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
 45   46   47   48   49 
Polynomial Integrated PLS Regression for Predicting Corrosion Inhibition Efficiency of Ionic Liquids Petrus Praditya Aswangga; Muhammad Akrom
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.v9i4.10158

Abstract

Corrosion degrades and weakens metal surfaces, leading to structural failure and significant safety hazards across various sectors. Data driven machine learning offers a rapid, cost-effective alternative to the expensive and time consuming traditional experimental methods by predicting inhibitor performance computationally. This study addresses the challenge of accurately predicting corrosion inhibition efficiency (CIE) of ionic liquid compounds. Integration of a polynomial function, especially in higher degrees, inevitably grows the dimensionality and escalates multicollinearity, but it captures deeper nonlinear interactions that the original variables alone would miss. To counterbalance this curse of dimensionality, Partial Least Squares (PLS) Regression was applied after polynomial integration to project the high-dimensional variables into a smaller set of predictors. Besides PLS, Gradient Boosting Regressor (GBR) and Support Vector Regressor (SVR) models were also developed to establish baseline performance. Although these polynomial integrated models outperformed their baseline version, the Polynomial Integrated PLS outperformed their predictive performance, yielding R2, RMSE, and MAPE of 0.73, 4.730, and 3.73%, respectively. The result of this study highlights that the integration of a polynomial function can improve the predictive performance of PLS for corrosion inhibitors.
Personal Protective Equipment Completeness Monitoring System Using YOLO-Based Computer Vision Akmal, Baasith Khoiruddin; Lestari, Wiji; Pradana, Afu Ichsan
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.v9i4.10172

Abstract

Workplace safety in the construction sector remains a critical concern, primarily due to low compliance with Personal Protective Equipment (PPE) standards. To address this, this study develops and evaluates a real-time PPE monitoring system, conducting a comparative analysis of two state-of-the-art object detection models: YOLOv8s and YOLOv11s. The system is designed to detect three essential PPE items: helmets, masks, and vests, and both models were trained on a custom dataset of 9,202 augmented images over 200 epochs. The final evaluation on an unseen test set revealed highly competitive performance. While YOLOv8s achieved a marginally higher mAP@0.5 (90.8%), YOLOv11s demonstrated superior precision (92.0%) and better performance on the stricter mAP@0.5:0.95 metric (54.4%). Based on this nuanced trade-off and its significantly higher computational efficiency (15% fewer parameters), YOLOv11s was selected as the optimal model. The chosen model achieved a real-time inference speed of approximately 112 FPS. A functional web-based prototype was developed using Flask to demonstrate the system's practical application. These findings confirm that YOLOv11s offers a more balanced and efficient solution for automating PPE compliance monitoring and highlight that a holistic evaluation beyond a single metric is crucial for deploying robust computer vision systems in real-world safety applications.
Mushroom Classification Using Convolutional Neural Network MobileNetV2 Architecture for Overfitting Mitigation and Enhanced Model Generalization Prayogi, Fauzan Arif; Arvianto, Fariz Hasim; Pratama, Dimas Rizki; Sugiyanto, Sugiyanto
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.v9i4.10183

Abstract

Fungal identification is a significant challenge due to the morphological similarities among different species. Previous studies using Convolutional Neural Networks (CNNs) for mushroom classification still face overfitting issues, which lead to poor performance on new data. Therefore, this research develops a MobileNetV2-based Convolutional Neural Network (CNN) model capable of classifying three mushroom species (Amanita, Boletus, and Lactarius) with a primary focus on mitigating overfitting. The dataset consists of 3,210 RGB images, divided into 1,979 training data, 493 validation data, and 738 testing data. The model is developed using transfer learning with MobileNetV2, combined with additional layers such as Conv2D, pooling, and Dense, along with Dropout for regularization. The training process employs the Adam optimizer with a learning rate of 1.0×10⁻⁵ and is monitored with EarlyStopping and ModelCheckpoint. The model successfully addresses overfitting, achieving a minimal generalization gap of 1.33%, compared to 7% in previous studies. The evaluation results show a training accuracy of 77.35%, validation accuracy of 78.79%, and testing accuracy of 76.02%, with precision of 80.6% and recall of 68.1%. The consistent performance, with a maximum difference of only 2.77% across the three datasets, demonstrates superior generalization ability and provides a strong foundation for the implementation of a reliable automatic mushroom identification system.
Early Detection of Type 2 Diabetes Using C4.5 Decision Tree Algorithm on Clinical Health Records Setiani, Hani; Arridho, Muhammad Noor; Supriyanto, Supriyanto
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.v9i4.10190

