Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Infomedia KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Bulletin of Information Technology (BIT) Prosiding Snastikom Jurnal Sistim Informasi dan Teknologi Prosiding Seminar Nasional Teknik UISU (SEMNASTEK)
Herdianto Herdianto
Dosen Sistem Komputer Universitas Pembangunan Panca Budi Medan Jl Gatot Subroto KM 4,5, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
IMPLEMENTASI METODE CNN UNTUK KLASIFIKASI OBJEK Herdianto Herdianto; Darmeli Nasution
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No1.pp54-60

Abstract

Objects can be interpreted as all inanimate and living things that have various shapes and sizes. For humans to determine the presence of objects, to classify and estimate the distance of objects around them is not difficult. But for a computer to do the work mentioned above with an accuracy level that reaches up to greater than 90% is not easy. Object detection is important in the field of computer vision because it is used to monitor and track objects, while robots that use cameras as sensors are used to avoid obstacles, follow objects, classify and so on. Therefore, the purpose of this study was to determine the level of accuracy of the CNN method in classifying objects. The steps used to complete this research were literature study, collecting digital image data, determining training, and testing data, designing the CNN program, conducting training and testing. From the results of testing the CNN method that has been carried out, it is known that the level of accuracy in classifying objects reaches 98%.
Sistem Monitoring dan Deteksi Dini Pencemaran Udara Berbasis Internet Of Things (IOT) Muhammad Hasanuddin; Herdianto Herdianto
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4034

Abstract

The problem of environmental pollution has been a concern for a long time and continues to be an important issue today. One form of pollution that is very detrimental is air pollution, which causes a decrease in air quality and disrupts its normal function. . The ISPU Scale usually consists of several categories that describe the level of air quality, such as the range 1-50 is said to be "Good", the range 51-100 is said to be "Moderate", the range 101-200 is said to be "Unhealthy", the range 201-300 is said to be "Very Unhealthy", and a range of 301+ is said to be "Dangerous". To monitor the level of air pollution, it is necessary to use a tool that can measure air quality accurately. Indoor pollution, as is often the case in office environments, is one of the most serious threats to the health of modern society. A worker can spend up to eight hours a day in the room. The air we breathe everyday comes from the building's air duct system. However, these duct systems not only circulate air, they can also spread contaminants from one room to another. Therefore, it is important to maintain indoor air quality for the comfort and health of workers, because this has a direct impact on their productivity. Based on this, an idea emerged to develop a prototype detector of carbon monoxide gas that is sensitive to indoor air pollution using a microcontroller and web-based. This research begins with analyzing and designing the system, both in terms of hardware and software. After that, the next step involves coding and programming configuration and setup on the Thingspeak web. The last stage is testing tools and systems to ensure that they operate according to the plan that has been set. This system is designed to be able to detect and monitor the level of air pollution, as well as provide a graphical display on Thingspeak. This tool works by using a microcontroller to read the MQ-135 sensor attached to the Ardino Wemos ESP8266. This sensor is used to measure gas levels in a predetermined room. The tool will connect to the Wi-Fi hotspot on the Android device to provide notifications via Thigspeak based on a graph
Co-Authors Adisastra Pengalaman Tarigan Amin, Muhammad Ari Pratama Aulia Manurung Ayu Nuriana Sebayang Chairul Rizal Darmeli Nasution Dewi Mahrani Rangkuty Fery Anugrah Hidayati Purnama Lubis Mita Mutiara Zora Muhammad Hasanuddin Munadi Munadi Mursyidah . Rusli Rusli Silvia Anggraini Ucmariance