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All Journal Jurnal Media Infotama Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTECOMS: Journal of Information Technology and Computer Science JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Scientific Journal of Informatics Jurnal Teknologi Informasi Mura
Rudi Kurniawan
Universitas Bina Insan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Deteksi Dini Terhadap Penyakit Tumor Otak Menggunakan Citra Magnetik Resonance Imaging (MRI) dengan Pendekatan Deep Convolutional Neural Network Muhamad Salman; Rudi Kurniawan; Bunga Intan; Budi Santoso
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.37-41

Abstract

This study aims to develop an early detection system for brain tumors using MRI images with a Deep Convolutional Neural Network (DCNN) based on the ResNet152V2 architecture. Rapid detection of brain tumors is crucial for improving recovery chances; however, manual processes often face challenges due to limitations in technology and medical expertise. Therefore, this research offers an automated solution for analyzing MRI images.The methods used include data collection from public datasets, image preprocessing, and training the DCNN model. The ResNet152V2 model was chosen for its ability to address the vanishing gradient problem and its effectiveness in feature extraction. The results show that the model achieved an accuracy of 92.38% in classifying four types of brain tumors: Meningioma, Glioma, Pituitary, and No Tumor. Evaluation using a confusion matrix and classification report indicates good performance. This research is expected to contribute to the early diagnosis of brain tumors and serve as a reference for future studies in the application of artificial intelligence in the medical field.
Klasifikasi Penyakit pada Buah Jeruk Berdasarkan Citra dengan Pendekatan Transfer Learning Menggunakan Arsitektur Densenet-121 Sheli Agustina; Asep Toyib Hidayat; Satrianansyah; Rudi Kurniawan
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.42-47

Abstract

This study aims to develop a classification system for citrus fruit diseases based on digital images using a machine learning approach. The primary challenge in citrus cultivation is disease attacks that affect both the quality and quantity of production. In this research, image processing techniques were applied to extract color, shape, and texture features from citrus fruit images, which were then used as input for classification algorithms. This study uses the DenseNet-121 architecture for orange fruit image classification. The dataset used consisted of images of healthy citrus fruits and those affected by various diseases, such as blackspot, canker, and greening. The testing results showed that the DenseNet-121 architecture achieved the highest accuracy in classifying citrus diseases, with an accuracy rate of up to 99%. This system is expected to assist farmers and relevant stakeholders in early disease detection and in taking appropriate control measures.
A MODEL HIBRID RESNET-SVM UNTUK KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERBASIS CITRA DAUN: HYBRID RESNET-SVM MODEL FOR MAIZE LEAF DISEASE CLASSIFICATION BASED ON LEAF IMAGES Andri Anto Tri Susilo; Hasan Basri; Rudi Kurniawan
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2744

Abstract

Abstrak Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak signifikan dalam bidang pertanian, khususnya pada deteksi dan klasifikasi penyakit tanaman. Penelitian ini mengusulkan model hibrid yang mengintegrasikan Residual Network (ResNet) sebagai ekstraktor fitur dengan Support Vector Machine (SVM) sebagai classifier utama untuk mengklasifikasikan penyakit pada tanaman jagung berbasis citra daun. Dataset yang digunakan mencakup empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, serta daun jagung Healthy atau sehat. Hasil pengujian menunjukkan bahwa model hibrid ResNet-SVM mampu mencapai akurasi akhir sebesar 94,61%. Berdasarkan laporan klasifikasi, performa terbaik ditunjukkan pada kelas Healthy dengan nilai precision, recall, dan f1-score mencapai 1,00. Kelas Common Rust juga memperoleh hasil tinggi dengan f1-score 0,96, sedangkan kelas Blight mencapai f1-score 0,92. Namun, kelas Gray Leaf Spot masih menjadi tantangan dengan f1-score 0,62 akibat jumlah data yang relatif lebih sedikit. Secara keseluruhan, nilai macro average f1-score tercatat sebesar 0,88, sementara weighted average f1-score mencapai 0,94. Temuan ini menunjukkan bahwa kombinasi ResNet dan SVM efektif dalam meningkatkan akurasi klasifikasi penyakit jagung, sekaligus memperkuat potensi penerapan metode hibrid deep learning dan machine learning dalam sistem deteksi penyakit tanaman berbasis citra digital. Kata kunci: Resnet, SVM, Model Hibrid, Klasifikasi, Penyakit Jagung Abstract The advancement of Artificial Intelligence (AI) has significantly impacted agriculture, particularly in plant disease detection and classification. This study proposes a hybrid model that integrates Residual Network (ResNet) as a feature extractor with Support Vector Machine (SVM) as the main classifier for classifying corn leaf diseases based on image data. The dataset consists of four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy leaves. Experimental results show that the hybrid ResNet-SVM model achieved a final accuracy of 94.61%. The best performance was obtained in the Healthy class with precision, recall, and f1-score of 1.00. The Common Rust class also achieved a high f1-score of 0.96, while the Blight class reached 0.92. However, the Gray Leaf Spot class remained more challenging, with an f1-score of 0.62 due to the relatively smaller number of samples. Overall, the model achieved a macro average f1-score of 0.88 and a weighted average f1-score of 0.94. These findings demonstrate that the combination of ResNet and SVM is effective in enhancing classification accuracy compared to single methods, highlighting its potential application in developing automated corn disease detection systems based on digital leaf images. Keywords: ResNet, SVM, hybrid model, classification, corn disease
PREDIKSI HARGA SAHAM BANK MANDIRI BERDASARKAN DATA BMRI HISTORICAL STOCK PRICE Khairul Imam Mahmud; Rudi Kurniawan; Harma Oktafia Lingga Wijaya
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10983

