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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Keteknikan Pertanian IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika International Journal of Advances in Intelligent Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Science and Mathematics Education Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Jurnal Pusat Inovasi Masyarakat Cylinder : Jurnal Ilmiah Teknik Mesin Jurnal Pengabdian Rekayasa dan Wirausaha
Karlisa Priandana
Computer Science Department Faculty Of Mathematics And Natural Sciences IPB University (Bogor Agricultural University)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Long Short Term Memory-Based Marine Data Prediction with Pearson Correlation Mukhlis, Mukhlis; Jaya, Indra; Nurdiati, Sri; Priandana, Karlisa; Hermadi, Irman
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10731

Abstract

Marine data prediction plays a vital role in supporting decision-making in the field of marine environment and resources. However, the complexity of marine data, which is nonlinear and dynamic, is a significant challenge in producing accurate predictions. This study aims to explore the role of Long Short-Term Memory (LSTM) models in computer systems to predict marine data, focusing on Pearson Correlation analysis. The methods applied include collecting historical marine data, implementing LSTM models for prediction, and evaluating performance using metrics such as Mean Absolute Error (MAE). In addition, Pearson Correlation analysis is used to understand the relationship between variables in marine data. The results show that the LSTM model is able to produce predictions with a low error rate with a composition of training data and testing data of 80:20, resulting in Sea Surface Temperature (SST) = 0.0053, Sea Surface Salinity (SSS) = 0.0026, sea Surface Height (SSH) = 0.0061 and CHL-a = 0.0002 and shows a significant relationship between variables through Multivariate correlation analysis. This research contributes to the development of marine data-based prediction systems and provides implications for the world of marine resource research and management.
Multi-Platform Detection of Melon Leaf Abnormalities Using AVGHEQ and YOLOv7 Ishak, Sahrial Ihsani; Priandana, Karlisa; Wahjuni, Sri
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1441

Abstract

This research develops a multiplatform system for detecting abnormalities in melon leaves, integrating an Internet of Things (IoT) approach using Jetson Nano, a Streamlit-based website, and a mobile application for real-time monitoring. The system employs preprocessing with Average Histogram Equalization (AVGHEQ) to enhance image quality, followed by modeling with the YOLOv7 algorithm on a dataset of 469 training images and 52 test images, validated through 5-fold cross-validation. The model achieved a mean Average Precision (mAP) of 84% with an inference detection time of 4.5 milliseconds. Implementation on Jetson Nano resulted in a 25% increase in CPU usage (from 25% to 50%) and a 20% increase in RAM usage (from 70% to 90%). By combining these platforms and leveraging robust data preprocessing and modeling techniques, the system provides an accessible, efficient, and scalable solution for agricultural monitoring, enabling farmers to address plant health issues promptly and effectively.
Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
Hybrid convolutional vision transformer for extrusion-based 3D food-printing defect classification Mawardi, Cholid; Buono, Agus; Priandana, Karlisa; Herianto, Herianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3311-3323

Abstract

Deep learning is generally used to perform remote monitoring of three-dimensional (3D) printing results, including extrusion-based 3D food printing. One of the widely used deep learning algorithms for defect detection in 3D printing is the convolutional neural network (CNN). However, the process requires high computational costs and a large dataset. This research proposes the Con4ViT model, a hybrid model that combines the strengths of vision transformer with the inherent feature extraction capabilities of CNN. The locally extracted features in the CNN were merged using the transformers’ global features with four transformer encoder blocks. The proposed model has a smaller number of parameters compared to other lightweight pre-trained deep learning models such as VGG16, VGG19, EfficientNetB2, InceptionV3, and ResNet50. Thus, the proposed model is simplified. Simulations were conducted to classify defect and non-defect images obtained from the printing results of a developed extrusion-based 3D food printing device. Simulation results showed that the model produced an accuracy of 95.43%, higher than the state-of-the-art techniques, i.e., VGG16, VGG19, MobileNetV2, EfficientNetB2, InceptionV3, and ResNet50, with accuracies of 77.88, 86.30, 82.95, 90.87, 84.62, and 93.83%, respectively. This research shows that the proposed Con4ViT model can be used for 3D food printing defect detection with high accuracy.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Buono, Agus; Priandana, Karlisa; Paruhum Silalahi, Bib; Sri Hendrastuti, Elisabeth
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States Hidayat; Buono, Agus; Priandana, Karlisa; Wahjuni, Sri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4949

Abstract

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.
An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History Imantho, Harry; Seminar, Kudang Boro; Damayanthi , Evy; Suyatma , Nugraha Edhi; Priandana, Karlisa; Ligar, Bonang Waspadadi; Seminar, Annisa Utami
Jurnal Keteknikan Pertanian Vol. 12 No. 1 (2024): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.012.1.140-152

Abstract

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.
Pengenalan Robotika sebagai Media Pembelajaran STEM di SMA Labschool Unsyiah Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Islamy, Fajrul; Priandana, Karlisa; Basir, Nurlida; Safitri, Rini
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v2i2.50575

