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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Luluk Elvitaria Elvitaria
Abdurrab
Computer Science & IT Engineering

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PEMODELAN ADDIE DALAM PENGEMBANGAN KURIKULUM 13 PADA PEMBELAJARAN BAHASA INGGRIS TINGKAT SEKOLAH DASAR BERBASIS ANDROID Luluk Elvitaria Elvitaria; Debi Setiawan; Lasiah Susanti; Syafrizal Syafrizal
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3351

Abstract

The proliferation of science and technology that is so rapid today supports and makes it easier for all the activities in various areas of human life, one of them being education. The K–13 curriculum requires schooling. The excess K–13 is to require students to be more independent, creative, and innovative, emphasizing character education. And the judgment process is made up of all the aspects of attitude, activity, skill, and knowledge. English is essential to study and be understood by the students because of the challenge of global development, and one of its supporting tools is mastery of English as a communication medium in the global and international world. Purpose: This study made it easier for students to tackle mainly the English learning process pronunciation in the form of interactive systems by applying speech and voice technologies recognition. Data collection from library studies, interviews, and observatories Methods of quantitative analysis are used in this research process for testing. Testing systems using black box testing with a successful walk and user acceptance test (UAT) and getting an average of 85% of student responses The results of this study prove that interactive system applications generate 86% and 84% of the result of media appropriations and material content, respectively, that are perfectly feasible in the learning process.
KLASIFIKASI TELUR CACING BERBASIS GAMBAR MENGGUNAKAN JARINGAN SARAF KONVOLUSIONAL: IMAGE-BASED CLASSIFICATION OF HELMINTHS EGGS USING CONVOLUTIONAL NEURAL NETWORKS Luluk Elvitaria Elvitaria; Ira Puspita Sari; Tengku Imam Buchari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6207

Abstract

This research aims to develop an image processing-based classification system for helminths eggs using Convolutional Neural Network (CNN) with a transfer learning and finetuning approach. Helminths eggs are important indicators in the diagnosis of helminth diseases in humans. However, the manual classification of helminths eggs requires significant time and effort. Therefore, an automated system that can classify helminths eggs with high accuracy would be highly beneficial in the diagnosis of these diseases. In this study, experiments were conducted using three CNN architectures that have proven effective in image classification tasks, namely EfficientNetB0, MobileNetV3, and ResNet50. Transfer learning method was employed by utilizing pre-trained models on large-scale image datasets. Subsequently, fine tuning was performed on the last layers of the models to adapt them to the helminths egg data. Testing was conducted using a dataset of helminths eggs collected from IEEE Dataport. The experimental results show that all three CNN architectures were able to classify helminths eggs with high accuracy, with EfficientNet-B0 achieving the highest accuracy (95.36%). The developed system in this study has the potential to be used in the efficient and accurate diagnosis of helminth diseases.
Co-Authors Debi Setiawan Ira Puspita Sari Lasiah Susanti Syafrizal Syafrizal Tengku Imam Buchari