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Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 28084519     DOI : https://doi.org/10.53842/jaiea.v1i1
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 430 Documents
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Artificial Neural Network for Classification of Dengue Fever Using Backpropagation Algorithm Ririn Eka Andrianti Tarigan; Fuzy Yustika Manik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.357

Abstract

Fever is an increase in body temperature to higher than usual. Normal human body temperature is at 37oC, if the body temperature is more than this figure, it indicates a fever caused by infectious or non-infectious factors. The main symptom of Dengue hemorrhagic fever is high fever with a temperature between 30oC - 40oC which appears suddenly, the fever lasts for 7 days and occurs continuously, body temperature can be normal or low, then will rise slowly every day and can reach 40oC . These two diseases are still a public health problem in urban areas, including in the cities of Binjai and Medan. The problem that has occurred so far is that people in general cannot differentiate the symptoms of Dengue Fever from Malaria, so the treatment given only provides ordinary fever medicine, so that within three days there is no change and the high body temperature makes the patient know that someone has dengue fever. Therefore, the solution provided in this research is to find out the physical characteristics experienced by the sufferer before further diagnosis is carried out. If someone has a fever above 38oC, the body has red spots, irregular breathing, immediately go to the doctor because these symptoms indicate symptoms of dengue hemorrhagic fever or malaria fever. Artificial neural networks are an information processing system designed to imitate the workings of the human brain by carrying out a learning process through changing the weights of synapses. The human brain consists of millions of interconnected neurons known as biological neurons. Each neuron consists of a cell that has a number of dendrites (input) and an axon (output). Axons connect to other neurons through connecting pathways that produce chemical reactions when responding to incoming input. The input required includes the number of input variables, input variable values, weights, learning rate, threshold, maximum epoh and target (output) with the error value classification used is Mean Absolute Error (MAE), there are 2 types of disease with fever symptoms used. The types of disease are dengue hemorrhagic fever and malaria and the system will be designed using the Visual Basic 2010 programming language. From the results of the research that has been carried out, classification results are obtained with a value of 0.893619481 or rounded to equal 1 and classified as dengue hemorrhagic fever.
Web-Based Expert System for Early Diagnosis of Skin Diseases in Cats Using the Naïve Bayes Method Ririn Pratiwi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.364

Abstract

This journal discusses the development of a web-based expert system for early diagnosis of skin diseases in cats using the Naïve Bayes method. Skin disease in cats is a health problem that often occurs and requires fast and accurate diagnosis. This expert system is designed to assist cat owners and veterinarians in identifying potential causes of skin symptoms in cats. The Naïve Bayes method is used in this system because of its ability to process symptom data and produce predictions based on probability. Symptom data is collected from various sources and used to train a Naïve Bayes model. Next, the system allows users to enter symptoms observed in their cat, and the system will provide an initial diagnosis based on the information provided. The experimental results show that this expert system is able to provide an initial diagnosis of skin diseases in cats with a sufficient level of accuracy. This provides a great benefit to cat owners in taking early action and further veterinary consultation. Apart from that, this expert system can also be used as a supporting tool for veterinarians in the process of diagnosing skin diseases in cats. Thus, this research provides an important contribution to the development of expert systems in the field of animal health, especially in the early diagnosis of skin diseases in cats.
Clustering Data On Underage Marriage Using The Clustering Method Sonadi Perangin Angin; Rusmin Saragih; Marto Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.370

Abstract

In Law no. 1 of 1974, article 7 paragraph (1) states that marriage is only permitted if the man has reached the age of 19 and the woman has reached the age of 16. Nationally, early marriage to the age of under 16 is 26.95%. In fact, based on the findings of Bappenas in 2008, it was stated that 34.5% of the 2,049,000 marriages in 2008 until now were child marriages which were increasing rapidly (Rifiani, 2011: 126). The influence of foreign culture is also one of the causes of the large number of underage marriages, foreign cultures which are very famous for freedom of dating, are the views of today's youth to have relations outside of legal marriage. Not only culture, information technology in the 4.0 era has greatly influenced the occurrence of underage marriages, adult video sites that are easily accessible via the internet. For this reason, the K-means clustering method is used as the right solution for the problem of underage marriage data by grouping the data based on age, gender, and occupation to get definite data, so that data grouping using the applicationmatlab andrapid miner can produce output from data mining that can be used in making decisions in the future Keywords: Underage marriage, clustering, matlab
Decision making for determining promotional targets for the STMIK Kaputama campus using the Promethee method Feni Yasari Br Surbakti; Achmad Fauzi; Suci Ramadani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.371

