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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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A Real-Time Hand Gesture Control of a Quadcopter Swarm Implemented in the Gazebo Simulation Environment Wijaya, Ryan Satria; Prayoga, Senanjung; Fatekha, Rifqi Amalya; Mubarak, Muhammad Thoriq
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9578

Abstract

With the advancement of technology, human-robot interaction (HRI) is becoming more intuitive, including through hand gesture-based control. This study aims to develop a real-time hand gesture recognition system to control a quadcopter swarm within a simulated environment using ROS and Gazebo. The system utilizes Google's MediaPipe framework for detecting 21 hand landmarks, which are then processed through a custom-trained neural network to classify 13 predefined gestures. Each gesture corresponds to a specific command such as basic motion, rotation, or swarm formation, and is published to the /cmd_vel topic using the ROS communication framework. Simulation tests were performed in Gazebo and covered both individual drone maneuvers and simple swarm formations. The results demonstrated a gesture classification accuracy of 90%, low latency, and stable response across multiple drones. This approach offers a scalable and efficient solution for real-time swarm control based on hand gestures, contributing to future applications in human-drone interaction systems.
Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques Dina Wulan Yekti rahayu; Khothibul Umam; Maya Rini Handayani
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9584

Abstract

This study explores the performance of five sentiment classification algorithms—Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest—on an imbalanced sentiment dataset, with the SMOTE technique applied as a comparison. The research follows the Knowledge Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and evaluation. The evaluation uses metrics such as accuracy, precision, recall, F1-score, and macro average F1-score. Initial results show that all five algorithms performed fairly well even without using a balancing technique, with Naïve Bayes achieving the highest F1-score of 0.84 and recall of 0.81. After applying SMOTE, only small improvements were observed in some models, such as Random Forest (F1-score increased from 0.81 to 0.85), while other models like Naïve Bayes experienced a decrease in performance, dropping to 0.77. This suggests that the effect of balancing techniques like SMOTE can vary depending on the algorithm. Thus, this study provides empirical contributions that highlight the importance of selecting appropriate approaches and the need for a deep understanding of each algorithm's behavior in the context of imbalanced data. Researchers are encouraged to carefully consider these aspects when designing experiments and interpreting results.
Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) Enjelia, Lola; Cahyana, Yana; Rahmat; Wahiddin, Deden
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9593

Abstract

This study compares the performance of the K-Nearest Neighbors (K-NN) and Naive Bayes Classifier (NBC) algorithms in sentiment analysis of the 2024 Regional Election (Pilkada) based on Indonesian local data sourced from platform X. A total of 1,187 tweets were collected through crawling, followed by extensive preprocessing and manual sentiment labeling by a professional linguist to ensure data validity and reliability. The study highlights NBC's superior accuracy (81.05%) compared to K-NN (75.26%), largely due to the characteristics of short-text social media data that align with NBC's independence assumptions. Key terms identified through TF-IDF analysis include “pilkada”, “2024”, and “damai” in positive sentiment, while “mahkamah konstitusi” and “kalah” dominated negative sentiment. The results imply that although public discourse largely supports the election process, critical sentiments toward election dispute issues persist. These findings offer practical implications for election authorities, policymakers, and digital campaign strategists, particularly in optimizing public communication strategies, early detection of potential conflicts, and designing public opinion monitoring systems based on real-time sentiment analysis. By leveraging high-quality labeled local data, this study makes a significant contribution to modeling public opinion dynamics in Indonesia during political events.
Geographic Information System for Mapping Accommodation Locations in Lhokseumawe City Using the AHP Method and Dijkstra's Algorithm Wahdana, Aldi; Nurdin; Sujacka Retno
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9597

