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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Exploring AI-Based Music Tool Affordances to Enhance Gen Z Creativity: A Study Using Affordance Theory in Papua Padwa, Charles; Juita, Ratna; Inan, Dedi I.; Indra, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9910

Abstract

Artificial intelligence (AI) plays an important role in the world of music, especially for Generation Z who grew up in the digital age. However, understanding of how AI drives musical creativity in the context of music education is still limited, especially in West Papua and Papua. This study aims to analyze the influence of awareness, perception, and intention to use AI on Generation Z's musical creativity and innovation, using the theory of affordance as a theoretical foundation. The study employs a quantitative approach with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method on 219 respondents from West Papua and Papua provinces. The analysis results indicate that the intention to use AI (p = 0.000) and positive perceptions of AI in general (p = 0.000) have a significant influence on AI-based musical creativity, with moderate predictive power (R² = 0.461). This creativity was then found to encourage the use of AI in musical activities (p = 0.000) and the music creation process (p = 0.000), although both showed weak predictive power (R² = 0.292 and R² = 0.251). Conversely, awareness of AI (p = 0.509) and perceptions of AI in the context of music education (p = 0.135) did not significantly influence creativity. These findings suggest that positive intentions and perspectives toward AI are more decisive in driving creativity than awareness levels. Therefore, a contextual approach to digital music education that encourages active exploration is needed to optimize the potential of AI in enhancing creativity.
Stunting Risk Detection and Food Recommendation via Maternal Diagnosis Using the CF Method Kautsar, Al; Asrianda, Asrianda; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9949

Abstract

Stunting in children often stems from maternal health conditions during pregnancy. This study aims to develop an intelligent rule-based IF–THEN system using the Certainty Factor method as a decision-support tool for the early detection of stunting risk factors. The detection is performed indirectly by diagnosing maternal health conditions during pregnancy. The knowledge base was constructed through interviews with obstetricians and nutritionists, encompassing 20 symptoms categorized into three primary conditions namely Chronic Energy Deficiency (CED), anemia, and preeclampsia. A total of 119 pregnant women from 11 villages in Muara Satu District participated as respondents. Implementation results revealed that among the respondents, 20 were identified with CED, 96 had anemia, and 3 exhibited signs of preeclampsia. Based on Certainty Factor (CF) calculations, the confidence distribution for CED included 2 respondents with CF <50%, 5 respondents within the 50–80% range, and 13 respondents with CF >80%. For anemia, 1 respondent had a CF value <50%, 4 fell within the 50–80% range, and 91 respondents had CF values above 80%. Meanwhile, for preeclampsia, all respondents exceeded the 50% CF threshold, with 1 respondent in the 50–80% range and 2 respondents >80%. In addition to diagnosis, the system provides tailored meal recommendations (breakfast, lunch, and dinner) based on the identified health conditions. Expert validation indicated a 90% agreement rate. However, results still require confirmation through clinical examinations and consultations to ensure medical accuracy.
Classification of the Number of Malaria Cases in Asahan Regency Using Random Forest Application Naza Amarianda; Eva Darnila; Lidya Rosnita
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9960

Abstract

This study aims to classify the number of malaria cases in Asahan Regency using the Random Forest method. This method was chosen because it is able to handle data with many and complex variables and reduce the risk of overfitting. Data were collected from the Asahan Regency Health Office. The research stages include data collection, preprocessing, model training, and model evaluation. The dataset used consists of 568 malaria case data from 25 sub-districts. The data is divided into 80% for training and 20% for testing. Of the total data, there are 109 data 19.2% in the low category, 334 data 58.8% in the medium category, and 125 data 22.0% in the high category. This classification aims to assist in mapping the level of malaria risk in the area. In this study, several variables were used for model training, including health centers, sub-districts, age, month, and gender. The results of the analysis showed that the most influential variables were health centers 47.53%, followed by sub-districts 43.77%, age 6.07%, months 2.18%, and gender 0.45%. The Random Forest model built was evaluated using accuracy, precision, recall, and F1-Score metrics. The evaluation results showed that the model was able to classify the number of malaria cases well, with an accuracy value of 0.97. With these results, Random Forest has proven effective as a classification method in malaria cases in Asahan Regency.
Performance Comparison of Embeddings and Keyword Selection Methods in Enterprise Document Cristin, Putri; Natalia, Brenda; Limantara, Joseph Clio; Sarwosri
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9971

