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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING CSRID (Computer Science Research and Its Development Journal) Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Noor Ageng Setiyanto, Noor Ageng
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm Daurat Sinaga; Feri Agustina; Noor Ageng Setiyanto; Suprayogi Suprayogi; Cahaya Jatmoko
Journal of Applied Intelligent System Vol 6, No 2 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i2.4627

Abstract

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.
Recommendation System for Major University Determination Based on Student’s Profile and Interest Desi Purwanti Kusumaningrum; Noor Ageng Setiyanto; Erwin Yudi Hidayat; Khafiizh Hastuti
Journal of Applied Intelligent System Vol 2, No 1 (2017): April 2017
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v2i1.1389

Abstract

Failure study on university students is one of the serious problems we face today. Data from the Centre for Education Statistics Research and Development of the Ministry of National Education Republic of Indonesia showed that the percentage of students graduate on time from 2001 to 2011 only reached 51.97%. In addition, cases of students dropping out at the beginning of the semester is also quite significant. One of the causes of failure of this study was the selection of major’s errors when applying to university. This study offers a selection subject recommendation system that builds on the profile data and student’s interest using the technique of Association Rule. Results of the rules of the relationship will then be matched with prospective students using questionnaires dynamic, so expect new students get recommendations more valid subject fit the profile and interest respectively. The system built on this research utilizes student data stored on the academic system of Dian Nuswantoro University. This model however can be adapted by all the universities that has a system of academic information. At the end of this system is expected to be used to minimize failures caused students study majors election mistakes
Co-Authors Abdussalam Abdussalam Achmad Wahid Kurniawan Achmad Wahid Kurniawan Akrom, Muhamad Aprilyani Nur Safitri Ayu Pertiwi Ayu Pertiwi Budi, Setyo Cahaya Jatmoko Christy Atika Sari Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Dhendra Marutho Dyarsa Singgih Pamungkas Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Erwin Yudi Hidayat Feri Agustina Hanny Haryanto Harun Al Azies Herawati, Wise Hidayat, Novianto Hidayat, Novianto Nur Khafiizh Hastuti Kurnia, Lisa Lukman Sandjaya Moch Anjas Aprihartha Moch Arief Soeleman Muhamad Akrom Muhammad Naufal, Muhammad Novianto Hidayat Prabowo, Wahyu Aji Eko Prabowo, Wahyu Eko Aji Pujiono Pujiono Pulung Nurtantio Andono Putra Sumarjono, Cornellius Adryan Rana, Bastion Jader Safitri, Aprilyani Nur Setiawan, Marcell Adi Sinaga, Daurat Sudibyo, Usman Suprayogi Suprayogi T. Sutojo Tirta Indra Wibowo Trisnapradika, Gustina Alfa Usman Sudibyo Waseso, Bambang Mahardhika Poerbo Wise Herawati