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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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Analyzing Sentiment of SiCepat Express User Reviews Wicaksana, Endra Maulia; Rijati, Nova
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8056

Abstract

The development of e-commerce in Indonesia has led to an increase in the number of users of product delivery services to deliver their customers' orders to their destination. SiCepat Ekspres is the number one fastest delivery service in Indonesia, besides JNE and JNT Express. The study aims to evaluate the performance of sentiment analysis methods in identifying and classifying sentiments related to SiCepat Ekspres. Data from Twitter media as many as 10,000 dataset records. The experimental results show that Random Forest with SMOTE is the best method, as it has the highest accuracy (91.10%), followed by improvements in precision, recall, and F-measure. SVM with SMOTE is in second place, with 90.50% accuracy and stable performance in other metrics. Naive Bayes with SMOTE shows improvement, but its performance remains slightly below Random Forest and SVM, with an accuracy of 88.80%.
Energy Efficiency and Time Management through IoT-Based Automated Electrical Control System and Digital Prayer Schedule Integration Syaputra, Muhammad Adie; Sutomo, Budi; Saprudin, Usep; Putra, Muhammad Aqida
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8848

Abstract

This study aims to develop an Internet of Things (IoT)-based automated electrical control system integrated with a digital prayer schedule to enhance energy efficiency and time management in mosques. Using IoT devices, electronic equipment such as air conditioners, lights, and loudspeakers can be automatically controlled according to prayer times, reducing energy waste and minimizing fire risks due to negligence in operating electrical devices. The study employs a prototyping method, where a system prototype is tested to evaluate its performance and effectiveness. A machine-to-machine bridge communication model is implemented to integrate the digital prayer schedule with mosque electrical devices. The research results are expected to provide an environmentally friendly technological solution while improving mosque operational efficiency. However, technical challenges, such as integrating the system with older electrical infrastructure, limited internet connectivity, and user readiness to operate new technology, must be addressed. Thus, this study contributes not only to energy efficiency but also to improving mosque management quality through digital technology.
Analyzing the Impact of Artificial Intelligence on Student Learning: A Case Study of SMK Tri Arga 2 Lestari, Tri Putri
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8902

Abstract

The use of artificial intelligence (AI) technology in every aspect of life is a solution that provides an important contribution to the continuity of the wheel of life, not to mention in the world of education. In this digital era, AI has become an important partner and tool for students in completing assignments and assisting in carrying out their learning activities, especially in completing assignments given by teachers. The purpose of this study is to analyze the effect of artificial intelligence on learning, especially for students of SMK Tri Arga 2. The formulation of the research problem involves a description of the use of AI in the process of completing assignments, the benefits and challenges experienced by students, and the most dominant AI applications used in completing these assignments. The research method used is a quantitative descriptive method using a questionnaire, Likert Scale, which is created on Google Form and then sent to the Whatsapp Group and student personal chat. Using a random method. 105 students of SMK Tri Arga 2 participated in this study. the results of this study showed that 51.4% stated that they often use AI. Then 52.4% or 55 students answered that they often use AI in completing school assignments. These results certainly provide a new perspective on the role of AI in helping students complete the school assignments they have been given. It is also hoped that the results of this research can help Senior High Schools (SMA) and Vocational High Schools (SMK) in improving the quality of education and learning by integrating AI more effectively in their students' academic processes, improving supervision and regulations regarding the use of AI in completing assignments and making AI a companion tool while still paying attention to the ethics of plagiarism and the growth of students' skill development.
Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification Santoso, Ilham; Manurung, Ayub Michaelangelo; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8968

Abstract

Cataract is a leading cause of blindness worldwide, emphasizing the need for an effective early detection approach. This study evaluates the capabilities of three widely-used deep learning models—ResNet-50, EfficientNet-B1, and VGG-16—in classifying visual data. The analysis was conducted on a dataset of 2,112 images, comprising 1,074 normal cases and 1,038 cataract cases. The findings reveal that ResNet-50 achieved the best accuracy at 98.61%, followed by EfficientNet-B1 at 96.64% and VGG-16 at 93.82%. In comparison, previous research using Convolutional Neural Network (CNN) techniques reported an accuracy of 92.93%. These results highlight ResNet-50's superior potential for image classification tasks in this domain. This study contributes significantly to the selection of robust models for building an automated cataract detection framework.
Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics Lubis, Quiin Latifah Almatin; Huda, Arif Akbarul
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8978

Abstract

Independent musicians face significant challenges in enhancing the visibility and appeal of their work amid intense competition on music streaming platforms. Although numerous studies have been conducted to analyze and predict song popularity, most of them focus on English-language songs. This creates a research gap for Indonesian-language songs, particularly in the context of predicting popularity based on lyrics. The dataset used includes 652 Indonesian songs from 2017 to 2024. The research methodology includes data pre-processing, feature extraction using TF-IDF, handling data imbalance with SMOTE, implementing SVM, and model optimization. The results show an improvement in model accuracy from 84% to 89% after parameter optimization using GridSearchCV. In the model evaluation with 5-fold cross-validation, an average accuracy of 86.19% with a standard deviation of 0.90% was obtained. Precision, Recall, and F1-score metrics for the Less Popular class are 0.98, 0.85, and 0.91; for the Moderately Popular class, 0.79, 0.95, and 0.86; and for the Very Popular class, 0.92, 0.86, and 0.89. The implementation of the model in a Streamlit application allows for the prediction of song popularity based on lyrics, providing valuable insights for musicians in choosing word choices that can potentially increase the popularity of their songs.
Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews Lestari, I Gusti Ayu Nandia; Dewi, Ni Made Rai Masita; Meiliana, Komang Gita; Aryanto, I Komang Agus Ady
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9077

