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Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Published by Politeknik Negeri Banjarmasin
ISSN : 25983245     EISSN : 25983288     DOI : -
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
We are the Editor of Jurnal ELTIKOM, invites Mr. / Ms Lecturer, researcher and practitioner to be able to publish your paper on topics covering Electrical Engineering, Electronics Engineering, Telecommunications Engineering, Computer Engineering, Information Technology.
Arjuna Subject : -
Articles 243 Documents
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On-Off Control System for Temperature and Humidity in A Sweet Potato Chip Dryer using Heating Elements Khoerun, Bobi; Putra, Cahyono; Kurnianingtyas, Rahajeng
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2105

Abstract

Sweet potato chips are an innovative processed product that has been widely developed by Micro, Small, and Medium Enterprises in several regions. Most sweet potato chips are dried using traditional methods, such as direct sun drying. However, during the rainy season, solar heat cannot be used optimally, so the drying pro-cess tends to be slower. This study aims to create a temperature and humidity control system for a sweet potato chip drying machine using a dual air heater. Based on previous studies, temperature and humidity are crucial factors in food drying because they affect product quality. Earlier studies on drying ovens and temperature-controlled drying systems have shown that on-off control can maintain temperatures close to the setpoint with a simple design and low cost for small-scale applications. In addition, the development of microcontrollers such as the Arduino Uno has made it possible to design automatic control systems that can monitor and adjust temperature and humidity in real time. The method used in this study was the design of a laboratory-scale dry-ing oven equipped with a DHT22 sensor, an Arduino Uno, and an on-off control system using a heater and fan as actuators. System performance was evaluated under several operating conditions, namely with and without the control system. The results showed that the control system was able to maintain the dryer temperature with-in the range of 45°C to 55°C and relative humidity between 35% and 40%. In addition, increasing the drying time from 2 hours to 3 hours reduced the product moisture content from 39.9% to 7.7%.
Domain Adaptation of Bert Models for Biomedical Entity Extraction from Indonesian Health News Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Ignasius, Darnell
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2116

Abstract

Health-related news articles play an increasingly important role in public health monitoring. However, their unstructured linguistic style complicates the automatic extraction of biomedical information. Indonesian health news shows high lexical variation by combining medical terms, colloquial expressions, borrowed Eng-lish words, and culturally specific symptom descriptions. This condition creates challenges for Named Entity Recognition (NER). To address the limited availability of domain-specific resources, this study compares four Transformer-based models, namely BERT, IndoBERT, RoBERTa, and BioBERT, for biomedical NER in Indone-sian health news. A new BIO-annotated dataset consisting of 272 manually labeled articles was constructed and validated, achieving strong inter-annotator agreement (Cohen’s Kappa = 0.88). To reduce data limita-tions, an additional 103 articles were automatically annotated using the best-performing model, RoBERTa, through a semi-supervised approach. All models were fine-tuned under identical settings and evaluated at both BIO and entity levels. The results show that RoBERTa achieves the highest weighted F1-score (0.9543). Howev-er, its macro F1-score (0.3873) indicates uneven performance across entity classes because of severe label im-balance, with non-entity tokens dominating the dataset. This finding highlights the importance of emphasizing macro-level evaluation to better reflect entity recognition performance. RoBERTa consistently outperforms the other models, which may be explained by its robust architecture and adaptability to diverse linguistic patterns. In contrast, BioBERT underperforms because of cross-lingual and domain mismatch, as it is pretrained on Eng-lish biomedical corpora and optimized for scientific text rather than journalistic language. The error analysis further identifies boundary inconsistencies and under-detection of low-frequency entities, especially in the drug and symptom categories.
LSTM-Based Causal Attribution Modeling of the 2025 Sumatra Flash Flood Discourse on YouTube Jalia, Kunti Najma; Suwondo, Adi; Sibyan, Hidayatus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2132

Abstract

Existing disaster sentiment analysis mainly focuses on emotional polarity classification, while often over-looking the causal reasoning that shapes public discourse on responsibility for disaster outcomes. This study proposes and assesses a Long Short-Term Memory (LSTM)-based causal attribution classification framework to examine YouTube comments related to the 2025 Sumatra flash flood. It compares LSTM performance with Sup-port Vector Machine (SVM) and Naïve Bayes baselines. A total of 17,503 publicly available comments were collected through the YouTube Data API v3 and processed into a final dataset of 12,299 comments. The com-ments were classified into two causal categories, human factor and nature/prayer factor, using lexicon-based scoring validated by three independent annotators (Cohen's κ = 0.81). The experimental results show that LSTM achieves 98.17% accuracy with strong stability (±0.25% standard deviation) under stratified five-fold cross-validation, substantially outperforming SVM (82.83%) and Naïve Bayes (75.04%). These findings indi-cate that sequence-based architectures can capture the contextual dependencies in causal attribution dis-course, offering a replicable framework for disaster risk communication monitoring systems.

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