Abstract

Keyword extraction is widely used in domains such as social media and e-commerce, but its application for enterprise document retrieval remains limited. Most organizations still depend on structured systems or rule-based approaches for indexing, which often lack semantic understanding and scalability. While several techniques like TextRank and RAKE have been explored, few studies assess their effectiveness on operational document retrieval in institutional settings, revealing a research gap. This study investigates the use of KeyBERT to extract keywords from university documents, including SOPs, manuals, and guidelines. KeyBERT leverages transformer-based embeddings to generate semantically relevant keywords and is chosen for its ease of use, model flexibility, and no need for labeled data. Additionally, it supports diversification strategies such as Maximum Marginal Relevance (MMR) and MaxSum to reduce redundancy and enhance keyword variety. We evaluate six embedding models combined with three keyword selection methods: Cosine similarity, MMR, and MaxSum. The best F1 score of 0.78 is achieved using Cosine with the paraphrase-MiniLM-L3-v2 model, along with an average extraction time of 184.02 seconds. These findings highlight the effectiveness of combining lightweight embeddings with strategic keyword selection for enterprise-scale document indexing.
Real-Time Braille Letter Detection System Using YOLOv8 Himawan, Reyshano Adhyarta; Rachmawanto, Eko Hari; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10060

Abstract

The purpose of this research is to create a system capable of detecting and recognizing Braille letters in real-time using the YOLOv8 algorithm for object detection, integrated with image processing technology and a user interface based on Tkinter. This system is developed to support visually impaired individuals in reading Braille text through the use of a webcam that captures and identifies Braille letters from images. The identification process is carried out by comparing the obtained images with a precompiled database of Braille letters. This research utilizes a dataset consisting of images of Braille code from letters A to Z, collected through public and private methods, with a total of 6013 images that comprehensively represent Braille letters. The model training is done using YOLOv8 to recognize Braille letter objects, with model performance evaluation using the Mean Average Precision (mAP) metric.The results of the tests show a very satisfactory model performance, with a mAP50 score of 0.98 and a mAP50-95 score of 0.789, as well as a high accuracy rate for almost all Braille letters tested. In addition, the system is equipped with a Tkinter-based graphical user interface (GUI) that allows users to operate the Braille letter detection process interactively and easily. This research proves that the YOLOv8-based object detection approach has significant potential for Braille letter recognition applications, which is expected to enhance accessibility and the independence of visually impaired individuals in reading text effectively.
Real-Time Drug Classification Using YOLOv11 for Reducing Medication Errors Lungido, Joshua; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10117

Abstract

Advancements in digital imaging and machine learning have transformed healthcare, enabling innovative solutions for automated drug identification. This study develops an image-based system to classify pharmaceutical drugs, tackling errors arising from visual similarities in their shape, color, or size. Accurate drug identification is crucial for healthcare professionals and patients to access reliable information on drug composition, usage instructions, and potential side effects, enhancing safety and efficiency in medical practice. The system leverages the YOLO (You Only Look Once) algorithm, renowned for its speed and precision in object detection. A dataset comprising 5,000 drug images sourced from Kaggle was curated, with annotations and augmentation techniques such as horizontal flipping, rotation, and scaling to improve model robustness. The YOLOv11 model achieved a precision of 97.4%, a recall of 97.6%, and a mean average precision (mAP@50) of 98.4%, demonstrating high reliability in real-world scenarios. Integrated with a user-friendly Tkinter interface, the system facilitates real-time drug detection and information retrieval, streamlining access to critical data. This research underscores the YOLO algorithm’s effectiveness in delivering rapid and accurate drug classification, offering a scalable solution for healthcare applications. The system’s success highlights its potential to reduce medication errors and improve patient outcomes through precise and accessible drug identification technology.
Information Cascades in Professional Networks: A Graph-Based Study of LinkedIn Post Engagement Revesai, Zvinodashe; Mutanga, Murimo Bethel; Chani, Tarirai
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10212

