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 110 Documents
 1   2   3   4   5 
Search results for , issue "Vol. 10 No. 1 (2026): February 2026" : 110 Documents clear
The Comparative Analysis of K-Nearest Neighbors Algorithm and Random Forest Regressor for House Price Prediction in Bandung City Ananda, Dimas Yudhistira; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.10718

Abstract

The rapid population growth and continuous urban expansion in Bandung have contributed to volatile and escalating housing prices, creating significant challenges for market transparency and affordability. This study aims to develop and evaluate machine-learning models to predict house prices in the Bandung region using a publicly available dataset consisting of 7,609 property records. Following the CRISP-DM methodology, the research includes data exploration, preprocessing (outlier handling using IQR, one-hot encoding, and feature standardization), model training, and performance evaluation. Two regression models K-Nearest Neighbors (KNN) Regressor and Random Forest (RF) Regressor—were compared through systematic hyperparameter tuning using Grid Search and Random Search techniques. The experimental results show that the Random Forest Regressor achieves the best performance with an R² score of 0.7838 and a mean absolute error (MAE) of approximately Rp 399.7 million, outperforming the optimized KNN model. Feature importance analysis also indicates that land area, building area, and location are the most influential predictors of property prices. The findings highlight the effectiveness of ensemble methods in handling complex real-estate data and demonstrate the potential of machine-learning-based predictive tools to support buyers, sellers, and policymakers in making informed and data-driven decisions in the Bandung housing market.
Weight Estimation of Broiler Ducks Based on Image Processing and Machine Learning with IoT Integration Mukti, Sindhu Hari; ardi, Ardi Pujiyanta; Fadlil, Abdul
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11259

Abstract

The broiler duck farming industry in Indonesia faces challenges in efficiently monitoring body weight, as traditional manual weighing methods are labor-intensive, time-consuming, and stressful for the animals. To address this issue, this study aims to develop a non-invasive and automated weight estimation system that integrates digital image processing, machine learning, and Internet of Things (IoT) technologies. The methodology involves acquiring multi-angle images of ducks, applying preprocessing steps such as resizing, normalization, and contrast enhancement, and extracting hand-crafted features, including Histogram of Oriented Gradients (HOG) and HSV color histograms. These features are then fused, reduced via Principal Component Analysis (PCA), and processed using a Support Vector Regression (SVR) model with optimized hyperparameters for weight prediction. While previous studies have focused on cattle, broilers, or fish, research specifically targeting meat-type ducks remains limited, particularly those that combine image-based regression with IoT-enabled real-time monitoring. Experimental results demonstrate that the proposed system achieves a mean absolute error (MAE) of approximately 110 grams on the validation set, with per-duck averaging improving stability compared to per-image predictions. Visualization through scatter plots, boxplots, and learning curves further confirms that the model effectively captures general weight distribution trends but exhibits higher errors in certain mid-weight ranges. The integration with IoT facilitates continuous, stress-free monitoring of duck growth, underscoring the system’s potential as a practical and sustainable solution for precision livestock farming.
Implementation of the Random Forest Algorithm for Anomaly Detection of Phishing Attacks on Computer Networks Slameto, Andika Agus; Kahmas, Ben Rafi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11346

Abstract

Phishing attacks are among the most common and dangerous cyber security threats, as they exploit manipulation techniques to steal sensitive user information. This research focuses on leveraging the Random Forest algorithm to identify anomalies caused by phishing attacks in computer network environments. Random Forest was selected for its superior classification performance and its capability to handle a wide variety of data types with minimal over fitting. The experimental dataset consists of captured network traffic, containing both benign activities and malicious events labeled as phishing. The data underwent pre-processing, feature selection, and model training using Random Forest. The experimental results show that the model achieved 98% accuracy, with precision 98%, recall 98%, and F1-score 98%. This study also reveals that URL features such as the percentage of external links redirecting back to the original domain, frequent domain name mismatches, the number of hyphens (-) in the URL, and the presence of data submission via email are relevant and effective in distinguishing phishing from non-phishing URLs. These findings confirm that Random Forest can serve as an effective method for identifying phishing attacks based on URL characteristics.
Performance Analysis of KNN and BERT Algorithms for Classifying Student Sentiments Towards Campus Services Mutmainna, Mutmainna; Hamrul, Heliawati; Firgiawan, Wawan
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11365

