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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 JATI (Jurnal Mahasiswa Teknik Informatika) 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 Penelitian Teknologi Informasi dan Sains Jurnal Pengabdian Masyarakat Nian Tana
Nur Rachmat
Universitas Multi Data Palembang, Kota 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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PERBANDINGAN ALGORTIMA RANDOM FOREST DAN SVM UNTUK MULTI-LABEL KLASIFIKASI MOOD BUKU BERBASIS DESKRIPSI TEKS Intan Sanu; Nur Rachmat
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16741

Abstract

Banyaknya buku dengan tema dan genre yang beragam sering menyulitkan pembaca dalam memilih bacaan yang sesuai dengan suasana hati, karena genre tidak selalu merepresentasikan nuansa emosional isi cerita. Permasalahan ini mendorong perlunya sistem klasifikasi mood buku berbasis teks deskripsi. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Support Vector Machine (SVM) dan Random Forest dalam klasifikasi multi-label mood buku. Dataset yang digunakan adalah 7k Books dari Kaggle, dengan atribut title dan description. Karena dataset tidak memiliki label emosi, dilakukan pelabelan otomatis menggunakan model emotion classifier berbasis Transformer (DistilRoBERTa) menjadi tujuh kategori mood, yaitu joy, sadness, anger, fear, disgust, surprise, dan neutral. Metode yang digunakan meliputi pra-pemrosesan teks, representasi fitur menggunakan TF-IDF (unigram dan bigram), serta pendekatan Binary Relevance dengan optimasi parameter menggunakan GridSearchCV. Evaluasi dilakukan menggunakan metrik macro-averaging, micro-averaging, dan Hamming Loss. Hasil penelitian menunjukkan bahwa Linear SVM memperoleh micro-averaging F1-score sebesar 0,7909, sedikit lebih tinggi dibandingkan Random Forest sebesar 0,7856, dengan Hamming Loss sekitar 0,11 untuk kedua model. Hasil ini menunjukkan bahwa Linear SVM lebih unggul secara keseluruhan, meskipun performa pada beberapa label masih dipengaruhi oleh ketidakseimbangan data
Perbandingan Kinerja Arsitektur MobileneTV2 dan MobileneTV3 Dalam Klasifikasi Penyakit Retina pada Citra Optical Coherence Tomography (OCT) Menggunakan Optimizer AdamW dan SGD Ricko Andreas Kartono; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.915

Abstract

Retinal diseases are serious visual disorders that can lead to decreased visual function and even blindness. The diagnosis of retinal diseases is generally still performed manually by medical professionals through the examination of Optical Coherence Tomography (OCT) images, a process that requires considerable time, high precision, and is prone to diagnostic errors. Previous studies have mostly employed larger and more complex CNN architectures, with optimization limited to a few commonly used optimizers. This study aims to develop an automatic retinal disease classification model using Convolutional Neural Network (CNN) methods by leveraging the lightweight and efficient MobileNetV2 and MobileNetV3 architectures, enabling faster applications that can be deployed on resource-constrained devices. The architectures evaluated include MobileNetV2, MobileNetV3-Large, and MobileNetV3-Small, along with a comparison of two optimizers, namely AdamW and Stochastic Gradient Descent (SGD). The dataset used consists of 4,000 OCT images divided into four classes: Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen. The training process was conducted using a transfer learning approach, and model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the combination of the MobileNetV2 architecture with a batch size of 16 and either the AdamW or SGD optimizer achieved the best performance, reaching an accuracy of 85.75%, which is the highest among all tested configurations. These findings highlight the strong potential of lightweight architectures to be developed into fast, accurate, and field-deployable retinal disease diagnostic applications on mobile devices using deep learning.
Evaluasi Efektifitas Optimizer Adam dan SGD pada Klasifikasi Citra Dermoskopi dengan MobileNetV4 Ahmad Naufal; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1165

