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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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INDONESIA
Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 235 Documents
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Comparative Analysis of Green Snake Identification using Head Structure and Body Patterns with Vision Transformer Putriany, Eva; Ariatmanto, Dhani
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2992

Abstract

Snakebites remain a major global health concern, with over 4.5 million cases annually, primarily affecting rural populations in tropical regions. Accurate snake species identification is critical for proper treatment, yet challenges persist due to morphological similarities, particularly among visually similar green snake species. We test five Vision Transformer (ViT)-based models to see how well they can classify snakes based on pictures of their heads and bodies. The models are ViT-B16, DeiT, PoolFormer, Swin-T, and CaiT. Results indicate that head structure classification achieved higher accuracy than body pattern classification due to more distinct morphological features. CaiT outperformed other models, achieving 87% accuracy, particularly when trained on RGB images. These findings highlight the importance of model selection and dataset characteristics in improving snake species classification, especially for species with high visual similarity.
Improving Alzheimer's Disease Prediction Accuracy using Feature Selection, K Fold Cross Validation, and KNN Imputer Techniques Kirso, Kirso; Anasanti, Mila Desi
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3055

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss; it accounts for 60–70% of dementia cases. Early diagnosis remains challenging due to the subtlety of its symptoms. This study explores the effectiveness of ensemble methods, feature selection techniques, and imputation strategies in enhancing the accuracy of AD diagnosis. We applied an ensemble method with Chi-Square feature selection, achieving a high accuracy of 95.733% with 7 optimal features. The combination of classifiers, including Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LR), contributed to the high performance. Additionally, the use of KNN Imputer and K-Fold Cross Validation significantly improved accuracy, regardless of whether feature selection was employed. Notably, feature selection slightly reduced model complexity but resulted in a marginal decrease in accuracy. The study highlights the importance of these methods in achieving reliable AD predictions, though dataset dependency and potential biases from methodological choices are acknowledged. Future work may involve exploring alternative classifiers and validating findings across diverse datasets to enhance generalizability and address these limitations.
Toward a Modular, Low-Latency Architecture with BERT-based Big Media Data Analysis Widyawan, Widyawan; Murti, Handoko Wisnu; Putra, Guntur Dharma; Nurmanto, Eddy; Affandi, Achmad
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3151

Abstract

The significant growth of digital and social media platforms has introduced massive streams of unstructured media data. However, current big data approaches are not specifically tailored to the high volume and velocity of media data, which consists of unstructured and lengthy full-text messages. This study proposes a modular and stream-oriented big data architecture for media data. The proposed architecture consists of data crawlers, a message broker, machine learning modules, persistent storage, and analytical dashboards, with a publish-subscribe communication pattern to enable asynchronous, decoupled data processing. The system integrates IndoBERT, a transformer-based model fine-tuned for the Indonesian language, enabling real-time semantic tagging within the streaming pipeline. The proposed solution has been implemented as a prototype using open-source technologies in an on-premise cluster. As such, the primary novelty is the successful integration and operationalization of a large, transformer-based language model (IndoBERT) within a low-latency streaming pipeline. The experimental results underscore the feasibility of deploying scalable, vendor-neutral media analytics platforms for institutions with high sensitivity to privacy and cost. Architectural quality is quantitatively evaluated through Martin's Instability Metric and Coupling Between Objects (CBO), confirming high modularity across components. The system demonstrates an end-to-end latency of 3.121 seconds, deep learning latency of 2.333 seconds, and processes 32,102 messages per day, making an explicit trade-off where the 2.333-second deep learning inference provides advanced semantic depth. This study presents a reference architecture for scalable, intelligent real-time media analytics systems that support public sector and academic deployments, requiring data privacy and control over infrastructure.
Violence and Robbery Detection System Using YOLOv5 Algorithm Based on IoT Technology Khoiriyah, Hani'atul; Abdillah, Fauzan; Aziz, Afris Nurfal; Wiryawan, I Gede
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3088

Abstract

Violence and robbery are two common forms of crime that often cause material losses, psychological trauma, and insecurity within society. Conventional CCTV systems are limited in preventing such incidents, which highlights the need for more intelligent and responsive security solutions. The primary objective of this research is to design and evaluate SmartGuard, a real-time detection system for violence and robbery based on artificial intelligence (AI) using the YOLOv5 algorithm, integrated with Internet of Things (IoT) technology for remote monitoring. This study employed an experimental design with several stages: dataset preparation, model training, testing, model analysis, and system integration with Raspberry Pi, Firebase, and a mobile application. The dataset consisted of 6,900 labeled images across three classes: violence, robbery, and normal activity. Model evaluation was conducted using a separate test dataset and analyzed with a confusion matrix. The results show that the model achieved an overall accuracy of 70.94%. The system performed relatively well in detecting violence, with a precision of 71.13% and an F1-score of 62.47%. However, recall values for robbery (47.53%) and normal activity (48.99%) were considerably lower, indicating challenges in consistently recognizing these classes. Despite these limitations, SmartGuard allows users to view and receive notifications in emergency situations, enabling them to take quick action and monitor the situation effectively.
Automatic Analysis of Natural Disaster Messages on Social Media Using IndoBERT and Multilingual BERT Safitri, Yasmin Dwi; Faisal, Mohammad Reza; Kartini, Dwi; Saragih, Triando Hamonangan; Abadi, Friska; Bachtiar, Adam Mukharil
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3140

Abstract

Information about natural disasters disseminated through social media can serve as an important data source for mitigation processes and early warning systems. Social media platforms, such as X (formerly known as Twitter), have become primary channels for conveying real-time information, especially during disaster emergencies. With the large amount of unstructured disaster-related text that must be processed, the main challenge is accurately filtering and classifying messages into three categories: eyewitness, non-eyewitness, and don’t know. This research aims to compare the performance of four BERT-based natural language processing models, namely IndoBERT, IndoBERT with Masked Language Modeling (MLM), Multilingual BERT, and Multilingual BERT with MLM, in classifying Indonesian-language disaster messages. The dataset used in this study was obtained from previous research and publicly available data on GitHub, consisting of annotated messages related to floods, earthquakes, and forest fires. The method applied is a deep learning approach using the hold-out technique with an 80:20 ratio for training and testing data, and the same ratio applied to split the training data into training and validation subsets, with stratification to maintain balanced class proportions. In addition, variations in batch size were explored to evaluate their effect on model performance stability. The results show that the IndoBERT model achieved the highest performance on the flood and earthquake datasets, with accuracies of 80.67% and 81.50%, respectively. Meanwhile, IndoBERT with MLM pre-training recorded the highest accuracy on the forest fire dataset, 88.33%. Overall, IndoBERT demonstrated the most consistent and superior performance across datasets compared to the other models. These findings indicate that IndoBERT has strong capabilities in understanding Indonesian disaster-related text, and the results can be used as a foundation for developing automatic classification systems to support real-time disaster monitoring and early warning applications

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