Article Per Year (5 Year)
p-Index From 2021 - 2026
1.857
P-Index
This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Jurnal Pengabdian Masyarakat IPTEK Digital Transformation Technology (Digitech) Journal Of Artificial Intelligence And Software Engineering eProceeding of TIK
Umri Erdiansyah
Politeknik Negeri Lhokseumawe
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
Klasifikasi Penyakit Diabetic Retinopathy Menggunakan Multilayer Perceptron Umri Erdiansyah; Ahmadi Irmansyah Lubis; Guntur Syahputra
Journal of Artificial Intelligence and Software Engineering Vol 2, No 1 (2022)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v2i1.3084

Abstract

Diabetic Retinopathy merupakan salah satu komplikasi penyakit diabetes yang dapat menyebabkan kematian. Komplikasi ini berupa kerusakan pada retina mata. Kadar glukosa yang tinggi dalam darah dapat menyebabkan kapiler kecil pecah dan menyebabkan kebutaan. Penyakit ini dimulai dengan melemahnya atau rusaknya kapiler kecil di retina, memungkinkan darah mengalir dan kemudian menyebabkan penebalan jaringan, pembengkakan, dan pendarahan hebat. Penelitian ini bertujuan untuk menganalisis diagnosis retinopati diabetik berupa data rekam medis. Multilayer Perceptron merupakan salah satu algoritma jaringan syaraf tiruan yang sering digunakan untuk klasifikasi data dan digunakan dalam proses klasifikasi pada penelitian ini. Dataset yang digunakan dalam penelitian ini diperoleh dari UCI Machine Learning Repository, kumpulan data dari University of Debrecen, Hongaria, termasuk data pasien untuk retinopati diabetik. Evaluasi hasil klasifikasi yang digunakan adalah confusion matrix. Dari hasil perhitungan yang telah dilakukan, maka didapatkan hasil akurasi pada Multilayer Perceptron sebesar 71.80%, dengan nilai precision 72.50%, dan Recall 71.80%.
Webcam-Based Finger Detection Using Mediapipe Rudi, Fachri Yanuar; Syahputra, Guntur; Erdiansyah, Umri; Safar, Ilham
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6996

Abstract

The development of multimedia-based learning tools continues to advance alongside technological innovations, particularly in recognizing parts of the human body such as fingers. This study aims to develop an interactive learning application for identifying the names of fingers using motion capture technology through Mediapipe and the Python programming language. The application utilizes a webcam as the input device to detect hand positions and movements in real-time. The system is designed to recognize individual fingers by comparing the length of finger segments and the width of the palm, based on hand landmark data provided by the Mediapipe Hands module. Testing results show that the application achieved a 90% success rate across 50 trials. These results indicate that Mediapipe technology holds significant potential for use in the development of interactive and educational learning media.
Smart Infusion Digitalization Based on IoT, Long-Range Communication, and Cloud Ananta, Adam; Nasir, Muhammad; Erdiansyah, Umri
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6135

Abstract

Currently, the monitoring of infusion fluids is performed by periodically checking each patient, regardless of whether there is an obstruction or not. To address this challenge, a system based on the Internet of Things (IoT), Long Range (LoRa) at a 2.4 GHz frequency, and Cloud technology, known as the digital smart infusion system, has been developed. This system aims to enhance the efficiency and safety of infusion fluid delivery, facilitate real-time monitoring by nurses, and provide accurate and up-to-date data. The testing results indicate that the implementation of the MQTT protocol in this system yields positive outcomes, with delay times varying between 42 ms (5 minutes), 84.3 ms (10 minutes), and 73.8 ms (15 minutes), along with very low packet loss rates of 0.03% at 5 minutes, 0.02% at 10 minutes, and 0.01% at 15 minutes. Additionally, the system's throughput remains stable, with values of 92.6 Kbps at 5 minutes, 83.8 Kbps at 10 minutes, and 86.2 Kbps at 15 minutes. In tests of LoRa without obstructions, packet loss percentages remain low up to a distance of 10 meters, with a value of 0%, but then increase to 68.29% at 25 meters. Tests with obstructions show a more drastic decline in signal quality, with packet loss reaching 6.98% at 5 meters and increasing to 70.97% at 25 meters.
Implementation of Website-Based Graduate Learning Outcomes Measurement System Erdiansyah, Umri; Syahputra, Guntur; Rudi, Fachri Yanuar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8584

Abstract

Digital transformation in higher education quality assurance has become imperative following the enactment of the Minister of Education, Culture, Research, and Technology Regulation No. 53 of 2023. This study proposes the development of a web-based Graduate Learning Outcomes (GLO) measurement system to accelerate academic evaluation effectiveness at Politeknik Negeri Lhokseumawe. Employing a Research and Development (RD) approach with the Waterfall development model, this research designs the system architecture using the Model-View-Controller (MVC) pattern to ensure application scalability and modularity. The system integrates academic data management features, automated GLO calculations, and analytical data visualization to support data-driven decision-making. System testing involved User Acceptance Testing (UAT), yielding a System Usability Scale (SUS) score of 82.3, indicating an 'excellent' user acceptance level. The results confirm that this platform not only meets national regulatory compliance standards but also enhances transparency and objectivity in graduate quality reporting. This implementation makes a strategic contribution to the modernization of institutional academic governance.
Co-Authors Abdi, Mustainul Ahmadi Irmansyah Lubis Ananta, Adam bin Amirudin, Iphonsyah Feri Setiawan Guntur Syahputra Guntur Syahputra, Guntur Ilham safar kamil erwansyah Lusiyanti Lusiyanti Muhammad Nasir Mukhlis Ramadhan Mursyidah Mursyidah Rifki, Muhammad Aidil Rosma Siregar Rudi F, Fachri Yanuar RUDI, FACHRI YANUAR