Abstract

Skin disease is one of the most common health problems and requires fast and accurate diagnosis. The limited availability of dermatology specialists and the high subjectivity of conventional diagnosis have driven the development of artificial intelligence-based automatic classification systems. This study aims to compare the performance of the Adam and Stochastic Gradient Descent (SGD) optimizers on the MobileNetV4 architecture for classifying eight classes of skin diseases using the ISIC 2019 dataset. The dataset consists of 23,257 valid dermoscopic images after preprocessing, which includes duplicate image removal, hair artifact elimination using the blackhat morphology method, and an asymmetric sampling strategy in which majority classes were capped at a maximum of 2,000 images while minority classes were augmented to reach the target count, in order to address extreme class imbalance with a ratio of up to 53:1. The model was trained using a three-phase training strategy with gradual unfreezing of the MobileNetV4 backbone initialized with pretrained ImageNet weights. All training configurations were made identical for both optimizers except for the optimization algorithm and learning rate, ensuring a fair comparison. Evaluation results on the test set show that the Adam optimizer achieved an accuracy of 71.07% with a macro F1-score of 0.72, while SGD achieved an accuracy of 58.06% with a macro F1-score of 0.57. Adam outperformed SGD across all eight skin disease classes. The performance difference of 13.01% indicates that Adam's adaptive learning rate mechanism is more effective for dermoscopic datasets with imbalanced class distributions compared to SGD. Nevertheless, it should be noted that Adam requires greater computational memory than SGD due to the storage of first and second moment estimates per parameter, and therefore the computational efficiency trade-off should be considered when deploying the model on resource-constrained devices. This study provides empirical contribution in selecting the optimal optimizer for skin lesion classification based on lightweight architectures.
Klasifikasi Penyakit Daun Mangga Menggunakan YOLOv11 Berbasis Deep Learning dan Computer Vision Andrian Wijaya; Nur Rachmat
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9168

Abstract

Indonesia’s mango agriculture sector continues to face significant challenges due to leaf diseases that reduce crop productivity. Conventional disease identification methods remain inefficient because they rely on subjective visual observation. This study aims to develop a mango leaf disease classification model using the YOLOv11 deep learning algorithm. YOLOv11 is chosen for its capability in real-time object classification with an optimal balance between accuracy and processing speed. The research will utilize the Mango Leaf Disease dataset from Kaggle, consisting of eight classes (seven disease types and one healthy class). The planned methodology includes preprocessing, image augmentation, data splitting using K-Fold Cross Validation, and hyperparameter tuning on optimizer, learning rate, epoch, and batch size. Model performance will be evaluated using the Confusion Matrix. This research is expected to produce an accurate and efficient classification model that enables objective and rapid early detection of mango leaf diseases. The research utilizes a dataset from Kaggle consisting of 4,000 images across eight classes—comprising seven disease types and one healthy leaf class. The methodology involves preprocessing (resizing to 640x640 pixels and normalization), image augmentation, and data splitting using 10-Fold Cross Validation. Performance was optimized through hyperparameter tuning of the Adam optimizer, a learning rate of 0.001, a batch size of 16, and various epoch settings. The experimental results demonstrate that the YOLOv11s model achieves exceptional and stable performance. Evaluation using a Confusion Matrix shows that the model reached a 100% accuracy, precision, recall, and F1-score on the dataset used in this study. The model recorded an average training loss of 0.0979 and a validation loss of 0.0027. These findings confirm that YOLOv11s is not only highly accurate but also computationally efficient, making it a viable candidate for real-time detection systems on mobile or edge computing devices to support early disease detection in mango orchards. As the main contribution, this study provides a comprehensive evaluation of YOLOv11s for mango leaf disease classification using a 10-Fold Cross Validation scheme, stability analysis based on validation loss, and an assessment of its potential for real-time deployment on mobile and edge computing devices.
Comparison Of Adam and SGD For The Classfication Of Palm Tree Leaf Diseases With ResNet50 Ardi al Ghifari Ardi; Nur Rachmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research 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.
Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification Wesley Maximilliano; Nur Rachmat
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.
Indonesian-Language Spam Email Classification Using Support Vector Machine Muhammad Alfa Rizi; Nur Rachmat
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.
Co-Authors Adhytio Mahendra Ahmad Naufal Andrian Wijaya Ardi al Ghifari Ardi Bobby Jaya Saputra Calvin Oliver Saputra Davin Hansel Winata Devella, Siska Dicky Ryanto Fernandes Elizabeth Fiona Hartono Farisi, Ahmad Fionna Caroline Hakiki, Muhammad Anugrah Indrawan Indrawan Intan Sanu Izha Mahendra Johannes Petrus Michael Lorens Molavi Arman Monica Rizki Ayustin Muhammad Alfa Rizi Muhammad Ezar Al Rivan Muhammad Naufal Anugrah Ricko Andreas Kartono Riski Ariza, Agung Russel Wijaya Saputra, Muhammad Redho Ubaidillah Ubaidillah Vannes Wijaya Vincent Wahyu Sudrajat Wesley Maximilliano Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes