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All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering JAPEPAM, Jurnal Pengabdian kepada Masyarakat
Hidra Amnur
Politeknik Negeri Padang
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Capacity Building for Farming System Digitalization Using Farming Management System Hidayat, Rahmat; Amnur, Hidra; Alanda, Alde; Yuhefizar; Satria, Deni
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.186

Abstract

Agriculture is one of the most important areas of human activity around the world. As the population increases, it is necessary to increase agricultural production. In the age of information technology, information plays a key role in people's lives. Agriculture is rapidly becoming a highly development-intensive industry where farmers need to collect and evaluate a large amount of information in their business processes to become more efficient in production and communicate information accordingly. Modernizing agriculture requires technological know-how for the efficient use of agricultural inputs. It deals with factors such as ecological footprint, product safety, labor welfare, nutritional responsibility, plant/animal health and welfare, economic responsibility and local market presence. They cover almost all stages in the production chain concerning day-to-day agricultural tasks, transactional activities for all stakeholders involved, and support for information transparency in the food chain. The use of information technology and network infrastructure currently enables the application of technology in agricultural business processes, but there is no standardized solution to enable interoperability and integration among services and stakeholders. Farming Management System (FMS) is expected to be a solution and standard in the use of technology in agriculture. Farming management system is a management system specifically designed to assist farmers or farm managers in managing their farming operations more efficiently and effectively. This system usually consists of integrated software and hardware to monitor and collect data from various aspects of agriculture, such as irrigation, fertilization, pesticide spraying, etc.
Application of IoT-based Intelligent Control Devices Empowered with Fuzzy Inference System in the Garment Industry Rizki, Agung Mustika; Ashari, Faisal; Yuliastuti, Gusti Eka; Haromainy, Muhammad Muharrom Al; Aditiawan, Firza Prima; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3344

Abstract

The garment industry in Indonesia has experienced significant development in recent years. A critical aspect of this development is the increasing role of Micro, Small, and Medium Enterprises (MSMEs). Swari Garment Industries (SGI) is an example of an MSME that focuses on the garment sector. In practice, various problems and negligence can affect the course of the production process. One potential issue is using the machine inappropriately or excessively, which can lead to a short electrical circuit. Short electrical circuits are one of the problems that must be faced because they can cause various severe impacts, including equipment damage and even fire. Based on this risk analysis, a possible solution to be applied to SGI, one of the MSMEs in the garment sector, is the implementation of an intelligent control device. The implementation of intelligent control tools based on the Internet of Things (IoT) can enhance the efficiency of the production process and mitigate significant risks to workers and the environment. The Fuzzy Inference System, in which the equity, temperature, and humidity are the input values of the Intelligent Control Device. A hardware device for temperature and humidity control, accessible through an Android phone application, was implemented in SGI. Experiments have verified that we can achieve excellent results. The average percentage of temperature measurement error was 0.2% and for humidity, 0.26%. The average percentage of measurement error from the comparison between the system and MATLAB is 0.49%.
Penguatan Sistem CHSE, Maintenance dan Repair Kendaraan Pada BUMNag MADANI Nagari Lubuk Malako Hasanah, Miftahul; Wahyu, Dian; Amnur, Hidra; Kurniawan, Roni
JAPEPAM, Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 2 (2023): JAPEPAM
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/japepam.v2i2.19

Abstract

Perawatan dan perbaikan kendaraan operasional merupakan hal yang sangat penting untuk memastikan kendaraan dapat berfungsi dengan baik dan aman untuk digunakan. Dengan melakukan perawatan dan perbaikan secara teratur, kendaraan dapat memiliki umur pakai yang lebih panjang, menghindari kecelakaan, meningkatkan efisiensi bahan bakar, mencegah biaya yang lebih besar, serta mempertahankan nilai kendaraan. Oleh karena itu, perawatan dan perbaikan kendaraan operasional harus dijadikan prioritas untuk memastikan kendaraan selalu berada dalam kondisi yang baik dan aman. Badan Usaha Milik (BUMNag) Madani Lubuk Malako memiliki unit usaha transportasi yang melayani sewa kendaraan. Unit yang dimiliki saat ini berjumlah 3 (tiga) unit kendaraan yaitu Ambulan, Micro Bus dan Truk Canter. Agar kendaraan operasional BUMNag dapat memenuhi kriteria efisiensi dan efektifitas penggunaan, perlu dilakukan aktivitas perawatan dan perbaikan. Oleh karena itu kegiatan Pengabdian Kepada Masyarkat (PKM) ini bertujuan untuk meningkatkan kapasitas personel/pengelola unit transportasi BUMNag dalam melaksanakan prosedur perawatan dan perbaikan kendaraan.
Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator BinJubier, Mohammed; Ismail, Mohd Arfian; Othman, Muhaini; Kasim, Shahreen; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3449

Abstract

The Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization.
A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms Chang Yu, Chiung; A Hamid, Isredza Rahmi; Abdullah, Zubaile; Kipli, Kuryati; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2703

