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All Journal Jurnal Rekayasa Sistem Informasi dan Teknologi Indonesian Journal of Sustainability
Rohman, Andhi
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Smart Farming: Improving Agricultural Productivity and Efficiency using Robotics Technology Anaam, Wahyu Syaiful; Saputra, Wahid Ivan; Rohman, Andhi; Anggraeni, Martha Richa; Rafli, Muhammad; Ghufron, Ghufron
INDONESIAN JOURNAL OF SUSTAINABILITY Vol 3, No 1 (2024): January
Publisher : Library of Sultan Agung Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ijsunissula.3.1.%p

Abstract

Agriculture is a vital sector in providing food for the world's growing population. However, challenges such as climate change, limited land, and a lack of skilled agricultural labor have driven the development of innovative agricultural technologies. One promising solution is the use of robots in agriculture. This paper discusses robotic farming, namely the use of robots and related technology in agricultural activities. The main focus is to explain the benefits, applications, and challenges associated with the use of robotic technology in agriculture. In this paper, several examples of the implementation of robotic technology in agriculture will also be discussed.
IMPLEMENTASI METODE TRANSFER LEARNING UNTUK KLASIFIKASI MALARIA MENGGUNAKAN RESNET-50 Rohman, Andhi; Subroto, Imam Much Ibnu
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 3 No. 2 (2025): November
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v3i2.3138

Abstract

Malaria merupakan penyakit menular yang disebabkan oleh parasit Plasmodium dan masih menjadi masalah kesehatan serius di Indonesia, khususnya di wilayah timur. Diagnosis malaria yang cepat dan akurat sangat penting untuk mencegah komplikasi serta memutus rantai penularan. Penelitian ini bertujuan untuk mengimplementasikan metode transfer learning menggunakan arsitektur Convolutional Neural Network (CNN) ResNet-50 dalam mendeteksi sel darah yang terinfeksi malaria pada citra mikroskopis. Dataset yang digunakan terdiri dari dua kelas, yaitu Parasitized (terinfeksi) dan Uninfected (tidak terinfeksi). Tahapan penelitian meliputi preprocessing data, augmentasi citra, pemanfaatan model pretrained ResNet-50, penambahan lapisan klasifikasi, pelatihan model, serta evaluasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC. Hasil penelitian menunjukkan bahwa model ResNet-50 mencapai akurasi sebesar 93,00%, dengan presisi 96,00%, recall 97,00%, F1-score 93,00%, dan AUC 98,00%. Dengan performa tersebut, pendekatan ini berpotensi menjadi solusi pendukung diagnosis malaria yang cepat, objektif, dan efisien, serta dapat diintegrasikan dalam sistem skrining laboratorium dan mendukung pengambilan keputusan medis berbasis kecerdasan buatan.
Co-Authors Anaam, Wahyu Syaiful Anggraeni, Martha Richa ghufron, ghufron Imam Much Ibnu Subroto Rafli, Muhammad Saputra, Wahid Ivan