Abstract

Currently there are many entertainment platforms that provide various movies, TV shows, games, and other content. These platforms usually offer a variety of features, one of which is reviews. Review data written by viewers plays an important role in influencing public interest in the film. However, the increasing number of reviews makes it difficult to assess the sentiment of the film quickly and accurately. This highlights the need for a system that can analyze reviews based on sentiment, making it easier for viewers to evaluate the film and supporting the entertainment industry in understanding the needs of the audience. Therefore, this study develops a sentiment analysis model to identify whether a review contains positive or negative sentiment using machine learning algorithms. The data used to build the model is obtained from user reviews of a film on the IMDb platform. This dataset is available on Kaggle with 50,000 movie reviews in text format. The characteristics of the data include two columns: review_text and sentiment. The methods used to create the classification model are AdaBoost and XGBoost. The data preprocessing process includes several stages such as text cleaning, tokenization, stopword removal, lemmatization, and vectorization using TF-IDF to convert the review text into numeric form, as well as converting the positive and negative labels into 1 and 0. Based on the results of model training with cross-validation, the accuracy of the XGBoost model is 85% and AdaBoost is 77%. Feature selection showed an improvement in the XGBoost model's accuracy from 85% to 86%, while the AdaBoost model's performance remained stable at 77%. Thus, it can be concluded that the XGBoost model demonstrates better performance than the AdaBoost model in sentiment classification.
Balancing CICIoV2024 Dataset with RUS for Improved IoV Attack Detection Firmansyah, Muhammad David; Rizqa, Ifan; Rafrastara, Fauzi Adi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9079

Abstract

This study addresses the cybersecurity challenges within the Internet of Vehicles (IoV) by exploring the efficacy of Random Under-Sampling (RUS) in balancing the class distribution of the CICIoV2024 dataset for improved intrusion detection. IoV technology connects vehicles to digital infrastructure, fostering communication and enhancing safety but is simultaneously vulnerable to cyber threats such as Denial of Service (DoS) and spoofing attacks. This research employed RUS to mitigate data imbalance within the CICIoV2024 dataset, which often impedes effective threat detection in machine learning models. Four machine learning classifiers Random Forest, AdaBoost, Gradient Boosting, and XGBoost were evaluated on both imbalanced and balanced datasets to compare their performance. Results demonstrated that RUS significantly enhances model accuracy, precision, recall, and F1-score, reaching perfect scores across all classifiers post-balancing. Additionally, RUS contributed to substantial reductions in training and testing times, thereby boosting computational efficiency. These findings underscore the potential of RUS in addressing data imbalance in IoV cybersecurity, establishing a foundation for future research aimed at safeguarding IoV systems against evolving cyber threats.
Improving Attack Detection in IoV with Class Balancing and Feature Selection Widyatama, Thierry; Rizqa, Ifan; Rafrastara, Fauzi Adi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9080

Abstract

The Internet of Vehicles (IoV) represents a specialized application of the Internet of Things (IoT), enabling vehicles to communicate with their surrounding infrastructure to enhance transportation safety and efficiency. However, IoV systems are susceptible to various cyberattacks, including Denial of Service (DoS) and spoofing attacks, which necessitate effective and efficient detection mechanisms. This study investigates the enhancement of detection efficiency for DoS and spoofing attacks in IoV by employing Ensemble Learning methods combined with feature selection techniques. The selected feature selection methods include Information Gain Ratio, Chi-Square (X²), and Fast Correlation-Based Filter (FCBF). The CICIoV2024 dataset, utilized in this study, was balanced using the Random Under Sampling technique to address data imbalance issues. The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. Results indicate that all three algorithms achieved high accuracy and F1 scores, reaching 0.985. Moreover, the application of feature selection significantly reduced computational time without compromising detection performance. These findings are expected to contribute to the advancement of IoV security systems in the future.
Magnetic Resonance Imaging for Breast Cancer Classification Using Convolutional Neural Networks Mahiruna, Adiyah; Destriana, Rachmat; Riansyah, Rahmat
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9101

Abstract

Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the urgent need for accurate diagnostic methods. This research addresses the challenges of early detection by leveraging Convolutional Neural Networks (CNNs) for the classification of Magnetic Resonance Imaging (MRI) data. Using a publicly available Kaggle dataset consisting of 54,676 MRI images categorized into "Normal" and "Cancer" classes, the dataset was split into 80% for training and 20% for validation. A modified CNN architecture was developed, incorporating optimized layers and hyperparameters, such as the ADAM optimizer, a learning rate of 0.0001, and a mini-batch size of 128. The proposed model achieved exceptional performance, with an accuracy of 99.72%, precision and recall of 99.98% and 99.97%, respectively, and an F1-score of 99.98%, as evaluated through a confusion matrix. These results demonstrate the model’s robustness in distinguishing between healthy and cancerous tissues, providing a reliable and efficient diagnostic tool. This study highlights the potential of CNNs to improve diagnostic precision in medical imaging, aiding clinicians and advancing AI applications in healthcare.
Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison Dharmawan, I Putu Yogi Prasetya; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9167

Abstract

Baby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has unique characteristics that represent specific needs such as hunger, discomfort, sleepiness, flatulence, and abdominal pain. With the advancement of technology, identification of baby cries can now be done automatically through AI-based applications, but the implementation is still limited. This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. Random Forest showed the lowest performance among the three models. Transformer-based models such as Whisper-small proved to be superior in capturing the complex patterns of infant cries, compared to tree-based models. These findings indicate the great potential of accurate and reliable models to help parents understand the needs of infants more effectively, thereby improving the quality of infant care.

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