Abstract

Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. However, machine learning algorithms still suffer from high margin error, which makes them unreliable as every algorithm uses a different way of prediction. Deep learning requires high computation power and a large dataset to operate the classification model. A filtering model with a consensus layer in a multi-tier model is introduced in this research paper. The multi-tier model filters the news label correctly predicted by the first two-tier layer. The consensus layer acts as the final decision when collision results occur in the first two-tier layer. The proposed model is applied to the WEKA software tool to test and evaluate the model from both datasets. Two sequences of classification models are used in this research paper: LR_DT_RF and LR_NB_AdaBoost. The best performance of sequence for both datasets is LR_DT_RF which yields 0.9892 F1-Score, 0.9895 Accuracy, and 0.9790 Matthews Correlation Coefficient (MCC) for ISOT Fake News Dataset, and 0.9913 F1-Score, 0.9853 Accuracy, and 0.9455 MCC for CHECKED Dataset. This research could give researchers an approach for fake news detection on different social platforms and feature-based
Feature Selection Approach to Detect DDoS Attack Using Machine Learning Algorithms Azmi, Muhammad Aqil Haqeemi; Foozy, Cik Feresa Mohd; Sukri, Khairul Amin Mohamad; Abdullah, Nurul Azma; Hamid, Isredza Rahmi A.; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.734

Abstract

Distributed Denial of Service (DDoS) attacks are dangerous attacks that can cause disruption to server, system or application layer. It will flood the target server with the amount of Internet traffic that the server could not afford at one time. Therefore, it is possible that the server will not work if it is affected by this DDoS attack. Due to this attack, the network security environment becomes insecure with the possibility of this attack. In recent years, the cases related to DDoS attacks have increased. Although previously there has been a lot of research on DDoS attacks, cases of DDoS attacks still exist. Therefore, the research on feature selection approach has been done in effort to detect the DDoS attacks by using machine learning techniques. In this paper, to detect DDoS attacks, features have been selected from the UNSW-NB 15 dataset by using Information Gain and Data Reduction method. To classify the selected features, ANN, Naïve Bayes, and Decision Table algorithms were used to test the dataset. To evaluate the result of the experiment, the parameters of Accuracy, Precision, True Positive and False Positive evaluated the results and classed the data into attacks and normal class. Hence, the good features have been obtained based on the experiments. To ensure the selected features are good or not, the results of classification have been compared with the past research that used the same UNSW-NB 15 dataset. To conclude, the accuracy of ANN, Naïve Bayes and Decision Table classifiers has been increased by using this feature selection approach compared to the past research.
Transformer in mRNA Degradation Prediction Yit, Tan Wen; Hassan, Rohayanti; Zakaria, Noor Hidayah; Kasim, Shahreen; Moi, Sim Hiew; Khairuddin, Alif Ridzuan; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1165

Abstract

The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Ranger’s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively.
Big Healthcare Data: Survey of Challenges and Privacy Bin Jubeir, Mohammed; Ismail, Mohd Arfian; Kasim, Shahreen; Amnur, Hidra; Defni, -
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.246

Abstract

The last century witnessed a dramatic leap in the shift towards digitizing the healthcare workflow and moving to e-patients' records. Health information is consistently becoming more diverse and complex, leading to the so-called massive data. Additionally, the demand for big data analytics in healthcare organizations is increasingly growing with the aim of providing a wide range of unprecedented potentials that are considered necessary for the provision of meaningful information about big data and improve the quality of healthcare delivery. It also aims to increase the effectiveness and efficiency of healthcare organizations; provide doctors and care providers better decision-making information and help them in the early detection of diseases. It also assists in evidence-based medicine and helps to minimize healthcare cost. However, a clear contradiction exists between the privacy and security of big data and its widespread usage. In this paper, the focus is on big data with respect to its characteristics, trends, and challenges. Additionally, the risks and benefits associated with data analytics were reviewed.
Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm Toresa, Dafwen; Shahril, Mohamad Azrul Edzwan; Harun, Nor Hazlyna; Bakar, Juhaida Abu; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.664

Abstract

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.
Co-Authors A Hamid, Isredza Rahmi Abdullah, Nurul Azma Abdullah, Zubaile Agung Mustika Rizki, Agung Mustika Alde Alanda, Alde ASHARI, FAISAL Azmi, Muhammad Aqil Haqeemi Bakar, Juhaida Abu Bin Jubeir, Mohammed BinJubier, Mohammed Chang Yu, Chiung Defni, - Deni Satria Dian Wahyu Firza Prima Aditiawan Foozy, Cik Feresa Mohd Gusti Eka Yuliastuti Hamid, Isredza Rahmi A. Harun, Nor Hazlyna Ismail, Mohd Arfian Khairuddin, Alif Ridzuan Kipli, Kuryati Miftahul Hasanah, Miftahul Moi, Sim Hiew Muhaini Othman, Muhaini Muhammad Muharrom Al Haromainy Rahmat Hidayat Rohayanti Hassan, Rohayanti Roni Kurniawan Shahreen Kasim, Shahreen Shahril, Mohamad Azrul Edzwan Sukri, Khairul Amin Mohamad Toresa, Dafwen Yit, Tan Wen Yuhefizar Yuhefizar Zakaria, Noor Hidayah