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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Anton Yudhana
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sistem Pemantau Suhu Cooler Box Berbasis Telemetri Dengan Thermoelectric Cooler Sebagai Bakteriostatik Pada Ikan Renangga Yudianto; Anton Yudhana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 6 No. 2 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i2.551

Abstract

Indonesia has enormous marine resource potential, which can be explored as a prime mover of national economic development, one of which is fisheries resources. The factor that determines the selling value of fish is the freshness of the fish. This study aims to utilize Thermoelectric Cooler (TEC) in cooler boxes as a bacteriostatic system for fish that is environmentally friendly and monitored using telemetry based DS18B20 ESP8266 temperature sensor. The temperature data includes the temperature of the cooler box, the temperature of the Thermoelectric Cooler (TEC), and the temperature of the room. The parameters used to test the quality of DS18B20 temperature sensor are accuracy, normality test, homogeneity test, and independent samples t-test. The data of DS18B20 temperature sensor system is processed at nodeM-CU then sent and displayed via the thingspeak website and LCD in real time. Organoleptically, the obser-vation results of fish A placed in a cooler box are categorized as fresh, and the observation results of fish B which are placed at room temperature are categorized as not fresh according to (SNI 2729:2013). The sensor test results obtained in this study showed value of the temperature accuracy of cooler box was 97.8%, the temperature accuracy value of Thermoelectric Cooler (TEC) was 98.56%, the room tempera-ture accuracy value was 99.67%, the results of normality test of three temperature sensors are normally distributed, the results of homogeneity test of three temperature sensors are homogeneous, and the results of independent samples t-test are not significantly different, which indicates that three DS18B20 tempera-ture sensors are accurate.
The UTAUT Model for Measuring Acceptance of the Application of the Patient Registration System Tugiman Tugiman; Herman Herman; Anton Yudhana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2844

Abstract

The Covid-19 pandemic forced hospitals to innovate so that services comply with health protocols in the new adaptation period. Electronic Health (E-Health) such as online patient registration is expected to be a solution for hospitals with high patient visit rates. The purpose of this study was to analyze the level of user acceptance and the factors that influence the implementation of the online patient registration system for hospital patients. This research was conducted at PKU Muhammadiyah Gombong Hospital, which has implemented an online patient registration system based on Android since May 2020. The evaluation model uses Unified Theory of Acceptance and Use of Technology(UTAUT) and the analysis uses the Structural Equation Model (SEM) method using smart PLS. The results of the research show that all the hypotheses formed show valid values. So it can be said that the application of SIPENDOL in hospitals has been well received by users.
Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image Miftahuddin Fahmi; Anton Yudhana; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2819

Abstract

Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.
Co-Authors Herman Miftahuddin Fahmi Renangga Yudianto Sunardi Sunardi Tugiman Tugiman