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All Journal ETNIK : Jurnal Ekonomi dan Teknik EXPLORER
Aditya Pratama, Bayu
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pemanfaatan Metode Fuzzy Logic Mamdani Dalam Menentukan Lokasi Terbaik Penjualan Coffee Shop Menggunakan Matlab Aditya Pratama, Bayu; Valdhano Oka, Valdi; Gustiawan, M. Yoris
Jurnal Etnik: Ekonomi-Teknik Vol 2 No 3 (2023): ETNIK : Jurnal Ekonomi dan Teknik
Publisher : Rifa'Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54543/etnik.v2i3.166

Abstract

MSME business growth today continues to increase as stated on the www.indonesia.go.id website, MSME growth in 2021 reached 61.07 percent. So that it can help increase employment. One example of MSMEs is Coffee Shop which is increasingly mushrooming in certain areas. This contains business competition in the management of the coffee shop business must be the main calculation for business actors. Determining the right location will increase attractiveness for potential customers who will come, such as the location of crowds in the area such as close to lectures, in cities that have high mobility and so on, this needs to be carefully calculated in order to determine the right sales strategy. In determining it, a decision support system is needed that will help business actors in choosing a place of sale
Analisis Komparatif CNN Ringan untuk Klasifikasi Penyakit Daun Tomat Menggunakan Visualisasi Grad-CAM Rahman, Sayuti; Hartono, Hartono; Sembiring, Arnes; Khahfi Zuhanda, muhammad; Aditya Pratama, Bayu; Martini, Dewi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2601

Abstract

Tomato leaf disease classification based on digital imagery has become an important approach in supporting smart agriculture, particularly for early detection of plant disease attacks. This study aims to compare the performance of several lightweight Convolutional Neural Network (CNN) architectures, namely MobileNetV3-Small, MobileNetV2, and EfficientNet-B0, in classifying tomato leaf diseases using the PlantVillage dataset. The dataset consists of 3,628 images distributed across 10 classes (9 disease classes and 1 healthy class), with a data split scheme of 80% for training and 20% for validation. Performance evaluation was conducted using classification reports, confusion matrices, and interpretability analysis through Grad-CAM and feature map visualization. The experimental results show that all models achieved very high accuracy, exceeding 99%. EfficientNet-B0 obtained the best performance with a validation accuracy of 99.59%, followed by MobileNetV2 at 99.45% and MobileNetV3-Small at 99.04%. However, model complexity increased along with accuracy, where EfficientNet-B0 had the largest number of parameters and FLOPs. Grad-CAM analysis revealed that higher-accuracy models demonstrated more precise activation focus on leaf lesion regions. This study confirms that lightweight CNN architectures are capable of delivering excellent classification performance while offering strong potential for deployment in plant disease detection systems on resource-limited devices
Co-Authors Gustiawan, M. Yoris Hartono Hartono Khahfi Zuhanda, Muhammad Martini, Dewi Rahman, Sayuti Sembiring, Arnes Valdhano Oka, Valdi