Jurnal Teknik Informatika C.I.T Medicom https://medikom.iocspublisher.org/index.php/JTI <p align="justify">The Jurnal Teknik Informatika C.I.T Medicom a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.<br /><br />1. Expert systems<br />2. Decision Support System<br />3. Datamining<br />4. Artificial Intelligence</p> <p align="justify"> </p> <p align="justify"><strong>Acceptance Ratio:</strong></p> <table width="100%"> <tbody> <tr> <td bgcolor="#F0F8FF"><strong>Volume 13 Issue 1 (2021)</strong></td> <td bgcolor="#F0F8FF"><strong>18% </strong></td> </tr> <tr> <td bgcolor="#F5F5DC"><strong>Volume 12 Issue 2 (2020)</strong></td> <td bgcolor="#F5F5DC"><strong>33% </strong></td> </tr> <tr> <td bgcolor="#F0F8FF"><strong>Over All (Vol 1-13)</strong></td> <td bgcolor="#F0F8FF"><strong>15%</strong></td> </tr> </tbody> </table> <p> </p> <table style="border-collapse: collapse; width: 100%;" border="1"> <tbody> <tr> <td style="width: 43.6097%;"> Citation Analysis :</td> <td style="width: 56.3903%;"> <a href="https://medikom.iocspublisher.org/index.php/JTI/SCOPUS"><img src="https://jurnal.polgan.ac.id/public/site/images/polgan/scopus1.jpg" /></a> <a href="https://scholar.google.co.id/citations?hl=id&amp;authuser=5&amp;user=vB5ZokUAAAAJ"><img src="https://jurnal.polgan.ac.id/public/site/images/polgan/google1.jpg" /></a> <a href="https://sinta.ristekbrin.go.id/journals/detail?id=6844"><img src="https://jurnal.polgan.ac.id/public/site/images/polgan/sinta1.jpg" /></a></td> </tr> </tbody> </table> Institute of Computer Science (IOCS) en-US Jurnal Teknik Informatika C.I.T Medicom 2337-8646 Dynamic Pathfinding for Non-Player Character Follower on Game https://medikom.iocspublisher.org/index.php/JTI/article/view/68 <p>Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.</p> Paulus Harsadi Siti Asmiatun Astrid Novita Putri Copyright (c) 2021 Jurnal Teknik Informatika C.I.T Medicom https://creativecommons.org/licenses/by-nc/4.0 2021-09-01 2021-09-01 13 2 51 58 10.35335/cit.Vol13.2021.68.pp51-58 Smartphone Price Grouping by Specifications using K-Means Clustering Method https://medikom.iocspublisher.org/index.php/JTI/article/view/98 <p>The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.</p> Ahmad Agung Zefi Syahputra Annisa Dwi Atika Muhammad Adam Aslamsyah Meida Cahyo Untoro Winda Yulita Copyright (c) 2021 https://creativecommons.org/licenses/by-nc/4.0 2021-09-01 2021-09-01 13 2 59 68 10.35335/cit.Vol13.2021.98.pp59-68