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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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News Recommendation System Using Content-Based Filtering through RSS Customization Service Nandita, Ida Ayu Widya; Dwi Suarjaya, I Made Agus; Bayupati, I Putu 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.v9i4.9807

Abstract

News refers to stories or information about current events or incidents. Several news websites offer a service called RSS (Really Simple Syndication), which enables users to easily receive updates on the latest news. News RSS feeds are typically generated based on the order of publication time or general categories. The content of these news RSS feeds can be customized to align with user interests or preferences. A recommendation system can be utilized as an approach to customize RSS feeds. This study was conducted to design a system capable of generating RSS feeds based on news recommendations using the content-based TF-IDF method and cosine similarity. Data scraping and preprocessing of news articles from various RSS feeds of Indonesian news websites were automated using cron jobs. Content-based filtering modeling was carried out using TF-IDF and cosine similarity. The design and customization of RSS feeds were implemented in a Flask application and packaged within several endpoints. The recommendations generated based on user click interactions were reasonably relevant, as they successfully presented news titles similar to the clicked articles, with cosine similarity scores ranging from 0.2 to 1.0. The majority of respondents agreed that the recommended news articles were relevant to the articles they had clicked and aligned with their interests. The RSS feed evaluation yielded highly satisfactory results, with all aspects assessed in the user acceptance survey achieving an average score exceeding 80%, and the overall results of the customer satisfaction survey indicated scores starting from 90%.
Implementation of Convolutional Neural Network in Image-Based Waste Classification Qurrota A'yun, Adila; Suhartono, Suhartono; Lestari, Tri Mukti
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.v9i4.9829

Abstract

The increasingly complex issue of waste management, particularly in the sorting process, demands efficient and accurate technology-based solution. This study aims to implement the Convolutional Neural Network (CNN) method for image-based waste classification, focusing on two classes paper and plastic. The dataset used consists of 2000 images, with an 80% proportion for training and 20% for testing. This study tested four scenarios combining image augmentation and classification methods, namely threshold and one-hot encoding, and evaluated model performance using accuracy, precision, recall, and F1-score metrics. The best results were obtained in the scenario using image augmentation with the one-hot encoding classification method, with an accuracy of 89%, precision of 88.5%, recall of 89%, and F1-score of 88.5%. These findings indicate that implementation of CNN can enhance the effectiveness of image-based waste classification and support recycling efforts through a smarter and more automated sorting system.
UI/UX Optimization of GOBIS Suroboyo Application with User Centered Design Approach and Short User Experience Questionnaire Mayangsari, Mustika Kurnia; Fatihia, Wifda Muna
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.v9i4.9859

Abstract

GOBIS Suroboyo is a mobile application designed to assist Suroboyo Bus passengers in accessing route information, schedules, and general bus details. Despite its potential, the application has lacked systematic user experience evaluation, resulting in usability issues that require improvement. This study aims to optimize the user interface (UI) and user experience (UX) of the GOBIS Suroboyo application using the User-Centered Design (UCD) approach. The research was conducted through four main stages: analysis of the existing application, identification of user needs, redesign of the interface, and evaluation of the resulting prototype. The usability evaluation was performed using the Short User Experience Questionnaire (UEQ-S), which assessed both hedonic and pragmatic qualities. The results showed mean scores of 1.69 for hedonic quality and 1.425 for pragmatic quality, which fall into the "Excellent" and "Above Average" categories, respectively, based on the benchmark scale. These results indicate that the redesigned prototype is engaging, motivating, efficient, and user-friendly. This study concludes that the UCD approach, with active user involvement, is effective in enhancing the user experience of mobile applications.
Application of the PSI-VIKOR Method in Determining Priorities for Poor Areas Based on Poverty Indicators in Central Java Kusuma, Kasa; Cholil, Saifur Rohman
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.v9i4.9888