Abstract

Stock price movements exhibit dynamic and highly fluctuating characteristics, making accurate prediction challenging when using conventional approaches. Therefore, this study aims to develop and evaluate a stock price prediction model for PT Bank Mandiri (Persero) Tbk using a deep learning approach based on Long Short-Term Memory (LSTM). The LSTM model is applied for time series forecasting by utilizing historical stock price data, with a primary focus on the closing price as the target variable. The historical stock price data of Bank Mandiri undergo preprocessing stages, including data cleaning and normalization using the Min–Max Scaling method, to align data scales and improve training stability and convergence. The proposed LSTM architecture consists of two LSTM layers with dropout mechanisms for regularization, followed by fully connected layers to generate stock price predictions. The model is trained using the Adam optimizer with Mean Squared Error (MSE) as the loss function. Model performance is evaluated using the Mean Absolute Percentage Error (MAPE) metric on the testing dataset. The experimental results show that the LSTM model achieves a MAPE value of 2.7572%, indicating a very high prediction accuracy. Furthermore, the future forecasting results suggest a relatively stable stock price movement with a gradual upward trend in the short term. Based on the findings, it can be concluded that the Long Short-Term Memory (LSTM) method is effective for predicting Bank Mandiri’s stock prices and has strong potential as a data-driven decision support tool for investment analysis, although it should be complemented with fundamental analysis and external market factors
KLASIFIKASI JENIS JAMUR BERDASARKAN CITRA DIGITAL MENGGUNAKAN METODE HYBRID FUSION DEEP LEARNING Dilla Monalisa; Rudi Kurniawan; Budi Santoso
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 2 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i2.2954

Abstract

Mushroom species identification based on digital images remains challenging due to high visual similarity among species and the limitations of manual identification. This study proposes a Hybrid Fusion Deep Learning classification system that integrates two Convolutional Neural Network (CNN) architectures, DenseNet201 and MobileNetV3Large, as feature extractors through feature-level fusion. The novelty of this research lies in the complementary integration of two CNN architectures not previously combined for mushroom classification, further coupled with Random Forest as the final classifier to improve stability and generalization. The dataset comprises 2,573 images of five mushroom classes from Kaggle, split at 70:15:15 (training: 1,801; validation: 386; testing: 386 images). Preprocessing includes resizing to 224×224 pixels, pixel normalization, and data augmentation. Evaluation was conducted using stratified 5-fold cross-validation, with accuracy, precision, recall, F1-score, and AUC metrics. The proposed model achieves a validation accuracy of 95.53%, with micro-average AUC = 0.9961 and macro-average AUC = 0.9951. Compared to single-model baselines (DenseNet201: 91.45%; MobileNetV3Large: 89.73%), the proposed method demonstrates significant improvement. These findings confirm that the hybrid fusion approach effectively enhances mushroom image classification and has strong potential for computer vision-based biological identification in food safety and biodiversity conservation.
PENGEMBANGAN CHATBOT INFORMASI HUKUM BERBASIS RETRIEVAL AUGMENTED GENERATION: STUDI KASUS KUHP 2023 Yuda Putra Pratama; Rudi Kurniawan; Budi Santoso
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 1 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Maret
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i1.2959

