International Journal Of Electrical Engineering And Intelligent Computing
type: channel
description: Journal title : International Journal of Electrical Engineering and Intelligent Computing Initials : ijeeic License : CC-BY-NC-SA Frequency : 2 issues per year June and December DOI : prefix 10.33387/ijeeic by Crossref E-ISSN : 3031-5255 (Online) Editor in chief Managing Editor : Prof. Dr. Umar, S.T., M.T : Dr. Andi Muhammad Ilyas, M.T Layout Editors Publisher : Mochammad Apriyadi Hadi Sirad, S.T., M.T : Universitas Khairun Citation Analysis : Google Scholar | DOAJ | Garuda | SINTA International Journal of Electrical Engineering and Intelligent Computing (IJEEIC) E-ISSN (Online) : 3031-5255 is an official publication of the Universitas Khairun. The IJEEIC is an international journal is a peer-reviewed open-access. The IJEEIC that has been published online since 2023 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication, and computer engineering from the global world, and publishes reviews, original research articles, and short communications. Our aim is to provide an international forum for scientists and engineers to share research and ideas, and to promote the crucial field of electrical & power engineering, circuits & electronics, power electronics & drives, automation, instrumentation & control engineering, digital Signal, image & video processing, telecommunication system & technology, computer science & information technology, internet of things, big data & cloud computing, and artificial intelligence & soft computing. Authors are invited to submit manuscripts that fall within the scope of the IJEEIC. Please read the information on the peer-review process. The articles published in IJEEIC are going through a single-blind peer-review process. Hence, the decision on whether the scientific article is accepted or not, will be the Editorial Board right based on the peer reviewer's recommendation. Please read and understand the author's guidelines for the preparation manuscript. The …
publisher: Universitas Khairun
language: en-US
publication name: International Journal Of Electrical Engineering And Intelligent Computing
issn: 3031-5255
copyright: Authors who publish with this journal agree to the following terms: The copyright of the accepted for publication articles shall be assigned toInternational Journal of Electrical Engineering and Intelligent Computing (IJEEIC) as the publisher of the journal. The intended copyright includes the rights to publish articles in various forms (including reprints). International Journal of Electrical Engineering and Intelligent Computing (IJEEIC) maintain the publishing rights of the published articles. Authors are permitted to republish or disseminate published articles by sharing the link/DOI of the article at International Journal of Electrical Engineering and Intelligent Computing (IJEEIC). Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from International Journal of Electrical Engineering and Intelligent Computing (IJEEIC) with an acknowledgment of initial publication to this journal. International Journal of Electrical Engineering and Intelligent Computing (IJEEIC) by Universitas Khairun This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
items: A digital image recommendation system using Semantic Segmentation
items: Big Data Driven Approach for Stock Price Forecasting Using Machine Learning
items: Performance Analysis of 100 KVA Generator Set as AC Backup Supply at Panakukang Electrical Substation

A digital image recommendation system using Semantic Segmentation
type: item
description: The research aims to determine the nature and features of a digital image in order to suggest or recommend relative images (candidate) to the image (query) that is determined by the user which may contain high scientific or valuable contents. The proposed method processes the semantic segmentation firstly, then computing the feature similarity, by segmenting the image into meaningful object regions then extracting isolating features for the determined objects, then the system will enhance the matching range between the recommendation query and the output of the semantic level. The sample was consisting of 1380 images based on 70/30 split with selected 3 labels (cars, persons and trees). The preprocessing step has been applied where the features were extracted from the determined image in order to determine its contents then we can recommend (candidate) the images with the relative features, this process is applied using the concept of semantic segmentation, where the procedure is partitioned into several steps: The first step includes grouping (cars, persons and trees) images from multiple online and real-world datasets, the grouped raw data that can be processed by the system normalization have been processed using Min-Max normalization that is used for standardizing the input data. The second step is the feature extraction process which is achieved using a modified VGG-16 net and the fully connected layers have been removed and the output will be a 2-D features map, then we convert this map to a 1-D vector called feature vector using a Global Average Pooling. The next step is obtaining feature labels by applying a Support Vector Machine SVM classifier, then we recommended the images with relative features to the determined (query) image, this step is achieved using a Pearson correlation coefficient. The final step is constructing a Confusion Matrix and applying the (F1-score, Recall, Precision and Accuracy) metrices in order to estimate the performance.
creator: Omar Muayad Abdullah
rights: Copyright (c) 2026 International Journal Of Electrical Engineering and Inteligent Computing https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 3
number: 2 June
starting page: 48
ending page: 54
doi: 10.33387/ijeeic.v3i2.11397

