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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.
Comparison of Holt’s Exponential Smoothing and Weighted Moving Average Methods in Predicting the Proportion of Alma Mater Sizes Eko Dwi Nugroho; Miranti Verdiana; Leslie Anggraini; Radhinka Bagaskara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12645

Abstract

The annual admission of new students requires the early procurement of university jackets to ensure distribution during the inauguration ceremony. However, the lengthy production lead time necessitates ordering months before the actual sizing data is fully collected. This issue is further complicated by low participation rates in size registration and extreme population spikes. This study proposes a time series forecasting approach to predict the proportional distribution of jacket sizes rather than absolute quantities. Specifically, the research compares the performance of Holt’s Exponential Smoothing and the Weighted Moving Average (WMA) method using historical size proportions from 2019 to 2025. Walk-forward validation was employed to evaluate the models based on the Mean Absolute Percentage Error (MAPE). The results demonstrate that WMA outperforms Holt’s Exponential Smoothing by achieving a lower MAPE of 6.56%. By extrapolating the WMA proportions to the 2026 target of 5,250 students and mathematically integrating the 6.56% error rate as a safety stock buffer, the final procurement quantities for sizes S through XXXL were precisely determined. This proportional forecasting framework provides a robust, quantitative foundation for institutional supply chain management, allowing early and accurate ordering despite incomplete preliminary data.
Forecasting the Demand for Freshmen Alma Mater Jackets and Sports T-Shirts by Size Using a Hybrid Bayesian–Machine Learning Approach Miranti Verdiana; Eko Dwi Nugroho; Leslie Anggraini; Radhinka Bagaskara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12679

Abstract

This study addresses an operational procurement problem in university admissions, where alma mater jackets and sports T-shirts for incoming students must be ordered several months before complete size information becomes available. In the case of ITERA admissions, procurement decisions are typically made in March or April, whereas actual student size data are only gradually collected during the re-registration period from April to July. To support earlier and more reliable procurement planning, this study formulates the problem as a size-demand forecasting task covering six categories: S, M, L, XL, XXL, and XXXL. Historical data from 2015 to 2025 were analyzed, with reliable size records concentrated in the 2019–2025 period. The main novelty of this study lies in formulating freshman uniform procurement as a staged forecasting problem that follows the actual admissions workflow. Specifically, the study proposes a hybrid framework that combines: (i) a time-weighted Bayesian Dirichlet–Multinomial model for early-stage aggregate forecasting when current-year size data are not yet available, and (ii) a CatBoostClassifier-based multiclass machine learning model for prediction updates when student attributes become available. Model performance was evaluated using an expanding-window rolling/forward chaining scheme with a one-year forecasting horizon. In addition to conventional historical baselines, the study also included Simple Exponential Smoothing (SES) as a time-series benchmark. Performance was assessed using cross-entropy for size-distribution accuracy, MAE/size and WAPE for quantity prediction, and stockout/overstock for operational impact. The results show that the previous-year proportion remains a strong baseline, while the Bayesian model provides competitive performance and yields posterior uncertainty estimates that are useful for determining safety-oriented order quantities. The statistical analysis further confirms that gender is the most influential predictor of size, while study program, admission track, and province provide complementary but weaker signals. The findings indicate that the proposed framework can support more adaptive and evidence-based procurement planning, reduce the risk of size shortages and excess inventory, and provide a transferable forecasting workflow that may be adapted to other institutions after local recalibration.
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 Leslie Anggraini 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