Tech-E

Design and Analysis of a Knowledge Management System for Sawit Seberang Health Center Using the Inukshuk Methodology

2024-08-01T04:55:28+00:00

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.

Implementing and Monitoring Water Consumption Using IoT-Based Smart Dispensers

2024-07-24T06:21:33+00:00

Conventional dispensers have limitations in providing drinking water tailored to user preferences and do not focus on efficient resource use. This research aims to address these issues by designing and implementing a smart, efficient automatic dispenser. An experimental method was used to develop an Arduino-based prototype consisting of several components: flow sensor, color sensor, fingerprint sensor, proximity sensor, DC pump, motor driver, NodeMCU, and LCD. The flow sensor measures water volume, the color sensor detects glass color, the fingerprint sensor identifies the user, and the proximity sensor detects the presence of the glass. The DC pump flows water from the tank to the glass, relays and solenoids control the water flow, NodeMCU processes sensor data and connects to IoT, and the LCD displays the required information. A battery backup ensures functionality during power outages. The research results show that the automatic dispenser performs well and meets the research objectives. It provides drinking water according to user preferences: warm water for red glasses, cold water for blue glasses, and room temperature water for green glasses. Additionally, it identifies users through fingerprints and sends notifications via Telegram chatbots. This smart dispenser offers a more efficient and user-friendly solution compared to conventional dispensers.

Implementasi dan Perbandingan Kinerja Algoritma Fuzzy Tsukamoto dan Mamdani pada Sistem Exhaust Fan Berbasis IoT

2024-07-24T06:18:47+00:00

Dalam produksi kerupuk, proses penggorengan sering menghasilkan asap dan panas berlebih yang dapat berdampak buruk pada kesehatan pekerja. Asap dapur mengandung senyawa berbahaya seperti sulfur oksida, nitrogen dioksida, dan karbon monoksida. Diperlukan kipas pembuangan untuk mengeluarkan asap dan menstabilkan suhu, namun kontrol manual kurang efektif. Sistem kontrol otomatis, termasuk mikrokontroler, set points, PID, dan logika fuzzy, telah dikembangkan. Kontrol berbasis fuzzy dianggap paling baik untuk beradaptasi dengan kondisi lingkungan. Penelitian ini mengevaluasi perbedaan antara metode fuzzy Mamdani dan fuzzy Tsukamoto dalam mengontrol kipas pembuangan. Pengujian dilakukan dengan 100 titik data selama 5 kali percobaan untuk masing-masing metode. Hasil penelitian menunjukkan bahwa metode logika fuzzy Tsukamoto mencapai akurasi lebih baik yaitu 99,35%, dibandingkan dengan logika fuzzy Mamdani yang hanya mencapai 95,45%. Oleh karena itu, sistem kontrol kipas pembuangan lebih efektif menggunakan metode logika fuzzy Tsukamoto. Metode fuzzy Tsukamoto memberikan respon yang lebih cepat dan tepat dalam menyesuaikan kecepatan kipas terhadap perubahan kondisi asap dan suhu di dapur. Hal ini dikarenakan metode Tsukamoto mampu menangani perubahan input yang lebih kompleks dan menghasilkan output yang lebih halus. Di sisi lain, metode fuzzy Mamdani memiliki kelebihan dalam hal kesederhanaan dan kemudahan implementasi, namun kurang responsif terhadap perubahan kondisi yang cepat.

Development of Machine Lerning-Based Website for Diabetes Patient Health Classification

2024-07-26T08:49:24+00:00

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

2024-08-06T08:11:40+00:00

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.

Implementation of Sugeno Fuzzy Logic Methods for Predicting Pie Crust Raw Material Stock

2024-08-01T04:52:40+00:00

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.