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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Implementation of the ORESTE Method in Determining the Selection of Service Ambassador Events Akbar Idaman; Hamjah Arahman; Abdul Muis; Tar Muhammad Raja Gunung; Handry Eldo
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3225

Abstract

The selection of candidates to become Service Ambassadors is an important and complex process. Assessors need to consider many factors and the relative weight of each factor to ensure the best candidate is selected for the position. One method that can be used in candidate selection is the ORESTE method. The ORESTE method is a multi-criteria decision-making method developed by J.P. Brans and B. Mareschal in 1994. This method allows assessors to aggregate multiple criteria and consider the relative weight of each criterion to compare alternatives and produce a ranking of candidates based on the highest relative value. In the context of Service Ambassador selection, the ORESTE method can assist assessors in solving complex decision-making problems and ensuring the best candidate is selected for the position. The method allows raters to consider multiple criteria and consider the relative weight of each criterion, resulting in a ranking of candidates based on the highest relative value. Thus, the use of the ORESTE method in determining the selection of Tourism Ambassadors can simplify and speed up the candidate selection process, as well as increase accuracy and satisfaction in decision making. By using the ORESTE method, the results of the decision to select the winner of the Service Ambassador event are obtained with a preference value of 4.42
Identification of Nervosa Disease using Case-Based Reasoning Tar Muhammad Raja Gunung; Riko Muhammad Suri; Nopi Purnomo; Akbar Idaman; Abdul Muis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3227

Abstract

Among teenagers or adults, body shape greatly affects a person's confidence. Most people will be very confident if their body shape is ideal, where the person will be very confident speaking in public. Different things with people who have a body shape that is too thin/fat, so their confidence is less to appear in public. To get the ideal body shape they will do everything possible, one of which is dieting, taking weight loss capsules, and so on. They do not know the dangers of dieting too strictly and taking weight loss capsules carelessly which can make them contract Nervosa disease. To get the results of the expert system, the case-based reasoning method will be used. After this application is built, the result obtained is the type of nervosa disease based on the symptoms felt by the user.
Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Abwabul Jinan; Manutur Siregar; Vicky Rolanda; Dede Fika Suryani; Abdul Muis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3381

Abstract

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.
Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach Agung Prabowo; Sumita Wardani; Abdul Muis; Radiman Gea; Nathanael Atan Baskita Tarigan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3611

Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches