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INDONESIA
Journal of System and Computer Engineering
Published by Universitas Pancasakti Makassar
ISSN : -     EISSN : 27231240     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Programming Languages Algorithms and Theory Computer Architecture and Systems Artificial Intelligence Computer Vision Machine Learning Systems Analysis Data Communications Cloud Computing Object Oriented Systems Analysis and Design Computer and Network Security Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 105 Documents
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Prototipe Sistem Manajemen Tangki Pintar Berbasis Internet of Things (IoT) hidayat, Ircham; Marlina, Marlina; Nurani, Nurani
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1239

Abstract

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 kusumo, bayu
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1278

Abstract

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 Sardjono, Mochamad Wisuda; Ramadhan, Valdy; Cahyanti, Margi; Swedia, Ericks Rachmat
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1292

Abstract

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 Mardewi, Mardewi; Wungo, Supriyadi La
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1302

Abstract

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 inda, nur
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1334

Abstract

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 jeffry, Jeffry; aziz, firman; usman, syahrul
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1339

Abstract

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 Usman, Syahrul; Syam, Rahmat Fuadi
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1418

Abstract

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%
Implementation of an Internet of Things (IoT)-Based Air Quality Monitoring System for Enhancing Indoor Environments Enal Wahyudi, Abdi; Kurniyan Sari, Sri; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1466

Abstract

This research investigates the development and implementation of an IoT-based air quality monitoring system designed to improve indoor environmental conditions. The primary objective of this study is to develop a comprehensive system that continuously monitors air quality parameters, including smoke, LPG gas, carbon monoxide (CO), temperature, and humidity. The system integrates real-time data collection from various sensors, which is then processed and transmitted to a cloud platform for secure storage and detailed analysis. The user-friendly interface of the software allows for intuitive monitoring and reporting, while built-in notification and alert features ensure timely responses to significant air quality changes. Testing results demonstrate that the system operates with high reliability, providing accurate data and stable performance. The findings confirm that the system effectively addresses indoor air quality concerns and offers valuable insights for maintaining a healthy and safe environment. This research contributes to the field by showcasing a practical application of IoT technology in environmental monitoring.
Recognition of Human Activities via SSAE Algorithm: Implementing Stacked Sparse Autoencoder Batau, Radus; Kurniyan Sari, Sri; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1470

Abstract

This study evaluates the performance of Stacked Sparse Autoencoder (SSAE) combined with Support Vector Machine (SVM) against a standard SVM for classification tasks. We assessed both models using accuracy, precision, sensitivity, and F1 score. The SSAE Support Vector Machine significantly outperformed the standard SVM, achieving an accuracy of 89% compared to 37%. SSAE also achieved higher precision (87% vs. 75%) and sensitivity (89% vs. 37%), with an F1 score of 88% versus 36% for the standard SVM. These results indicate that SSAE enhances the model’s ability to capture complex patterns and provide reliable predictions. This study highlights the effectiveness of SSAE in improving classification performance, suggesting further research with larger datasets and additional optimization techniques to maximize model efficiency
Application of Advanced Encryption Standard (AES) Algorithm in E-Commerce Login System for User Data Security Ifani, Aulyah Zakilah; S.Intam, Rezki Nurul Jariah; Syair, Andi Irfandi; Husnawati, Husnawati
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1511

Abstract

E-commerce becomes an electronic media that uses a login system used by users. User data in the form of usernames and passwords is vulnerable to hacking. One technique to improve user security is the implementation of AES algorithms on login systems in E-Commerce applications. The purpose of this study is to apply the AES algorithm in the login system of e-commerce websites and analyze the improvement of information security for users after the implementation is carried out. The research method used is an experiment with the application of the use of the AES algorithm before and after. Therefore, the application of the AES algorithm on the login system of e-commerce websites can be used as a solution to improve user data security. Testing using Wireshark and Burpsuite tools. The results obtained are that AES successfully secures the username and password on the e-commerce login system .

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