Abstract

Pemberlakuan KUHP 2023 sebagai sistem hukum pidana baru di Indonesia memerlukan mekanisme penyaluran informasi hukum yang cepat dan akurat. Namun kompleksitas regulasi dan keterbatasan akses informasi menjadi kendala utama bagi masyarakat di dalam memahami sistem hukum baru yang berlaku. Penelitian ini mengembangkan chatbot berbasis RetrievalAugmented Generation (RAG) untuk mengoptimalkan Large Language Model dengan menyediakan konteks informasi hukum pidana dari KUHP 2023. Sistem RAG dibangun melalui tahapan parsing dokumen, chunking per-pasal, indexing, embedding menggunakan Qwen3Embedding, penyimpanan vektor ke ChromaDB, dan retrieval konteks relevan. Model LLM Qwen2.5 melalui Ollama menghasilkan respons akurat berdasarkan konteks yang diperoleh. Chatbot diintegrasikan ke Telegram untuk kemudahan akses. Evaluasi menggunakan RAGAs menunjukkan nilai faithfulness 0.825 dan context precision 0.947, mengindikasikan kemampuan sistem memberikan respons akurat dan relevan. Pengujian Black Box Testing memvalidasi seluruh fungsi chatbot berjalan optimal. Secara keseluruhan sistem terbukti dapat diandalkan untuk menyalurkan informasi hukum pidana KUHP 2023.
A MODEL HIBRID RESNET-SVM UNTUK KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERBASIS CITRA DAUN: HYBRID RESNET-SVM MODEL FOR MAIZE LEAF DISEASE CLASSIFICATION BASED ON LEAF IMAGES Andri Anto Tri Susilo; Hasan Basri; Rudi Kurniawan
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2744

Abstract

Abstrak Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak signifikan dalam bidang pertanian, khususnya pada deteksi dan klasifikasi penyakit tanaman. Penelitian ini mengusulkan model hibrid yang mengintegrasikan Residual Network (ResNet) sebagai ekstraktor fitur dengan Support Vector Machine (SVM) sebagai classifier utama untuk mengklasifikasikan penyakit pada tanaman jagung berbasis citra daun. Dataset yang digunakan mencakup empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, serta daun jagung Healthy atau sehat. Hasil pengujian menunjukkan bahwa model hibrid ResNet-SVM mampu mencapai akurasi akhir sebesar 94,61%. Berdasarkan laporan klasifikasi, performa terbaik ditunjukkan pada kelas Healthy dengan nilai precision, recall, dan f1-score mencapai 1,00. Kelas Common Rust juga memperoleh hasil tinggi dengan f1-score 0,96, sedangkan kelas Blight mencapai f1-score 0,92. Namun, kelas Gray Leaf Spot masih menjadi tantangan dengan f1-score 0,62 akibat jumlah data yang relatif lebih sedikit. Secara keseluruhan, nilai macro average f1-score tercatat sebesar 0,88, sementara weighted average f1-score mencapai 0,94. Temuan ini menunjukkan bahwa kombinasi ResNet dan SVM efektif dalam meningkatkan akurasi klasifikasi penyakit jagung, sekaligus memperkuat potensi penerapan metode hibrid deep learning dan machine learning dalam sistem deteksi penyakit tanaman berbasis citra digital. Kata kunci: Resnet, SVM, Model Hibrid, Klasifikasi, Penyakit Jagung Abstract The advancement of Artificial Intelligence (AI) has significantly impacted agriculture, particularly in plant disease detection and classification. This study proposes a hybrid model that integrates Residual Network (ResNet) as a feature extractor with Support Vector Machine (SVM) as the main classifier for classifying corn leaf diseases based on image data. The dataset consists of four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy leaves. Experimental results show that the hybrid ResNet-SVM model achieved a final accuracy of 94.61%. The best performance was obtained in the Healthy class with precision, recall, and f1-score of 1.00. The Common Rust class also achieved a high f1-score of 0.96, while the Blight class reached 0.92. However, the Gray Leaf Spot class remained more challenging, with an f1-score of 0.62 due to the relatively smaller number of samples. Overall, the model achieved a macro average f1-score of 0.88 and a weighted average f1-score of 0.94. These findings demonstrate that the combination of ResNet and SVM is effective in enhancing classification accuracy compared to single methods, highlighting its potential application in developing automated corn disease detection systems based on digital leaf images. Keywords: ResNet, SVM, hybrid model, classification, corn disease
Two-stage Transfer Learning for Chilli Leaf Disease Classification using CNN and Vision Transformer Rudi Kurniawan; Samsuryadi Samsuryadi; Fatma Susilawati Mohamad; Bunga Intan; Nelly Khairani Daulay
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7674