Abstract

Information cascades in professional networks represent a critical mechanism for knowledge transfer and career development, yet their dynamics remain poorly understood. This study presents a comprehensive empirical analysis of information cascades in LinkedIn professional networks, focusing on computer science professionals and academic-industry knowledge transfer. We analysed 50,000 CS professionals, 500,000 connections, and 100,000 technical posts over 12 months using a Modified Independent Cascade Model that incorporates professional context factors. Our analysis reveals that hybrid professionals, representing only 25% of the network, account for 52% of inter-cluster connections and achieve 2.8× higher cross-domain transfer rates. Educational content demonstrates superior cross-domain appeal (0.47) compared to research papers (0.23), with optimal posting windows between 10 AM-12 PM achieving 23% higher cross-domain engagement. Bridge users in academic-industry transitions show significantly higher transfer effectiveness (Cohen's d = 1.47, p < 0.001). These findings provide evidence-based strategies for optimising professional networking and knowledge dissemination across academic and industry domains
Hybrid Decomposition ICEEMDAN-EWT Deep Learning Framework for Wind Speed Forecasting Alif Hidayat, Dedi Arman; Aditya Pradana , Muhamad Hilmil Muchtar; Saikhu, Ahmad
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10241

Abstract

Accurate wind speed forecasting plays a crucial role in supporting early warning systems for extreme wind events. However, the inherent non-linearity and non-stationarity of wind speed data pose significant challenges. This study addresses these issues by evaluating the effectiveness of targeted Empirical Wavelet Transform (EWT) denoising applied to specific Intrinsic Mode Functions (IMFs) derived from Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Daily wind speed data from 2000 to 2023 were decomposed using ICEEMDAN, and denoising was selectively applied to IMF1, IMF2, and IMF3. Each IMF was then modeled using a Bidirectional Long Short-Term Memory (BiLSTM) network under a time-series cross-validation framework. Among all model configurations, the ICEEMDAN+EWT(IMF1 & IMF2)+BiLSTM model achieved the highest predictive accuracy, with an R² of 0.8885, RMSE of 0.501, and MAPE of 7.64%. This result outperformed both the baseline BiLSTM model (R² = 0.0501) and the ICEEMDAN+BiLSTM model without EWT denoising (R² = 0.6433). Moreover, denoising on IMF1 alone also yielded a strong performance (R² = 0.8879), emphasizing the importance of early component selection. Conversely, applying EWT to IMF2 or IMF3 individually resulted in lower R² values of 0.6639 and 0.6327, respectively, indicating limited individual contribution. These findings confirm that selective denoising, especially on the high-frequency IMFs, substantially enhances forecasting accuracy. The proposed approach holds significant potential to improve the timeliness and reliability of wind-related early warning systems, thus contributing to more effective disaster risk reduction strategies.
Turtle Dove Classification Using CNN Algorithm With MobileNetV2 Transfer Learning Muhammad Minanul Lathif; Novianto, Sendi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9173

Abstract

This study aims to optimize the performance of a Convolutional Neural Network (CNN) model based on the MobileNetV2 architecture in classifying Java sparrow images by testing four main parameters: optimizer, learning rate, number of epochs, and batch size. The dataset consists of 800 images divided evenly into four classes. The results show that using the Adam optimizer yields the best accuracy with a training accuracy of 97.50%, validation accuracy of 98.75%, and testing accuracy of 98.13%. A learning rate of 0.001 produces the same results, indicating consistent performance with this configuration. Epoch testing shows that 35 epochs yield the highest performance with a training accuracy of 98.39%, validation accuracy of 100%, and testing accuracy of 98.75%. Meanwhile, batch size testing shows that a batch size of 32 yields the highest testing accuracy of 98.85%, a batch size of 64 yields the highest training accuracy of 98.63%, and a batch size of 128 yields the highest validation accuracy of 99.58%. These findings suggest that smaller batch sizes tend to yield better performance in terms of model generalization, while larger batch sizes provide higher stability in the training process but do not always reflect actual performance on the test data. The results of this study can serve as a reference for selecting parameter configurations to improve the accuracy and generalization of image classification models using MobileNetV2. These results emphasize the importance of proper parameter settings in improving the accuracy and stability of image classification models. They can be a reference in model development in object recognition.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.

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