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International Journal of Applied Mathematics and Computing.
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30481988     EISSN : 3047146X     DOI : 10.62951
Core Subject : Science, Education,
Computer Science & IT Mathematics
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. This journal is a peer-reviewed and open access journal of Mathematics and Computing
Arjuna Subject : Matematika - Matematika Komputer
Articles 54 Documents
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Stabilization of Distance Measurement Between Landmarks for Gesture Recognition Using Polynomial Regression
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.120

Abstract

Gesture recognition technology enables computers and digital devices to detect, understand, and interpret human body movements through image processing techniques. This technology has significant potential to facilitate communication between individuals with hearing impairments and those without, thereby improving interaction and mutual understanding. However, the accuracy of gesture recognition systems is often influenced by variations in the distances between hand landmark points, which can introduce instability and reduce recognition performance. To address this issue, this study proposes a polynomial regression-based approach to stabilize distance measurements between hand landmarks in gesture recognition tasks. The proposed method calculates and normalizes landmark distances using polynomial regression to minimize measurement fluctuations and improve recognition accuracy. The system is implemented using the MediaPipe framework for real-time hand detection and tracking, while OpenCV is utilized for video processing and management. Experimental results demonstrate that the proposed approach significantly enhances the stability and accuracy of gesture detection. The developed system successfully recognizes hand gestures representing the letters A through F with an average accuracy exceeding 98.3%. Furthermore, the application of polynomial regression effectively reduces noise in landmark data, contributing to more reliable and accurate gesture recognition performance.
Traffic Condition Classification Using IoT on Raden Inten II Road
International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i4.121

Abstract

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.
Implementation of an RFID Card Based Automatic Door Lock System Using NodeMCU with Integration of Telegram Notifications and IoT Services
International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i4.126

Abstract

In the digital era, the demand for practical and efficient security systems has significantly increased, particularly in the context of access control for restricted rooms or buildings. This research aims to develop an automatic door locking system utilizing an RFID card and a NodeMCU ESP32 microcontroller integrated with Internet of Things (IoT) technology through real-time notifications using the Telegram application. The system is designed to replace conventional locking methods that often present various weaknesses, such as key loss, physical duplication, and lack of remote access capabilities. The development method employed is the Research and Development (R&D) approach, consisting of needs analysis, system design, hardware and software implementation, followed by testing and evaluation. The main components used in the system include the RC522 RFID reader for user identification, Espressif manufactures the ESP-32 microcontroller, which is equipped with Wi-Fi and Bluetooth modules to enable wireless internet connections. NodeMCU ESP32 as the control center and internet connector, a relay module as an electronic switch, and a solenoid door lock as the actuator. The results show that the system is capable of accurately reading RFID card UIDs, granting access to registered cards, activating the solenoid to unlock the door, and sending access status notifications to Telegram in an average of less than three seconds. The system also effectively denies access to unregistered cards and sends warning messages accordingly. Therefore, this system enhances the security and efficiency of room access control and has the potential to be adopted as a prototype solution in the development of smart homes or modern access control systems.
Sentiment Analysis of the Trending Topic #Indonesiagelap on X Using a Naive Bayes Algorithm Based on Particle Swarm Optimization
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.127

Abstract

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.
Development of an IoT-Based Smart Health Monitoring System with Heart Attack Prediction Using the SVM (Support Vector Machine) Algorithm
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.128

Abstract

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.
Analisis Sentimen Publik terhadap Hashtag #kaburajadulu Menggunakan Kombinasi Algoritma Support Vector Machine (SVM) dan Random Forest
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.129

Abstract

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.
Implementation of Personal Data Security Using Advanced Encryption Standard (AES) Encrypted QR Codes on Digital Images Processed by the Discrete Cosine Transform (DCT) Method
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.230

Abstract

The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.
Design of a Financial Saving Challenge to Enhance Saving Interest Using the Reinforcement Learning Method
International Journal of Applied Mathematics and Computing Vol. 2 No. 1 (2025): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i1.247

