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All Journal Journal of Applied Data Sciences Jurnal Sistem Informasi dan Teknik Informasi INOVTEK Polbeng - Seri Informatika
Noratama Putri, Ramalia
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Improved Deep Learning Model for Prediction of Dermatitis in Infants Setiawan, Debi; Noratama Putri, Ramalia; Fitri, Imelda; Nizar Hidayanto, Achmad; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.542

Abstract

Indonesia's equatorial climate, characterized by summer and rainy seasons, presents environmental conditions that contribute to a high incidence of dermatitis in infants. Dermatitis, an inflammatory skin condition, can lead to significant discomfort in infants, affecting their sleep, growth, and development. Early diagnosis is crucial for effective treatment; however, conventional diagnostic methods in clinics and hospitals—such as physical observation and parental interviews—are often time-consuming, subjective, and may lack precision, creating a need for more efficient diagnostic tools. This study explores the application of deep learning models to enhance the accuracy and speed of dermatitis diagnosis in infants. Four convolutional neural network (CNN) models were evaluated: MobileNet, VGG16, ResNet, and a Custom CNN model specifically designed for this study. Using a dataset of 1,088 skin images collected from three regions in Riau Province, Indonesia, we conducted training and testing to assess each model’s performance in distinguishing between dermatitis-affected and healthy skin. Results show that MobileNet and the Custom CNN outperformed other models, achieving accuracy rates of 97% and 85%, respectively. MobileNet’s high accuracy and efficiency make it a viable option for mobile applications, enabling rapid, on-site diagnosis in resource-limited settings. The Custom CNN model, tailored to the unique features of infant skin, also showed promising results. These findings demonstrate the potential of automated, image-based diagnostic tools for assisting medical professionals in early dermatitis detection, improving patient outcomes. This study contributes a valuable diagnostic solution that leverages deep learning to support healthcare providers, particularly in areas with limited access to specialized medical resources.
Soursop Leaf Disease Detection With CNNs:   From Training to Deployment Hidayatullah Nuriadi, Siti; Sabri, Erlin; Hajjah, Alyauma; Noratama Putri, Ramalia
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): Juli
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/sn8avr92

Abstract

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.
Perancangan Aplikasi Smart Farming Berbasis Design Thinking untuk Optimalisasi Manajemen Lahan Pertanian Debi, Debi Setiawan; Noratama Putri, Ramalia
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 5 No. 2 (2025)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/santi.v5i2.1637

Abstract

Perubahan iklim, keterbatasan sumber daya, dan rendahnya efisiensi manajemen pertanian menjadi tantangan utama dalam sektor pertanian modern. Studi ini bertujuan untuk merancang sebuah aplikasi smart farming yang dapat membantu petani dalam mengelola lahan pertanian secara efektif. Pendekatan Design Thinking digunakan untuk memastikan aplikasi dikembangkan berdasarkan kebutuhan nyata petani. Penelitian ini melalui lima tahap: empathize, define, ideate, prototype, dan test. Hasilnya adalah sebuah prototipe aplikasi yang menyediakan fitur manajemen tanam, pemantauan pertumbuhan tanaman, prediksi cuaca, serta pengingat pemupukan dan irigasi. Uji coba awal menunjukkan bahwa aplikasi ini mudah digunakan dan meningkatkan efisiensi operasional petani. Temuan ini menunjukkan potensi besar aplikasi smart farming dalam mendukung pertanian berkelanjutan berbasis teknologi lunak yang terjangkau dan adaptif.
Sistem Pakar Diagnosa Penyakit Pada Perokok Dengan Metode Forward Chaining Berbasis Web Putri, Etika Melsyah; Apriliza, Tari; Noratama Putri, Ramalia
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 1 No. 1 (2021)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (730.886 KB) | DOI: 10.58794/santi.v1i1.9

