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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. 15 No. 1: April 2024" : 6 Documents clear
Sistem Artificial Intelligence Deteksi Penyakit THT dan Jantung Menggunakan Forward Chaining dan Image Processing Ari Purno Wahyu Wibowo; Dani Hamdani; Heri Heryono; Rizky Fajar Pratama
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 Ach Sirojul Munir; Agus Bima Saputra; Abdul Aziz; Mula Agung Barata
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 M. Miftah Darussalam; Marshanda Amalia Vega; Putri Octaria; Shinta Puspasari
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 Rezki Nurul Jariah S.Intam; Wulandari; Andi Akram Nur Risal; Dewi Fatmarani Surianto
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 Abdul Halim Anshor; Ahmad Turmudi Zy
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.
The LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture Adi Purnama; Atep Aulia Rahman; Esa Fauzi
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.v15i1.3805

Abstract

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

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