Abstract

This study aims to develop a web-based Geographic Information System (GIS) to provide recommendations for the best accommodation and the fastest route to the accommodation location in Lhokseumawe City. The Analytical Hierarchy Process (AHP) method is used to determine the priority of accommodation based on five main criteria, namely price, public facilities, cleanliness, security, and year founded. The Dijkstra algorithm is applied to calculate the shortest path from the user's position to the selected accommodation. This study involved 21 accommodations as study objects. The results of the analysis showed that Hotel Diana obtained the highest value of 0.08873, so it was recommended as the main accommodation. The shortest distance from the Faculty of Engineering, Malikussaleh University to Hotel Diana is 11.53857 km. These results indicate that the combination of the AHP method and the Dijkstra algorithm is effective in supporting location-based decision making, as well as making it easier for users to determine appropriate accommodation and the fastest route efficiently.
LSTM-Based Hand Gesture Recognition for Indonesian Sign Language System (SIBI) on Affix, Alphabet, Number, and Word Ho, Patricia; Santoso, Handri
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9607

Abstract

Sign language plays a critical role in enabling communication for the Deaf and hard-of-hearing community in Indonesia, yet there remains a significant gap in technological support for recognizing the official Indonesian sign language, Sistem Isyarat Bahasa Indonesia (SIBI). This study presents a deep learning-based hand gesture recognition system for SIBI, focusing on four primary gesture categories: affix, alphabet, number, and word. A large and diverse dataset of 21,351 videos was collected, covering 18 affix, 26 alphabet, 35 number, and 29 word classes. Hand keypoints were extracted using MediaPipe Holistic, and a bidirectional long short-term memory (BiLSTM) model was trained using 5-fold stratified cross-validation. The model achieved high recognition performance in the alphabet, number, and word categories, with mean test accuracies of 93.94%, 91.48%, and 92.41%, respectively, and slightly lower performance in the affix category at 68.17%. The affix category posed particular challenges due to subtle hand shape differences and high variability between signers, while the alphabet category consistently showed the highest accuracy due to its distinct and static handshapes. Evaluation metrics, including precision, recall, F1-score, and confusion matrix analysis, provided further insights into model strengths and limitations. Overall, the study demonstrates the effectiveness of LSTM models for sequential hand gesture recognition in SIBI and highlights areas for future improvement, such as handling non-manual features and improving generalization across signers.
Comparison of Random Forest and Support Vector Machine Methods in Sentiment Analysis of Student Satisfaction Questionnaire Comments at ITB STIKOM Bali Sidik, Purnama; I Made Gede Sunarya; I Gede Aris Gunadi
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9617

Abstract

ITB STIKOM Bali is one of the higher education institutions in Bali that focuses on academic activities, particularly in the field of Information Technology. To maintain its educational quality, the Quality Assurance Department collaborates with the Center for Information and Communication (Puskom) to distribute a student satisfaction questionnaire at the end of each semester. In evaluating student satisfaction with campus facilities, the comment section is one of the key indicators, featuring the question: “Based on your experience, please describe which AAK services you found disappointing and in need of improvement.” This study compares the performance of the Random Forest and Support Vector Machine (SVM) methods in conducting sentiment analysis on historical student satisfaction comments. The research involved several stages, including literature review, data collection, preprocessing, transformation, data mining, evaluation, and visualization. The results demonstrate strong accuracy, precision, recall, and F1-scores for both methods using an 80:20 data split. Before applying the SMOTE technique, the best result was achieved by the Support Vector Machine method with a score of 0.90, while the Random Forest method yielded an accuracy of 0.81, precision of 0.85, recall of 0.81, and F1-score of 0.76. After applying SMOTE, both methods achieved an improved and equal score of 0.90. The study also produced an excellent classification result based on the ROC curve. It is expected that this research can serve as an additional reference for the assessment of student satisfaction at ITB STIKOM Bali at the end of each academic semester.
Application of Convolutional Neural Network (CNN) Algorithm with ResNet-101 Architecture for Monkey Pox Detection in Human Al Fathir Rizal Januar; Indra, Jamaludin; Kusumaningrum, Dwi Sulistya; Faisal, Sutan
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9621

