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All Journal Jurnal Penelitian dan Pengabdian Masyarakat Jotika Nuansa Informatika
Umi Khultsum
Universitas Bina Sarana Informatika
Computer Science & IT Other

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Journal : nuansa informatika

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The Implementation of the TOPSIS Method in Determining Stunting Toddlers: Penerapan Metode TOPSIS dalam Penentuan Balita Stunting Umi Khultsum; F. Lia Dwi Cahyanti; Elly Firasari
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.400

Abstract

Stunting is a growth disorder in children due to chronic malnutrition and recurrent infections, especially in the first 1,000 days of life. Assessment of stunting status that only relies on height and weight measurements is considered ineffective because it does not cover all aspects that affect a child's nutritional status. At Posyandu Bougenvile, stunting identification is still done manually and is at risk of causing errors in decision making. This study aims to develop a web-based Decision Support System (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to assist Posyandu cadres in determining toddler stunting status quickly, accurately, and efficiently. This system processes data from four main anthropometric indicators, namely Height/Age, Weight/Age, Weight/Age, and BMI/Age. The results of the system calculations show agreement with manual calculations, which proves that the system is working optimally. An example of the results shows that toddlers with code A1 (Rafasya Malik) have the lowest preference value of 0, followed by A4 (Ihsan Dwi Hanggoro) with a value of 0.4022149 which is included in the high stunting risk category. This system has proven to be able to help Posyandu cadres in prioritizing the handling of at-risk toddlers, as well as supporting the stunting monitoring process in a more structured and data-based manner.
Website-Based Potato Leaf Disease Detection Using the EfficientNetV2L Deep Learning Architecture Umi Khultsum
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.593

Abstract

Potato plants are seasonal crops, where potatoes are classified as a nutrient-rich food ingredient. The leaves of potato plants can be attacked by diseases caused by bacteria and viruses. The modern agricultural era makes farmers face many challenges related to the recognition of potato leaf diseases. Potato leaf diseases can be recognized visually, but the recognition carried out by farmers has a drawback, namely the process of identifying the type of disease that takes a long time, this can disrupt productivity and sustainability of the harvest. The purpose of this study is to produce a model that is able to recognize the type of potato leaf disease using the Convolution Neural Network (CNN) method using the EfficientNetV2L architecture. The EfficientNetV2Larchitecture has the ability to perform good feature extraction and high computational efficiency compared to other CNN architectures. The dataset used in this study is potato leaf images taken from Kaggle, there are 2 disease classes and 1 healthy class with a total of 2,300 potato leaf images. The model proposed in this study is able to identify potato leaf disease images well and has a testing accuracy of 99.00%, training accuracy of 99.35%, validation accuracy of 98.60%, precision of 98.95%, recall of 99.02% and F1-Score of 99.98%. The model was developed into a website-based system that can quickly and accurately identify potato leaf diseases. Implementing this website system offers the advantages of easy access and speed in the potato leaf disease identification process.
Co-Authors Ali Mustopa, Ali Dahlia Dahlia Desyandri Desyandri Endah Wiji Lestari F. Lia Dwi Cahyanti Firasari, Elly Muhammad Fahmi Julianto Muhammad Iqbal Rabiatus Sa’adah Siti Juleha Wahyu Nugraha