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All Journal Jurnal Sistem Informasi Kaputama (JSIK) JTIK (Jurnal Teknik Informatika Kaputama) Djtechno: Jurnal Teknologi Informasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Manajemen Dan Akuntansi Medan International Journal of Community Service Implementation Jurnal Pengabdian Masyarakat Multi Disiplin Ilmu
I Gusti Prahmana
STMIK Kaputama
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra I Gusti Prahmana; Kristina Annatasia Br Sitepu
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 3 (2022): June 2022
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (250.231 KB) | DOI: 10.59934/jaiea.v1i3.97

Abstract

A process to explain the results of the KNN algorithm analysis with the prediction of Breast Cancer Coimbra disease (Breast Cancer). The prediction output of the KNN algorithm will be added with the Simple Linear Regression algorithm modeling to measure the predictive data through a straight line as an illustration of the correlation relationship between 2 or more variables. Linear regression prediction is used as a technique for the relationship between variables in the prediction process of the Breast Cancer Coimbra data set (Breast Cancer). for the value of K in analyzing the KNN algorithm, take the nearest neighbor with the ranking results with K = 5 nearest neighbors which are taken in the KNN calculation. Which is where the output of the KNN algorithm classification will be analyzed with the Simple Linear Regression algorithm with Dependent (Cause) and Independent (effect) variables. The test results determine that the patient has breast cancer and the number of predictions based on age with glucose means that the patient is predicted to have breast cancer. analyze the KNN algorithm with Simple Liner Regression modeling with Python programming language.
Identification Identification of land and water Centella asiatica leaf herbal plants using digital imagery with the Sobel Edge Detection algorithm I Gusti Prahmana; Kristina Annatasia Br Sitepu
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 2 No. 2 (2023): February 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v2i2.158

Abstract

Centella asiatica leaves or gotu kola leaves are wild plants that grow in Asian countries such as China, Indonesia, Japan and India. Since thousands of years ago, this gotu kola leaf has been known to treat various diseases. This plant is even used as a traditional herbal medicine in China and India. Centella asiatica is an annual herbaceous plant that grows and flowers throughout the year. Plants will thrive if the soil and environment are suitable to be used as a ground cover. Types of gotu kola that are often found are red gotu kola and green gotu kola. Centella asiatica is also known as antanan taman or antanan batu because it is found in rocky, dry and open areas. Centella asiatica grows with stolons and has no stems, but has rhizomes (short rhizomes). Meanwhile, green gotu kola is often found in rice fields and on the sidelines of the grass. Based on this problem, a study is needed to develop a system to determine the shape of leaf fiber density with a comparison of ground gotu kola and water gotu kola using image processing techniques to find the diameter. This measurement process uses the Matlab application and tests with the Sobel edge detection method and image processing to see edges that are more clearly visible. The results showed that the developed system was capable of obtaining images and identifying the fiber density of Centella asiatica leaves. The system was designed with Jupyter Notebook Python-based programming language analysis with image data taken via internet sources as research material.
Analysis Sentiment On Social Media Instagram Towards Metaverse Games Saindbox Aplha 2 With Support Vector Machine Algorithm I Gusti Prahmana; Kristina Annatasia Br Sitepu; 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 .
Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final Muhammad Rifa'i; Relita Buaton; I Gusti Prahmana
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.v3i1.274

Abstract

This thesis aims to analyze sentiment on text data from social media related to the 2023 SEA Games, especially in the final match of the soccer sport. The method used is the Text Mining Technique with the SVM (Support Vector Machine) algorithm to classify user sentiment as positive or negative regarding the match. Text data is retrieved from various social media platforms during and after the match. The results of the sentiment analysis are expected to provide insight into the public's view of the sporting event. This research can contribute to the understanding of public sentiment towards the 2023 SEA Games final football match through the analysis of text data from social media.
Categorying Sugarcane Production Based On Factors Affecting Productivity With The K-Nearest Neighbor Algorithm Anggi Pratiwi; A M H Pardede; I Gusti Prahmana
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.v3i1.295

Abstract

Sugarcane (Saccharum Officanarum is an annual plantation crop, which has its own characteristics, because the stem contains sugar. To classify the results of sugarcane production, currently still using the manual method by only looking at the current conditions of sugarcane production. This is less efficient because there is no calculation process in grouping sugarcane. So that mistakes can occur in grouping sugarcane production to get good results or not in the assessment of sugarcane grouping at PTPN II Kwala Madu. For this reason, the author will create an alternative application system that can group sugarcane production with the K-Nearest Neighbor algorithm to find out the best type of sugarcane production based on the factors. The application made by the author uses the PHP programming language and uses the MySQL database as data storage. The system is made as easy as possible to make it easier for users to use and understand later.
Co-Authors Adek Maulidya Ambarita, Indah Anggi Pratiwi Buaton, Relita Hotler Manurung Katen Lumbanbatu Kristina Annatasia Br Sitepu Maulida Azmy Muhammad Arief Muhammad Gifary Muhammad Hiszat Muhammad Rifa'i Muhammad Yoga Sabilla Pardede, Akim Manaor Hara Ratih Puspadini Rika Oktavia Rozaq Habibi Rozaq Habibi Selfira Yap Simanjuntak, Magdalena Suria Alamsyah Putra Toni Prabowo Tria Pusvita Dewi