Abstract

Monkeypox is a zoonotic disease that has spread to various countries, including Indonesia. It is transmitted through direct contact with skin lesions, respiratory droplets, or contaminated objects. Early and accurate detection is crucial to reduce the risk of transmission and improve treatment effectiveness. This study aims to detect monkeypox using a Convolutional Neural Network (CNN) with the ResNet-101 architecture. The pre-processing steps include normalization and resizing of images to 224×224 pixels. The model is trained using the Adam optimizer, categorical crossentropy loss function, and an adaptive learning rate reduction. Evaluation results show that the model achieved an accuracy of 94%, with a precision of 0.92, recall of 0.92, and an F1-score of 0.92. The model is capable of classifying images effectively, although some misclassifications still occur. This system is intended to function as an initial image-based screening tool, but its results should be confirmed through clinical diagnosis and laboratory testing to ensure accuracy.
Fine-Tuned Transformer Models for Keyword Extraction in Skincare Recommendation Systems Ni Putu Adnya Puspita Dewi; Putri, Desy Purnami Singgih; Trisna, I Nyoman Prayana
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9687

Abstract

The skincare industry in Indonesia is experiencing rapid growth, with projected revenues reaching nearly 40 billion rupiah by 2024 and expected to continue to increase. The large number of products in circulation makes it difficult for consumers to find products that suit their needs. In this context, a text-based recommendation system that utilizes advances in Natural Language Processing (NLP) technology is a promising solution. This research aims to develop a skincare product recommendation system based on user needs by applying the DistilBERT model, which is specifically fine-tuned with text in the skincare recommendation domain to perform keyword extraction. The resulting keywords are then used as parameters to provide recommendations by using co-occurrence as well as using a modification of Jaccard Similarity to assess the suitability between the content and benefits of the product and user preferences. The trained extraction model achieved the best performance with a micro F1-score of 0.96 at the token level and an exact match rate of 74.25% at the entity level. The evaluation of the recommendation system showed excellent results, with an nDCG value of 0.96 and a user satisfaction rate (CSAT) of 91.9%.
Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns Rafi Dio; Aulia Agung Dermawan; Dwila Sempi Yusiani; Rifaldi Herikson; Andikha, Andikha; Dwi Ely Kurniawan; Adyk Marga Raharja
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9698

Abstract

Veterinary clinics routinely generate transactional data that contain valuable information about both operational workflows and customer preferences. This study aims to differentiate between procedural and customer-driven service patterns by applying the FP-Growth association rule mining algorithm to 1,000 anonymized transactions comprising 94 unique items, collected from a veterinary clinic in West Java, Indonesia, during 2023. Two distinct analytical scenarios were constructed: Scenario 1 includes all services (procedural and customer-driven), while Scenario 2 excludes procedural items such as “Vet” and “Visit Dokter” to focus solely on client-initiated behaviors. Data preprocessing involved aggregating transaction items into a market basket format suitable for frequent pattern mining. The FP-Growth algorithm was employed to extract association rules, evaluated using support, confidence, and lift metrics. Results from Scenario 1 revealed rule patterns reflective of standard clinical protocols and operational dependencies, informing bundled service packages and inventory management. In contrast, Scenario 2 uncovered customer-driven associations, highlighting opportunities for personalized promotions and service innovation. The comparative analysis demonstrates the utility of scenario-based association rule mining for both operational optimization and customer engagement. While the findings provide actionable insights for clinic management, further validation with practitioners and implementation in multi-clinic settings are recommended to confirm real-world applicability and enhance generalizability.
Automatic License Plate Recognition (ALPRON) Using Optical Character Recognition Method Prasetyawan, Purwono; Aulia, Muhammad Athallah Cahya; Utami, Nia Saputri; Ramadhani, Uri Arta
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9903

Abstract

Manual parking systems are prone to inefficiencies and human error, especially with increasing vehicle density. This study proposes ALPRON, an automatic license plate recognition system using Optical Character Recognition (OCR) to automate motorcycle parking management. The system integrates Raspberry Pi 4, USB cameras, and Tesseract OCR to detect and recognize license plates in real-time. Performance testing was conducted under varying distances, lighting intensities, and camera angles. The results show that the system achieves a peak recognition accuracy of 98.75% at 70 cm, in bright lighting, and a 0° camera angle. These findings suggest that ALPRON is a potentially cost-effective and efficient solution for smart parking applications, particularly in controlled campus environments. While current limitations include daylight dependency and difficulty recognizing skewed angles plates, future improvements will address these through infrared support and deep learning enhancements.

Page 39 of 70 | Total Record : 695

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