Abstract

This study addresses the limitations of campus service evaluation processes that are still conducted manually and are unable to optimally process students’ textual opinions. The objective of this research is to analyze and compare the performance of the K-Nearest Neighbor (KNN) and BERT algorithms in classifying student sentiments toward campus services. The research stages include text preprocessing, the generation of IndoBERT embeddings for the KNN model, and fine-tuning IndoBERT for direct sentiment classification. The dataset consists of student evaluation texts from the Faculty of Engineering at UNSULBAR, labeled as negative, neutral, and positive sentiments. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics. The results show that the basic KNN model achieves an accuracy of 79%, while KNN with hyperparameter tuning improves performance to 86%. The BERT model delivers the best performance, achieving an accuracy of 88.68%, precision of 87.87%, recall of 90.19%, and an F1-score of 88.79%. These findings indicate that transformer-based approaches, particularly IndoBERT, are more effective in understanding the contextual nuances of student language than traditional methods, and are therefore more recommended for sentiment analysis implementation in campus service evaluation.
Prediction of Nile Tilapia (Oreochromis niloticus) Harvest Yield in Brackishwater Pond Aquaculture Using XGBoost Himawan, Salamet Nur; Wisnu, Arif; Nugraha, Nur Budi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11378

Abstract

Nile tilapia aquaculture is one of the aquaculture subsectors with significant development potential. However, the productivity of Nile tilapia cultured in brackishwater ponds is often constrained by variability in technical factors such as the number of fingerlings stocked, pond area, stocking density, land status, planting season, and feed quantity. To address these challenges, a predictive model based on machine learning was developed. Data were collected through field observations and interviews with Nile tilapia farmers in Wanantara, Sindang, Indramayu. The data were then processed using label encoding and normalization techniques. The dataset was divided into 80% for training and 20% for testing. XGBoost, Random Forest, and Support Vector Regression algorithms were trained using hyperparameter tuning and five-fold cross-validation, and evaluated using RMSE and R² metrics. The results show that XGBoost achieved the best performance (R² = 0.9798 and RMSE = 442.05 kg), followed by Random Forest (R² = 0.955 and RMSE = 679.742 kg) and SVR (R² = 0.888 and RMSE = 1065.367 kg).
Image Processing and Object Detection in the Indonesian Sign System (SIBI) for Hearing-Impaired Communication Faroek, Dewi Astria; Yusuf, Muhammad; Haris, Haris
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11395

Abstract

Communication is a fundamental human need, yet individuals with hearing impairments continue to face barriers due to limited access to sign language translation technologies. In Indonesia, the adoption of such technologies remains low, particularly in regions such as Sorong, Southwest Papua, creating a communication gap between the Deaf community and the general public. This study develops a web-based detection system for 36 classes of the Indonesian Sign System (SIBI) using the YOLOv5 algorithm. The dataset consists of 5,682 images of SIBI hand poses with variations in lighting and background, divided into 4,970 training images (87%), 376 validation images (7%), and 335 test images (6%). All data were processed through labeling, preprocessing, augmentation, balancing, and model training. The training was conducted for 150 epochs, and the evaluation results show that YOLOv5 is capable of detecting SIBI signs with significant accuracy. Performance evaluation using a confusion matrix achieved a detection accuracy of 95%, supported by stable precision and recall values and real-time inference performance on common web browsers. Usability testing with 20 respondents indicated satisfaction levels above 72.8%, demonstrating that the system is practical and easy to use. This research presents a validated real-time, web-based SIBI detection system that supports inclusive computer vision applications and enhances accessibility in public services such as education, healthcare, and administrative environments.
Analysis of the Impact of Lateral Stock Transfers in Distribution Network with a Central Warehouse and Two Storage Points Dambo Punga; Mabela Makengo Rostin; MATONDO MANANGA, Herman; Muhala Luhepa Blaise; Boono Yaba Benjamin
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11408

