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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 257 Documents
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Electricity Energy Demand in Banjarnegara District for the Year 2021-2030 Using Linear Regression Method and Leap Software Mustofa, Amin; Setiawan, Hendra
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 Suhendar, Sidik; Purnama, Adi; Fauzi, Esa
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 Pradhana, Risky Reza; Prasetyo, Bagus Alit; Syukriyah, Yenie
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 Sanmorino, Ahmad
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 Rahman, Atep Aulia; Fauzi, Esa; Prasetyo, Bagus Alit; Utomo, Bimo Cokro
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.
Sistem Artificial Intelligence Deteksi Penyakit THT dan Jantung Menggunakan Forward Chaining dan Image Processing Wibowo, Ari Purno Wahyu; Hamdani, Dani; Heryono, Heri; Pratama, Rizky Fajar
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3573

Abstract

Heart disease and ENT (Ear, Nose and Throat) disease are serious can threaten human health and even life, the attacks of these two diseases can come suddenly without us realizing it, the symptoms are similar to ordinary diseases, the aim of this research is created based on AI (artificial intelligence) which is able to provide a quick response when a disease attacks us by looking for signs of physical changes or early symptoms in the body, examples of these symptoms are changes in skin color or finger nails which can indicate the presence of a serious disease, the method used is a combination of two detection methods using forward chaining and the heart disease detection system using the image processing method, the application created is able to detect disease symptoms and measure the level of accuracy of the detection results, so that the patient or doctor is able to measure the severity of the disease, from the experimental results it can be concluded that this system can recognize with an accuracy of above 80%, this application doesn't replace the doctor as a medical expert but is used for recognize early symptoms and carry out  prevention processes  disease becomes more serious.
Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara Munir, Ach Sirojul; Saputra, Agus Bima; Aziz, Abdul; Barata, Mula Agung
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3578

Abstract

Cancer is one of the deadliest diseases in the world with a high increase in the number of cases every year Cancer disease with significant growth in cases, is a serious global challenge. The main focus of this research is breast cancer in Indonesia. Using a data mining approach, this study compares two main classification algorithms, namely Naive Bayes and Decision Tree, to identify breast cancer. Naive Bayes is a simple probabilistic approach, calculating probabilities assuming attribute independence. Decision Tree, as a popular algorithm, represents decision rules in the form of a tree. Through comparison with previous research on algorithms in other contexts, this study aims to find the algorithm with the highest accuracy in breast cancer classification. With the final result, the decision tree has a higher accuracy of 92.04% and naïve Bayes has an accuracy of 91.15%.This result proves that the decision tree is superior in the classification of breast cancer disease compared to naïve Bayes. The results of the study are expected to make an important contribution to the development of effective approaches for the diagnosis and treatment of breast cancer.
Perbandingan Metode Ekstrapolasi Polinomial dan Ekstrapolasi Chebyshev pada Prediksi Total Ekspor Migas Tahun 2022 Darussalam, M. Miftah; Vega, Marshanda Amalia; Octaria, Putri; Puspasari, Shinta
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3624

Abstract

International trade plays a crucial role in strengthening relationships between nations, where production specialization and exports serve as strategies to address a country's production deficiencies. The focus on exports, particularly oil and gas exports in Indonesia, is key to economic growth. This study compares two primary extrapolation methods, namely polynomial and Chebyshev, to predict the volume of oil and gas exports in 2022. Actual data from 2019 to 2021 is utilized to evaluate the performance of these methods. The analysis results indicate that although both methods provide accurate predictions, polynomial extrapolation has a slightly lower error rate compared to Chebyshev. Using MAPE as the evaluation metric, polynomial extrapolation obtains a value of 28.48, while Chebyshev obtains 31.46. Furthermore, for the relative error, polynomial method yields 0.304466997 percent, and Chebyshev method yields 0.327263854. Therefore, the polynomial method is chosen as the preferred approach, predicting the total oil and gas exports to be 11,127.29.
Klasifikasi Mahasiswa Berprestasi Menggunakan Fuzzy C-Means Dan Naive Bayes S.Intam, Rezki Nurul Jariah; Wulandari; Risal, Andi Akram Nur; Surianto, Dewi Fatmarani
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3666

Abstract

Success in the world of education is often associated with successful academic achievements. Therefore, processing information is very important to determine the selection of students who excel. However, study programs and student services often face difficulties in recognizing students who have achievements. In this research, outstanding students from the Faculty of Engineering, Makassar State University were determined using the Naive Bayes classification method combined with the Fuzzy C-Means (FCM) method to identify data patterns before classification. The criteria measured are GPA, achievements achieved, organizations attended, and the number of Semester Credit Units (SKS) that have been programmed. By using the Confusion Matrix, the evaluation results show an accuracy level of 98%, recall of 97%, precision of 100%, and F1-Score of 99%.
Implementasi Metode Decision Tree pada Sistem Prediksi Status Kualitas Produk Minuman A Anshor, Abdul Halim; Zy, Ahmad Turmudi
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3778

Abstract

The quality of a beverage product is one of the important items that beverage product entrepreneurs must pay attention to. Good quality beverage products will have an impact on consumers' health. UMKM Buah Sabar is one of the MSMEs located in Bekasi district which produces beverage products A. In the distribution of these beverage products, MSME workers in the delivery section have conditions where the product is out of stock or left over. The reseller must be able to understand whether the status of the remaining product is still of good quality or has been damaged. This is very important to pay attention to because the cooling conditions of each reseller have varying degrees of cold, sometimes also influenced by blackouts and unstable electricity voltage. This condition can cause the quality of product A to decrease. The large number of resellers and products sent will make it difficult for MSME workers to detect the quality of beverage product A. To overcome this problem the researchers found a solution that requires a machine learning method to predict the quality status of product A. In this research, the researchers used the decision tree method to predict the quality status of the product Drink A. The data used are 500 samples of drink product A in the production period from November 2023 to February 2024. The parameters used include temperature, color, taste, aroma, and quality status class of drink product A. The results of this research will show the presentation The accuracy value for the quality of product A is 99.59%, this shows that the decision tree algorithm has very good performance in the process of classifying the quality of beverage product A.

Page 20 of 26 | Total Record : 257

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