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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika MDP Student Conference
Muhammad Redho Saputra
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Perancangan Desain UI/UX Pada Aplikasi HealMed Menggunakan Metode Design Thinking Naufal Akbar; Stephen Setyawan; Kelvin Dwi Wahyudi; Lipi Amanda Putra; Muhammad Redho Saputra; Muhammad Rizky Pribadi
MDP Student Conference Vol 1 No 1 (2022): The 1st MDP Student Conference 2022
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.967 KB)

Abstract

Abstract: HealMed is one of the drug sales applications that has developed an e-commerce method and has been verified by the BPOM and MUI institutions. To provide the best experience in buying and selling medicines, HealMed designs their application platform using the Design Thinking approach. This method is a human-centered approach to solving problems and presenting new ideas or innovations. To get feedback and explore problems, a research and testing process is carried out. The use of the Design Thinking method is expected to be able to meet the needs and solve user problems when using the HealMed application. After successfully finding and understanding the problem, the results of the application design include User Interface (UI) and User Experience (UX) which are able to be solutions to existing problems. Abstrak: HealMed merupakan salah satu aplikasi penjualan obat yang telah mengembangkan metode e-commerce serta telah sudah terverifikasi lembaga BPOM dan MUI. Untuk memberikan pengalaman yang terbaik dalam pembelian dan penjualan obat, HealMed melakukan perancangan desain terhadap platform aplikasi mereka dengan metode pendekatan Design Thinking. Metode ini merupakan metode pendekatan yang berpusat pada manusia atau human centris untuk menyelesaikan masalah dan menghadirkan ide atau inovasi baru. Untuk mendapatkan feedback dan menggali permasalahan dilakukan proses research dan pengujian. Penggunaan metode Design Thinking diharapkan mampu memenuhi kebutuhan dan dapat menyelesaikan permasalahan pengguna saat menggunakan aplikasi HealMed. Setelah berhasil menemukan dan memahami permasalahan didapatkan hasil desain aplikasi mencakup User Interface(UI) dan User Experience(UX) yang mampu menjadi solusi dari permasalahan yang ada.
Penggunaan MobileNetV2 untuk Klasifikasi Penyakit Daun Cabai Muhammad Redho Saputra; Nur Rachmat
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13562

Abstract

Chili peppers (Capsicum annuum L.) are an important horticultural commodity in Indonesia with high economic value, but they are susceptible to leaf diseases such as leaf spots, curled leaves, yellowing leaves, and whitefly pests. This study aims to classify chili leaf diseases using a MobileNetV2-based Convolutional Neural Network (CNN) architecture utilizing the Depthwise Separable Convolution mechanism for filter decomposition and model complexity reduction. Based on previous studies, MobileNetV2 has been proven to maintain a highly competitive level of accuracy. The dataset used consisted of 6000 images from five categories: healthy, leaf spot, leaf curl, yellowish, and whitefly, which were taken from open sources and equalized in number for each class. The data was divided into training, validation, and testing sets with a ratio of 80:10:10. The training process used depthwise separable convolution, dropout, and Adam and SGD optimization techniques to prevent overfitting. Model evaluation was carried out through 12 scenarios with variations in batch size, dense layer, optimizer, and epoch. The results show the highest accuracy of 98.40% in the scenario with a batch size configuration of 32, a dense layer of 128, a learning rate of 0.001, an Adam optimizer, and 20 epochs. Most scenarios achieved an accuracy above 96%, proving that MobileNetV2 is effective for classifying chili leaf diseases. The contribution of this study is the identification of an optimal and efficient MobileNetV2 parameter configuration for chili leaf disease classification.
Co-Authors Kelvin Dwi Wahyudi Lipi Amanda Putra Muhammad Rizky Pribadi Naufal Akbar Stephen Setyawan