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All Journal Jurnal Cendekia : Jurnal Pendidikan Matematika Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) JOURNAL OF ICT APLICATIONS AND SYSTEM RJOCS (Riau Journal of Computer Science) Global Science: Journal of Information Technology and Computer Science
Enda Ribka Meganta P
STMIK Kaputama
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences

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Evaluasi Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Daun Jagung Menggunakan Pendekatan Systematic Literature Review Riski Rahmadan; Nurliani; Efendi Rahayu; Saudah; Ayu Puspita Sari Sinaga; Enda Ribka Meganta P
RJOCS (Riau Journal of Computer Science) Vol. 11 No. 1 (2025): RJOCS (Riau Journal of Computer Science)
Publisher : Fakultas Ilmu Komputer, Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjocs.v11i1.3068

Abstract

Penyakit pada tanaman jagung dapat menyebabkan kerugian besar dalam produksi pangan, yang berdampak pada perekonomian Indonesia. Salah satu metode yang berkembang untuk mendeteksi dan mengklasifikasikan penyakit tanaman adalah penggunaan Convolutional Neural Network (CNN), yang telah terbukti efektif dalam analisis citra. Penelitian ini bertujuan untuk mengevaluasi dan menganalisis penerapan CNN dalam klasifikasi penyakit pada daun jagung, dengan merujuk pada studi literatur yang ada. Melalui pendekatan Systematic Literature Review (SLR), penelitian ini menilai berbagai arsitektur CNN yang diterapkan pada klasifikasi penyakit tanaman, termasuk jagung, cabai, kentang, dan lada. Hasil penelitian menunjukkan bahwa metode CNN, khususnya dengan arsitektur seperti EfficientNet, mampu memberikan akurasi yang tinggi, dengan rata-rata akurasi sebesar 93.76%. Arsitektur CNN yang berbeda menunjukkan performa yang bervariasi tergantung pada dataset dan teknik preprocessing yang digunakan. Penelitian ini memberikan wawasan tentang bagaimana model CNN dapat dioptimalkan untuk mendeteksi penyakit tanaman dengan akurasi yang lebih baik, serta mengidentifikasi tantangan dan potensi dalam penerapannya pada berbagai jenis tanaman
Optimized Detection of Red Devil Fish in Low-Quality Underwater Images from Lake Toba Using a Hybrid CNN and Transfer Learning Approach Enda Ribka Meganta P; Yanto, Budi
Journal of ICT Applications System Vol 4 No 1 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i1.429

Abstract

The detection of freshwater fish in turbid underwater environments presents significant challenges due to poor image quality caused by low lighting, suspended particles, and visual noise. This study proposes an optimized detection model for Amphilophus labiatus (Red Devil fish) in the murky waters of Lake Toba, Indonesia, using a hybrid Convolutional Neural Network (CNN) integrated with transfer learning and visual enhancement techniques. The proposed architecture combines MobileNetV2 and ResNet50 backbones with CLAHE (Contrast Limited Adaptive Histogram Equalization) and median filtering to improve image clarity and feature extraction. A custom dataset comprising 3,500 annotated underwater images was used to train and evaluate the model. The hybrid model achieved a detection accuracy of 96.1%, a precision of 95.6%, a recall of 94.8%, and a mean Average Precision (mAP@0.5) of 0.941—outperforming baseline models such as YOLOv5 and Faster R-CNN. Visual diagnostics and Grad-CAM attention maps confirm the model's ability to focus on key anatomical features under varying image conditions. The architecture is optimized for real-time deployment on edge-AI devices, supporting conservation efforts and biodiversity monitoring in freshwater ecosystems
Pelatihan Dan Sosialisasi Sistem Informasi Inventori Barang Dengan Metode EOQ Berbasis Web di SMK Putra Anda Kota Binjai Izhari, Fahmi; Dhany, Hanna Willa; Harahap, Ricky Ramadhan; P, Enda Ribka Meganta
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 4 No 1 (2025): Juli 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v4i1.448

Abstract

Pengabdian Kepada Masyarakat ini bertujuan untuk memberikan pelatihan dan sosialisasi mengenai penggunaan sistem informasi inventori barang berbasis web dengan metode Economic Order Quantity (EOQ) di SMK Putra Anda Kota Binjai. Sistem informasi inventori ini dirancang untuk membantu sekolah dalam mengelola persediaan barang secara lebih efisien dan efektif, dengan memanfaatkan metode EOQ untuk menentukan jumlah pemesanan yang optimal dan mengurangi biaya penyimpanan barang. Pelatihan ini diikuti oleh staf pengelola inventori dan bertujuan untuk meningkatkan pemahaman serta keterampilan mereka dalam mengoperasikan sistem yang telah dibangun. Sosialisasi ini juga mencakup aspek teknis penggunaan aplikasi berbasis web, yang memungkinkan pengelolaan data persediaan secara real-time dan lebih terstruktur. Hasil dari pelatihan ini diharapkan dapat meningkatkan efisiensi operasional di SMK Putra Anda, terutama dalam hal pengelolaan barang yang lebih tepat, terkontrol, dan sesuai dengan kebutuhan sekolah. Dengan demikian, diharapkan penggunaan sistem ini dapat mengoptimalkan pengelolaan inventori barang di lingkungan sekolah, serta memberikan manfaat dalam pengambilan keputusan yang lebih berbasis data.
Pengembangan Model Pembelajaran Berbasis Pendidikan Matematika Realistik Berbantuan Media Animasi untuk Meningkatkan Kemampuan Komunikasi Matematis Meganta P, Enda Ribka; Syahputra, Edi; Ahyaningsih, Faiz
Jurnal Cendekia : Jurnal Pendidikan Matematika Vol 7 No 1: Jurnal Cendekia: Jurnal Pendidikan Matematika Volume 7 Nomor 1 Tahun 2023
Publisher : Mathematics Education Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cendekia.v7i1.2036

