Jurnal Teknik Informatika dan Komputer

Analisis Prediksi Jumlah Pengunjung Perpustakaan Berdasarkan Jenis Kelamin di Kabupaten Malang Dengan Menggunakan Metode Monte Carlo

Achmad Rifki — 2024-09-30

Libraries are crucial institutions supporting the development of education and knowledge within society. As centers of information, they serve not only as repositories for books and other informational resources but also as spaces for learning and interaction for diverse segments of the population. Therefore, understanding visitor numbers and their visiting patterns is crucial for library managers to enhance planning and optimize services. This study aims to analyze predictions of library visitor numbers in Malang Regency using the Monte Carlo method. The research utilizes data on visitor counts by gender from 2018 to 2020 obtained from the Department of Libraries and Archives in Malang Regency. These data will be used to forecast visitor numbers for the years 2019 to 2021. The analysis reveals that the most accurate prediction occurred in 2019, achieving a 52% accuracy rate. Lower accuracy in subsequent years was attributed to the COVID-19 pandemic, which restricted public movement and non-essential outings.

Pengembangan Game Edukasi 2D Mata Pelajaran IPA Menggunakan Unity Berbasis Mobile

Ilham Teguh Prayudha — 2024-09-30

Technological advances have had a great influence in various areas of life, including in the digital entertainment industry such as video games. There are many children who are more interested in playing games than learning, which can result in a decrease in interest in learning academic activities. Therefore, finding an effective method to balance children's interest in learning by playing games is very important. One solution is to create educational games that provide a fun and interactive learning experience. This research aims to develop an educational game that can help children play while learning. The development of 2D educational games for science subjects uses Unity Engine and MDLC (Multimedia Development Life Cycle) as development methods. From the results of the discussion, the creation of the game was successful. Based on the results of application testing that has been carried out on educational games, all components of this game function properly and there are no problems. In addition, beta testing shows that this educational game has been proven to be able to increase students' interest in learning

Klasifikasi Penyakit dan Kelainan Bentuk Kuku Manusia Menggunakan Convolutional Neural Network

Rafika Aulia Madani — 2024-09-30

Nails are one of the body parts that have an important role, because nails can provide signals of a disease starting from the color, shape, and size of the nails. This study aims to identify diseases and abnormalities of human nail shape using digital images. This study uses seven classes of diseases, namely beausline, clubbing, koilonychia, yellow nail, white nail, onychomycosis, and normal nail. To identify diseases and abnormalities of human nail shape, the method used is Convolutional Neural Network with VGG-16 architecture. The results obtained from this study are an accuracy of 92.9% from a total dataset that has been augmented as much as 3900 data, for an average precision measurement result of 86.1%, an average recall result of 85.8%, an average f-1 score result of 85.8%.

Penerapan Support Vector Machine Untuk Analisis Sentimen pada X (Twitter) Mengenai Obat Penyebab Gagal Ginjal Akut pada Anak

Tasya Rizki Salsabilla — 2024-09-30

In October 2022, there were many cases of children suffering from acute kidney failure due to harmful chemical compounds detected in the history of children's cough medicine use. The statement caused controversy and became a conversation on social media, especially Twitter. Exploring this opinion can lead to a decision that can be applied in machine learning and sentiment analysis, namely support vector machines (SVM). The purpose of this research is to find out the sentiment of the community towards drugs that cause acute kidney failure in children and see the performance of the support vector machine algorithm. The data used was 1128. Based on the results of the study, the community responded negatively to this topic, as evidenced by the fact that the negative sentiment obtained was greater than the positive sentiment, and the support vector machine algorithm with a linear kernel performed very well, as evidenced by the excellent accuracy value of 91%.