Abstract

Type 2 Diabetes is a chronic metabolic disorder marked by elevated blood glucose levels. It is the most prevalent form of diabetes in society, commonly triggered by poor lifestyle habits and hereditary factors. If left unmanaged, the disease can lead to serious complications such as hypertension and other chronic conditions. Therefore, early detection plays a critical role in minimizing long-term impacts and promoting healthier behavioral changes. This research focuses on classifying Type 2 Diabetes using clinical data with the C4.5 Decision Tree algorithm. The dataset encompasses attributes including gender, age, height, weight, waist circumference, BMI, systolic and diastolic blood pressure, respiratory rate, and pulse rate. The model was evaluated under two scenarios: without data balancing and after applying the SMOTE technique for balancing. In the first scenario, the best performance was achieved with a training-testing split of 80:20, resulting in an F1 Score of 67.76%. However, the performance varied across different data proportions. In contrast, the second scenario showed more consistent results, with the 60:40 split yielding the highest F1 Score of 66.67%. These findings suggest that SMOTE effectively reduces bias toward the majority class and enhances sensitivity to the minority class. Therefore, data balancing is a crucial step in developing a reliable classification model for Diabetes Mellitus diagnosis.
Detection of Ripeness in Oil Palm Fresh Fruit Bunches Using the YOLO12S Algorithm on Digital Images Nur'aini, Linnda Prawidya; Rahardi, Majid
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.v9i4.10250

Abstract

Indonesia is the world's largest producer of palm oil, with a production volume reaching 46.82 million tons in 2022. This industry heavily relies on the quality of Fresh Fruit Bunches (FFB) harvests, which is determined by the accuracy of ripeness at the time of harvest. Unfortunately, ripeness assessment of FFB is still conducted manually and subjectively by field workers, posing risks to both efficiency and production accuracy. Although various studies have employed YOLOv5 and YOLOv8 for fruit ripeness detection, few have explored the potential of YOLO12s in classifying FFB ripeness in a comprehensive and efficient manner. In this study, we present the application of the YOLO12s algorithm to automatically classify the ripeness levels of oil palm FFB using digital images. The YOLO12s model was trained on 14,620 FFB images categorized into four ripeness levels: unripe, under-ripe, ripe, and overripe. Evaluation results showed a precision of 93.1%, recall of 95.9%, mAP@0.50 of 97.8%, and mAP@0.50–0.95 of 78.8%. The model was able to perform inference in approximately 4.7 milliseconds per image and demonstrated good generalization despite challenges related to varying lighting conditions. These results indicate that YOLO12s holds great potential to replace subjective manual methods with a more accurate, consistent, and efficient classification solution to support the harvesting process in the palm oil industry.
Analysis of Public Sentiment Towards President Prabowo's Work Program Using The CNN Thenata, Angelina Pramana; Saputra, Dimas Sakti Reka
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.v9i4.9394

Abstract

Digital media has now become the primary means for Indonesians to receive and respond to information, including the work programs presented by Prabowo Subianto. One of the programs that is widely discussed by the public is related to efforts to improve the national economy. Public responses to this issue are widespread on social media, reflecting diverse sentiments. Therefore, this study aims to analyze the sentiment of comments from social media users X regarding President Prabowo's work programs in the economic sector, using a deep learning approach based on the Convolutional Neural Network (CNN) architecture. The methods employed include data collection, text preprocessing, and training a CNN model. The dataset used consisted of 2,467 data points, with 1,086 labeled as positive and 1,381 labeled as negative. The test results showed that the model achieved an accuracy of 87.45% and an Area Under the Curve (AUC) score of 0.9373, indicating excellent classification performance in distinguishing between positive and negative sentiments. This study proves that the combination of CNN and FastText is a practical approach to understanding text-based public opinion from social media.
Implementation of Ant Colony Optimization (ACO) Algorithm for Route Optimization of Tourist Paths in Takengon Suryana, Fitra; Nurdin, Nurdin; Hamdhana, Defry
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.v9i4.9706

Abstract

This study aims to design and implement a system for determining the shortest route between tourist destinations in Takengon using the Ant Colony Optimization (ACO) algorithm. The system is developed to assist travelers in obtaining efficient visitation routes based on distance and travel time. Experiments were conducted on 20 tourist locations, resulting in an optimized route with a total travel distance of 40.40 km and an estimated travel time of 81 minutes. The computation process took only 0.024001 seconds with a memory usage of 20.23 KB. The ACO algorithm was executed using 10 ants with key parameters set to alpha (α) = 1, beta (β) = 2, and rho (ρ) = 0.5. ACO demonstrated high effectiveness in exploring route combinations and iteratively generating near-optimal solutions. The chosen parameters were determined through experimentation to balance solution quality and convergence speed. In addition to generating the optimal visitation sequence, the system also provides complete turn-by-turn navigation instructions, including major roads such as Jalan Lintas Tengah Sumatera and Jalan Lebe Kader. The actual estimated travel route based on the generated navigation covers a distance of 97.4 km with a travel duration of approximately 2 hours and 42 minutes. The results indicate that ACO is an effective and efficient approach for solving medium- to large-scale tourist route optimization problems. The developed system can serve as a practical tool in the tourism sector and has the potential to be adapted and implemented in other tourist regions with similar routing challenges.
A Forecasting Modeling of Imported Goods Release Waiting Time in Importer Logistics Operations Using Multiple Linear Regression Alfad Zebua, Vivid Kristiani; Rusdah, Rusdah
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.v9i4.9725

