cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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
 39   40   41   42   43 
Stock Sentiment Prediction of LQ-45 Based on News Articles Using LSTM Kristina, Kristina; Agus Dwi Suarjaya, I Made; Cahyawan Wiranatha, Anak Agung Ketut Agung
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.v9i4.9699

Abstract

The growth in the number of investors in the financial market indicates that the investment world is currently experiencing rapid development. One of the long-term investment instruments that has experienced significant growth in the financial market is the stock market. Growth data as of September 2024 sourced from the Indonesia Stock Exchange report reveals that the number of stock market investors has reached more than 6 million single investor identification (SID). The share price of a company can be influenced by two main factors, namely internal factors and external factors. Internal factors come from within the company itself, while external factors come from conditions outside the company. Model development uses the Long Short-Term Memory (LSTM) method to predict daily stock sentiment in realtime. Labeling is done based on the history of stock price changes taken from Yahoo Finance. Stock market news data is obtained automatically every day through Really Simple Syndication (RSS) with the help of cronjob. The results of the LSTM model showed good performance, with a macro F1-Score of 0.73, a macro precision of 0.72, and a macro recall of 0.75. When compared to baseline models such as Logistic Regression, Naive Bayes, and Random Forest which only achieve a macro F1-Score of 0.58, 0.54, and 0.65, respectively, it can be concluded that the developed LSTM model has superior performance. This research can provide new considerations to investors, so as to reduce the risk of loss due to errors in choosing companies to invest in.
Performance Analysis of Deep Learning Model Quantization on NPU for Real-Time Automatic License Plate Recognition Implementation Alexander, Daniel; Wildanil Ghozi
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.v9i4.9700

Abstract

Neural Processing Units (NPUs) are dedicated accelerators designed to perform efficient deep learning inference on edge devices with limited computational and power resources. In real-time applications such as automated parking systems, accurate and low-latency license plate recognition is critical. This study evaluates the effectiveness of quantization techniques, specifically Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), in improving the performance of YOLOv8-based license plate detection models deployed on an Intel NPU integrated within the Core Ultra 7 155H processor. Three model configurations are compared: a full-precision float32 model, a PTQ model, and a QAT model. All models are converted to OpenVINO’s Intermediate Representation (IR) and benchmarked using the benchmark_app tool. Results show that PTQ and QAT significantly enhance inference efficiency. QAT achieves up to 39.9% improvement in throughput and 28.6% reduction in latency compared to the non-quantized model, while maintaining higher detection accuracy. Both quantized models also reduce model size by nearly 50 percent. Although PTQ is simpler to implement, QAT offers a better balance between accuracy and speed, making it more suitable for deployment in edge scenarios with real-time constraints. These findings highlight QAT as an optimal strategy for efficient and accurate license plate recognition on NPU-based edge platforms.
Performance Comparison of Random Forest, SVM, and XGBoost Algorithms with SMOTE for Stunting Prediction Maulana As'an Hamid; Egia Rosi Subhiyakto
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.v9i4.9701

Abstract

Stunting is a growth and development disorder caused by malnutrition, recurrent infections, and lack of psychosocial stimulation in which a child’s length or height is shorter than the growth standard for their age. With a prevalence of 21.5% in Indonesia by 2023, stunting is a global health problem that requires technology-based detection approaches for early intervention. This study evaluates the performance of three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) in predicting childhood stunting, and applying the SMOTE technique to handle data imbalance.  The evaluation results show that XGBoost with SMOTE achieves the best performance with 87.83% accuracy, 85.75% precision, 91.59% recall, and 88.57% F1-score, superior to RF (84.56%) and SVM (68.59%). These results show that the combination of XGBoost and SMOTE is the best solution for an accurate and effective machine learning-based stunting detection system, so it can be used in public health programs to accelerate stunting risk identification.
Implementation of the Hybrid K-Nearest Neighbour Algorithm for Dangdut Music Sub-Genre Classification Tria Hikmah Fratiwi; I Gede Harsemadi; Putu Tjintia Kencana Dewi; Luh Rediasih; M. Alvinnur Filardi; I Dewa Made Dharma Putra Santika
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.v9i4.9702

