Article Per Year (5 Year)
p-Index From 2021 - 2026
1.642
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri Jurnal Manajemen Informatika Jurnal Teknologi dan Informasi Jurnal Informatika Global Jurnal Sistem Komputer & Kecerdasan Buatan Abdimasku : Jurnal Pengabdian Masyarakat Jurnal AbdiMas Nusa Mandiri Paradigma
Nawawi, Hendri Mahmud
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Mustopa, Ali; Sasongko, Agung; Nawawi, Hendri Mahmud; Wildah, Siti Khotimatul; Agustiani, Sarifah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): 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.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Klasifikasi Resiko Diabetes terhadap Gaya Hidup dengan Algoritma K-Nearest Neighbor (k-NN) dan Naive Bayes Jonhar, Thaufik Darma; Fiananta, Maulana; Purwanto, Dicky; Nawawi, Hendri Mahmud
Jurnal Ilmiah Informatika Global Vol. 17 No. 01 (2026): April 2026
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v17i01.6705

Abstract

Diabetes melitus merupakan gangguan metabolik yang terjadi ketika pankreas tidak mampu memproduksi insulindalam jumlah yang cukup atau ketika tubuh tidak dapat menggunakan insulin secara efektif, sehingga menyebabkan kadar gula darah tidak terkontrol dan berpotensi menimbulkan komplikasi jangka panjang. Penelitian ini menerapkan pendekatan data mining untuk mengklasifikasikan tingkat risiko diabetes menggunakan dua algoritma supervised learning, yaitu Naive Bayes dan k-Nearest Neighbor (k-NN), dengan tujuan membandingkan kinerja prediksi serta mengidentifikasi pola dari dataset. Proses penelitian mengikuti tahapan Knowledge Discovery in Database (KDD), meliputi seleksi data, pra-pemrosesan, transformasi, pemodelan, dan evaluasi. Seluruh eksperimen dilakukan menggunakan RapidMiner dengan skema pembagian data 80:20 melalui stratified split dan random seed tetap untuk menjaga konsistensi hasil. Evaluasi model menggunakan confusion matrix, akurasi, precision, recall, dan F1-score. Hasil menunjukkan bahwa k-NN memperoleh akurasi sebesar 81,72%, lebih tinggi dibandingkan Naive Bayes sebesar 75,42%. Dari sisi precision dan recall, Naive Bayes menghasilkan precision 86,69%, recall 93,12%, dan F1-score 89,79%, sedangkan k-NN memperoleh precision 90,68%, recall 80,33%, dan F1-score 85,19%. Meskipun k-NN unggul dalam akurasi dan precision, Naive Bayes menunjukkan keseimbangan performa yang lebih baik melalui nilai recall dan F1-score yang lebih tinggi, khususnya dalam mendeteksi kelas positif. Temuan ini menegaskan bahwa pemilihan model tidak hanya mempertimbangkan akurasi keseluruhan, tetapi juga performa pada tiap kelas, terutama pada dataset kesehatan yang cenderung tidak seimbang.
Analisis Tingkat Penerimaan Mahasiswa Terhadap Aplikasi Zoom Meeting Sebagai Media Perkuliahan Menggunakan Metode TAM Rifani Ihsan, Muhammad Ifan; Ihsan, Muhammad Ifan Rifani; Saadah, Rabiatus; Dahlia, Rizka; Lailiah, Badariatul; Nawawi, Hendri Mahmud
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i1.973

Abstract

The increasingly advanced technology makes it easier for anyone to use it for various activities, such as lecture activities. Especially during the Covid-19 pandemic that has occurred since 2019 until this research was conducted. Covid-19, formerly known as SARS-CoV2, is an outbreak of pneumonia that originates from a virus. This virus was first heard in Wuhan, China in December 2019 (Ciotti et al., 2020). Lectures are conducted online to prevent the spread of this virus. One of the technologies widely used during the Covid-19 pandemic is video conferencing. With video conference meetings can be held even if the people present are far from each other. Zoom is a cloud-based application that is currently often used as a video communication (Azkiya, 2021). Zoom is a video conferencing application that is currently being used in various activities, including lectures. Because it is important to know how much student acceptance of the Zoom application they use for lectures is. The research was conducted using the Technology Acceptance Model or TAM method which consists of three constructs, namely Perceived Usefulness, Perceived Ease of Use and Acceptance of Technology. The data was obtained by distributing questionnaires with a Likert scale of 1 to 5. The data obtained were then calculated using the Structural Equation Model or SEM method consisting of the Outer Model and Inner Model calculations. SEM is an analytical technique that allows testing of a series of simultaneous relationships (Gardenia, 2018). The conclusion is that by taking the R-Square value, the construct in the TAM method is able to measure as much as 63% of student admission case studies on Zoom as a lecture medium. Keywords: Analysis, TAM, Zoom.
Co-Authors Agung Baitul Hikmah Agung Sasongko Agustiani, Sarifah Ali Mustopa, Ali ANI OKTARINI SARI, ANI OKTARINI Badariatul Lailiah Eri Bayu Pratama, Eri Bayu Fiananta, Maulana Gata, Windu Jajang Jaya Purnama Jonhar, Thaufik Darma Junianto, Rahmad Kahfi, Ahmad Hafidzul Kholifah, Desiana Nur M. Rangga Ramadhan Saelan Muhammad Ifan Rifani Ihsan Nugraha, Fitra Septia Purwanto, Dicky Ridwansyah Ridwansyah Risnandar, Risnandar Rizka Dahlia Saadah, Rabiatus Sarwono Sarwono Siti Khotimatul Wildah Susy Rosyida Thira, Indra Jiwana