Abstract

Every foundation or educational institution certainly has efforts to maintain its existence amidst competition from educational institutions that continue to innovate to attract public interest in an educational institution . The results of good and appropriate promotion can be seen from the development of student admissions each year. To carry out promotions, of course you have to pay attention to things such as the type of school, travel time, number of computer science enthusiasts. Apart from that, determining promotional targets to get good, effective and efficient results. So a decision-making system is needed that is able to assist in the analysis of determining promotional targets at STMIK Kaputama. With the existence of a decision support system and based on STMIK Kaputama promotion target criteria, we are able to get the right promotion target results for the advancement of STMIK Kaputama development and realizing the vision and mission for the future, apart from that, so that prospective STMIK Kaputama students increase, because of the right promotion targets . To make decisions effective and efficient, this decision making system was built using the Promethee method , which is one of the decision making methods used to obtain a problem solution. Promethee is used to determine and produce decisions from several alternatives. From the results of the research conducted, it was found that the Promethee method was able to produce the best concise decisions.
Application of Case Based Reasoning Method to Diagnose Rice Plant Diseases Windi Tamara Windi; Novriyenni; Husnul Khair
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.372

Abstract

Rice (Latin: Oryza sativa) is one of the most important cultivated crops in civilization. Although it mainly refers to a type of cultivated plant, rice is also used to refer to several types of the same genus (genus), commonly referred to as wild rice. The problem that often arises is that many rice plants are susceptible to pests and diseases during the planting period. Some pests and diseases that can attack rice plants include: leaf blight, grass, tongguo, rice spout, and dwarf grass. Generally, when rice plants are attacked by pests and diseases, farmers will immediately use pesticides or treatment methods that are sometimes not in accordance with pests. As a result, treatment is not optimal and can even cause new pests and diseases. The purpose of this study is to assist farmers in identifying early symptoms of plant diseases and pests of rice plant diseases using the case base reasoning method, so that the treatment of plant diseases and insect pests is more concentrated and maximal.
Expert System for Diagnosing Lipoma Disease in Hospital Patients Latersia Using the Certainty Factor (CF) Method Muhammad Al Hafiz; Novriyenni; Fuzy Yustika Manik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.376

Abstract

Lipoma disease is a disease characterized by a lump filled with a layer of fat that gradually accumulates under the skin, where this lump is between the skin and the muscle layer. This disease often appears on the neck, back, shoulders, arms, and thighs. In general, fat lumps or lipomas can be said to have slow growth between the skin and muscle layers. People tend to just let the lumps happen to them and think they are just normal lumps, without carrying out further examinations. The queue to see a doctor for further examination is also a factor. Therefore, it is necessary to make efforts so that the public can obtain information and be able to diagnose lipoma early without having to visit a doctor. From the description above, it is the basis for building a system that can provide information on lipoma disease and diagnose lipoma disease early. The system to be built can produce an early diagnosis analysis based on symptoms that are felt like a doctor, this system is commonly called an expert system, to support accuracy in building an expert system a method is needed in the analysis of its completion. One of the methods to be used is Certainty Factor (CF). The CF method is a clinical parameter value given by MYCIN to indicate the level of trust. The php programming language and MySQL database can build a system for diagnosing lipoma disease using the Certainty Factor method. type of lipoma Lipo Sarcoma 42.24%, Spindle cell lipoma, 56.59%, Myxoid liposarcoma 51.36%, Hibernoma 32%, Intramuscular hemangioma 51.48%, Chondroid lipoma 51.48%, Atypical lipoma 24%. From these results it can be said that the greatest confidence value is the type of Spindle cell lipoma disease with a confidence value of 56.59%.
Implementation of the Smart Method in Selection of Contraceptive Devices in Couples of Childbearing Age Case Study: Datar City Health Center Rahmawati; Rusmin Saragih; Mili Alfhi Syari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.384

