Article Per Year (5 Year)
p-Index From 2021 - 2026
3.208
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Ilmiah KOMPUTASI Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Informasi MURA Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Journal of Computer Networks, Architecture and High Performance Computing Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer JEECS (Journal of Electrical Engineering and Computer Sciences) MDP Student Conference Jurnal Teknologi Informasi Mura
Nur Rachmat
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Penerapan SMOTE dan Regresi Logistik Pada Website Skrining Awal Kesehatan Mental Mahasiswa Wijaya, Vannes; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9046

Abstract

Mental health is a very important aspect in realizing overall health. Students are an age group that experiences a transition from adolescence to adulthood, students tend to experience stress, especially those originating from the academic process. In this study, a website-based questionnaire system was developed to predict mental health profiles consisting of optimal mental health profiles (+-), maximum mental health profiles (++), minimal mental health profiles (--), and minimal mental health profiles (-+). The questionnaire questions and grouping of mental health profiles used the SKM-12 mental health measurement tool. The dataset used was obtained from 78 students at Multi Data University Palembang. The method used in this research is Logistic Regression using the data imbalance method, namely SMOTE with parameter solver newton-cg with data division, 70% training data and 30% test data. The results obtained in this study using confusion matrix model evaluation obtained an accuracy of 89.28% and model evaluation using K-fold cross validation obtained an accuracy of 87.43% for training data and 82.66% for test data.
PEMANFAATAN APLIKASI PENDAFTARAN DALAM BAKTI SOSIAL KESEHATAN TZU CHI KE-145 Rachmat, Nur; Indrawan, Indrawan; Petrus, Johannes
FORDICATE Vol 4 No 2 (2025): April 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i2.11335

Abstract

Bakti Sosial Kesehatan Tzu Chi ke-145 adalah kegiatan kemanusiaan yang memberikan layanan kesehatan bagi masyarakat yang membutuhkan. Sebelumnya, pendaftaran peserta dilakukan menggunakan Google Form, yang menyebabkan keterlambatan administrasi, duplikasi data, dan penyebaran informasi yang kurang efektif. Untuk mengatasi masalah ini, dikembangkan aplikasi berbasis web yang mempermudah pendaftaran, verifikasi data, dan koordinasi antara tim kesehatan serta relawan. Hasilnya, proses administrasi menjadi lebih cepat dan tertata. Dari 582 peserta yang mengikuti screening, berdasarkan data di aplikasi, sebanyak 114 peserta katarak, 29 hernia, 2 bibir sumbing, dan 60 bedah minor lolos untuk operasi. Pada hari operasi, 106 peserta katarak, 25 hernia, 2 bibir sumbing, dan 57 bedah minor berhasil menjalani tindakan kesehatan. Aplikasi ini membuat proses pendaftaran dan pelayanan lebih cepat, akurat, dan terorganisir. Sistem ini diharapkan dapat terus digunakan dan dikembangkan untuk kegiatan bakti sosial kesehatan berikutnya agar lebih banyak masyarakat yang terbantu.
Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification Maximilliano, Wesley; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6259

Abstract

Corn leaf disease represents a significant threat to agricultural productivity, capable of causing substantial economic losses in Indonesia. Conventional identification methods, which rely on visual observation by farmers, are frequently subjective, time-consuming, and inaccurate. This study conducts a systematic comparative analysis of two efficient Convolutional Neural Network (CNN) architecture variants, MobileNetV3-Large and MobileNetV3-Small, for the classification of four corn leaf conditions: Gray Leaf Spot, Common Rust, Northern Leaf Blight, and Healthy. The research further evaluates the influence of two prevalent optimizers, Adam and Stochastic Gradient Descent (SGD), to ascertain the most optimal model configuration through hyperparameter tuning. The models were trained and evaluated using a local image dataset from Sampang, Indonesia, comprising 4000 images. The methodology included image preprocessing, data augmentation, and hyperparameter tuning of the learning rate and batch size. The results demonstrate that both architectures achieved exceptionally high accuracy. The principal finding reveals that MobileNetV3-Small unexpectedly outperformed its larger variant, attaining a peak accuracy of 99.5% with the SGD optimizer, a learning rate of 0.01, and a batch size of 32. In comparison, MobileNetV3-Large reached a maximum accuracy of 99.0% under a similar configuration. These findings underscore the considerable potential of lightweight architectures for the development of rapid, accurate, and field-deployable plant disease diagnostic applications on mobile devices using deep learning.
Klasifikasi Spesies Jamur Menggunakan Convolutional Neural Network dengan Arsitektur MobileNetV2 Hakiki, Muhammad Anugrah; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.11077

