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REMOTE LABORATORY BERBASIS PROTOKOL VIRTUAL NETWORK COMPUTING SEBAGAI MEDIA PEMBELAJARAN JARAK JAUH PROGRAMMABLE LOGIC CONTROLLER Rachman, Isa; Hasin, Muhammad Khoirul; Adhitya, Ryan Yudha; Rahmat, Mohammad Basuki; Adianto, Adianto; Nurcahyo, Agus; Ruwahida, Dewi Rizani
TEKTRIKA Vol 8 No 1 (2023): TEKTRIKA Vol.8 No.1 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i1.6044

Abstract

One effective method that can be applied to online practical learning during the COVID-19 pandemic is remote laboratory which is a combination of the real world and the virtual world using the internet network. This research aims to design remote laboratory based on the Virtual Network Computing (VNC) on Programmable Logic Controller (PLC) which is one of the regular practical devices used in Industrial Automation Laboratory. Remote laboratory in the form of a remote laboratory website for administrators consisting of user management and course management; and for users it consists of login, course and schedule, user order and remote desktop containing PLC Trainer, XG5000 and camera. From the results of remote laboratory testing starting from registration, remote laboratory website, login, course selection, suitability of course schedules, user order and remote desktop can be accessed so that the designed remote laboratory can work properly. The existence of a delay time of 0,52–1,73 seconds for the results of shooting by the camera as a display visualization for online monitoring of practice activities in the laboratory on the client computer is influenced by the condition of the internet network used mainly on server computers. Key Words: practice, online, remote, laboratory, VNC, PLC.
Enhancing Fishing Efficiency with Geographic Information System and Optimized Methods Santosa, Anisa Fitri; Arfianto , Afif Zuhri; Hasin, Muhammad Khoirul; Sutrisno, Imam; Sukoco, Didik; Riananda, Dimas Pristovani
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.13859

Abstract

Traditional fishing techniques frequently lack efficiency and optimization, resulting in fishermen obtaining unsatisfactory yields. This study presents a novel approach by incorporating Geographic Information System (GIS) technology, notably utilizing Leaflet, to improve fishing techniques. The suggested system incorporates a LoRa node tool that logs the journeys of fishermen, offering comprehensive itineraries and data on the distribution of fish and unfavorable weather conditions. Notable outcomes were attained by employing the haversine approach to compute distances between the LoRa Gateway and different data points. The approach exhibited a negligible error margin of 0.157% in contrast to the calculations performed in Excel. In addition, the GPS accuracy testing produced remarkable results, with latitude and longitude errors of 0.000116% and 0.000002%, respectively. The LoRa system demonstrated a range of RSSI performance, with values ranging from -57 dBM at 50 meters to -121 dBM at 1500 meters. This range of performance guarantees dependable transmission of data over significant distances. The findings underscore the GIS-based strategy's efficacy in enhancing the effectiveness and precision of conventional fishing methods, presenting a promising technical improvement for the fishing sector.
Implementation of integrated temperature, humidity, and dust monitoring system on building electrical panel Khumaidi, Agus; Hasin, Muhammad Khoirul; Pujiputra, Anggarjuna Puncak; Irsyad, Sholahuddin Muhammad; Rinanto, Noorman; Rachman, Isa; Budi, Perdinan Setia; Malik, Alfianto Taufiqul; Bayu, Nurissabiqoh Binta
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.483

Abstract

This research aims to develop and implement an electrical power monitoring system at the Sub Sub Distribution Panel (SSDP) in the Building. The system is designed to monitor power usage in real-time, provide accurate information on energy consumption, and detect potential energy waste. The methodology used includes hardware and software design. The hardware consists of current and voltage sensors connected to a microcontroller. The data collected by the sensors is then transmitted via Wi-Fi network to the server for analysis. The software uses an Internet of Things (IoT) platform that displays the data in the form of graphs and tables. The implementation shows that the system is capable of monitoring power usage with a high degree of accuracy. The sensors used, namely PM2100 for voltage, SHT20 for temperature and humidity, and GP2Y101AU0F for dust concentration, proved effective in generating accurate real-time data. Based on the test results, the voltage measurement error with the PM2100 was only 0.035%, while the current measurement resulted in an error of 0.48%. The SHT20 sensor recorded an error of 2.4% for temperature and 1.0% for humidity. Dust measurements with the GP2Y101AU0F sensor had a very small error of 0.02%. These results indicate that the tested device has a sufficient level of precision for electrical power and environmental monitoring applications.
Eksplorasi Keandalan Sistem Sortir dan Klasifikasi Kecacatan Perekat Kemasan Menggunakan Arsitektur UNet-Inception Convolutional Neural Network Richo, Richo; Adhitya, Ryan Yudha; Hasin, Muhammad Khoirul; Syai’in, Mat; Setiawan, Edy
Jurnal Elektronika dan Otomasi Industri Vol. 10 No. 3 (2023): Jurnal Elkolind Vol. 10, No. 3, 2023 (September 2023)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v10i3.3835

Abstract

Kualitas standarisasi kelayakan kemasan menjadi parameter utama pada bidang industri untuk mencapai visi dan misi perusahaan dalam memastikan produk yang dihasilkan telah memenuhi standar yang diharapkan. Sistem pemilahan produk umumnya masih dilakukan dengan cara manual dengan pengamatan visual yang rentan terhadap ketidakakuratan dan interpretasi subjektif oleh operator yang menyebabkan kesalahan dalam mengenali produk. Penelitian ini melakukan perancangan sistem sortir produk dengan penambahan arsitektur UNet-Inception pada model CNN. Arsitektur UNet-Inception yang dikembangkan peneliti memiliki konstruksi layer konvolusi sebanyak 5 layer, pooling layer sebanyak 2 layer, up sampling 1 layer, serta pola concatenate sebanyak 1 layer, penambahan layer inception convolutional (Concv2D) dengan neuron hidden sebanyak 128 neuron. Model dengan penambahan arsitektur UNet-Inception berhasil mencapai tingkat akurasi training yang lebih tinggi daripada model tanpa arsitektur UNet-Inception dengan perbandingan yakni 98,39% berbanding 71,47%. Pada pengujian deteksi real-time didapatkan akurasi sebesar 93,34%. Sistem yang diciptakan mampu melakukan klasifikasi produk dengan sangat baik berdasarkan karakteristik bercak pada panjang bercak 3 cm, 5 cm, dan 7 cm, dengan akurasi keberhasilan mencapai 100%. Sistem integrasi dalam bentuk sortir yang telah diimplementasikan berhasil memberikan respons aksi reject yang sesuai dengan hasil deteksi produk cacat dengan akurasi keberhasilan mencapai 100%.