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Tech-E
Published by Universitas Buddhi Dharma
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Tech-E dikembangkan dengan tujuan menampung karya ilmiah Dosen dan Mahasiswa, baik hasil tulisan ilmiah maupun penelitian yang berupa hasil studi kepustakaan.
Arjuna Subject : -
Articles 116 Documents
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Design and Analysis of a Knowledge Management System for Sawit Seberang Health Center Using the Inukshuk Methodology Nurainun Syahdia; Ilka Zufria
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3181

Abstract

The Sawit Seberang Health Center as the technical implementation unit of the health service is responsible for carrying out health development in the Sawit Seberang area, both in terms of health services and providing health knowledge that is useful for medical personnel in particular and the community in general. The problem faced by the Community Health Center is the unavailability of a computer-based system that can be accessed online as a medium for storing and sharing knowledge and information about health, both knowledge for fellow medical personnel and knowledge for the community. This problem can be solved with the KMS application which can be accessed online. The method used is the Inukshuk KM Model Method. The Inukshuk Knowledge Management model is a framework that has been refined from the SECI model (socialization, externalization, combination and internalization) with the addition of components such as leadership, culture and technology. The relationship with Knowledge Management is that it can provide information about Tacit and Explicit Knowledge in the organization. The result of this research is the KMS Puskesmas application which can be accessed online as a medium for storing and sharing knowledge for fellow medical personnel as well as a medium for information and knowledge for the community.
Pengembangan Website Berbasis Machine Lerning untuk Klasifikasi Kesehatan Pasien Diabetes Safitri, Rahmi Dian; Handayani, Ade Silvia; Sopian Soim
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3184

Abstract

This research aims to develop a website utilizing the Support Vector Machine (SVM) algorithm for diabetes detection. The primary objective is to assist medical personnel in diagnosing diabetes efficiently by collecting and analyzing patient data to provide accurate health classifications. The SVM algorithm was chosen due to its high accuracy in managing complex and multidimensional medical data, making it ideal for diabetes detection. The website integrates SVM to process patient information and deliver precise predictions about their health status. By enhancing the diabetes diagnosis process, the system supports healthcare providers in making informed decisions and encourages patients to maintain regular check-ups. Additionally, the website features notifications for follow-up examinations, ensuring timely medical interventions and improving patient care and diabetes management. Its user-friendly interface allows medical staff to input and retrieve patient information with ease. This integration of advanced algorithms and intuitive design creates a valuable tool for both medical professionals and patients. By streamlining data collection and analysis, the website contributes to more accurate and timely diagnoses, fostering better health outcomes. This research highlights the potential of combining machine learning with healthcare to develop innovative solutions for chronic disease management, emphasizing the importance of regular monitoring and early detection in preventative healthcare.
Designing Air Quality Detection Systems with Over-the-Air Firmware Update Methods for Performance Enhancement Nanda Syaputra; Ade, Ade Silvia Handayani; Taqwa, Ahmad Taqwa
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3192

Abstract

Implementing the Over-The-Air (OTA) system, which facilitates wireless and remote updates of software or firmware through internet connectivity, offers a significant advantage by saving both time and effort. This approach allows for firmware updates to be performed directly from any location, eliminating the need to physically visit each device. This is especially advantageous in the manufacturing of air quality monitoring devices, where adjustments to programs and software are often needed, particularly with seasonal changes. Updating firmware manually on numerous devices can be a time-consuming and labor-intensive process. To address this issue, the proposed device will be designed to support air quality readings and will utilize an internet connection to enable virtual firmware updates. The device will periodically check its program storage for new firmware versions. When a new version is detected, the device will automatically download and install the latest firmware available. This process reduces the need for manual intervention and improves operational efficiency. Additionally, deploying multiple devices across a large area is crucial for ensuring comprehensive coverage. This approach not only simplifies maintenance but also enhances the operational management of air quality monitoring systems. By leveraging OTA technology, the process of updating devices becomes more streamlined, scalable, and efficient, contributing to more effective environmental monitoring and management.
Penerapan Metode Logika Fuzzy Sugeno Pada Prediksi Stok Bahan Baku Kulit Pie Yudha, Fajrul Aulia; Raissa Amanda Putri
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3193

Abstract

Accurate prediction of raw material stocks is essential for cost management and effective production planning in the food industry. The Sugeno fuzzy logic method is employed to predict the stock levels of pie leather raw materials. This method aims to offer a reliable prediction system that enhances stock management, thereby minimizing the risks associated with overstocking or stock shortages. The performance of the model is evaluated using the average error percentage test, which yielded a result of 3.94%. This indicates an accuracy level of 96.06%, demonstrating a high degree of precision. The findings suggest that the Sugeno fuzzy logic method is a highly effective tool for predicting raw material requirements in the pie leather production process. The study underscores the potential of fuzzy logic methods in supply management, ensuring smooth production operations. By implementing this method, manufacturers can achieve better inventory control, leading to more efficient production planning and cost savings. The results validate the application of Sugeno fuzzy logic as a robust approach for inventory prediction, capable of significantly improving the overall management of raw material stocks in the food industry. This research highlights the practical benefits of advanced predictive models in optimizing supply chains, supporting continuous production flow, and enhancing the overall efficiency of production systems. Consequently, the use of fuzzy logic methods can play a critical role in streamlining production processes and maintaining optimal inventory levels, ultimately contributing to the success and sustainability of food manufacturing operations.
Enhancing Inventory and Transaction Management with Integrated E-Commerce Solutions: A Case Study of Desasa Home Decor Utami, Yohana Tri; Faradila, Dita; Ramadhanti, Karina Adityas; Muhaqiqin; Taufik, Rahman
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3168

Abstract

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 A. Permana, Yadhi; Setiawan, Irwan; Diani, Fitri; Suprihanto
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3212

Abstract

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.
Implementasi Tarpit Firewall untuk Optimasi Keamanan Jaringan dengan Metode NIST SP800-86 Pada Ruangan KP Rahmat Novrianda Dasmen; ansyah, Ardi; Timur Dali Purwanto; Marlindawati
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3277

Abstract

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.
Tongue Detection For Identification Of Syndrome Diagnosis In Heart Disease Using Convolutional Neural Network Niko Suwaryo; Koniasari; Amat Basri
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3285

Abstract

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%.
PROTOTYPE WATER SPRAY OTOMATIS UNTUK PEMBERSIHAN DEBU BATUBARA PADA COAL CONVEYOR Arik Putra Pratama; Nina Paramitha; Rasmila; Tamsir Ariyadi
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3410

Abstract

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.
Design and Implementation of an RFID-Based Automatic Doorstop System with Website and Telegram Bot Integration Zainul Anwar Adi Putra; Rizal Tjut Adek; Hafizh Al Kautsar Aidilof
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3447

Abstract

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.

Page 11 of 12 | Total Record : 116

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