Abstract

This research focuses on the classification of dangdut sub-genres — classical, rock, and koplo — by collecting 136 songs from Ellya Khadam, Rhoma Irama, and Denny Caknan, each representing distinct eras of dangdut music. From these, 483 music segments of 30 seconds each were extracted and labelled with expert assistance to ensure accuracy. Six spectral features (centroid, skewness, rolloff, kurtosis, spread, and flatness) were computed and stored in a dataset divided into 70% training and 30% testing sets. The Hybrid K-NN algorithm, integrating Genetic Algorithm (GA) to optimize the k parameter, was applied and evaluated through 5-fold cross-validation. GA parameters were set to a population size of 10, 15 generations, 4-bit chromosome length, and 3-fold cross-validation during optimization. Hybrid K-NN achieved the highest accuracy of 74.31% at k=4 with a processing time of 4.86 seconds, outperforming conventional K-NN (68.75% at k=4, 0.04 seconds), Decision Tree (61.11%, 0.42 seconds), and SVM with ECOC (54.86%, 1.99 seconds). The Hybrid K-NN also demonstrated stable performance with an average accuracy of 72.04% and a standard deviation of 2.22 percent, while the average precision, recall, and F1-score were each around 0.72. Confusion matrix analysis revealed frequent misclassification of class 2 as class 1, highlighting a classification challenge. Overall, this research shows that Hybrid K-NN is more effective than the other methods in capturing data patterns, optimizing parameters, and generalizing to unseen data, though at the cost of longer computation time due to GA’s iterative optimization and validation processes.
Esscore: An OCR-Based Android App for Scoring Short Handwritten Answer Using Levenshtein Distance Apriana, Krisna; I Made Agus Dwi Suarjaya; Ni Kadek Dwi Rusjayanthi
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.v9i4.9708

Abstract

Manual evaluation of short answer tests is time-consuming and prone to subjectivity. This study presents Esscore, an Android-based application that automates the scoring of handwritten short answers using EasyOCR and the Levenshtein Distance algorithm. EasyOCR extracts text from student answers image, while Levenshtein Distance measures similarity against predefined answer keys, allowing tolerance for varied correct responses. The system was tested on 350 student’s handwritten answers, achieving 95.7% accuracy. Functional testing using 14 black box scenarios showed all features operated correctly without failure. A usability test conducted with the SUS method produced a score of 76.5, rated “Good” with a grade “B” and an “Acceptable” acceptance level. The Net Promoter Score (NPS) placed the application in the “Passive” category. These results confirm Esscore as a functional, accurate, and user-friendly solution for automated answer scoring in educational environments.
Implementation of The Logistic Regression Algorithm to Analyze Poverty Factors in Aceh Province Mursyidah, Mursyidah; Kesuma Dinata, Rozzi; Yunizar, Zara
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.v9i4.9715

Abstract

Aceh Province continues to face a high poverty rate despite its abundant natural resources. This study aims to analyze the factors influencing poverty status in Aceh Province by applying a binary logistic regression algorithm. The research specifically focuses on an inferential analytical approach to reveal significant relationships among socioeconomic variables. Secondary data were obtained from the Aceh Provincial Statistics Agency (Badan Pusat Statistik/BPS) for the period 2019–2023. Inferential analysis was conducted using the entire dataset through the statsmodels library to identify variables that are statistically significant to poverty status. In addition, a classification approach was implemented using scikit-learn, with a data split between training data (2019–2022) and testing data (2023), yielding an accuracy of 0.70, precision of 0.81, recall of 0.70, F1-score of 0.66, and AUC of 0.69. These findings provide empirical evidence that improving access to education and equitable infrastructure development in densely populated areas can serve as effective policy focuses in efforts to alleviate poverty in Aceh Province.
YOLOv11-Based Detection of Indonesian Traffic Signs: Transfer Learning vs. From-Scratch Training Ramadhan, Ibnu Cipta; Hendriawan, Akhmad; Oktavianto, Hary
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.v9i4.9718

Abstract

Traffic sign detection is a fundamental component in intelligent transportation systems (ITS), autonomous driving, and advanced driver assistance systems (ADAS), enabling vehicles to interpret road conditions and enhance safety. Developing robust traffic sign detection models for specific regions requires high-quality, well-annotated local datasets, which are often challenging and costly to create. Even when such datasets are available, training deep learning models from scratch demands substantial computational resources and time. This study compares models trained from scratch and those using transfer learning based on the lightweight YOLOv11s architecture on an Indonesian traffic sign dataset. Evaluations using precision, recall, mean Average Precision at IoU 0.5 (mAP@0.5), and mean Average Precision across IoU thresholds 0.5 to 0.95 (mAP@0.5:0.95) demonstrate that the transfer learning model consistently outperforms the from-scratch model across all metrics. These findings highlight the effectiveness and efficiency of transfer learning for developing accurate and practical traffic sign detection systems adapted to local contexts.
Sentiment Classification of MyPertamina Reviews Using Naïve Bayes and Logistic Regression Dwi Yuni Saraswati; Handayani, Maya Rini; Umam, Khothibul; Mustofa, Mokhamad Iklil
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.v9i4.9723

