Seiring berjalannya waktu teknologi terus berkembang semakin canggih. Dimulai dari website, sampai dengan teknologi mobile. Teknologi website dan mobile memiliki peran yang sangat penting sebagai sumber informasi. iBadung merupakan sebuah aplikasi mobile yang memiliki fungsi sebagai wadah yang menyediakan berbagai jenis buku bacaan atau dapat disebut perpustakaan digital yang dapat diakses oleh semua orang. Untuk dapat mengukur tingkat kenyamanan user, kelayakan aplikasi, dan interface aplikasi maka dilakukan pengujian usability, tujuan untuk menganalisis user experience dalam penggunaan aplikasi iBadung. Fungsi dari metode Heuristic Evaluation adalah mengetahui tingkat usability pada iBadung. Pengujian ini mengisi kuesioner dilakukan oleh 17 orang responden dengan kategori pengguna awam, pengguna biasa dan pengguna administrator. Hasil yang didapat dari pengujian pertama pengguna awam mendapat nilai severity rating skala 2 kategori minor usability problem, pengguna biasa dan pengguna administrator mendapat nilai severity rating skala 1 kategori cosmetic problem. Temuan permasalahan yaitu pada logo aplikasi, tata letak icon menu, tampilan menu login, penambahan pop up exit pada tampilan aplikasi iBadung dan dilakukan perbaikan tampilan sesuai rekomendasi user selanjutnya melakukan pengujian kedua setelah perbaikan tampilan selesai dikerjakan.
Kanker payudara termasuk salah satu penyakit tidak menular yang cenderung terus meningkat setiap tahunnya. Penyakit ini terjadi hampir seluruhnya pada wanita, tetapi dapat juga terjadi pada pria. Cara terbaik untuk mengidentifikasi keberadaan kanker payudara pada tahap awal adalah dengan menafsirkan gambar mammogram yang menggunakan sinar-X yang dapat memperlihatkan keabnormalan atau kelainan pada payudara dalam bentuk yang sangatkecil. Identifikasi secara visual memerlukan skill penglihatan dan pengetahuan dalam mengklasifikasikan hasil dari citra mammogram. Berdasarkan hal tersebut penelitian ini mengusulkan identifikas/klasifikasi kanker payudara pada citra mammogram secara visual ke dalam komputer dengan menggunakan metode segmentasi k-menas, ekstraksi fitur tekstur Gray Level Co-Occurence Matrix (GLCM) dan metode klasifikasi Support Vector Machine (SVM). Aplikasi pada penelitian ini dapat mengklasifikasi/mengenali citra mammogram yang normal dan abnormal dengan nilai akurasi yang diperoleh sebesar 80%.
Big companies that have many branches in different locations often have difficulty with analyzing transaction processes from each branch. The problem experienced by the company management is the rapid delivery of massive data provided by the branch to the head office so that the analysis process of the company's performance becomes slow and inaccurate. The results of this process used as a consideration in decision making which produce the right information if the data is complete and relevant. The right method of massive data collection is using the data warehouse approach. Data warehouse is a relational database designed to optimize queries in Online Analytical Processing (OLAP) from the transaction process of various data sources that can record any changes in data that occur so that the data becomes more structured. In applying the data collection, data warehouse has extracted, transform, and load (ETL) steps to read data from the Online Transaction Processing (OLTP) system, change the form of data through uniform data structures, and save to the final location in the data warehouse. This study provides an overview of the solution for implementing ETL that can work automatically or manually according to needs using the Python programming language so that it can facilitate the ETL process and can adjust to the conditions of the database in the company system.
E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.
Balinese traditional carvings are Balinese culture that can easily be found on the island of Bali, starting from the decoration of Hindu temples and traditional Balinese houses. One of the types of Balinese traditional carving ornaments is Kekarangan ornament carving. Apart from the many traditional Balinese carvings, Balinese people only know the shape of the carving without knowing the name and characteristics of the carving itself. Lack of understanding in traditional Balinese carving is caused by the difficulty of finding sources of materials to study traditional Balinese carvings. A traditional Kekarangan Balinese carving classification system can help Balinese people to identify classes of traditional Balinese carving. This study used the Gabor CNN method. The Multi Orientation Gabor Filter is used in feature extraction and image augmentation, coupled with the Convolutional Neural Network method for image classification. The usage of the Gabor CNN method can produce the highest image classification accuracy of 89%.
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