Abstract

Chili cultivation is considerably affected by leaf diseases, making timely and reliable disease identification essential for maintaining agricultural productivity. While deep learning has demonstrated strong capabilities in plant disease recognition, many existing studies primarily focus on modifying network architectures rather than optimizing the training process. This study introduces a two-stage transfer learning framework that combines frozen-backbone adaptation with controlled fine-tuning to enhance classification performance. The experiments utilized a publicly available dataset comprising 12,000 augmented chili leaf images representing six disease categories. Several deep learning architectures were investigated, including DenseNet121, EfficientNetB0, MobileNetV2, ResNet152V2, and the Vision Transformer (ViT-B16). The experimental results indicate that the proposed two-stage training strategy improves classification performance consistently across the evaluated architectures. Among the models, ViT-B16 obtained the highest test accuracy of 99.61%, together with stable macro-averaged precision, recall, and F1-score values. Furthermore, the confusion matrix and ROC analysis revealed strong discrimination among the disease classes, with only limited errors occurring between visually similar categories. These findings highlight the importance of optimizing the training schedule when adapting pretrained models for plant disease recognition. Overall, the proposed framework provides an effective and scalable approach for chili leaf disease classification while offering an alternative to approaches that primarily rely on architectural modifications.
MODEL MACHINE LEARNING UNTUK KLASIFIKASI ANOMALI SERANGAN DENIAL OF SERVICE (DOS) PADA INFRASTRUKTUR JARINGAN MENGGUNAKAN DATASET CIC IIOT 2025 Tri Swastiningsih; Rudi Kurniawan; Elmayati Elmayati; Nelly Khairani Daulay
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 5 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/y01jc532

Abstract

Perkembangan Industrial Internet of Things (IIoT) meningkatkan efisiensi sistem industri, namun juga meningkatkan risiko serangan siber, khususnya Denial of Service (DoS) yang dapat mengganggu ketersediaan layanan jaringan. Penelitian ini bertujuan untuk membangun dan mengevaluasi model machine learning dalam mengklasifikasikan anomali serangan DoS pada infrastruktur IIoT menggunakan dataset CIC IIoT 2025. Dataset ini memuat trafik normal (benign) serta berbagai jenis serangan DoS seperti TCP SYN Flood, UDP Flood, ICMP Flood, dan MQTT Publish Flood. Permasalahan ketidakseimbangan data ditangani menggunakan metode Synthetic Minority Over-sampling Technique (SMOTE). Tiga algoritma supervised learning digunakan, yaitu Random Forest, XGBoost, dan Support Vector Machine (SVM). Evaluasi dilakukan menggunakan metrik accuracy, precision, recall, F1-score, confusion matrix, ROC-AUC, serta K-Fold Cross Validation. Hasil penelitian menunjukkan bahwa Random Forest dan XGBoost memperoleh akurasi tertinggi sebesar 97%, sedangkan SVM memperoleh akurasi 95%. Secara keseluruhan, menunjukkan performa paling stabil dalam mendeteksi berbagai variasi serangan DoS. Random Forest Penelitian ini berkontribusi dalam pengembangan sistem deteksi intrusi berbasis machine learning untuk meningkatkan keamanan jaringan IIoT.   Kata Kunci: IIoT, Denial of Service, Machine Learning, SMOTE.
IMPLEMENTASI BROAD LEARNING SYSTEM (BLS)  UNTUK IDENTIFIKASI PENYAKIT DAUN TANAMAN TOMAT BERDASARKAN CITRA DIGITAL Elien Marnes Kusmina; Rudi Kurniawan; Armanto Armanto; Tri Hasanah Bimastari Aviani
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 5 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/qsba4219

Abstract

Penyakit daun pada tanaman tomat dapat menurunkan produktivitas sehingga diperlukan metode identifikasi berbasis karakteristik visual citra. Penelitian ini bertujuan menerapkan Broad Learning System (BLS) sebagai classifier untuk klasifikasi penyakit daun tomat dengan MobileNetV2 sebagai feature extractor. Dataset yang digunakan berasal dari PlantVillage dan terdiri atas 18.160 citra dalam 10 kelas. Tahapan penelitian meliputi preprocessing, augmentasi citra, ekstraksi fitur menggunakan MobileNetV2, L2 normalization, reduksi dimensi menggunakan Principal Component Analysis (PCA), klasifikasi menggunakan BLS, dan evaluasi model. Hasil pengujian terhadap data testing menunjukkan accuracy sebesar 78%, dengan macro precision 0,78, macro recall 0,78, dan macro F1-score 0,77. Confusion matrix menunjukkan bahwa sebagian besar data dapat diklasifikasikan dengan benar, meskipun masih terdapat kesalahan pada beberapa kelas dengan karakteristik visual yang serupa. Hasil penelitian menunjukkan bahwa kombinasi MobileNetV2 dan BLS dapat digunakan sebagai salah satu pendekatan untuk klasifikasi penyakit daun tomat berbasis citra digital.   Kata kunci: Broad Learning System, MobileNetV2, penyakit daun tomat, PlantVillage.
Co-Authors Andri Anto Tri Susilo Armanto Armanto Asep Toyib Hidayat Budi Santoso Bunga Intan Dilla Monalisa Elien Marnes Kusmina Elmayati Elmayati Fatma Susilawati Mohamad Hasan Basri Khairul Imam Mahmud Lingga Wijaya, Harma Oktafia Muhamad Salman Nelly Khairani Daulay Samsuryadi Samsuryadi Satrianansyah Sheli Agustina Tri Hasanah Bimastari Aviani Tri Swastiningsih Yuda Putra Pratama