet monitoring. Field validation involving 50 electric buses operating under Jakarta's TransJakarta network over a twelve-month period achieved a prediction accuracy of 94.7% for thermal runaway events, with an average warning time of 8.3 minutes. The system successfully prevented 23 potential battery failures while maintaining a false alarm rate below 2.1%. An economic analysis further indicated a favourable cost-benefit ratio of 1:7.4. The proposed solution demonstrates significant potential in enhancing EV battery safety through predictive analytics and automated emergency response, offering a scalable model for broader industry adoption.
Privacy-Preserving Automated QA Dataset Generation for Fine-Tuning LLMs with Local Models and Information Retrieval Ary Suryadi; Dedi Dwi Saputra; Windu Gata; Riza Fahlapi; Angge Firizkiansah; Nuryani Mawar Putri
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1388

Abstract

This paper introduces a novel framework for automated question-answering (QA) dataset construction, integrating information retrieval (IR) with a lightweight local large language model (LLM), SmolLM2- 360M-Instruct, to ensure privacy and scalability for domain-specific applications. Addressing the limitations of manual dataset creation and cloud-based LLMs, our approach leverages PyPDF2 for robust PDF text extraction and a novel sentence segmentation algorithm to generate concise, contextually relevant QA pairs from domain-specific corpora. The framework employs IR techniques to align questions with precise answers, enhancing dataset quality while maintaining data privacy through localized processing. Rigorous evaluation using automated metrics and manual expert review confirms the high quality and semantic alignment of the generated QA pairs. This approach offers significant benefits for fine-tuning LLMs in niche domains, such as education and technical support, by providing scalable, privacy-preserving datasets that improve contextual understanding and adaptability. Our work contributes to efficient NLP dataset generation, offering a robust solution for advancing LLM performance in specialized real-world applications.
IOT-Based Electrical Energy Consumption Monitoring Application on Machine Tools Putra Bismantolo; Kurnia Anggriani; Nurul Iman Supardi; Gusta Gunawan; Emilio Oktori
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1395

Abstract

Deep Learning for Periodontitis Diagnosis on Two Dimensional Dental Radiograph: A Systematic Review Jordan Valentino Lomanto; Monica Widiasri
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1401

Abstract

Periodontitis is an inflammatory disease that affects the supporting structures of the teeth and is a major contributor to tooth loss. Traditional diagnosis through clinical examination and manual interpretation of two-dimensional (2D) dental radiographs is prone to variability and subjectivity. The emergence of deep learning (DL) offers a powerful tool in medical image analysis, including dental radiography. This study aims to systematically review the existing literature on the use of DL approaches for diagnosing periodontitis using two-dimensional (2D) dental radiographic images, and to assess their diagnostic effectiveness in comparison to conventional clinician-based evaluation. A systematic literature review (SLR) was conducted following the PRISMA 2020 protocol and guided by the PICO framework. Five major databases (Scopus, PubMed, Semantic Scholar, Web of Science, and ScienceDirect) were searched for relevant studies published between 2016 and 2025. A total of 27 studies (across 29 reports) were included based on eligibility criteria, covering classification, segmentation, or detection tasks using panoramic, periapical, or bitewing radiographs. The results indicate that DL models show high diagnostic potential, with classification accuracies often exceeding 80% and segmentation models achieving Dice coefficients above 0.88. Although some models outperformed clinicians, external validation and real-world deployment remain limited. This review highlights both the diagnostic potentials and present limitations of DL in 2D dental radiographs. In conclusion, DL shows substantial promise for automated periodontitis diagnosis using 2D radiographs, though challenges still remain in standardization, external validation, and integration into clinical workflows.

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