Journal of System and Computer Engineering

Perancangan Aplikasi Kepuasan Mahasiswa Terhadap Dosen Fakultas Teknik Universitas Islam Makassar Menggunakan Metode Simple Additive Weighting

2024-07-26T01:05:07+00:00

Faculty of Engineering UIM is one of the faculties at private universities in Makassar City. Currently, measuring student satisfaction with lecturers by filling out questionnaires through google forms. Disadvantages in filling out google forms because you can use gmail freely so that data manipulation can occur. So that a web application is created that facilitates student access and participation in providing assessments of lecturer performance. The purpose of the study was to design a web-based student satisfaction application for lecturers to increase student participation and produce more representative assessments. Waterfall research method as an application development method from the needs analysis, design, implementation, testing and maintenance phases while the Simple Additive Weighting method functions in the decision-making process. The results of application research where students log in to enter the dashboard page that displays a list of lecturers. On the lecturer list page there is a list of questions in assessing lecturer performance while on the admin page there is a menu that displays the results of the SAW calculation with a scale of 20 - 100 information is not good, not good, good enough, good and very good. Conclusion The application is able to provide solutions in mapping, understanding and assessing student satisfaction performance with lecturers in improving the quality of learning with a user-friendly interface.

Keywords : Satisfaction, Simple Additive Weighting, Website

Prototipe Sistem Manajemen Tangki Pintar Berbasis Internet of Things (IoT)

2024-07-26T01:06:25+00:00

The purpose of the study was to design a prototype of a smart tank management system that aims to help gas station managers measure water content and monitor fuel content in gas station tanks automatically and realtime so that fuel quality is maintained, information can be monitored through web-based systems and mobile applications with Internet of Things technology. The IoT system design consists of an Arduino Uno R4 microcontroller, an ultrasonic sensor HC-SR04 to measure the contents of the tank level, a conductivity sensor to measure the moisture content contained in the fuel while a temperature sensor to measure the temperature inside the tank and a selenoid valve to remove the water in the tank. The accuracy test results of the ultrasonic sensor used are good enough to measure the contents of the tank with an average error of 1.3%, while the conductivity sensor measurement has an average error of 0.292% with the validation process using the centrifuge method, the temperature sensor has an accuracy of 1.6%. The selenoid valve works well which is activated through a web-based monitoring app and a mobile app.

Simulasi Rancang Bangun Alat Pemberi Pakan Ayam Dan Monitoring Suhu Kandang

2024-07-26T01:07:46+00:00

In the livestock sector in Indonesia, the manual method is used, namely the breeder must put the feed into the feed container. Breeders also have to check the temperature in the cage and this certainly requires a lot of effort and time for livestock management. The application of IoT in chicken farms can be implemented to help farmers monitor and provide automatic feed. Of course this will help livestock farming activities using automatic feeders and temperature controllers that can be monitored anywhere with just a smartphone. The automatic system consists of an automatic system that provides ideal air and a chicken feed system. Determination of tool specifications and manufacture that aims to find the form. Testing the sending of data on the Telegram application has been carried out 10 times. Sending data has a success percentage of 100% and an average delay of 17.40 seconds. Testing of sending data for the feeding time test on an automatic feed system was carried out 2 times. This test aims to open the valve in the dining area during breakfast and evening meals. The automatic feed system feature can be adjusted according to the needs of animal feed, with the intention that the amount of feed per day can be adjusted easily. Sending mealtime test data has a success percentage of 100%..

Klasifikasi Bentuk Bingkai (Frame) Kacamata Menggunakan CNN dengan Arsitektur Inception V3 dan Augmented Reality Berbasis Android

2024-07-26T01:08:58+00:00

Glasses are not only a type of vision aid for people with eye diseases, but they are also an increasingly popular part of the fashion world. The choice of eyeglass frame design can influence a person's appearance in clothing, so when making a choice you must pay attention to two important aspects, namely style and comfort, and can change the impression on a person's face. When designing eyeglass frames, it is necessary to use the science of measuring the human body, because each human organ's size and shape are different from each other. So, with the diversity of human facial shapes, it becomes very important in making the choice of eyeglass frames and the challenge in conducting research to build an application to recommend eyeglass frames according to face shape. With the current technological era, it is possible to apply Artificial Intelligence (AI) and Machine Learning (ML) to be the best solution to answer these challenges. Several studies have tried to classify facial shape using ML, with the best results using the Inception V3 architecture. In this research, a Unity 3D-based application was developed that combines Augmented Reality (AR) with ML to recommend eyeglass frame shapes based on face shape. Inception V3 model training results show performance improvements over time. However, it is necessary to overcome overfitting in validation data. In testing the test data, the model achieved an accuracy of around 78.6%, indicating good prediction ability. This technology has the potential to help consumers make more informed decisions when selecting glasses

