Tech-E

Implementation of Tarpit Firewall for Network Security Optimization with The NIST SP800-86 Method in KP Room

2025-01-07T02:16:26+00:00

Network optimization is one of the important aspects that aims to improve the performance, efficiency and reliability of network systems and security on the network, but if the optimization is not carried out effectively it will pose a security threat to the network, one of the real threats is DdoS Attack, DdoS attack is a dangerous attack because this attack can paralyze the network server, Therefore, optimization needs to be carried out in the KP Room in order to avoid the threat of DdoS attacks, so the initial stage of this research will test the network to find out how optimal the network is in the KP room so that optimization is needed. The research method used is NSIT, which includes collection, examination, analysis, and reporting, the results after the research is carried out where, on the network in the kp room after testing at the examination stage and then by identifying the test results, it can be concluded that the network is not optimal enough against DdoS attacks and connection type attacks which, The optimization step taken is to apply a Tarpit firewall on the router. The implementation of Tarpit Firewall successfully overcomes DdoS attacks by slowing down incoming connections and stopping attacks, thereby improving network security from Port Scanning, DDoS, and Brute force attacks.

Prototype Automatic Water Spray for Coal Dust Cleaning on Coal Conveyor

2025-01-07T03:42:55+00:00

Coal dust generated during the transfer process via belt conveyors at PT Bukit Asam Tbk has significant negative impacts on the environment and the health of workers. The current manual method, involving direct water spraying, is ineffective in controlling airborne dust and increases safety risks due to water exposure and unstable working conditions. To overcome these challenges, this study developed a prototype of an Arduino-based automatic water spraying system as a safer and more efficient solution.The system employs a SHARP GP2Y1010AU0F dust sensor to monitor coal dust concentrations in real-time and an HC-SR04 ultrasonic sensor to regulate water spraying automatically, based on the detected levels. The prototype was tested under operational conditions and showed optimal performance, effectively reducing coal dust concentrations while improving health and safety standards in the workplace.This innovation offers a practical and sustainable approach to coal dust management, addressing the shortcomings of manual methods. By automating the process, it minimizes worker exposure to dust and eliminates hazards associated with direct water application. The system's efficient and safe operation highlights its potential for broader implementation in similar mining environments. This technology not only resolves critical issues in coal dust control but also introduces a forward-thinking solution that aligns with industry goals for improved occupational safety and environmental protection.

Enhancing Inventory and Transaction Management with Integrated E-Commerce Solutions: Case Study of Desasa Home Decor

2024-07-25T01:16:23+00:00

Esasa Home Decor is a store that specializes in selling various types of artificial flower home decorations. The use of information technology in data management is essential to ensure that inventory and transaction management are conducted swiftly and generate accurate reports. This system is integrated with the Shopee API to automatically retrieve product and transaction data. This integration allows for better monitoring of stock levels and transactions on the e-commerce platform, ensuring that the information remains up-to-date. The development method used in this study is Extreme Programming, which emphasizes close collaboration within the team and continuous testing to produce high-quality software. Data collection was conducted through interviews, analysis, and direct observation of the ongoing business processes at Esasa Home Decor. The result of this research is a management information system that facilitates store management and is integrated with the Shopee e-commerce platform. The User Acceptance Testing (UAT) yielded a score of 97.714%, indicating that the system is highly suitable for use. Additionally, the Black-Box testing concluded that the system functions as expected and according to plan. Thus, this system enhances the operational efficiency of Esasa Home Decor by streamlining inventory and transaction management while providing more accurate and timely reports.

Enhancing Sundanese News Articles Classification: A Comparative Study of Models and Feature Extraction Techniques

2025-01-07T02:52:58+00:00

This paper presents a comprehensive investigation into the classification of Sundanese news articles, focusing on the evaluation of various classification models and feature extraction methods. Using a dataset obtained from Sundanese news websites, this study conducts a systematic comparison of Naive Bayes and Logistic Regression classifiers combined with TF-IDF and Bag-of-Words feature extraction methods. The research process involves critical steps such as data preprocessing, model training, hyperparameter optimization, and performance assessment based on standard metrics, including accuracy, precision, recall, and F1-score. Results demonstrate high accuracy across all combinations, with the Logistic Regression model using Bag-of-Words feature extraction achieving the highest accuracy of 98.20%. Beyond model evaluation, the research delves into qualitative data analysis. Word clouds and TF-IDF weighting are employed to uncover prominent themes and topics within the news articles, highlighting recurring patterns in the Sundanese language. The study identifies key challenges, including the scarcity of annotated datasets for low-resource languages like Sundanese and the limitations of traditional models in capturing complex linguistic structures. Future opportunities are highlighted, such as leveraging deep learning models, including transformers, to enhance classification performance and address current limitations. Additionally, ensemble methods and domain-specific adaptations could further improve accuracy. Overall, this research contributes to advancing Sundanese language processing and provides a roadmap for future innovations in text classification and natural language processing applications.

Design and Implementation of an RFID-Based Automatic Doorstop System with Website and Telegram Bot Integration

2025-01-10T05:10:20+00:00

This research develops a prototype of an automatic doorstop control system based on Radio Frequency Identification (RFID) and the Internet of Things (IoT) integrated with a website-based information system and Telegram bot. This system is specifically designed to improve efficiency and security in access management at Malikussaleh University, by overcoming the vulnerabilities and limitations of traditional manual access control systems that are prone to security risks. The system uses RFID sensors to read user identity cards as access verification, while infrared (IR) sensors detect objects near the door to ensure security during automatic door operation. The system has an easy-to-use web interface for efficient management of data and activity records. In addition, real-time notifications are sent via Telegram bot to provide administrators with detailed information on access attempts. Tests show that the RFID sensor is capable of accurately reading ID cards at distances of up to 2 cm, while the IR sensor detects objects near the door quickly and precisely. The servo motors used had an average response time of 2 seconds to open and close the door. With a 98% accuracy rate on the RFID sensor, this system provides a reliable solution for automatic access control needs. With the advantages of high accuracy, fast response, and ease of integration, this prototype is expected to be implemented in various educational institutions and other public facilities.

Tongue Detection For Identification Of Syndrome Diagnosis In Heart Disease Using Convolutional Neural Network

2025-02-07T10:43:39+00:00

Convolutional Neural Network (CNN) which is one of the Deep Learning methods for Image identification and CNN models can identify images well but in this case it requires higher accuracy because the case is very crucial to determine the risk of heart disease. The initial stage in this study was the collection of tongue image data, 4836 training data and 1209 testing data. The image data used were the front, right side, left side of the tongue and under the tongue. The dataset was obtained from taking pictures using a smartphone camera centimeters above the object. The distribution of data in each class is shown in the following figure. The model from the two CNN algorithm experiments has accuracy performance. Based on the training results the model from the algorithm gets an accuracy value and Testing by identifying 20% of the total dataset as test data. The identification results are formed in a Confusion Matrix to then be poured into a classification report and obtain: train loss 0.301446, train accuracy 0.862696, test loss 0.314132 and test accuracy 0.850290 so that from the results of the tongue data test it can be concluded that the accuracy value is quite good, above 80%.