Big Data Driven Approach for Stock Price Forecasting Using Machine Learning
type: item
description: Big data is becoming a major factor that changes or influences various things in real world. One of such is the financial market where the use of advanced analytical techniques, which leads to the changes in investment decisions, can be really of huge effect. Simply put, the main point of implementing the most modern tools and methods to analyze the data inflow basing on the idea of data exploitation is clear and obvious. Thus, the impact of big data on the financial markets is massive enough since it can lead to the improvement of the stock price predictions as well as making the decision process of investors more transparent, easier and quicker. This report is a comprehensive review of the Apple Inc. stock trading data from 2020 to 2025. Big data tools will be necessary to load and process a vast amount of financial data, thus, laying a solid foundation for the analysis in order to uncover phenomena and market trends caused by various events. In addition, machine learning algorithms will be deployed to construct accurate forecasting models that can recognize complex data patterns. The objective of this study is to apply big data and machine learning techniques to forecast Apple Inc. stock price which would be an example of how such technological innovations can lead to a significant increase in the accuracy of financial forecasting. In addition, this paper will also compare the predicted data with the real ones so as to figure out the models' performance.
creator: Wael Hadeed
creator: Nagham Sultan
creator: Dhuha Abdullah
rights: Copyright (c) 2026 International Journal Of Electrical Engineering and Inteligent Computing https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 3
number: 2 June
starting page: 55
ending page: 62
doi: 10.33387/ijeeic.v3i2.11518

Performance Analysis of 100 KVA Generator Set as AC Backup Supply at Panakukang Electrical Substation
type: item
description: Substation AC backup supply reliability is critical for sustaining auxiliary equipment operation during grid blackouts. Prior studies on genset sizing and motor starting have not specifically addressed substation AC backup systems integrating continuous loads, non-continuous motor loads, and comparative starting methods. This study evaluates the adequacy of the existing 100 kVA prime / 110 kVA standby genset at Panakkukang Substation using field data collection and ETAP 19.0.1 simulation. The total continuous load is 9.342 kW, requiring a minimum genset capacity of 11.675 kW after applying a 46.7% demand factor and 125% safety factor (per NFPA 110 and ISO 8528-1). The existing genset adequately covers continuous loads at only 10.62% of standby capacity. However, simultaneous starting of the hydrant and oil pump motors exceeds genset capability under all methods tested: Direct-On-Line (DOL) causes a 24.00% voltage dip requiring 463.74 kVA; Wye-Delta reduces voltage dip to 17.48% requiring 231.87 kVA; and VFD limits voltage dip to below 3% requiring only 159.17 kVA, compliant with IEEE Std 1159-2019. This study recommends upgrading genset capacity to at least 160 kVA and adopting VFD motor starting to ensure reliable substation AC backup supply.
creator: Muh. Imran Bachtiar
creator: Andi Wawan Indrawan
creator: Muammar Abidin
creator: Syarifuddin Nojeng
creator: Mochammad Apriyadi Hadi Sirad
rights: Copyright (c) 2026 International Journal Of Electrical Engineering and Inteligent Computing https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 3
number: 2 June
starting page: 63
ending page: 71
doi: 10.33387/ijeeic.v3i2.12162