Klasifikasi Liver Cirrhosis Menggunakan Teknik Ensemble: Studi Perbandingan Model Boosted Tree, Bagged Tree, dan Rusboosted Tree

2024-07-26T01:10:10+00:00

Liver cirrhosis, as a significant chronic liver disease, exhibits a rising global prevalence, demanding more effective preventive approaches. In an effort to enhance early detection and patient management, this research proposes the development of a liver cirrhosis risk prediction model using machine learning technology, specifically comparing the performance of three ensemble tree models: Ensemble Boosted Tree, Ensemble Bagged Tree, and Ensemble RUSBoosted Tree. Utilizing clinical and laboratory data from adults with a history or risk of cirrhosis, the study reveals that Ensemble Bagged Tree achieved the highest accuracy at 71%, followed by Ensemble Boosted Tree (67.2%) and Ensemble RUSBoosted Tree (66%). Analysis of clinical and laboratory variables provides further insights into the most significant contributors to risk prediction. The findings lay the groundwork for the advancement of a more sophisticated liver cirrhosis risk prediction tool, supporting a vision of more personalized and effective preventive strategies in liver disease management.

Analisis Perbandingan Kinerja Model Yolov7 dalam Deteksi Kuku Diabetes

2024-07-26T01:11:14+00:00

Abstract

Diabetes mellitus (DM) is a degenerative and non-communicable disease that can be seen from the color of the fingernails. In analyzing color the human eye has limitations in color recognition and texture analysis while computers are able to classify millions of colors and slight texture changes to recognize changes in individual nail color to prevent early symptoms of diabetes using the YOLOv7 method to represent a one-stage model for detecting objects using a Convolutional Neural Network ( CNN).

This research was carried out at the Polewali Community Health Center. Sampling was carried out by taking medical records and conducting interviews with the relevant doctors. Sample data was taken from several diabetes mellitus patients and several workers at the Polewali Community Health Center for healthy nail sample data.

The results of testing the YOLOv7 model with epoch 100 showed accuracy of 81%, precision of 82.4%, recall of 95.5% and F1-Score of 88.5%. Testing the YOLOv7 model with epoch 200 resulted in an accuracy of 90%, precision of 93.3%, recall of 93.3% and F1-Score of 93.3%. Testing the YOLOv7-x model with epoch 100 resulted in an accuracy of 71.4%, precision 72.3%, recall 82.9% and F1-Score 77.2%. Testing the YOLOv7-x model with epoch 200 resulted in an accuracy of 63.3%, precision 60.4%, recall 90.6% and F1-Score 72.5%. Testing the YOLOv7-tiny model with epoch 100 resulted in an accuracy of 91.4%, precision 95.6%, recall 93.5% and F1-Score 94.5%. Testing the YOLOv7-tiny model with epoch 200 resulted in an accuracy of 94.6%, precision 93%, recall 100% and F1-Score 96.4%. The results of comparative testing of the YOLOv7 model in detecting diabetic nails, concluded that the ideal model that can be used is the YOLOv7-tiny model with an epoch value of 200.

Keywords: Confusion Matrix, CNN, Diabetes Mellitus, Nails, YOLOv7

Sistem Pendukung Keputusan Penentuan Destinasi Objek Wisata Dengan Metode Simple Additive Weighting (SAW) Berbasis Web

2024-07-26T01:16:23+00:00

One of the biggest regional proceeds of the North Toraja Regency comes from the utilization of tourist objects as recreational objects whether for the local communities or the overseas. However, the lack of information and the lack of systems technology in Toraja destination caused many tourists to visited a few of the many tourism objects available. This problem causes tourists to tend to visit only a fraction of the many tourism objects. Based on these problems, we need a system that helps provide information and determine tourist objects suitable for each tourist, and the tour is more varied. This study produces a decision support system for selecting tourism objects in North Toraja using the “Simple Additive Weighting” method based on a website in the goal of assisting tourists to determine tourist place

Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor

2024-07-26T01:12:43+00:00

Spare parts are components that make up a single object that has a specific function. In car vehicles, spare parts have the function of maintaining the performance and function of the vehicle. Predictive Spare Part Maintenance is an effort to improve operational efficiency, customer service, and reduce vehicle downtime through the application of analysis and machine learning algorithms to predict spare part replacement times. A machine learning approach can be used to predict maintenance times for car spare parts, where one of the algorithms that can be used is XGBoost Regressor. Through this approach, this research aims to improve service planning by predicting spare part replacement times based on certain indicators, With the implementation of this research, it is hoped that it can increase operational efficiency in automotive after-sales services, increase customer satisfaction, reduce vehicle downtime, and improve overall service planning and most importantly can provide preventive maintenance information to customers. This research provides prediction results with R2-Score values as follows: train data: 93%, Valid: 90%, Test: 90%