Abstract

This research conducts a comparative evaluation of the effectiveness of the Naïve Bayes and Logistic Regression algorithms in mapping public perceptions of the MyPertamina application on the Google Play Store. The data consists of 2,000 user reviews obtained through a scraping technique. The research steps include labeling the reviews as positive or negative, followed by pre-processing and TF-IDF weighting. The dataset was systematically divided into two parts, with 80% allocated for model training and the remaining 20% for evaluation. The Naïve Bayes and Logistic Regression models were implemented using the Python programming language and evaluated based on accuracy, precision, recall, and F1-score metrics. The analysis shows that Logistic Regression achieved an accuracy of 86%, while Naïve Bayes achieved 81%. Logistic Regression demonstrated superior performance as it effectively captures linear relationships between features in TF-IDF representations and provides a more balanced outcome in terms of precision and recall. In contrast, Naïve Bayes is more influenced by high-frequency word distributions and does not account for feature correlations, which can limit its performance in certain contexts. Therefore, Logistic Regression is considered more suitable for sentiment classification tasks in this study. These findings emphasize the importance of selecting appropriate algorithms for sentiment analysis and suggest opportunities for future research using alternative methods to enhance predictive accuracy.
Implementation of Text Mining for Evaluating the Relevance Between News Headlines and Content on a Web-Based Platform Purnawati, Desak Gede Inten; Singgih Putri, Desy Purnami; Piarsa , I Nyoman
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.v9i4.9732

Abstract

Technological advancements in the era of the Industrial Revolution 4.0 have significantly transformed how society accesses and consumes information, particularly through online news portals. This study aims to analyze the relevance between news headlines and article content on Indonesian online news platforms by employing text mining techniques and similarity checking methods. To enhance the accuracy of relevance assessment, this research utilizes two deep learning-based modeling algorithms: Long Short-Term Memory (LSTM) and IndoBERT. The data was collected from three leading Indonesian news portals detik.com, kompas.com, and suara.com with a total of 52,242 articles from the entertainment and national news categories, gathered between July 1 and September 30, 2024. The dataset includes attributes such as headline, category, publication date, author, article URL, and news content. The research process consists of several stages, including data collection through web scraping, data pre-processing (which involves cleaning the category, author, and content columns), content summarization, text similarity calculation, and data labeling into three classes (relevan, berlebihan, and nonrelevan). Evaluation results show that the IndoBERT model outperforms LSTM, achieving the best performance with a training accuracy of 0.9048 and a training loss of 0.2514, as well as a validation accuracy of 0.8604 and a validation loss of 0.4039. These findings demonstrate that IndoBERT is effective in assessing the coherence between news headlines and content in today’s digital age.
Determining Eligibility for Smart Indonesia Program (PIP) Recipients Using the Backpropagation Method Rizkya, Ghinni; Nurdin, Nurdin; Meiyanti, Rini
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.v9i4.9733

Abstract

The government provides financial assistance, educational opportunities, and expands access for students from poor or vulnerable families through the Smart Indonesia Program (PIP). At Madrasah Ibtidaiyah Negeri 20 Bireuen, the selection process for underprivileged students is still carried out manually by homeroom teachers by collecting data on students and their parents. This study aims to design, implement, and evaluate a classification method using the Backpropagation Neural Network to determine the eligibility of PIP scholarship recipients. The dataset consists of 309 entries, comprising 217 training data and 92 testing data, collected from MIN 20 Bireuen students between 2021 and 2023. The attributes used include father's occupation, mother's occupation, father's income, mother's income, number of dependents, number of vehicles, home ownership status, and card ownership status. Prior to training, the data were normalized using Min-Max scaling. The model was built with one hidden layer using a hard-limit activation function and a learning rate of 0.01. The classification results are categorized as "Eligible" and "Not Eligible". The model achieved an accuracy of 98%, precision of 100%, recall of 95%, and F1-score of 97%.

Page 41 of 70 | Total Record : 695

 39   40   41   42   43 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 6 (2025): December 2025 Vol. 9 No. 5 (2025): October 2025 Vol. 9 No. 4 (2025): August 2025 Vol. 9 No. 3 (2025): June 2025 Vol. 9 No. 2 (2025): April 2025 Vol. 9 No. 1 (2025): February 2025 Vol. 8 No. 2 (2024): December 2024 Vol. 8 No. 1 (2024): July 2024 Vol. 7 No. 2 (2023): December 2023 Vol. 7 No. 1 (2023): July 2023 Vol. 6 No. 2 (2022): December 2022 Vol 6 No 1 (2022): July 2022 Vol. 5 No. 2 (2021): December 2021 Vol 5 No 2 (2021): December 2021 Vol 5 No 1 (2021): July 2021 Vol. 5 No. 1 (2021): July 2021 Vol 4 No 2 (2020): Desember 2020 Vol. 4 No. 2 (2020): Desember 2020 Vol 4 No 1 (2020): Juli 2020 Vol 3 No 2 (2019): Desember 2019 Vol 3 No 1 (2019): Juli 2019 Vol 2 No 2 (2018): Desember 2018 Vol 2 No 1 (2018): Juli 2018 Vol. 2 No. 1 (2018): Juli 2018 Vol 1 No 2 (2017): Desember 2017 Vol 1 No 1 (2017): Juli 2017 More Issue