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All Journal TEKNIK INFORMATIKA Telematika : Jurnal Informatika dan Teknologi Informasi InComTech: Jurnal Telekomunikasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development) IJISTECH (International Journal Of Information System & Technology) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Berdaya Mandiri IJISTECH JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Parta: Jurnal Pengabdian Kepada Masyarakat Applied Science and Technology Research Journal
Eko Dwi Nugroho
Institut Teknologi Sumatera
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Geohash-Based Maize Plant Monitoring System Utilizing Drones Algifari, Muhammad Habib; Nugroho, Eko Dwi
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6362

Abstract

Corn is one of the important food crops in the world. To ensure optimal results, farmers usually monitor crop conditions manually. Unfortunately, manual monitoring can take time and effort due to the large area of maize fields (approx.: 1 ha). In addition, corn plants are also susceptible to diseases and pests which often result in corn farmers experiencing losses due to crop failure. This can be supported by several cases of corn crop failure in Lampung caused by pests and water shortages, such as in Bumidaya Village, South Lampung. Therefore, this research will develop a corn crop monitoring system using geohash and drones. The primary objective of this research is to develop a comprehensive design for a corn crop monitoring system, leveraging the capabilities of machine learning for corn plant recognition. The application of geohash is expected to assist farmers in handling and early detection of plants that experience a decrease in health quality before it spreads to all other maize crops. The results of the model training carried out with the R-CNN are that the detection model is able to detect with an accuracy of 88.9% with a low distance of the drone in taking pictures or close to plants.
Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm Nugroho, Eko Dwi; Ashari, Ilham Firman; Nashrullah, Muhammad; Algifari, Muhammad Habib; Verdiana, Miranti
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6409

Abstract

Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.
Emotion Classification of Indonesian Tweets using BERT Embedding Algifari, Muhammad Habib; Nugroho, Eko Dwi
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6528

Abstract

Twitter is one of the social media that has the largest users in the world. Indonesia is one of the countries that has the 5th largest number of Twitter users in the world which causes a high possibility of conflict between Indonesian Twitter users due to emotional tension in tweets. In this paper, we will compare the BERT embedding method with CNN and LSTM. The results of this experiment are BERT-CNN has the best performance results which has an accuracy of 61% compared to BERT-LSTM. In the experiment several stages of data preprocessing, data cleaning, data spiting and data training were carried out and the results were evaluated using confusion metrics.
Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naive Bayes at Institut Teknologi Sumatera Algifari, Muhammad Habib; Ashari, Ilham Firman; Nugroho, Eko Dwi; Afriansyah, Aidil; Vebriyanto, Mario
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6761

Abstract

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
Co-Authors Adri Priadana Adrian Putradinata, Gusti Made Afriansyah, Aidil Algifari, Muhammad Habib Alkarkhi, Makruf Amelia Oktavia Amirul Iqbal Andre Febrianto Andrianto, Dodi Devrian Anggraini, Leslie Bagaskara, Radhinka David Gunawan, David Firmansyah, Hafiz Budi Idza Ramaulkim Ilham Firman Ashari Kurniawansyah, Apri Kusumaningtyas, Kartikadyota Miranti Verdiana Muhammad Nadhif Athalla Muhammad Reza Kahar Aziz Nashrullah, Muhammad Nela Agustin Kurnianingsih Perdana, Agung Mahadi Putra Praseptiawan, Mugi Prasetyawan, Purwono Radhinka Bagaskara Rizkita, Alya Khairunnisa Untoro, Meida Cahyo URI ARTA RAMADHANI Vebriyanto, Mario Verdiana, Miranti Verlina Agustine Winarta, Richard Arya Winda Yulita Yulita, Winda Yusuf, M. Asyroful Nur Maulana