Abstract

Perkembangan teknologi robotika telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dunia pendidikan. Robotika, sebagai bidang multidisiplin yang menggabungkan aspek mekanika, elektronika, dan ilmu komputer, memiliki potensi besar untuk diterapkan dalam pembelajaran berbasis Science, Technology, Engineering, and Mathematics (STEM). Artikel ini bertujuan mendeskripsikan pelaksanaan kegiatan pengabdian masyarakat berupa pengenalan sistem robotika di SMA Labschool Unsyiah Banda Aceh serta mengkaji dampaknya terhadap pengetahuan dan motivasi siswa. Metode pelaksanaan kegiatan meliputi identifikasi kebutuhan mitra, perencanaan materi, pelaksanaan kegiatan, praktik sederhana, hingga evaluasi. Dokumentasi kegiatan menunjukkan antusiasme tinggi dari siswa selama mengikuti seluruh rangkaian kegiatan. Hasil evaluasi melalui kuesioner dan observasi lapangan mengindikasikan bahwa siswa memperoleh peningkatan pemahaman tentang konsep dasar robotika serta terdorong untuk lebih berminat pada bidang STEM. Guru pendamping juga menilai kegiatan ini relevan dengan kebutuhan pembelajaran dan membuka peluang pengembangan robotika sebagai kegiatan ekstrakurikuler di sekolah. Dengan demikian, kegiatan pengenalan robotika tidak hanya berkontribusi pada peningkatan literasi teknologi siswa, tetapi juga menjadi langkah awal dalam membangun ekosistem pendidikan berbasis teknologi yang berkelanjutan di SMA Labschool Unsyiah Banda Aceh.
Development of an ethnoscience-based vocational learning model for solar-wind hybrid energy systems in small-scale fishing vessel applications Zakiah, Diah; Jaya, Indra; Rahmat, Ayi; Priandana, Karlisa; Iskandar, Budhi Hascaryo
Indonesian Journal of Science and Mathematics Education Vol. 8 No. 3 (2025): Indonesian Journal of Science and Mathematics Education
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijsme.v8i3.29177

Abstract

Coastal communities increasingly require accessible renewable-energy education, yet conventional vocational programs rarely integrate local maritime knowledge or hands-on, culturally relevant practices. This study aims to develop and evaluate an ethnoscience-based vocational learning model that uses a simple wave-energy converter as a contextual tool. A qualitatively driven mixed-methods design was employed involving fishers, vocational students, teachers, and local stakeholders through interviews, focus group discussions, observations, and classroom implementations. Findings show that the model effectively bridges indigenous maritime knowledge with renewable-energy engineering concepts, producing pedagogically meaningful outcomes: a 21% increase in students’ conceptual understanding and a 139–270% improvement in psychomotor skills related to device design, measurement, and troubleshooting. Teachers reported enhanced capacity in contextual and multimodal instructional design, while community members recognized the model’s relevance to local energy needs. The study demonstrates that culturally grounded, low-cost engineering activities can strengthen STEM learning in resource-constrained coastal settings. These results have implications for integrating ethnoscience into vocational curricula, supporting equitable education, preserving local knowledge, and accelerating the adoption of community-based renewable technologies. This model offers a measurable pathway for policy development in vocational education, energy transition, and fisheries.
Co-Authors Agus Buono Ahmad Zulfikar S Alex Coccia Arsy Novita Syahada Arsy Novita Syahada Auzi Asfarian Basir, Nurlida Bib Paruhum Silalahi Budhi Hascaryo Iskandar Diah Zakiah Doli Aryo Dani Sinaga Doli Aryo Dani Sinaga Elisabeth Sri Hendrastuti Evy Damayanthi Farohaji Kurniawan Handoko, Dadang Hasan, Rozaimi Che Henry Munandar Manik Herianto Herianto Hestirianoto, Totok Hidayat Hidayat Hidayat Husin Alatas Iis Rodiah Imantho, Harry Indra Jaya Irene Pricella Situmorang Irene Pricella Situmorang Irman Hermadi Ishak, Sahrial Ihsani ISKANDAR ZULKARNAEN SIREGAR Islamy, Fajrul Kahfi Gunardi Karina Eku Dwinanda Gunawan Kudang Boro Seminar Laksono Trisnantoro Leo P. Ligthart Ligar, Bonang Waspadadi M Alwan Farhandany M. Cholid Mawardi Medria Kusuma Dewi Hardhienata Melinda Melinda Michael Julyus Christopher Manullang Mohamad Solahudin Muhammad Hazim Muhammad Irhamsyah Muhammad Rafi Muttaqin Mukhlis Mukhlis Muttaqin, Muhammad Rafi Nazala Hafidhullah Nugraha Edhi Suyatma Peter John Morley Puspita Kartika Sari Puspita Kartika Sari Rahmat, Ayi Rini Safitri RR. Ella Evrita Hestiandari Santoso, Syifa Afnani Sarah Arisanti Seminar, Annisa Utami Siregar, Ardinsyah Sri Hendrastuti, Elisabeth Sri Nurdiati Sri Wahjuni Sukarman Sumanto, Sumanto Try Kusuma Wardana Wisnu Ananta Kusuma WULANDARI Yandra Arkeman Yunidar