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Alam Rahmatulloh
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International Journal of Informatics and Computing
Published by Meta Pustaka Intelektual
ISSN : -     EISSN : 30904722     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Informatics and Computing (JICO) is the official publication of the Institute of Advanced Informatics and Computing (IAICO). The journal is open to submission from scholars and experts in the wide areas of informatics and computing from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol. 1 No. 2 (2025): November 2025" : 6 Documents clear
Integrating Digital Payment and Monitoring Systems in On-Street Parking Services: A Cyber-Physical System Approach Nursuwars, Firmansyah Maulana Sugiartana; Andang, Asep; Shofa, Rahmi Nur; Sambas, Aceng
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

On-street parking is a critical component of urban mobility but often encounters challenges in terms of payment efficiency, transparency, and system supervision. This study aims to develop an on-street parking service system based on a Cyber-Physical System (CPS) that integrates digital payments via QRIS and Android Point of Sale (POS) devices. The proposed system is designed to streamline transaction processes, enhance accountability of parking attendants, and provide real-time monitoring data accessible to system administrators. The research methodology combines the Software Development Life Cycle (SDLC) and Hardware Development Lifecycle, with a modular system architecture and comprehensive functional testing. The results show that the system can process transactions in an average of 23 seconds, supports automated digital records, and improves user satisfaction with parking services. The implications of this development extend beyond digital transformation in the transportation sector, offering a foundation for data-driven and adaptive smart parking management. This study contributes to the advancement of efficient, transparent, and user-centered public service systems through integrated technological solutions.
Sentiment Analysis of ChatGPT Using the KNN Algorithm and K-Fold Cross-Validation Optimization of the K Value Azliza Yacob; Nurzal Effiyana Ghazali; Faez M. Hassan
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

ChatGPT is a cutting-edge artificial intelligence language model powered by advanced machine learning technologies, such as GPT-4.0. It’s remarkable ability to generate human-like text and engage in interactive conversations has captured widespread attention, particularly on social media. As a result, public sentiment toward ChatGPT has become a significant topic, necessitating detailed sentiment analysis to comprehend the broader societal reactions to this technology. This study focuses on optimizing the K value in sentiment analysis applied to Twitter data about ChatGPT. Selecting the appropriate K value is crucial, as improper values can result in overfitting or underfitting the model. The research methodology includes several stages: data collection, pre-processing, feature extraction, k-fold cross-validation for K optimization, implementing the K-Nearest Neighbors (KNN) algorithm, and evaluating results. The analysis determined that the optimal K value for this sentiment analysis is K=9. Using this value, the KNN algorithm achieved an accuracy of 88%, indicating robust performance in classifying sentiment effectively. These findings highlight the potential of this approach to provide meaningful insights into public perception and sentiment regarding ChatGPT on social media platforms. This result not only underscores the technical effectiveness of KNN for sentiment analysis but also demonstrates the practical application of machine learning in understanding societal trends in the context of emerging AI technologies.
Improving Emotion Recognition Accuracy with Combination of Bidirectional and Long Short-Term Memory Models Haerani, Erna; Rahmatulloh, Alam; Rizal, Randi
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

Emotions play a vital role in shaping human behavior and mental health, making accurate emotion recognition essential for mitigating potential negative impacts. This study explores the application of Bidirectional Long Short-Term Memory (Bi-LSTM) for recognizing emotions from text-based data. Bi-LSTM extends the standard LSTM by enabling the model to process input sequences in both forward and backward directions, thereby capturing contextual dependencies more effectively. The research methodology consists of data collection, manual emotion labeling, and pre-processing techniques, including stemming, tokenization, and one-hot encoding. Visualization of the dataset and the distribution of labeled emotions was conducted to gain deeper insights into the data. The Bi-LSTM model was trained for 25 epochs, achieving a training accuracy of 0.9954 and validation accuracy of 0.8790, along with a training loss of 0.0133 and validation loss of 0.658. A confusion matrix was used to further evaluate model performance and classification accuracy across various emotion categories. The experimental results confirm that the Bi-LSTM model is highly effective in recognizing emotions from textual input. Its ability to capture long-term dependencies in both directions contribute to improved learning and prediction. However, opportunities for enhancement remain, particularly in refining the model architecture, expanding the dataset, and exploring additional feature extraction techniques. This research demonstrates the potential of Bi-LSTM in building intelligent emotion-aware systems for applications in mental health monitoring, customer feedback analysis, and human-computer interaction.
Support Vector Machine Based Machine Learning for Sentiment Analysis of User Reviews of the Bibit Application on Google Play Store Ega Shela Marsiani; Natsir, Fauzan; Redo Abeputra Sihombing; Millati Izzatillah; Rajiansyah
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

