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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah KOMPUTASI Journal of Information Technology and Computer Science InComTech: Jurnal Telekomunikasi dan Komputer Communications in Science and Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Proceeding International Conference on Mathematics and Learning Research Prosiding SeNTIK STI&K
Sarifuddin Madenda
Universitas Gunadarma
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Deep learning based identification of Crocidolomia pavonana larvae on mustard plants using Grad-CAM Diana Tri Susetianingtias; Sarifuddin Madenda; Risnawati Risnawati; Maukar Maukar; Eka Patriya; Rodiah Rodiah
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11343

Abstract

Mustard greens are an important vegetable commodity, but their production is often affected by pest attacks, especially the cabbage worm Crocidolomia pavonana (C. pavonana). The larvae damage leaf tissues and cause significant yield losses, while chemical control is often ineffective due to differences in insecticide sensitivity across larval instars. This study proposes a deep learning based classification approach combined with gradient weighted class activation mapping (Grad-CAM) to identify larval instars of C. pavonana on mustard plants. A dataset of 684 images covering instars 1 to 4 was collected from laboratory rearing and field observations, then processed using resizing and augmentation techniques and divided into training, validation, and testing sets with an 8 to 1 to 1 ratio. Two convolutional neural network (CNN) models, visual geometry group 19 (VGG19), and Xception, were implemented with additional fully connected layers. The VGG19 model achieved 94.20% accuracy and outperformed Xception. Grad-CAM successfully highlighted larval regions and supported visual interpretation. The results show that the proposed method can improve pest identification accuracy and support more effective pest management.
A mathematical model for IoT malware propagation with adaptive patching strategy based on R₀: a simple optimal control approach Dwi Ely Kurniawan; Sarifuddin Madenda; Eri Prasetyo Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp219-232

Abstract

This paper presents a mathematical framework for modeling and controlling internet of things (IoT) malware propagation via an adaptive patching strategy governed by the real-time basic reproduction number R₀. We introduce the SEIR-P (susceptible–exposed–infected–recovered–patched) model, where the patching control rate u(t) is a sigmoid feedback function of R₀(t). All epidemiological parameters are calibrated from three empirical malware captures of the IoT-23 dataset (Stratosphere laboratory, Czech Technical University) a publicly available labeled collection of real IoT network traffic comprising 23 captures from infected and benign devices: CTU-IoT-1 (Mirai), CTU-IoT-9 (Torii), and CTU-IoT-17 (IRCBot) yielding the first empirically grounded parameter set for SEIR-type IoT epidemic models with confidence intervals. The optimal control problem is formulated via pontryagin’s maximum principle (PMP), and a closed-form R₀(u) expression is derived via the next-generation matrix (NGM), yielding the critical threshold u_crit = 0.142 day⁻¹. Five comparative simulation scenarios over a 365-day horizon show that the proposed R₀-adaptive strategy achieves a 91.3% reduction in peak infection (360 vs. 4,142 devices), eradicates malware by day 179, and attains the highest cost-effectiveness index (CEI = 1.142). Global asymptotic stability of the disease-free equilibrium under u*(t) is proven via Lyapunov’s method and LaSalle’s Invariance Principle. PRCC sensitivity analysis identifies u_max and β as dominant parameters. This closed-loop framework bridges the gap between abstract epidemic theory and deployable IoT security management.
Leveraging Convolutional Neural Networks Preprocessing for Accurate Age and Gender Classification in Personalized Nutrition Fadhillah, Muhammad Aulia Nur; Al Hakim, Shidiq; Rodiah, Rodiah; Data, Mahendra; Siagian, Al Hafiz Akbar Maulana; Riyanto, Slamet; Madenda, Sarifuddin; Apriani, Niken Fitria; Maukar, Maukar
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101923

Abstract

Imagine having a personalized nutrition plan that caters to your unique dietary needs based on your age and gender. Such a system could revolutionize the way we approach health and wellness. A key component of this vision is the accurate classification of age and gender from facial images, which can be leveraged to provide tailored nutritional recommendations. In this paper, we explored the use of convolutional neural networks and their preprocessing techniques to enhance the performance of age and gender classification models. Our age classification aimed to identify the age group according to the regulation of the Indonesian Republic's Health Ministry in 2014 about the guidelines for balanced nutrition, which includes the following categories: 10-12 years old, 13-15 years old, 16-18 years old, 19-29 years old, and 30-49 years old. We utilized the ResNet50 and Inception-v3 models, which were fine-tuned on the UTKFace dataset, a collection of more than 20,000 face images with corresponding age and gender labels. However, the UTKFace dataset suffers from a data imbalance problem. To address this issue, we proposed innovative data augmentation methods to create a more balanced dataset. Our experimental results demonstrated that our proposed augmented methods could significantly improve the classification performances of the models, leading to more accurate age and gender predictions. This advancement in facial attribute classification could pave the way for developing personalized nutrition systems that cater to individual needs and preferences, ultimately improving health outcomes and quality of life.
Co-Authors Achmad Fahrurozi Ahmad Hidayat Al Hakim, Shidiq Antonius Angga Kurniawan Apriani, Niken Fitria Aqwam Rosadi Kardian Atit Pertiwi Aviarini Indrati Bertalya Bertalya Cahyaningtyas, Rizqia Catur Suharinto Dewi Agushinta R. Dewi Putrie Lestari Diana Tri Susetianigtias Dina Indarti Djati Kerami Dwi Ely Kurniawan Eka Patriya Eri Prasetya Wibowo Eri Prasetyo Wibowo, Eri Prasetyo Ernastuti Ernastuti Ety Sutanty Fadhillah, Muhammad Aulia Nur Farina Pramanik Fitrianingsih Fitrianingsih Hariyanto Hariyanto Harmanto, Suryadi Hustinawaty Hustinawaty, Hustinawaty Ichsani Mursidah Iffatul Mardhiyah Ire Puspa Wardhani Johan Harlan Johan Harlan Johan Harlan Jufriadif Na`am, Jufriadif Julius Santony Khalida, Rakhmi Khoerul Anwar LATIFAH Linda Wahyu Widianti Lingga Hermanto Lussiana, ETP Mahendra Data Makmun Makmun Maukar Maukar Maukar Maukar Metty Mustikasari Muhammad Subali Nurma Nugraha Octaviani Hutapea Prayitno, Ragiel Hadi Prihandoko . Rahayu Noveandini Rahayu Widayanti Rama Dian Syah Ravi Ahmad Salim Rifki Kosasih Rini Arianty Risnawati Risnawati Risnawati Risnawati Robby Candra Rodiah Rodiah Ruddy J Suhatril Setia Wirawan SHABIRA, DIYANK SIAGIAN, AL HAFIZ AKBAR MAULANA Slamet Riyanto Sumijan Sumijan Sunny Arief Sudiro Suryadi Harmanto Suryadi MT Suryadi, S Suryarini Widodo Susetianingtias, Diana Tri Syamsiar, Syamsiar Tavipia Rumambi Tri Susetianingtias, Diana Tubagus Maulana Kusuma widyaningsih, tri wahyu Wiryana , I Made