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All Journal JITEK (Jurnal Ilmiah Teknosains) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jusikom : Jurnal Sistem Komputer Musirawas JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Dharma Wacana Informatics and Enginering Dedication Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) The Indonesian Journal of Computer Science
Desi Puspita
Institut Teknologi Pagar Alam
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Medicine & Pharmacology Social Sciences Veterinary Other

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KLASIFIKASI PENGUNJUNG WISATA DI KOTA PAGAR ALAM DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) Riduan Syahri; Desi Puspita
JITEK (Jurnal Ilmiah Teknosains) Vol 9, No 2/Nov (2023): JiTek
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jitek.v9i2/Nov.17329

Abstract

The city of Pagar Alam has many beautiful tourist options, fresh and cold air, and unique culture and culinary delights. So that it becomes an area that is visited by many local and foreign tourists. Of the many visitors who come, not a few of them leave impressions in the form of reviews of the places they have visited. The purpose of this study is to determine the classification and to determine the accuracy produced by the K-Nearest Neighbor (K-NN) method. The K-Nearest Neighbor (K-NN) method is used to classify visitor data on Pagar Alam tours. Tests carried out to get good accuracy results and evaluate using a confusion matrix. This research produces a classification system that can identify and classify Pagar Alam tourism visitors using the K-Nearest Neighbor (K-NN) algorithm with the results obtained the greatest accuracy with a value of k = 3 with 99% accuracy, K0 gets 98% precision, recall 100 and a fi-score of 99%, for k1 precision 100%, the recal is 89% and the fi-score is 92%, while for K2 the precision is 100%, the recal is 100%, the f1-score is 100%.
Pelatihan Pembelajaran Berdiferensiasi Berbasis Digital dengan Canva bagi Sekolah Penggerak Desi Puspita; Nadiya Citra Dewi; Ferry Putrawansyah
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Sistem Informasi dan Teknologi (Sisfokomtek)

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Abstract

Education units that are part of the driving school program found that the implementation of digital-based differentiated learning, especially using Canva media, is still a challenge. Several aspects that can be seen from the results of observations and interviews include that many teachers in this educational unit are not fully aware of the importance and benefits of digital-based differentiated learning with Canva. The service method chosen to achieve the target of differentiated learning training with Canva starts from Self, Concept Exploration, Collaboration Space, and Demonstration. The work produced by the participants is concrete evidence of the positive impact of this service activity. These works reflect the understanding obtained from the service team regarding the material presented. Participants not only become consumers of information but are also able to turn it into useful work in a learning context
Implementasi Learning Vector Quantization untuk Klasifikasi Jenis Buah Kelapa menggunakan Image Processing Desi Puspita
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): Indonesian Journal of Computer Science Volume 11. No. 3 (2022)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3108

Abstract

Coconut fruit is a versatile plant because all parts from the stem to the coconut fruit have their benefits. Coconut fruit is the most valuable part of the economy. The problem so far that has occurred is that the process of classifying coconut species is still done manually and has not been computerized, namely the classification of coconut types is still based on experience, color, and shape of the coconut. This of course takes a long time and errors still occur frequently. So this research can help classify coconuts with Learning Vector Quantization (LVQ). The purpose of this research is to organize the types of coconuts with image processing and Learning Vector Quantization (LVQ) by using mean extraction from RGB (Red, Green, Blue) and standard deviation from RGB (Red, Green, Blue). The results of the study were taken from 2 different types of coconuts against the 80 training data, the accuracy of the training data was 83.75%. The evaluation results with the Confusion Matrix with a test accuracy value of 90% of the 20 test data.
Co-Authors Alfis Arif Alfis Arif Asminah Asminah Barrorotul Azizah Buhori Muslim Dewi, Nadiya Citra Ferry Putrawansyah Lesi Anggraini Melani Anggita Nadiya Citra Dewi Riduan Syahri Riduan Syahri Risnaini Masdalipa Sasmita Sasmita Siti Aminah Stiti Aminah Yogi Isro Mukti, Yogi Isro