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Abdul Muis
Universitas Satya Terra Bhinneka
Computer Science & IT Education

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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
Rancang Bangun Sistem Informasi Pemasaran Barang Antik Berbasis Mobile Web Rofiqoh Dewi; Abdul Muis
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this era of digitalization, the use of technology in developing a product is very commonly used to introduce products, sell products and other things related to recognizing product needs such as product information, product functionality, price, or uniqueness that a product has but is not found in other products. like antiques. Currently, it is very rare to find the marketing of antiques in modern and traditional markets. The need to buy antiques is also very rare, but that doesn't mean it has disappeared with time because there are still some people who are interested in antiques if they appear displayed in a window at the market. Based on this, to make it easier for people to access antiques, technology is needed to be able to process data well to make it more efficient and effective. Based on this, the direction of the research carried out by the researcher aims to introduce antiques to all levels of society and build a mobile website to market these antiques so that people can get to know the types and benefits of antiques, the history of antiques and the prices of these antiques.
Co-Authors Abwabul Jinan Agung Prabowo Akbar Idaman Dede Fika Suryani Manutur Siregar Nathanael Atan Baskita Tarigan Nopi Purnomo Radiman Gea Riko Muhammad Suri Rofiqoh Dewi SUMITA WARDANI Tar Muhammad Raja Gunung Vicky Rolanda