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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
Rohana, Tatang
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect Antoni, Alan; Rohana, Tatang; Pratama, Adi Rizky
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3270

Abstract

Packaging is an important aspect of a product, because packaging can affect the quality and competitiveness of the product. Damaged packaging can result in decreased product quality. One popular packaging used is corrugated cardboard type box. To visually distinguish defect and no defect cardboard packaging, there are tears, holes and dents on the defect cardboard packaging. Whereas the no defect cardboard packaging has a visual that there are no tears, holes or dents. To simplify the classification, technology is needed that can distinguish between defect and no-defect cardboard packaging. In this study the total images used as a dataset are 1300 images, which are then divided into 2 with a percentage of 80% for training data and 20% for test data. The dataset first goes through the preprocessing stage before being used. Preprocessing consists of cropping, augmentation and resizing. And also do the segmentation process using Grabcut method. Then feature extraction is also performed using Local Binary Pattern (LBP). This study uses the Convolutional Neural Network algorithm with a total of 3 convolution layers, namely 16.32 and 64 and the Adam optimizer. Four experiments were carried out by differentiating the hyperparameter epoch, the input image size and the learning rate. The results showed that the model produced with an epoch hyperparameter of 30, an input image size of 300x300 and a learning rate of 0.001 obtained the best performance with an accuracy value of 95.77%, 96% precision, 96% recall, 96% f1-score and loss of 0.1478.
Perbandingan Kinerja Klasifikasi Penyakit Ginjal Menggunakan Algoritma Support Vector Machine (SVM) dan Decision Tree (DT) Madani, Puja Milenia Sriwildan; Rohana, Tatang; Baihaqi, Kiki Ahmad; Fauzi, Ahmad
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5206

Abstract

Chronic Kidney Disease is one of the deadliest diseases. In the early stages, the disease may go undetected, so patients tend to take it lightly, however, the disease can progress little by little and become serious without being detected. This can lead to complications of other diseases and can cause permanent damage to the kidney organs. Therefore, this study aims to classify individuals who are at risk of having Chronic Kidney Disease which can help medical personnel in an effort to reduce the number of people with the disease. This study uses Chronic Kidney Disease data obtained from the UCI Repository web. The data has 25 attributes with 400 rows. This research compares the Support Vector Machine and Decision Tree algorithms and uses the Confusion Matrix evaluation method. The results showed that the Support Vector Machine algorithm has superior accuracy, precision, recall, and f1-score results compared to the Decision Tree algorithm. The accuracy of the Support Vector Machine algorithm is 97.5, precision is 0.98, recall is 0.96, and f1-score is 0.97. While the Decision Tree algorithm obtained accuracy of 92.5, precision of 0.92, recall of 0.90, and f1-score of 0.91. with these results, this research can be continued into an application that can classify individuals at risk of Chronic Kidney Disease
Perbandingan Metode K-Means dan K-Medoids Untuk Clustering Jenis Kriminalitas Azizah, Nurul; Fauzi, Ahmad; Rohana, Tatang; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5723

Abstract

Crime in Indonesia includes acts that violate the law, social norms and religion which cause economic and psychological losses as well as social tensions in society. Crimes such as theft, violence, fraud and drugs are often triggered by factors such as poverty and environmental conditions that support criminal behavior. This research needs to be carried out to overcome the complex and far-reaching crime problem in Indonesia, especially in Karawang Regency. With crimes such as theft, violence, fraud and drugs on the rise, often fueled by factors such as poverty and environmental conditions, a more effective approach is needed to understand and address these problems. This research uses data mining techniques, especially cluster analysis, to group types of crime. The aim is to identify existing crime patterns and understand the factors that influence their spread. Thus, the results of this research can help the authorities in developing more targeted crime prevention and handling strategies, so as to minimize the negative impact of crime in the area. Apart from that, this research also contributes to increasing knowledge regarding the most effective methods for analyzing crime data, which can be applied in other areas with similar problems. The results of the research show that the K-Means algorithm is more effective than K-Medoids in handling data variability, with a Silhouette Coefficient value of 0.482 and a Davies Bouldin Index of 0.915. It is hoped that the implementation of this algorithm will make it easier to identify and handle crimes in the area.
Implementasi Algoritma Convolutional Neural Network dan YOLOV8 Untuk Klasifikasi Ras Kucing Adinata, Abdul Rohim; Rohana, Tatang; Baihaqi, Kiki Ahmad; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5913

Abstract

The cat with the scientific name Felis catus is a very popular pet and is often kept in various parts of the world. There are many types or breeds of cats, each of which has its own characteristics and characteristics, such as style, body shape, fur and color. However, because of the many breeds and the uniqueness of each breed, it is often difficult for ordinary people to differentiate between the types of cat breeds that exist. Therefore, technology is needed to identify and differentiate cat breeds. By comparing the Convolutional Neural Network (CNN) and YOLOV8 methods, this research aims to develop a cat breed classification model. This research uses data from six different cat breeds, namely Bengal, Bombay, Himalayan, Local, Persian and Sphynx. There are 1,200 images in all, with 200 images for each race. Before the data is used for training with the CNN and YOLOV8 methods, a preprocessing stage is carried out which includes resize and rescale for the CNN method, while for YOLOV8 a data labeling process is carried out. There are two parts to the dataset: 20% validation data and 80% training data. The training process is carried out with the same parameters for each model, namely a learning rate of 0.001, batch size of 15, and 100 epochs. From the test results with the confusion matrix, the YOLOV8 model shows the best performance with an accuracy value of 99%, precision 96.1%, recall 98.4%, and F1-score 97.2%.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agustin, Rachmayanti Tri Antoni, Alan Awal, Elsa Elvira Azhari, Febrian Akbar Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Bunga Tiara, Vira Butar Butar, Naomi Nova Meylica Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Faisal, Sultan Faisal, Sutan Farah Maulida, Oxana Fauzi Ahmad Muda Fauzi, Ahmad Fauzi, Rifqi Arul Hakim, Mirwan Abdurrahman Hartini, Dina Indra, Jamaludin Irawan, Muhamad Anggi Jamal, Malikil Juwita, Ayu Ratna Kamilah, Nur Azizah Karyanto, Dony Dwi Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Maulana, Moch Sigit Rizky Mudzakir, Tohirin Al Muhammad Khaerudin Niendha Biell Binna Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva NURUL AZIZAH Pratama, Adi Rizky Pratama, Irfan Nugraha Purnomo, Indarto Aditya Rahmat Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ratna Juwita, Ayu Siregar, Amril Mutoi Sukmawati, Cici Emilia tukino, tukino Wahiddin, Deden Winarni Yana Cahyana Yulia Novita, Hilda