Twitter is one of the social media with the number of users who reach millions of users. The number of Twitter users in 2019 increased by 17 percent in 2018 to 145 million users with a variety of good both positive and bad. The negative impacts that occur such as the spread of status, images, and videos that affect pornography especially among freedom groups. Homosexuals are sexually oriented people who like the same sex that occurs in men, the rejection often experienced by men makes one of the reasons intellectuals use Twitter social media to show their personal relationships, open to each other, socializing with same sex, looking for conversation, to become a place to find a partner. The purpose of this study is to determine the positive and negative sentiments to determine the level of accuracy of intellectual pornography tweets in Indonesia from data taken from Twitter tweets by using the TF-IDF and k-NN methods. The results of this study get an accuracy value of 88.25% containing pornography and the remaining 11.75% not containing pornography will contain news, news, and other information.Keywords: homosexual, sentiment analysis, twitterAbstrak: Twitter merupakan salah satu media sosial dengan jumlah pengguna mencapai jutaan pengguna. Jumlah pengguna Twit-ter pada tahun 2019 dicatat meningkat 17 persendari tahun 2018 menjadi 145 juta pengguna dengan berbagai dampak baik dampak positif maupun dampak negatif. Dampak negatif yang ditimbulkannya seperti penyebaran status, gambar, dan video yang bersifat pornografi khsusunya di kalangan kaum homoseksual. Homoseksual merupakan orang yang berorientasi seksual sebagai penyuka sesama jenis yang terjadi pada kaum pria, Penolakan yang sering dialami kaum homoseksual men-jadikan salah satu alasan kaum homoseksual menggunakan media sosial Twitter untuk menunjukkan identitas diri mereka, saling terbuka, bersosialisasi dengan sesama jenis, mencari penghasilan, hingga menjadi ajang pencarian pasangan. Tujuan dari penelitian ini adalah untuk mengetahui sentimen positif dan negatif untuk mengetahui tingkat akurasi terhadap tweet pornografi kaum homoseksual di Indonesia dari data yang diambil dari tweet Twitter dengan menggunakan metode TF-IDF dan k-NN. Hasil penelitian ini mendapatkan nilai accuracy sebesar 88,25% mengandung unsur pornografi dan sisanya sebesar 11,75 tidak mengandung unsur pornografi akan tetapi berisi iklan, berita, dan informasi lainnya.Kata kunci: homoseksual, sentimen analisis, twitter
E-Learning For Kids Education about Corona Virus (EduCovid-19) is an e-learning website for elementary school students to be able to bridge teachers, parents and students in providing correct information about the spread, danger, and handling of corona virus outbreaks. In addition, this website is equipped with Thematic material that students get at school. By implementing a primary school curriculum for thematic lessons, it is hoped that it can adjust the learning system that is available in schools. The design of EduCovid-19 contains material in the form of education and exercises about Covid-19 and thematic lessons where in one of these materials there will be questions and answers as one of the interactive assessment methods for teachers. The method used in the design of this system is RAD and the research methods used in data collection are interviews, observation and case studies. By applying the learning system using EduCovid-19, it will be able to increase children's interest in the learning process that is currently underway, namely school from home and provide students with knowledge about the dangers and ways to overcome Covid-19.Keywords: Elearning, School, Covid-19, EduCovid-19Abstrak: E-Learning For Kids Education about Corona Virus (EduCovid-19) merupakan website elearning bagi pelajar sekolah dasar untuk dapat menjembatani guru, orang tua dan siswa dalam memberikan informasi yang benar seputar penyebaran, bahaya, dan penangan terkait wabah virus corona. Selain itu, di website ini dilengkapi dengan materi Tematik yang siswa dapatkan di sekolah. Dengan menerapkan kurikulum sekolah dasar pelajaran tematik, diharapkan dapat menyesuaikan sistem pembelajaran yang terdapat di sekolah. Perancangan EduCovid-19 ini berisi materi-materi berupa edukasi serta latihan soal seputar Covid-19 dan pelajaran tematik dimana dalam salah satu materi tersebut akan ada tanya jawab sebagai salah satu metode penilaian interaktif bagi para guru. Dalam perancangan sistem ini menggunakan metode RAD dan metode penelitian yang digunakan dalam pengumpulan data yaitu wawancara, observasi dan studi kasus. Dengan penerapan sistem pembelajaran menggunakan EduCovid-19 nantinya mampu meningkatkan ketertarikan anak dalam proses pembelajaran yang saat ini sedang berlangsung yaitu school from home dan memberikan pengetahuan kepada siswa mengenai bahaya dan cara menghindari serta mengatasi Covid-19.Kata kunci: Elearning, Sekolah, Covid-19, EduCovid-19
Meat is a food ingredient that can be consumed by humans and consists of essential nutrients, especially protein, which are needed for various physiological functions in the human body. Beef, goat and pork are meats that are commonly used by Indonesian people as daily processed foods. A very high level of meat consumption results in a high economic value of meat consumption. However, many people do not know how to distinguish between the types of beef, mutton and pork. This study aims to classify types of beef, goat and pork using the ResNet152V2 algorithm. The data used are 600 images with 200 images of beef, 200 images of mutton and 200 images of pork. The process carried out is pre-processing using 4 stages, namely image augmentation, image sharpness process, then the image is resized to adjust the size needed by the algorithm. The last pre-processing is to perform the image normalization process. After the pre-processing is done, then the data training stage is carried out using the ResNet152V2 algorithm to build a classification model and then the model is tested against data testing to get the results of the optimal classification of pork, goat and beef images by looking at the results of accuracy and loss values.
Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school. This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measurement process. The test results show that the use of the Decision Tree algorithm produces an accuracy value of 97.30% while using the K-NN algorithm produces an accuracy of 89.60%. While the proposed method by combining the Decision Tree and K-NN algorithms produces an accuracy value of 98.07%. The results of evaluation measurements using the Area Under Curve (AUC) on the Decision Tree algorithm are 0.992 and the AUC on K-NN is 0.958 and on the combination of the Decision Tree and K-NN algorithms of 0.979. These results indicate that the proposed algorithm is very significant towards increasing accuracy in the classification of the interests of high school students continuing school
Abstract—Biometric technology is developing to be the most relevant mechanism in identity identification. The main purpose of an identity management system is to be able to establish a relationship between individuals and their identities when needed under certain conditions. Among the newly proposed identity verification and personal identification technologies, biometrics is rapidly becoming the most relevant mechanism for identity recognition. This study proposes a new biometric recognition method for authentication and personal identification. Palm image recognition based on image processing for authentication and personal identification is proposed, namely competitive coding using the Convolutional Neural Network (CNN) and Local Binary Pattern (LBP) texture extraction with hyperparameter modifications. The dataset used comes from the Birjand University Mobile Palmprint Database (BMPD) which consists of 20 classes with a total of 800 palm images. The research was conducted using a data distribution of 80% training data and 20% validation data. The tests carried out resulted in a good accuracy value of the proposed model of 93.3% for the training process and 90.6% for the validation process. Keywords: Biomethric, CNN, LBP Intisari— Teknologi biometrik berkembang menjadi mekanisme paling relevan dalam pengidentifikasi identitas. Tujuan utama dari sistem manajemen identitas adalah untuk dapat membangun hubungan antara individu dan identitas mereka ketika dibutuhkan dalam kondisi tertentu. Di antara verifikasi identitas yang baru diusulkan dan teknologi identifikasi pribadi, biometrik dengan cepat menjadi mekanisme yang paling relevan untuk pengenalan identitas. Penelitian ini mengusulkan metode pengenalan biometrik terbaru untuk otentikasi dan identifikasi pribadi. Pengenalan citra telapak tangan berbasis image processing untuk otentikasi dan identifikasi pribadi yang diusulkan yaitu pengkodean kompetitif menggunakan metode Convolutional Neural Network (CNN) dan ekstraksi tekstur Local Binary Pattern (LBP) dengan modifikasi hyperparameter. Dataset yang digunakan berasal dari Birjand University Mobile Palmprint Database(BMPD) yang terdiri dari 20 kelas dengan total 800 citra telapak tangan. Penelitian dilakukan dengan menggunakan distribusi data sebesar 80% data training dan 20% data validasi. Pengujian yang dilakukan menghasilkan nilai akurasi yang baik dari model yang diusulkan sebesar 93,3% untuk proses training dan 90,6% untuk proses validasi. Kata Kunci: Biometrik, CNN, LBP
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.