Abstract

Our study, Analysis of the impact of lateral Transfers in a stock distribution system with a central warehouse and two stocking points, aims to analyze the effet of lateral stock transfers between the two stocking points on minimizing the total inventory management cost system, retailers manage their inventories according to the (R,S) policy. This study also examines the service level and the stockout rate resulting from the implementation of lateral stock transfers. Each point i (i=1,2) manages its inventory independently in order to meet the consomer demand yi. Each stocking point has a maximun inventory level Si , when customer demand is less than or equal to the reorder point si , an order of quantity Qi= Si-si is placed with the central warehouse. This quantity is delivered after a known lead time Li. If the delivery lead time is too long, stocking point i may request a lateral transfer of quantity Xji from stocking point j, which has excess inventory, in order to avoid a stockout. The originality of this publication stems from the implementation of a numerical application using MATLAB, which allowed us to conduct this analysis.
Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest Febriani, Wulandari; Mambang, Mambang; Prastya, Septyan Eka; Sabella, Billy; Marleny, Finki Dona
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11415

Abstract

Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
A Fuzzy C-Means–Based Clustering Model for Analyzing TOEFL Prediction Scores in Higher Education Gulo, Filipus Mei Tri Boy; Hidayat, Rahmad; Hendrawaty, Hendrawaty; Hidayat, Rahmat Isma; Fasya, Muhammad Heikal; Syifaurrahman, Syifaurrahman; Ananda, Dea Syafira
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11468

Abstract

In the era of digital transformation, the application of data mining in academic data management has become an important requirement for improving the quality of education. One crucial aspect is English proficiency. One of the tools for measuring English proficiency is the Test of English as a Foreign Language (TOEFL) Prediction test, which is administered at every university, including the State Polytechnic of Lhokseumawe. The management of TOEFL Prediction scores can utilize data mining as a basis for more in-depth learning analysis, as well as evaluation material. This study aims to design and develop a model for grouping the TOEFL scores of students at State Polytechnic of Lhokseumawe by applying the Fuzzy C-Means (FCM) algorithm. The research methods included observation and interviews, data collection and pre-processing, cluster model design, web-based system development, and system testing. Evaluation was conducted through Black Box and White Box testing for the system, as well as cluster quality validation using the Xie-Beni Index (XB) and Partition Coefficient. The results showed that the pre-test dataset of first-year students (651 data) produced three clusters with an XB value of 0.623, while the dataset of final-year students (826 data) produced six clusters with an XB value of 0.181. The developed model proved to be able to map students' English language abilities in a more structured manner and could be used as a basis for academic planning and skill improvement.
Comparative Analysis of Foot Sole Classification Models: Evaluating Logistic Regression, SVM, and Random Forest Purba, Trie Dinda Maharani; Yuadi, Imam
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11550

Abstract

Accurate sole classification and types can aid applications in healthcare, sports, and biometrics such as diagnosis of high arch or flat foot disease, as well as in improved design of custom orthotics and enhanced gait analysis to improve sports performance. When applied to large-scale datasets, traditional methods for foot sole classification are inefficient as they are often manual, time-consuming and prone to human error. Machine learning has the ability to significantly improve accuracy and efficiency in automating this process. The proposed method uses Logistic Regression model compared to Support Vector Machines (SVM), and Random Forest using Orange Data Mining. The performance of these algorithms changes depending on the complexity of the data and model parameters. There are three types of feet that will be processed in this image analytics namely normal arch, flat foot and high arch. The pre-trained models used are Inception V3, VGG-19 and SqueezeNet. Logistic Regression model showed the best overall performance with superior parameter values such as AUC of 0.973, Classification Accuracy (CA) of 0.933, and MCC of 0.902, and demonstrated reliability and balance between precision and recall.

Page 1 of 11 | Total Record : 110

 1   2   3   4   5 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): February 2026 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