The increasing use of financial technology (fintech) applications has changed the investment patterns of users in Indonesia. Bibit, as one of the popular fintech investment platforms, receives many user reviews through the Google Play Store that reflect user perceptions and satisfaction levels. Although the volume of user reviews continues to increase, systematic analysis of user sentiment is still limited, making it difficult for developers to understand the needs and experiences of users. Therefore, an artificial intelligence-based approach is needed to efficiently and objectively extract and analyze user opinions. This study aims to conduct sentiment analysis of user reviews of the Bibit application using a Machine Vector Machine (SVM) based machine learning model. The research methodology includes data collection, pre-processing of texts, extraction of features using TF-IDF, as well as classification of sentiment into positive, negative, and neutral categories. Of the total review data, 7,801 data (79.99%) were used as training data, and 1,561 data (20.01%) were used as test data with a division ratio of 80:20 according to general standards in machine learning. The purpose of this study was to identify the dominant user sentiment and evaluate the classification performance of the SVM algorithm. The results of the experiment showed that the SVM model achieved high accuracy and was able to capture user opinions effectively, thus providing valuable input for developers in improving the quality of applications and user engagement on fintech platforms.
GANS: Genetic Algorithm and Neural Network Integration for Optimal Brain Selection in Snake Game Bambang Pudjoatmodjo; Mugi Praseptiawan; Ulka Chandini Pendit; Rusnida Romli
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

Snake games have emerged as an engaging subject in artificial intelligence and optimization research due to the growing interest in developing autonomous agents capable of controlling the snake intelligently. This study presents a hybrid approach by integrating a Genetic Algorithm (GA) with a Neural Network (NN) to enhance the snake game’s performance, effectively forming an adaptive and intelligent control system or “brain.” In this framework, the Snake game is modeled as an optimization problem, where the GA is employed to optimize the parameters of the NN to improve the decision-making process of the snake. The GA operates by evolving a population of individuals each representing a set of strategies through selection, crossover, and mutation. These operations are iteratively applied to discover optimal solutions within the vast parameter space. The integrated neural network enables the snake to make real-time decisions based on environmental stimuli, enhancing its survival and goal-seeking behavior. Fitness evaluation is performed based on everyone’s gameplay performance, where the most successful individuals contribute to the next generation. Experimental results demonstrate that the combination of GA and NN significantly improves snake gameplay performance. The fitness score acts as a performance indicator, showing that higher-generation populations tend to yield better results. For instance, snakes trained over 100 generations achieved scores around 8, while those trained over 500 generations exceeded scores of 15. This confirms the effectiveness of evolutionary optimization in training neural networks for game-based AI tasks.
A Transform-Domain Robust Watermarking Model Using Discrete Wavelet Transform for Image Copyright Security Randi Rizal; Nazwa Auliarahman; Siti Rahayu Selamat; Mae B. Lodana
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Abstract

This research presents the development of a Discrete Wavelet Transform (DWT)–based method designed to strengthen digital copyright protection in images. The proposed approach leverages multi-resolution decomposition to embed copyright information within high-frequency and mid-frequency sub-bands, enabling improved resistance against common image attacks such as compression, noise addition, and geometric manipulation. Experimental evaluation shows that the method maintains high imperceptibility, with minimal impact on visual quality, while achieving strong extraction accuracy under various distortion scenarios. The results confirm that DWT remains a reliable foundation for constructing secure and robust watermarking mechanisms suitable for modern digital content protection needs.

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