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Journal of Informatics Development
Published by Institut Teknologi dan Bisnis Widya Gama
ISSN : 2963055X     EISSN : 29630568     DOI : https://doi.org/10.30741/jid
Core Subject : Science,
Computer Science & IT
Focus and Scope Journal of Informatics Development cover all topics under the fields of Informatics, Information System, Information Technology, Computer Science, and Computer Engineering. Informatics and Information system IT Audit Software Engineering Big Data and Data Mining Internet Of Thing (IoT) Game Development IT Management Computer Network and Security Mobile Computing Security For Mobile Decision Support System Web and Cloud Computing Accounting Information system Electrical and Computer Engineering Sensors and Trandusers Signal, Image, Audio and Video processing Communication and Networking Robotic, Control and Automation Fuzzy and Neural System Artificial Intelligent
Arjuna Subject : Umum - Umum
Articles 34 Documents
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Implementation of Artificial Neural Network for IoT-Based Water Quality Classification in Fish Ponds Choiri, Achmad Firman; Murni, Cahyasari Kartika
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1752

Abstract

This study presents the implementation of an Artificial Neural Network (ANN) to classify water quality in fish ponds using a dataset derived from a fuzzy inference-based IoT system. The previous fuzzy system utilized three sensor parameters—pH, Total Dissolved Solids (TDS), and temperature—to determine water quality (good, moderate, poor) through rule-based reasoning. Although the fuzzy approach produced accurate and interpretable results, it lacked adaptability to new data variations and required manual rule adjustments. In this research, the ANN model was trained using MATLAB’s Neural Network Toolbox with 120 dataset samples obtained from the fuzzy system’s outputs. The model architecture consisted of three input neurons (pH, TDS, temperature), one hidden layer with ten neurons using a tansig activation function, and one output neuron with purelin. Training of the model was conducted using the Levenberg–Marquardt backpropagation algorithm, employing a dataset split of 80% for training, 10% for validation, and 10% for testing. The results showed that the ANN achieved a classification accuracy of 94.8%, with a Mean Squared Error (MSE) of 0.85942 and a regression coefficient (R) of 0.94, indicating a strong correlation between predicted and actual data. Compared to the fuzzy inference method, the ANN model demonstrated better adaptability to unseen data and a higher level of generalization. This system can be integrated into IoT-based monitoring platforms for real-time, intelligent, and adaptive water quality prediction to support sustainable aquaculture.
Integration of Natural Language Processing in a Web-Based Translanguaging System for Arabic-Indonesian Language Learning Reknadi, Danang Bagus; Abidin, Mohammad Mansyur; Choiri, Achmad Firman
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1753

Abstract

Arabic–Indonesian language learners often face challenges in understanding the contextual meaning of texts due to differences in morphological and syntactic structures between the two languages. To address this, this study proposes the development of a web-based translanguaging system integrated with Natural Language Processing (NLP) to help users understand and translate texts more meaningfully. This system was developed using the Waterfall model with stages of requirements analysis, design, implementation, testing, and maintenance. The implemented NLP module includes tokenization, part-of-speech tagging, and sentence structure analysis to produce translations that consider context, not just literal word equivalents. The implementation results show that the system is able to improve user comprehension of Arabic–Indonesian texts with a simple and accessible interface. Furthermore, the translation history feature supports continuous self-learning. Although the system still has limitations in handling idiomatic text and complex sentence structures, the NLP integration has proven effective in improving the quality of translanguaging. This research contributes to the development of bilingual learning technology and can be further developed using deep learning models such as BERT or mBERT to improve semantic and contextual accuracy.
Analysis of Twitter Sentiment on the Implementation of Regional Elections in Indonesia During Covid-19 Using the Support Vector Machine Method Nurdiansyah, Yanuar
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1757

Abstract

Sentiment analysis or opinion mining is a series of problem solving based on public opinion. The opinion is in the form of text or writing in the form of documents obtained from social media. Sentiment analysis serves to determine public opinion in responding to a policy, activity or issue that is happening and being discussed, one of which is on Twitter social media. Sentiment analysis in this study focuses on the activities of the 2020 regional elections during the Covid-19 pandemic which was held on 9 December 2020. Twitter social media works in real-time, so in retrieving research data using the Trending Topic feature to retrieve research datasets. The results of the dataset are then processed using text mining techniques and used as material for analysis to determine the public's response to the implementation of the elections during covid- 19 whether it tends to have a positive or negative sentiment, as well as knowing the opinion factors that often arise. The adoption of the Support Vector Machine (SVM) method for sentiment analysis was carried out by testing the composition of various datasets. From the test results using 4 scenarios of training data and test data, namely 90:10, 80:20, 70:30, 60:40, it is obtained that the SVM method can be implemented with an accuracy value of 87% in the data scenario of 80% training data and 20% test data. Variables that affect accuracy are the amount of data, the ratio of the number of training and test data and the ratio of the number of positive and negative data used.
Aspect-Based Sentiment Analysis of Tumpak Sewu Waterfall Tourist Reviews Using the Naive Bayes Classifier (NBC) Method Urrochman, Maysas Yafi; Asy’ari, Hasyim; Ro’uf, Abdur
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1758

Abstract

With the increasing popularity of Tumpak Sewu Waterfall, the volume of visitor reviews on Google Maps continues to grow. These reviews contain valuable insights into tourists’ experiences; however, conducting an in-depth manual analysis is inefficient. This study aims to perform aspect-based sentiment analysis on visitor reviews of Tumpak Sewu Waterfall using the Naive Bayes Classifier (NBC) method. This approach enables the classification of sentiments positive, negative, and neutral based on specific aspects such as facilities, accessibility, and natural scenery. Review data were collected from online platforms and processed through stages of text preprocessing and feature extraction before being trained using the NBC model. The results show that the model effectively classifies review sentiments with a high level of accuracy and provides detailed insights into which aspects most influence visitor satisfaction. These findings not only demonstrate the effectiveness of the Naive Bayes Classifier in aspect-based sentiment analysis tasks but also offer data-driven strategic recommendations for tourism managers to enhance service quality and improve visitor experience in the future.

Page 4 of 4 | Total Record : 34

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