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All Journal Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran Jurnal Riset Mahasiswa Matematika
Habibullah, Muhamad
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Humanities Computer Science & IT Education Mathematics Social Sciences

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The Role of Teachers in the Development of ICT-Based Learning Innovations Habibullah, Muhamad
Jurnal Inovasi dan Teknologi Pembelajaran Vol 9, No 3 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um031v9i32022p302

Abstract

Abstrak: Di era industri 4.0, para pendidik dituntut untuk bisa mengembangkan inovasi pembelajaran pada setiap pertemuan tatap muka. Hal inilah yang mendorong banyak guru sekolah dasar untuk dapat menerapkan media pembelajaran berbasis teknologi dan informasi. Tujuan penelitian ini adalah mengetahui sejauh mana para pendidik mengembangkan inovasi pembelajaran yaitu melalui adanya teknologi informasi dan komunikasi (TIK). Penelitian ini merujuk pada metode fenomenologi terhadap peristiwa yang terjadi dan merujuk juga terhadap refrensi buku sebagai penguat. Penelitian ini menggunakan analisis metode kualitatif deskriptif dengan memaparkan data-data melalui wawancara, observasi, dokumentasi, serta berdasarkan referensi dari buku dan jurnal. Hasil penelitian yang dicapai adalah sebanyak 30 pengajar mampu mengembangkan inovasi pembelajaran menggunakan teknologi komunikasi dan informasi berupa zoom, google meet, terms, Edmodo, dan youtube. Penelitian ini berkontribusi bagi para pendidik dalam pengguaan aplikasi media dalam teknologi komunikasi dan informasi di era industri 4.0.Abstract: In the era of industry 4.0, educators are required to be able to develop learning innovations in every face-to-face meeting, this is what encourages many elementary school teachers to be able to apply technology and information-based learning media. The purpose of this study is to find out the extent to which educators develop learning innovations through information and communication technology (ICT). This research refers to the phenomenological method of events that occur and to book references as reinforcement. This research uses descriptive qualitative method analysis by presenting data through interviews, observations, documentation, and based on references from books and journals. The results of the research were that as many as 30 teachers were able to develop learning innovations from communication and information technology in the form of zoom, google meet, terms, Edmodo, and YouTube. This research contributes to educators in using media applications in communication and information technology in the industrial era 4.0.
Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat Habibullah, Muhamad; Fahmi, Hisyam; Herawati, erna
Jurnal Riset Mahasiswa Matematika Vol 2, No 6 (2023): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v2i6.22023

Abstract

This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale ones before being processed using Gabor Filter. This Gabor Filter process segments the image to produce a magnitude value. The results of the image magnitude values here will be seen and will enter the classification process using SVM. The SVM algorithm aims to find the best hyperlane on tomato leaves that have been segmented to separate classes in the input space. The application of the SVM method with class classification of tomato leaves by calculating the energy value and entropy of the extraction results, assisted by 12 features, namely: CiriR, Feature G, FeatureB, Standard DeviationR, Standard DeviationG, Standard DeviationB, SkewnessR, SkewnessG, SkewnessB, Mean, Energy, Entropy are used to the simplity classification process with a high degree of accuracy. The process of classification of tomato leaf disease with test data of 600 images managed to get an accuracy value of 74.1667%. In order to facilitate the performance of farmers in predicting tomato leaf disease.
Co-Authors Erna Herawati, Erna Hisyam Fahmi