Abstract

This study aims to develop a financial saving application to improve the saving habits of students, particularly in Islamic boarding schools, through an adaptive challenge approach. The system integrates a mobile iOS application with a backend service and Large Language Model (LLM) processing via Ollama. Transaction data entered by users is processed by the backend to generate contextual and personalized saving challenges, applying Reinforcement Learning concepts in an adaptive and data-driven manner. The research adopts a descriptive quantitative method using surveys and system testing with 50 respondents. Results indicate that the application functions as designed, with no significant bugs detected. User evaluation shows high satisfaction, with an average score of 4.3 out of 5, covering ease of use, interface design, and increased awareness of saving. The combination of gamification, reward systems, and adaptive personalization successfully motivates users to save regularly. This system demonstrates the potential of integrating AI-driven personalization to strengthen financial literacy and healthy financial habits among students in a fun and interactive way.methods, and a summary of the results. The abstract should end with a comment about the significance of the results or conclusions brief.
Optimizing Bandwidth Settings Using the Y.1731 Method Based on Ethernet OAM on Raisecom Devices in a Metro Ethernet Network
International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i4.283

Abstract

The rapid development of network infrastructure demands high Quality of Service (QoS), especially in Metro Ethernet networks widely utilized by telecommunication service providers. A primary challenge is efficient bandwidth management to ensure network stability and performance. This research aims to optimize bandwidth management by implementing the Y.1731 method based on Ethernet Operations, Administration, and Maintenance (OAM) on Raisecom devices. The methodology employed is a quantitative experimental approach based on technical simulation within an Professional Network Emulator Tool Lab (PNET Lab), where real-time network performance measurements are conducted using the ITU-T Y.1731 protocol for key parameters such as delay, jitter, and packet loss on Raisecom devices (represented by Cisco routers). The expected outcomes include increased efficiency in bandwidth utilization through more adaptive allocation, comprehensive and accurate real-time network performance monitoring capabilities, validation of OAM functions on Raisecom devices, improved Quality of Service (QoS) and better Service Level Agreement (SLA) attainment, and the provision of technical recommendations for network management. The implementation of Y.1731 is anticipated to quickly detect and respond to service degradation, thereby providing a strong basis for decision-making in network management and contributing to the enhancement of service quality in Metro Ethernet networks through optimization based on proactive monitoring.
Performance Evaluation of Edge Computing Architecture for Latency Reduction in Real-Time Distributed Monitoring Systems Achmad, Refi Riduan; Yoas; Boimin; Karim, Abdul; Hernandez, Leonel
International Journal of Applied Mathematics and Computing Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i3.292

Abstract

The rapid proliferation of Internet of Things (IoT) devices and real-time monitoring applications has intensified the demand for low-latency, reliable, and scalable data processing in distributed systems. Conventional cloud-centric architectures, although flexible and scalable, often suffer from high end-to-end latency, bandwidth congestion, and dependency on continuous network connectivity, making them less suitable for latency-sensitive monitoring applications. This study aims to evaluate the effectiveness of an edge computing–based architecture in reducing latency and improving overall system performance in real-time distributed monitoring systems. A multi-layer architecture consisting of edge, fog, and cloud layers is proposed, where data processing tasks are partially offloaded to edge nodes located closer to IoT sensors. The proposed system integrates load balancing using the least connection algorithm and data caching mechanisms to optimize request handling and minimize network overhead. The architecture is implemented and evaluated in a real-world monitoring scenario involving 100 IoT sensors distributed across multiple locations. Experimental results demonstrate that the proposed edge-based approach significantly outperforms a conventional cloud-only architecture. The average end-to-end latency is reduced by 73.4%, from 245 ms to 65 ms, while system throughput increases by 58.3%. In addition, packet loss is reduced from 3.2% to 0.4%, and bandwidth usage to the cloud is decreased by approximately 68% due to local processing and data aggregation at the edge layer. These findings indicate that integrating edge computing with load balancing and caching mechanisms can effectively enhance the performance, reliability, and scalability of real-time distributed monitoring systems. The study concludes that edge computing provides a practical and efficient solution for meeting strict latency requirements in modern IoT-based monitoring applications.

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