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All Journal Jurnal INKOM Infotech Journal Sistemasi: Jurnal Sistem Informasi Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Jurnal Inovasi Penelitian Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Moneter : Jurnal Keuangan dan Perbankan
Setiawan, Foni Agus
Universitas Ibn Khaldun Bogor
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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Prototype of an Automatic Height and Weight Measurement System Based on Z-Scores for Determining the Nutritional Status of Toddlers Fayza, Maylaf; Harahap, Robby Kurniawan; Setiawan, Foni Agus
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1276.013 KB) | DOI: 10.34288/jri.v7i4.421

Abstract

Monitoring the nutritional status of children aged 24-60 months is a crucial aspect of ensuring their growth and development. The commonly used manual methods often have limitations in terms of accuracy and efficiency. This study aims to design and develop a prototype of an automated height and weight measurement system based on Z-Score to accurately and efficiently determine the nutritional status of children. The system is developed using the ESP8266 microcontroller as the control center, integrating an RFID module for child identification, an ultrasonic sensor for height measurement, and a load cell for weight measurement. The measurement data is then processed to generate a Z-Score value, which is displayed on an LCD screen. Based on the test results, the system demonstrates a measurement accuracy of 99.60% for children's height and weight. Additionally, the nutritional status assessment aligns with WHO standards. This system is expected to enhance the effectiveness and efficiency of nutritional monitoring for toddlers.
Chili Leaf Health Classification using Xception Pretrained Model Wulandari, Yestika Dian; Munggaran, Lulu Chaerani; Setiawan, Foni Agus; Satya, Ika Atman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3943

Abstract

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.
PERBANDINGAN METODE K-NN DAN RANDOM FOREST PADA KLASIFIKASI MAHASISWA BERPOTENSI DROPOUT Muhammad Maulana Rofi; Foni Agus Setiawan; Freza Riana
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.8856

Abstract

Perguruan tinggi bertanggung jawab memberikan pendidikan terbaik untuk menghasilkan individu berkualitas. Tingginya angka drop out dapat merusak akreditasi. Model dikembangkan menggunakan K-Nearest Neighbor (K-NN) dan Random Forest untuk mengklasifikasikan kasus drop out. Random Forest memiliki akurasi lebih tinggi (99.05%) dibanding K-NN (98.10%). Atribut Persentase Aktif menonjol sebagai faktor paling berpengaruh dalam mengklasifikasikan siswa yang berpotensi putus sekolah, menurut algoritma Random Forest. Ini menandakan pentingnya keterlibatan aktif dalam meminimalkan risiko drop out.
Co-Authors Abidin, Riswan Al Ikhsan, Safaruddin Hidayat Budi Sihabudin Dewi Primasari Fadhilah, Muhammad Rifqi Fayza, Maylaf Freza Riana Gibtha Fitri Laxmi Harahap, Robby Kurniawan Hendrawan, A. Hendri Hersanto Fajri Ika Atman Satya Laila Nadya Lulu Chaerani Munggaran Muhammad Maulana Rofi Mulyo, Arief Sekto Nadya, Laila Puspa Eosina Safaruddin Hidayat Al Ikhsan Safitri, Rianti Sudriani, Yuli Wahyudin Wahyudin Wulandari, Yestika Dian Yudicy Amelia Yuli Sudriani