Abstract

Poverty remains a significant challenge in developing countries, including Indonesia. Although the national poverty rate has declined, Central Java still shows relatively high rates. This study aims to identify priority areas in Central Java requiring government intervention to support effective poverty alleviation planning. Data were sourced from the Central Statistics Agency (BPS) of Central Java Province in 2023. A Decision Support System (DSS) approach was applied using the integrated Preference Selection Index (PSI) and VIKOR methods. PSI was used to determine objective criteria weights based on preference variations, while VIKOR ranked regions based on compromise solutions closest to ideal conditions.The ranking results were visualized spatially through a digitization process using QGIS to produce thematic maps. Analysis showed that Purworejo, Wonogiri, and Batang are high-priority regencies, whereas Semarang City, Banyumas, and Kendal have relatively stable socio-economic conditions. Validation using the Normalized Discounted Cumulative Gain (NDCG) method yielded a score of 0.9268, indicating strong alignment with historical data. These findings confirm the effectiveness of the PSI–VIKOR approach in supporting data-driven poverty alleviation strategies. The novelty of this study lies in the integrated application of PSI–VIKOR for spatial poverty prioritization, which has not previously been implemented in the Indonesian context.
Passenger Density Prediction at the Airport Using LSTM and SARIMA: A Case Study at Radin Inten Airport, Lampung Yugo Prasojo, Diaji; Muludi, Kurnia
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.v9i4.9935

Abstract

Passenger density prediction at airports is a critical aspect of operational planning and strategic decision-making. This study aims to develop a passenger count prediction model for Radin Inten Airport in Lampung using a combination of Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM), and to compare it with Random Forest and XGBoost models. The dataset consists of daily passenger counts from January 2023 to December 2024. The research includes data exploration, preprocessing, separate modeling with SARIMA and LSTM, and their integration through a residual learning approach. Evaluation results show that SARIMA achieved the best performance in capturing seasonal patterns with a Mean Absolute Percentage Error (MAPE) of 3.81%, followed by Random Forest with 5.81% and XGBoost with 5.84%. The LSTM model performed less effectively with a MAPE of 6.81%. Although the SARIMA–LSTM combination is theoretically promising, it produced a worse result with a MAPE of 14.27% due to error accumulation in the residual learning stage. This study highlights that the choice of prediction model strongly depends on data characteristics and forecasting objectives, as well as the importance of multi-model integration to improve prediction accuracy in airport passenger density forecasting applications.
Machine Learning-Based Sentiment Analysis on Twitter (X): A Case Study of the “Kabur Aja Dulu” Issue Using SVM Rohmatun, Lina; Baita, Anna
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.v9i4.9991

Abstract

This study aims to analyze public sentiment toward the phenomenon of “Kabur Aja Dulu” on Twitter (X) using the Support Vector Machine (SVM) method. The data used consists of 4,768 Indonesian-language tweets collected through web scraping. The pre-processing process includes data cleaning, tokenization, stemming, and translation into English for automatic sentiment labeling using TextBlob. The data is then classified into three sentiment categories: positive, negative, and neutral. To address class imbalance, the SMOTE method is applied to the training data, along with TF-IDF techniques for feature extraction. The model was evaluated using the K-Fold Cross Validation method and Grid Search for hyperparameter tuning. The results of the study show that the SVM model with a linear kernel and parameter C=10 provides the best performance with an accuracy value of 85.56%, precision of 845.19%, recall of 85.56%, and F1-score of 85.30%. The main finding of this study is that the linear SVM method is capable of classifying sentiment well, particularly for neutral sentiment data, and has proven effective as an approach to sentiment analysis in the context of social media using the Indonesian language.
Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic Irawan, Yoki; Pramitasari, Rina; Ashari, Wahid Miftahul; Yansyah, Aiko Nur Hendry
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.v9i4.10003