Abstract

Kepedulian manusia setiap harinya semakin menurun dengan meningkatnya jumlah perokok di Indonesia setiap tahunnya. Kurangnya rasa peduli pada diri sendiri, serta minimnya pengetahuan akan bahaya rokok membuat sebagian orang tidak lagi memikirkan kesehatan mereka di masa depan. Banyak yang mengesampingkan efek buruk yang ditimbulkan oleh asap rokok. Hal ini disebabkan karena efek tersebut tidak langsung terlihat saat pertama kali merokok.Banyak perokok yang enggan memeriksakan diri dengan berbagai alasan.Karena itu peneliti membuat sistem pakar diagnosis pada perokok dengan metode forward chaining berbasis web. Aplikasi ini dibuat dengan tujuan untuk mempermudah pengguna dalam mendiagnosa penyakit secara dini, serta memberikan pengetahuan tentang penyakit yang disebabkan oleh asap rokok. Sistem ini dibuat dengan menganalisa kebutuhan yang diperlukan, seperti data gejala, data penyakit, serta penanganannya. Data gejala akan dikelompokkan berdasarkan jenis penyakit yang sesuai. Sistem dibuat dengan menggunakan bahasa pemrograman PHP dan MySQL
Aplikasi Cegah Stunting Dengan Metode Design Thinking Berbasis Android Liem, Jordan; Noratama Putri, Ramalia
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 3 No. 2 (2023)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/santi.v3i2.549

Abstract

Masalah gizi stunting (balita pendek) merupakan salah satu masalah gizi yang krusial, khususnya di negara-negara miskin dan berkembang. Stunting merupakan bentuk kegagalan tumbuh kembang yang menyebabkan gangguan pertumbuhan linear pada balita akibat dari akumulasi ketidakcukupan nutrisi yang berlangsung lama, sejak dari masa kehamilan hingga pada usia 24 bulan. Design Thinking merupakan metode pendekatan desain yang berpusat pada manusia untuk menyelesaikan masalah dan menghadirkan inovasi baru. Metode ini memiliki beberapa tahapan yaitu emphatize, define, ideate, prototype, dan test. Pengembangan aplikasi ini melibatkan pengguna potensial (orang tua) dalam tahap desain dan pengembangan untuk memastikan bahwa aplikasi benar-benar memenuhi kebutuhan dan mudah digunakan. Aplikasi ini bertujuan agar dapat membantu orang tua dalam mengetahui perkembangan anak. Aplikasi memiliki fitur untuk melakukan penilaian pertumbuhan anak secara berkala. Berdasarkan data yang dimasukkan, aplikasi dapat memberikan informasi mengenai perkembangan pertumbuhan anak tersebut apakah berada dalam rentang normal atau ada indikasi stunting.
Rancang Bangun Sistem Informasi Penyewaan dan Rekomendasi Lahan Pertanian Menggunakan Metode KNN Natasya, Natasya; Noratama Putri, Ramalia
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/santi.v4i1.687

Abstract

Agriculture plays a crucial role in the economy of Indonesia, particularly in Kampar Regency, Riau Province. The agricultural, forestry, and fisheries sectors serve as the backbone in forming the Gross Regional Domestic Product (GRDP), making a significant contribution to the national economy. However, farmers in Karya Indah Village face challenges in finding and renting agricultural land that meets their needs. To address this challenge, the development of a web-based agricultural land rental information system is essential. This research aims to design a web-based information system using the K-Nearest Neighbor (KNN) algorithm to provide recommendations for agricultural land suitable for farmers' needs. This system facilitates farmers in finding and renting suitable agricultural land, while landowners can efficiently offer their land for rent. The research not only aims to improve efficiency in the process of renting agricultural land but also to enhance agricultural productivity and farmers' income. The research findings indicate that the KNN method can provide accurate recommendations for agricultural land, thus helping to improve farmers' welfare and the overall sustainability of the agricultural sector. The research findings show that the KNN method can recommend agricultural land with an accuracy rate of 80%.
Co-Authors Achmad Nizar Hidayanto Alyauma Hajjah Apriliza, Tari Debi Setiawan, Debi Debi, Debi Setiawan Erlin Etika Melsyah Putri, Etika Melsyah Fitri, Imelda Hidayatullah Nuriadi, Siti Hohashi, Naohiro Liem, Jordan Natasya Natasya Yuda Irawan