Abstract

Import activities play a critical role in international trade, directly affecting logistics efficiency and the competitiveness of importing companies. The process of releasing imported goods at ports often involves complex administrative procedures that can cause delays, leading to increased logistics costs. This study aims to predict the waiting time for the release of imported goods using a machine learning approach. A case study was conducted at PT. Sentra Sarana Logistic, a licensed customs broker responsible for import administration. The primary model applied was Multiple Linear Regression (MLR), and its performance was compared with Neural Network (NN) and Support Vector Machine (SVM) algorithms. Several influencing factors were considered, including tax payment time, inspection duration, and inspection status. Evaluation results indicate that the MLR model achieved the best performance, with an RMSE of 0.00653, MAE of 0.00544, and R-squared of 0.99999, demonstrating high prediction accuracy and a strong linear correlation. The SVM model yielded acceptable results (RMSE 0.74107, R-squared 0.98388) but underperformed compared to MLR. The NN model showed the lowest accuracy with RMSE 2.86599, MAE 2.38831, and R-squared 0.69510. The findings suggest that MLR, despite its simplicity, is highly effective for predicting waiting times in import logistics operations. This research not only offers a practical decision-support tool for importers but also contributes to the existing literature on machine learning applications in logistics operations and customs processing.
Implementation of BERTopic for Topic Modeling Analysis of the Free Nutritious Meal Program Based on YouTube Comments Wahyuni, Widya; Lestari, Tri Putri; Apriliana, Milla; Gumelta, Riyang
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.v9i4.9754

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis), represents a significant national effort aimed at mitigating stunting rates in Indonesia, having commenced its operations in January 2025. As the program progressed, public sentiment towards it evolved, resulting in a diverse array of opinions that were extensively debated on various social media platforms, notably YouTube. This study was conducted with the objective of examining the perceptions of the public regarding Makan Bergizi Gratis through a topic modeling methodology employing the BERTopic approach, which analyzed 19,843 comments from YouTube. The analytical framework entailed several stages, including data preprocessing, sentence-based embedding representation, dimensionality reduction via UMAP, clustering through HDBSCAN, and topic interpretation grounded in c-TF-IDF. The findings indicate that public commentary is categorizable into ten primary themes, encompassing issues such as the involvement of political figures, concerns over budget transparency, the program's educational benefits, and the need for equitable access in underserved regions. Evaluation results show that BERTopic outperformed the traditional LDA model, with a coherence score of 0.46 compared to 0.39 and topic diversity of 76 percent compared to 71 percent. This analysis reveals that public perception of Makan Bergizi Gratis is multifaceted, shaped by social experience, political context, and economic expectations. These insights may serve as a valuable foundation for a more comprehensive understanding of public opinion, thereby supporting more targeted and responsive policy development.
Browser-Based Detection of Harmful Content with Deep Learning Model Sikiandani, Ni Made Deni; Dwi Suarjaya, I Made Agus; Perdana Putra, Yohanes
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.v9i4.9804

Abstract

This study presents a browser extension that detects harmful content on both web pages and TikTok using a deep learning-based approach. The core model employs a Bidirectional Long Short-Term Memory (BiLSTM) network for multi-label classification, targeting six categories: Toxic, Severe Toxic, Obscene, Threat, Insult, and Identity Hate. The dataset combines 13,057 labeled samples from a public Kaggle dataset (2021) and 2,884 manually labeled tweets scraped from Twitter (X) between October–November 2024. Three feature extraction methods were tested: learned embeddings, FastText, and Word2Vec. The BiLSTM model architecture includes one embedding layer, a 32-unit bidirectional LSTM, three dense layers (128,256,128) using ReLU activation, and a six-unit sigmoid output layer. The model was trained using the Adam optimizer and binary cross-entropy loss, with early stopping applied after five stagnant validation checks across a maximum of 200 epochs. While the FastText-based model showed the best performance, the final deployed model used learned embeddings in Scenario 1 due to its smaller size (1.6M parameters) and near-optimal performance (Recall: 0.9786; Hamming Loss: 0.0052). The extension also integrates Whisper ASR for detecting harmful speech in video-based platforms like TikTok and supports five customizable censorship filters. User evaluation via Customer Satisfaction Score (CSAT) indicated strong acceptance, with 95.45% rating the user experience as Excellent, 84.09% confirming detection relevance, and 79.55% rating the system performance as Good. This highlights the extension’s effectiveness in promoting safer digital interaction across text and audiovisual content.

Page 47 of 70 | Total Record : 695

 45   46   47   48   49 

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