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All Journal Indonesian Journal of Electronics and Instrumentation Systems Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Pengabdian kepada Masyarakat Bina Darma Algoritme Jurnal Mahasiswa Teknik Informatika MDP Student Conference
Wijang Widhiarso
Universitas Multi Data Palembang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Journal : Algoritme Jurnal Mahasiswa Teknik Informatika

 1 
CLASSIFICATION OF PNEUMONIA USING K-NEAREST NEIGHBOR METHOD WITH GLCM EXTRACTION Chandra Wijaya; Hafiz Irsyad; Wijang Widhiarso
Jurnal Algoritme Vol 1 No 1 (2020): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1864.245 KB) | DOI: 10.35957/algoritme.v1i1.431

Abstract

Pneumonia is an inflammatory parenchymal disease caused by various microorganisms, including bacteria, micro bacteria, fungi, and viruses. This study used an X-ray to find out whether or not there was pneumonia. The objective of this study was to classify the X-ray results whether or not there was pneumonia in a fast and precise way through a program to produce good accuracy. The classification method used in this study were K-Nearest Neighbor (KNN) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before being classified, namely cropping, resizing, contrast stretching, and thresholding. The results showed that the best accuracy per class was 66.20% for K = 5.
Klasterisasi Topik Skripsi Informatika dengan Metode DBSCAN Zicola Vladimir VIky Khan; Derry Alamsyah; Wijang Widhiarso
Jurnal Algoritme Vol 3 No 1 (2022): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3337

Abstract

This research analyzed 176 Palembang public universities’ students’ theses which were published in 2020. The data was analyzed by conducting text processing and extraction with TF-IDF feature by using two scenarios, the reduced feature value and the unreduced one, with SVD method. In each scenario, three metrics, cosine, euclidean, and, manhattan were used, which generated six scenarios in total. The result found that the best quality of cluster which was measured by silhouette coefficient comes from metric cosine and reducted by SVD with the silhouette coefficient value of 0.88382763, intracluster value of 0.08688583, and intercluster value of 0.74671096. Therefore, the cluster quality value of the reducted feature is the best among all metrics. In addition, the use of DBSCAN method showed a positive correlation between epsilon and intracluster with the value of 0.97669, and also showed a negative correlation between epsilon and silhouette with the value of 0.9789.
Klasifikasi Pengenalan Wajah Untuk Mengetahui Jenis Kelamin Menggunakan Metode Convolutional Neural Network MUHAMMAD AKBAR SATRIAWAN; WIJANG WIDHIARSO
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.6095

Abstract

The face is the component that is most easily recognized and is often the center of attention of other people in the human body. There are often difficulties in distinguishing and analyzing large numbers of facial images manually due to the large number of similarities between males and females, which slows down the process of gender identification. This research was made to fix this problem by using the CNN method. The dataset used is 2280 images consisting of train, valid and test. The research process includes data pre-processing, model initialization, model training, hyperparameter validation and adjustment, and model performance evaluation. The test results show an increase in accuracy and a decrease in loss as training iterations increase. In this study, results were obtained with an accuracy rate of 92%, which shows the effectiveness of using a Convolutional Neural Network (CNN) with the ResNet-50 architecture in processing and classifying male and female facial images.
Co-Authors Ahmad Farisi Cendy Prakarsah Cerwyn Asyraq Chandra Wijaya Dafid Dafid Derry Alamsyah Edi Winarko Edo Renaldo Ericco Andreas Ervina Patricia Arifin Hafiz Irsyad Kersen Kersen MUHAMMAD AKBAR SATRIAWAN Ricky Putra Sardika Rizky Pribadi, Muhammad Sri Hartati Vera Nofita Verren Oktaviana Yohannes Yohannes Zicola Vladimir VIky Khan