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INDONESIA
Jurnal Informatika Global
Published by Universitas Indo Global Mandiri
ISSN : 2302500X     EISSN : 24773786     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of global informatics publish articles on architectures from various perspectives, covering both literary and fieldwork studies. The journal, serving as a forum for the study of informatics, system information, computer system, informatics management, supports focused studies of particular themes & interdisciplinary studies in relation to the subject. It has become a medium of exchange of ideas and research findings from various traditions of learning that have interacted in the scholarly manner as well become an effort to disseminate on computer research to the International community.
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Articles 6 Documents
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Search results for , issue "Vol. 14 No. 3" : 6 Documents clear
Analisis Komparasi Metode Sistem Pendukung Keputusan pada Gaya Belajar “VARK” Ambar Riani; Taswanda Taryo; Achmad Hindasyah
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3402

Abstract

Learning style is an important factor in the success of the learning process from learning resources to individuals or groups. Understanding learning styles can be a strategy in the learning process. One of the learning styles is "VARK" which is the preference of individuals or learning groups consisting of visual, aural, read/write, and kinesthetic. Various methods are developed to find out the learning style of an individual or group of learners. In this research, a decision support system (SK) will be developed that provides learning style recommendations for individuals or study groups whether the tendency is visual, aural, read/write, or kinesthetic. The method used is a combination of simple additive weighting (SAW) and weighted product (WP).  The SPK development stages consist of data collection through distributing surveys to 55 student respondents at the 1926 Education Foundation where the dominant characteristic of the respondent's learning style is visual, namely 56%, analyzing SAW and WP methods, developing SPK with a web-based system, testing the system using the black box testing method.
Electricity Energy Demand in Banjarnegara District for the Year 2021-2030 Using Linear Regression Method and Leap Software Amin Mustofa; Hendra Setiawan
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3436

Abstract

Electricity demand is expected to increase from year to year. Central Java's electricity demand in 2013 reached 18.205 GWh with a total of 8.092.964 customers. This energy demand has increased in 2018 to 22.945 GWh with a total of 10.011.388 customers. Continuous use of electricity, both directly and indirectly, will also affect economic needs and people's welfare. It is estimated that electricity sales will continue to increase in line with the growth of customers who will also continue to increase. In predicting the need for electrical energy, various forecasting methods are commonly used to predict the need for electrical energy, such as the Regression Method, Time Series Method, Causal Method, Neural Network Method, and Dynamic System Analysis Method. In predicting the need for electrical energy there are advantages and disadvantages of the forecasting method. Based on the availability of data, this research analyzes forecasting the demand for electrical energy in Banjarnegara district using the regression and time series method which is applied using LEAP software. The results show that the total demand for electrical energy using the regression method in 2030 will reach 50.616 GWh while the LEAP software in 2030 will be 59.677 GWh.
Deteksi Penyakit Pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network Sidik Suhendar; Adi Purnama; Esa Fauzi
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3478

Abstract

Sweet potatoes are the world's third most important root crop and the fourth most popular staple food in developing countries, including Indonesia. Some diseases commonly found in sweet potato leaves are early blight (identified by leaves containing batataezim) and late blight (characterized by leaves that have chlorosis). These two diseases have different symptoms and require different treatments, but a slow identification process can lead to additional costs for plant care. This research will classify image data of sweet potato diseases using the Convolutional Neural Network (CNN) method. CNN is a derivative of the Multilayer Perceptron (MLP) designed to process image data with high network depth and is often used for classification tasks. The research uses a total of 750 images divided into 3 classes: images of healthy leaves, images of leaves with chlorosis, and images of leaves containing batataezim. Each leaf class will be labeled with 250 image data, and the labeled data will be further divided into training and testing sets. From these sets, prediction data will be obtained from the testing process during the CNN model training. The training accuracy resulted in a value of 98.17%, while the testing accuracy reached 98.67%. Additionally, the resulting loss values are remarkably low, at 0.04% for training and 0.03% for testing. The research findings will provide insights into the CNN method's ability to detect diseases in sweet potato plants, potentially impacting agricultural supervision, plant disease identification, and enabling more precise decisions regarding plant care actions.
Implementasi Internet of Things Dalam Monitoring dan Controlling Variable Frequency Drive Risky Reza Pradhana; Bagus Alit Prasetyo; Yenie Syukriyah
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3519

Abstract

The use of the Internet of Things (IoT) has had a significant impact in various fields, including industry and automation. IoT connects devices and systems online, enabling fast and efficient exchange of data and control. In industry, the use of IoT has changed the way systems operate and monitor, including in terms of monitoring and controlling driver 3-phase induction motors/Variable Frequency Drive (VFD). The research method used in this research is making prototypes and trials. In the first stage, a prototype monitoring and controlling system using IoT was designed and developed. This system consists of a sensor to measure motor parameters, a microcontroller as a controller, and a communication module to connect the system to the internet. The implementation of IoT in this system allows real-time collection of motor data and sending this data to a cloud server for further analysis. With the implementation of IoT in monitoring and controlling 3-phase induction motor drivers/VFD, remote motor operation becomes more possible and efficient. Motor information in the form of speed and current obtained in real-time allows the operator to take appropriate steps in optimizing motor operation.
Emerging Trends in Cybersecurity for Health Technologies Ahmad Sanmorino
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3530

Abstract

The paper delves into the intricate relationship between technological advancements in healthcare and the pressing need for robust cybersecurity measures. It explores the escalating vulnerability of sensitive medical data due to the sector's digital transformation and the increased susceptibility to cyber threats. The interconnectedness of healthcare systems, from wearable devices to complex electronic health record systems, exposes healthcare organizations to relentless cyberattacks. Within this context, the article meticulously examines emerging trends and innovative solutions aimed at fortifying cybersecurity infrastructure and safeguarding sensitive medical data. It scrutinizes ten cybersecurity risks prevalent within the healthcare domain, highlighting the multifaceted nature of data security challenges faced by healthcare entities. Furthermore, the paper meticulously dissects ten AI-driven security mechanisms, ranging from behavioral analytics to AI-powered compliance management, showcasing their pivotal role in ensuring data integrity and confidentiality. Collaboration emerges as a pivotal strategy, with the article outlining ten collaborative initiatives that underscore the significance of joint efforts among healthcare institutions, technology providers, and cybersecurity experts. Collectively, these insights illuminate the imperative for proactive and adaptive cybersecurity strategies within the evolving landscape of healthcare technology integration.
Analisis Link Aggregated Group Interface Pada Switch Untuk Sistem Link Redudancy Di Universitas Widyatama Atep Aulia Rahman; Esa Fauzi; Bagus Alit Prasetyo; Bimo Cokro Utomo
Jurnal Ilmiah Informatika Global Vol. 14 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i3.3561

Abstract

Widyatama University is a Legal Entity Private Higher Education Institution under the main institution of the Ministry of Education, Culture, Research and Technology (Kemendikbudristek). Providing superior private universities is one of the results of performance, management and maintaining service quality. Link Redundancy is one of the technologies in the network that is used to maintain the stability of a network connection by using several physical network paths simultaneously. Link Redundancy is needed for performance and services to run well. Link Aggregation Group (LAG) is one of the link redundancy models whose way of working is to combine several physical interfaces into a single interface at the Layer 2 network layer (Data Link Layer). Implementation of Link Aggregation Group (LAG) makes network connections more secure by increasing bandwidth, dividing bandwidth loads, increasing network path availability, and having a minimal risk of data duplication of data errors.

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