Abstract

Indonesia has a high biodiversity of fungi, including edible and toxic species. Manual identification is often challenging due to morphological similarities between safe and poisonous species. Therefore, this study evaluates the use of deep learning-based Convolutional Neural Network (CNN) with the MobileNetV2 architecture for mushroom classification. The research method includes collecting a dataset of 1,500 images from 10 mushroom species (5 edible and 5 toxic), preprocessing data by normalizing image size and applying augmentation techniques, and training the model using the Adam optimizer with dropout and early stopping to prevent overfitting. Hyperparameter tuning was performed using grid search on batch size (64, 128, 256), epochs (20, 50, 100), and learning rate (0.1, 0.01, 0.001). The test results show that a combination of batch size 64, epoch 50, and learning rate 0.1 achieved 98% validation accuracy. The final model was tested and achieved 95.33% accuracy, with an average precision, recall, and f1-score of 95%. These results confirm that MobileNetV2 is effective in classifying mushroom species and can assist in more accurately identifying edible and toxic fungi.
Penerapan Algoritma Gradient Boosting dalam Mendiagnosa Penyakit Kucing dan Anjing Vincent; Rachmat, Nur
Jurnal Buana Informatika Vol. 16 No. 2 (2025): Jurnal Buana Informatika, Volume 16, Nomor 02, Oktober 2025
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Royal Canin selaku lembaga riset hewan domestik mengungkapkan bahwa hewan peliharaan di Indonesia jarang sekali melakukan pemeriksaan rutin ke klinik hewan, jika dipersentasekan hanya berada di angka 29,5%. Dengan persentase tersebut, semakin khawatir hewan dapat menularkan penyakit ke manusia atau disebut sebagai zoonosis, jika hewan sama sekali tidak mendapatkan perawatan dan identifikasi dini penyakit yang dialami. Pada penelitian ini menggunakan metode gradient boosting sebagai fokus utama untuk memprediksi penyakit berdasarkan gejala-gejala yang dialami hewan peliharaan. Melalui proses hyperparameter tuning menggunakan gridsearch, diperoleh model terbaik dengan kombinasi parameter: learning_rate 0,05, max_depth 7, min_samples_leaf 1, min_samples_split 2, n_estimators 200, dan subsample 0,9. Dari hasil hyperparameter tuning, model tersebut menunjukkan performa terbaik dengan accuracy 88%, precision 97%, recall 96%, f1-score 96%, dan hamming loss 0,29%. Hasil tersebut menunjukkan bahwa model memiliki kemampuan memprediksi multilabel yang akurat.
Co-Authors Adhytio Mahendra Antonius Wahyu Sudrajat Bobby Jaya Saputra Calvin Oliver Saputra Davin Hansel Winata Devella, Siska Dicky Ryanto Fernandes Elizabeth Fiona Hartono Farisi, Ahmad Hakiki, Muhammad Anugrah Indrawan Indrawan Johannes Petrus M Ezar Al Rivan Maximilliano, Wesley Michael Lorens Molavi Arman Monica Rizki Ayustin Riski Ariza, Agung Ubaidillah Ubaidillah Vannes Wijaya Vincent Wibowo, Suluh Arif Wiwik Handayani