Abstract

In a marriage, the presence of a child is something that is desired. The Indonesian government, in particular, the National Population and Family Planning Agency (BKKBN) advises husband and wife couples to have a maximum of 2 children. One way to plan the number and timing of pregnancies is to use contraception. This research implements the SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) Method in the selection of contraceptives for couples of reproductive age at the Kota Datar Health Center. The results obtained from the research conducted show that the SMART method used in this system has been proven to be effective in helping select contraceptives for couples of childbearing age in the Datar City Community Health Center case study, because it can select alternatives and carry out rankings in determining the right contraceptive for couples of childbearing age. according to needs based on predetermined criteria, where the injection alternative (A04) with a final score of 83 is a suitable contraceptive for couples of childbearing age according to their needs.
Classification For Predicting Heart Disease Using The K Nearest Neighbor Method Sylvani General Hospital Binjai City Eko Sulistio
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.396

Abstract

The heart is a hollow organ and has four chambers or chambers located between the two lungs in the middle of the thoracic cavity. The heart has an important function in the human body, namely as a pump that presses blood so that it can flow to all parts of the body through arteries or veins. Disease caused by plaque buildup in the coronary arteries which supply oxygen to the heart muscle, resulting in severe damage to the heart is called coronary heart disease. Many factors can increase the risk of heart disease. These risk factors consist of risk factors that cannot be modified such as family history, age and gender and risk factors that can be modified such as hypertension, smoking habits, diabetes, dyslipidemia, obesity, lack of physical activity, diet and stress. K-Nearest Neighbor is a method for classifying new objects based on their (K) closest neighbors. K-NN includes a Supervised Learning algorithm where the results of querying new instances are classified based on the majority of categories in KNN. The class that appears the most will be the classification result class. This algorithm only stores feature vectors and classifies the learning data. In the classification phase, the same features are calculated for the test data (whose classification is unknown). The distance of this new vector to all data vectors is calculated, and the k closest ones are taken. The newly classified point is predicted to be among the most classified of these points. From the data with the majority categories there are Positive and Negative categories. From the majority number (Positive > Negative) it can be concluded that new data (data No. 20) (K1=1, K2=0.5, K3=0, K4=1, K5=0, K6=1, K7=0, 5, K8=1) is included in the Positive category.
Analysis Sentiment On Social Media Instagram Towards Metaverse Games Saindbox Aplha 2 With Support Vector Machine Algorithm Prahmana, I Gusti; Br Sitepu, Kristina Annatasia; Selfira
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 2 (2024): February 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i2.230

Abstract

Metaverse is part from development technology in the metaverse SandBox Alpha 2 Game world taking place worldwide , games in the virtual world like real very possible thing done . metaverse now Already in progress for can be implemented most affected technology to opinion from particular society _ enthusiasts game metaverse saydbox alpha 2. where game can create her world alone and various game For look for missions and coins can make money to sell _ in metaverse sandbox alpha. since emergence exists game that has been appeared on facebook that has been replaced be meta, create attention world public increasingly highlight technology this , someone _ welcome game the with good and some have _ worries to development technology the . So study This will dig analysis sentiment public Indonesia against development and use metaverse technology uses method algorithm Algorithm Support Vector Machine. analysis sentiment that will done on social media Facebook. Programming language used _ is Language Jupyter Notebook Python. Study This get results opinion public Indonesia to metaverse technology that shows behave neutral , negative and positive .
Application of Multimedia Learning for Pancasila and Citizenship Education in SD Inpres Waingapu 3 Mbana, Marlyn Rambu Day; Hariadi, Fajar; Mira, Trisari Dewi Novyanti B
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 3 (2024): June 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i3.532

Abstract

PPKn is an essential subject in elementary schools to instill Pancasila values and national identity. However, the results of the End-of-Semester Assessment (PAS) at SD Inpres Waingapu 3 consistently show low PPKn scores. To address this issue, this study developed a multimedia learning application for PPKn. This application is designed to enhance student understanding of PPKn material, particularly related to Pancasila values. The SDLC (Software Development Life Cycle) Waterfall method was used in the application's development. The application's effectiveness was tested through pre-test and post-test, showing a significant increase in the average score of 79.5%. The application's usability was also tested using the System Usability Scale (SUS), with an average score of 85 and a category of "Excellent". These results indicate that the PPKn multimedia learning application is effective and ready for use in elementary schools. hopefully this application can help improve the quality of PPKn learning and instill Pancasila values more strongly in students.

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