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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 Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Jurnal Teknologi dan Manajemen Industri Terapan JEECS (Journal of Electrical Engineering and Computer Sciences) MDP Student Conference Jurnal Teknologi Informasi Mura Jurnal Pengabdian Masyarakat Nian Tana
Nur Rachmat
Universitas Multi Data Palembang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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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.
Klasifikasi Non-Destruktif Kemanisan Semangka Manohara Menggunakan Transfer Learning VGG-16 Dicky Ryanto Fernandes; Nur Rachmat
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 4 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/81wv2s06

Abstract

Semangka (Citrullus lanatus) merupakan buah tropis populer di Indonesia karena rasanya yang manis dan kandungan airnya yang tinggi. Penentuan tingkat kemanisan masih banyak dilakukan secara destruktif dengan refraktometer, sehingga kurang efisien. Penelitian ini bertujuan mengklasifikasikan tingkat kemanisan semangka Manohara secara non-destruktif berdasarkan ciri fisik luar menggunakan Convolutional Neural Network (CNN) dengan arsitektur VGG-16 dan pendekatan transfer learning. Data dikumpulkan secara mandiri dan dibagi menjadi 80% data latih, 10% validasi, dan 10% uji. Model menggunakan Adam Optimizer dan Softmax sebagai classifier. Hasil terbaik diperoleh pada skenario ke-4 dengan akurasi 67,42%. Namun, model menunjukkan gejala underfitting dan kecenderungan mengklasifikasi ke satu kelas. Penelitian ini menunjukkan potensi awal penggunaan visi komputer dalam seleksi kualitas semangka secara otomatis dan non-destruktif, meskipun masih diperlukan peningkatan akurasi agar dapat diimplementasikan secara praktis di lapangan.
Klasifikasi Penyakit Daun Tanaman Jagung Menggunakan Pendekatan Transfer Learning Arsitektur MobileNetV4 Muhammad Naufal Anugrah; Nur Rachmat
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 4 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v4i4.1393

Abstract

Jagung (Zea mays) merupakan komoditas pangan utama, namun produktivitasnya terhambat oleh serangan penyakit daun. Identifikasi penyakit daun jagung secara manual memakan waktu dan bersifat subjektif. Penelitian ini bertujuan mengembangkan model klasifikasi otomatis menggunakan Convolutional Neural Network (CNN) berbasis transfer learning dengan mengevaluasi tiga varian arsitektur MobileNetV4 (Small, Medium, Large) serta membandingkan optimizer Adam dan SGD. Peneliti menggunakan 4000 citra daun jagung dari empat kelas dengan pembagian 80% data pelatihan, 10% data validasi dan 10% data pengujian. Hasil eksperimen menunjukkan bahwa model terbaik diperoleh dari MobileNetV4-Conv-Medium dengan optimizer SGD, yang mencapai akurasi validasi tertinggi 95,25% dan F1-Score 92,00% dengan penggunaan hyperparameter learning rate 0.01, epoch 50 dan batch size 32. Kinerja ini menegaskan potensi MobileNetV4, khususnya varian Medium, dalam mencapai keseimbangan optimal antara efisiensi komputasi dan kinerja klasifikasi, menjadikannya model yang sangat menjanjikan untuk implementasi sebagai sistem mobile dalam pertanian presisi.
Comparison of XGBoost and LightGBM Algorithms in Predicting Heart Disease Caroline, Fionna; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7505

Abstract

Heart disease remains a leading cause of mortality worldwide, underscoring the need for early and accurate diagnosis to reduce complications and improve patient outcomes. Recent advances in machine learning have enabled the development of predictive models that assist healthcare professionals in disease detection using patient medical records. This study aims to develop and compare the performance of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) for heart disease prediction. The dataset used in this research was obtained from the UCI Machine Learning Repository and consists of 303 patient records with binary class labels indicating the presence or absence of heart disease. Data preprocessing involved feature standardization using StandardScaler and handling class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was conducted using Stratified K-Fold Cross Validation with K values of 3, 5, and 7 to ensure robust and unbiased performance assessment. Hyperparameter optimization was carried out using RandomizedSearchCV to efficiently identify optimal model configurations. Experimental results indicate that both XGBoost and LightGBM achieved strong classification performance, with accuracy exceeding 80% and AUC values above 0.89. LightGBM demonstrated slightly superior performance in terms of average accuracy, F1-score, and stability across folds, while XGBoost achieved higher precision, reflecting better control of false positives. Overall, both algorithms are effective for heart disease prediction, supporting the potential of machine learning in early disease detection and clinical decision-support systems.
Indonesian-Language Spam Email Classification Using Support Vector Machine Rizi, Muhammad Alfa; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7578

