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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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SISTEM PAKAR IDENTIFIKASI KERUSAKAN SEPEDA MOTOR SUZUKI SATRIA F150 DENGAN TEOREMA BAYES Setiawan, Foni Agus; Primasari, Dewi; Wahyudin, Wahyudin
Krea-TIF: Jurnal Teknik Informatika Vol 8 No 1 (2020)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/kreatif.v8i1.3489

Abstract

Sepeda motor merupakan jenis kendaraan beroda dua yang digerakkan oleh mesin. Sepeda motor terkadang mengalami kerusakan yang disebabkan oleh komponen yang telah aus, pemeliharaan yang tidak teratur, atau kesalahan pemakaian. Kerusakan tersebut ada yang bersifat ringan, sedang, atau berat. Sistem pakar identifikasi kerusakan sepeda motor Suzuki Satria F150 dibangun untuk dapat membantu mengidentifikasi kerusakan dan menawarkan solusi yang harus dilakukan untuk mengatasi kerusakan tersebut. Teorema Bayes digunakan sebagai model untuk pendugaan bagian atau komponen yang rusak. Metode inferensi backward chaining digunakan untuk menelusuri penyebab kerusakan sesuai dengan gejala-gejala yang diamati. Sistem pakar diimplementasikan dalam bentuk aplikasi berbasis web. Sistem pakar ini selain berguna bagi mekanik bengkel sepeda motor untuk mendukung pekerjaannya, juga bermanfaat bagi pengguna motor Suzuki Satria F150 sebagai alat bantu identifikasi awal kerusakan sehingga dapat diambil tindakan lebih lanjut.
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.
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 Mulyo, Arief Sekto Nadya, Laila Puspa Eosina Rofi, Muhammad Maulana Safaruddin Hidayat Al Ikhsan Safitri, Rianti Sudriani, Yuli Wahyudin Wahyudin Wulandari, Yestika Dian Yudicy Amelia Yuli Sudriani