Abstract

Distributed Denial of Service (DDoS) attacks represent a significant danger in network security because they can lead to extensive service interruptions. With these attacks increasingly mirroring regular traffic, smart and effective detection systems are essential. This research seeks to assess the efficacy of the Support Vector Machine (SVM) classification algorithm in identifying DDoS attacks in network traffic. The data utilized is CICIDS2017, focusing on the subset Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv, which contains both legitimate traffic and DDoS attacks like DoS-Hulk, DoS-GoldenEye, and DDoS. The preprocessing stage included eliminating duplicates and null entries, label binary encoding, normalization through Min-Max Scaler, and feature selection applying the Chi-Square technique. The data was divided into 80% for training and 20% for testing purposes. The Radial Basis Function (RBF) kernel was utilized to train the SVM model, and hyperparameter optimization was performed with GridSearchCV. The evaluation of the model's performance was conducted through accuracy, precision, recall, F1-score, confusion matrix, and visual representations including ROC and Precision-Recall Curves. The findings indicate that prior to tuning, the model reached an accuracy of 97%, which increased to 99% post-tuning, accompanied by an F1-score of 0.99. This shows that the SVM algorithm, when paired with appropriate preprocessing and optimization, is very efficient in identifying DDoS attacks within network traffic.
Sensor Fusion – Based Localization for ASV with Linear Regression Optimization Wijaya, Ryan Satria; Jamzuri, Eko Rudiawan; Wibisana, Anugerah; Sinaga, Jepelin Amstrong; Julanba, Vafin
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.v9i4.10048

Abstract

ASV (Autonomous Surface Vehicle) is one of popular innovations in the maritime field that is widely used for various missions on the water surface. The ASV itself has the ability to operate automatically without human intervention. Therefore, ASV requires an accurate and reliable localization system. This research focuses on developing an ASV localization system using waterflow sensors optimized through linear regression and integrated with orientation data from an IMU sensor through sensor fusion to obtain global coordinate position estimation. The experiments conducted showed a significant improvement in accuracy after optimization, with the Root Mean Square Error (RMSE) of the waterflow sensor data decreasing from 161.65 meters to 0.28 meters. Moreover, the yaw data reading by IMU achieved accuracy with RMSE 1.54 degrees. The localization system in the final test achieved RMSE values of 0.07 meters for the X-axis, 0.14 meters for the Y-axis, and 1.9 degrees for yaw during the ASV global positioning experiment. In addition, a GUI (Graphical User Interface) was developed for visualization with average communication latency of 113.6 milliseconds. This localization system is a promising solution in stable water condition.
Real-Time Chinese Chess Piece Character Recognition using Edge AI Wijaya, Ryan Satria; Anadia, Atika Yunisa; Fatekha, Rifqi Amalya; Prayoga, Senanjung
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.v9i4.10056

Abstract

This research focuses on developing a character analysis system on Chinese chess pieces (xiangqi) using computer vision technology with the deep learning framework PyTorch. The system is designed to detect and interpret text written on chess pieces in real time, making it easier for players to identify the function of each piece. The implementation is done using a web camera and can be applied to embedded devices such as Jetson Nano. This research aims to develop an automatic recognition system that can help players better understand the game of xiangqi by identifying characters on pieces in real time. The test results show that the system successfully recognized 14 pieces correctly. The system developed using Jetson Nano can directly process image data with a processing time of 0.0222 seconds. This data is obtained from the average of each FPS image from the web camera.
Decision Support System for Sunscreen Selection Based on Facial Skin Concerns Using the Analytic Network Process Sitorus, Andriani; Fakhriza, M.
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.v9i4.10112

Abstract

Exposure to ultraviolet (UV) radiation is one of the primary causes of premature skin aging and various facial skin problems. However, selecting an appropriate sunscreen product remains challenging due to limited consumer knowledge and the overlapping nature of facial skin concerns. This study proposes a decision support model using the Analytic Network Process (ANP) to determine the most suitable sunscreen product based on six common skin problems: acne-prone skin, very dry skin, outdoor-induced dullness, aging, hyperpigmentation and acne scars, and general dullness. These criteria were derived from literature and validated by a certified skincare expert. Nine sunscreen alternatives from the Wardah brand—chosen due to their wide usage in the Indonesian market and varying SPF, PA levels, and formulations—were evaluated. Expert judgment was used in pairwise comparisons, with Consistency Ratio (CR) used to ensure reliability. The ANP model was developed using unweighted, weighted, and limit supermatrices. Results showed that Wardah UV Shield Aqua Fresh Sunscreen Serum SPF 50 PA++++ had the highest global priority score. A prototype web-based system was built using PHP and MySQL to deliver personalized sunscreen recommendations. The novelty of this study lies in its integration of expert dermatological insights and the use of ANP to address interrelated skin concerns, which are rarely explored in prior skincare decision support research.

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