Abstract

Spam email remains a significant problem in digital communication, particularly for Indonesian-language emails, due to linguistic complexity, informal writing styles, and similarities between spam and legitimate (ham) messages. These factors often reduce the effectiveness of traditional spam filtering techniques. This study evaluates the performance of the Support Vector Machine (SVM) algorithm for classifying Indonesian spam emails using a combination of Term Frequency–Inverse Document Frequency (TF-IDF) and N-gram features. The proposed approach applies a text preprocessing pipeline, including case folding, text cleaning, tokenization, stopword removal, and stemming, to reduce noise and improve feature representation. Text data are transformed into numerical vectors using TF-IDF with unigram and bigram configurations to capture individual terms and contextual phrase patterns commonly found in spam emails. A linear kernel SVM is used as the classification model, and its performance is evaluated using K-Fold Cross-Validation to ensure robustness and reduce evaluation bias. The model is assessed using accuracy, precision, recall, and F1-score metrics. Experiments are conducted on the Indonesian Email Spam Dataset, consisting of 2,636 emails, with 1,368 spam messages and 1,268 non-spam (ham) messages. Experimental results show that the proposed model achieved an average accuracy of 98.71%, precision of 98.34%, recall of 99.20%, and F1-score of 98.76 across 10-fold cross-validation. This study contributes to the development of an efficient and lightweight spam detection model for Indonesian-language emails and provides empirical evidence that SVM combined with TF-IDF and N-gram features remains a reliable alternative to more complex deep learning approaches for medium-sized text datasets.
Comparison Of Adam and SGD For The Classfication Of Palm Tree Leaf Diseases With ResNet50 Ardi, Ardi al Ghifari; Nur Rachmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7501

Abstract

Plants from the palm tree family (Arecaceae), such as coconut, oil palm, and date palm, play an important role in the economy and food security, especially in Indonesia. However, leaf diseases such as leaf spot disease pose a serious threat that can reduce productivity. Manual disease identification is time-consuming and prone to errors, necessitating an image-based automatic classification system. This study aims to apply the ResNet50 Convolutional Neural Network (CNN) architecture for palm tree leaf disease classification and compare two popular optimization algorithms, Adam and Stochastic Gradient Descent (SGD), in terms of model training accuracy and efficiency. The dataset used is public, covering five classes of leaf images: Healthy, White Scale, Brown Spot, Leaf Smut, and Bacterial Leaf Blight. The research process includes data collection and preprocessing (resizing, normalization, and augmentation), dividing the dataset into three parts, namely training, validation, and testing data using the train/validation/test split approach. This approach provides a fairly representative evaluation of model performance while being computationally efficient. Model training was performed using transfer learning with ResNet50, and performance evaluation was performed using a confusion matrix to obtain accuracy, precision, recall, and F1-score values. The results of the two optimizers were compared to determine their effect on model performance. The experimental results show that the ResNet50 model optimized with Adam achieved a higher test accuracy of 87.23% compared to SGD with 85.96%, while SGD demonstrated more consistent performance between validation and testing phases, indicating better training stability.
Co-Authors Adhytio Mahendra Andrian Wijaya Antonius Wahyu Sudrajat Ardi, Ardi al Ghifari Bobby Jaya Saputra Calvin Oliver Saputra Caroline, Fionna 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 Muhammad Naufal Anugrah Ricko Andreas Kartono Riski Ariza, Agung Rizi, Muhammad Alfa Ubaidillah Ubaidillah Vannes Wijaya Vincent Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes