The quality of fruit is important to increase sales in the market. Right now, the quality selection of orange fruit is mostly still complete by humans. Several drawbacks such as inaccuracy and inconsistency in results happened due to the limitation of human perceptions. The development of computer vision, making it possible to train the computer to classify images based on specified characteristics. This paper proposes the classification model to classify orange images using Convolutional Neural Network (CNN). Five classes of orange namely good-orange-grade-1, good-orange-grade-2, immature-orange, rotten-orange, and damaged-orange are classified using deep learning CNN. Total of 1000 orange images is collected using the smartphone camera. Each class consists of 200 images which are divided into 60% as the training data, 20% as the validation data, and 20% as the testing data. K-Fold Cross-Validation method is used to validate the model. In this paper, the hidden layer of CNN consists of 256 nodes. Two activation functions, ReLU and Tanh, are employed for comparing the accuracy of classification with the Softmax classifier. The result shows that the accuracy of ReLU activation function is 96%, which is better than the Tanh activation function that gives only 93,8%.

Electricity is one of the most important needs for human life in many sectors. Demand for electricity will increase in line with population and economic growth. Adjustment of the amount of electricity production in specified time is important because the cost of storing electricity is expensive. For handling this problem, we need knowledge about the electricity usage pattern of clients. This pattern can be obtained by using clustering techniques. In this paper, clustering is used to obtain the similarity of electricity usage patterns in a specified time. We use K-Means algorithm to employ clustering on the dataset of electricity consumption from 370 clients that collected in a year. Result of this study, we obtained an interesting pattern that there is a big group of clients consume the lowest electric load in spring season, but in another group, the lowest electricity consumption occurred in winter season. From this result, electricity provider can make production planning in specified season based on pattern of electricity usage profile.

Pandemi Covid-19 mendorong dunia pendidikan untuk beralih dari proses pembelajaran tatap muka menuju pembelajaran jarak jauh. Pembelajaran jarak jauh menjadi salah satu solusi untuk tetap dapat menjalankan kegiatan belajar mengajar di tengah pembatasan kontak fisik akibat pandemi. Pembelajaran ini diwujudkan dalam bentuk pembelajaran berbasis digital atau e-learning. Penggunaan e-learning mulai diadaptasi di antaranya dengan penggunaan aplikasi Learning Management System (LMS) oleh berbagai macam instansi pendidikan. Seiring dengan berjalannya waktu, beberapa lembaga pendidikan mulai menggunakan aplikasi LMS buatan sendiri dengan menyesuaikan kebutuhan dan model pembelajaran di lembaga masing-masing. Untuk itu, dibutuhkan desain User Experience (UX) dan User Interface (UI) aplikasi LMS yang dapat memenuhi kebutuhan dan keinginan pengguna. Penelitian ini bertujuan untuk membuat desain model LMS berbasis mobile menggunakan pendekatan User Centered Design (UCD). Aktivitas yang dilakukan meliputi riset terhadap pengguna, studi literatur, implementasi fitur pada desain, serta evaluasi menggunakan usability testing dan System Usability Scale (SUS) pada purwarupa. Berdasarkan evaluasi yang dilakukan, diperoleh nilai efektivitas dengan perhitungan completion rate sebesar 84%, nilai efisiensi dengan perhitungan Overall Relative Efficiency sebesar 91%, dan nilai kegunaan dengan perhitungan SUS sebesar 75.38. Hasil pengujian ini menunjukkan bahwa aplikasi yang didesain sudah cukup baik.

Background: Social media has become a massive encyclopedia of almost anything due to its content richness. People tell stories, write comments and feedback, and share knowledge through social media. The information available on social media enables ‘clueless’ travelers to get quick travel recommendations in the tourism sector. Through a simple query, such as typing ‘places to visit in Bali’, travelers can get many blog articles to help them decide which places of interest to visit. However, doing this reading task without a helper can be overwhelming. Methods: To overcome this problem, we developed Bidirectional Encoder Representations from Transformers (BERT)-based tourism named entity recognition system, which is used to highlight tourist destination places in the query result. BERT is a state-of-the-art machine learning framework for natural language processing that can give a decent performance in various settings and cases. Our developed tourism named entity recognition (NER) model specifies three different tourist destinations: heritage, natural, and purposefully built (man-made or artificial). The dataset is taken from various tourism-related community articles and posts. Results: The model achieved an average F1-score of 0.80 and has been implemented into a traveling destination recommendation system. By using this system, travelers can get quick recommendations based on the popularity of places visited in the query frame. Discussion: Based on the survey that we conducted to target respondents who have never visited and have no or limited knowledge about tourist attractions in some example cities, their average interest level from the recommendation results is higher than four on a scale of 1 to 5. Thus, it can be considered a good recommendation. Furthermore, the NER model performance is comparable to another related research.

The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.