Abstract

This study aims to produce: the validity, practicality and effectiveness of learning tools based on the Realistic Mathematics Education (PMR) model, improving students' mathematical communication abilities using the developed learning tools and the process of student answers in solving mathematical communication skills questions. This research is development research with the ADDIE development model. From the results of the research, it is known that: (1) The learning model of Realistic Mathematics Education (PMR) assisted by animation media which was developed obtained an interactive learning model declared valid with an average of 4.31; (2) The Realistic Mathematics Education (PMR) learning model assisted by animation media that was developed meets the practical criteria of the learning model in terms of the analysis of the results of observations of the implementation of learning. The value obtained in trial I was 3.03 (medium category). In trial II, the score for observing the implementation of learning increased to 3.88 (category "high"); (3) The learning model of Realistic Mathematics Education (PMR) assisted by animation media developed meets the criteria of effectiveness. (4) Based on the normalized gain index it was found that in trial I there was an increase in students' mathematical communication skills with the "low" criteria with a score of 0.30 (g ≤0.3) and in trial II there was an increase in scores with the "moderate" criteria with a score of 0.42 (0.3 < N-Gain ≤0.7).
Context Sensitive Artificial Intelligence for Dynamic User Behavior Modeling in Next Generation Smart Information Platforms Rusmin Saragih; Enda Ribka Meganta P; Tiwuk Widiastuti; Ahmad Jurnaidi Wahidin; Erlita Sulistiati; Muhamad Furqon
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.194

Abstract

This study explores the development and implementation of a context sensitive artificial intelligence (AI) model designed to predict and personalize user behavior in smart information platforms. Traditional user behavior models often fail to adapt to dynamic and evolving user needs, especially in diverse environments where contextual factors such as time of day, location, and device type play a critical role in shaping user preferences. To address these limitations, the proposed context sensitive AI model integrates real time contextual data alongside traditional behavioral data, enabling it to make more accurate predictions and provide personalized, relevant content. The model utilizes advanced machine learning techniques, such as deep learning and reinforcement learning, to continuously update and refine user behavior models based on contextual shifts. Through the integration of contextual parameters, the model demonstrates improved prediction accuracy, system responsiveness, and overall user satisfaction compared to static, context agnostic models. Furthermore, the study discusses the key advantages of context aware AI, such as its ability to dynamically adjust to real time changes in user behavior, providing more adaptive, personalized services. Challenges encountered during the model's development, including issues related to data privacy, scalability, and the integration of multiple contextual data sources, are also addressed. The findings suggest that context sensitive AI can significantly enhance the effectiveness of smart platforms by improving user engagement and content relevance. Finally, the study provides recommendations for further research to explore deep learning methods for context detection and to improve the discoverability and integration of AI driven features in user interfaces.
Explainable Imbalance-Aware Spatiotemporal Learning for Traffic Accident Risk Prediction in Medan Metropolitan City Rusmin Saragih; Enda Ribka Meganta P; Theodora MV Nainggolan; Frans Ikorasaki; Fithry Tahel
Journal of ICT Applications System Vol 5 No 1 (2026): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v5i1.530

Abstract

Traffic accident prediction in rapidly urbanizing metropolitan regions remains a critical challenge due to the complex interplay of spatiotemporal dynamics, severe class imbalance, and the opacity of predictive models that limits actionable policy interpretation. Existing approaches tend to address these challenges in isolation—deploying graph neural networks without imbalance correction, or applying oversampling without incorporating spatial context—thereby falling short of the comprehensive decision-support capability demanded by intelligent transportation systems. This paper presents a novel integrated framework, designated SLT-SHAP, that systematically unifies spatiotemporal graph convolutional learning, Synthetic Minority Oversampling Technique (SMOTE) applied exclusively to the training partition, Long Short-Term Memory (LSTM) networks for sequential temporal dependency modeling, a Transformer encoder for long-range contextual attention across hourly traffic sequences, and SHapley Additive exPlanations (SHAP) for post-hoc model interpretability. The study employs a curated spatiotemporal dataset of 132,480 observations collected at hourly resolution across 48 administrative zones in Medan Metropolitan City, Indonesia, encompassing traffic, meteorological, infrastructural, and geospatial variables with an inherent accident class imbalance of 12.4%. Experimental results demonstrate that SLT-SHAP achieves an F1-score of 0.796, AUC-ROC of 0.963, AUPRC of 0.784, and Matthews Correlation Coefficient (MCC) of 0.783, surpassing all baseline and ablation variants. Ablation analysis confirms that each component—graph construction, SMOTE, LSTM, and Transformer—contributes independently to performance. SHAP analysis identifies congestion index, hour of day, and average speed as the three most influential predictors, with spatial heatmapping delineating persistent high-risk zones. The proposed framework offers a replicable and interpretable decision-support architecture for urban road safety analytics in the Indonesian and broader Southeast Asian metropolitan context.
Co-Authors Ahmad Jurnaidi Wahidin Ahyaningsih, Faiz Ayu Puspita Sari Sinaga Budi Yanto, Budi Dhany, Hanna Willa Edi Syahputra Efendi Rahayu Erlita Sulistiati Fithry Tahel Frans Ikorasaki Izhari, Fahmi Muhamad Furqon Nurliani Ricky Ramadhan Harahap Riski Rahmadan Rusmin Saragih Rusmin Saragih, Rusmin Saudah Theodora MV Nainggolan Tiwuk Widiastuti