Almost everyone looks at reviews before deciding to buy an item in e-commerce. Consumers say that online reviews influence their purchasing decisions. Based on these data, consumers need sentiment reviews to make a decision to choose a product/service. However, the results of the sentiment analysis are still less specific, so the review classification process is carried out based on the review category. Sentiment classification process based on clothing category is carried out using the Convolutional neural network method. The amount of data used is 3384 data with 3 categories. The category classification model shows good performance. When evaluated with testing data (unseen data), the accuracy value is 88%, the precision value is 88%, recall is 88% and the f1-score is 88%. For the sentiment classification model with the bottoms category, the resulting accuracy value is 80%, precision is 81%, recall is 80%, and f1-score is 79%. For the sentiment classification model with the dresses category, the accuracy value is 81%, precision is 81%, recall is 81%, and f1-score is 81%. For sentiment classification with the tops category the resulting accuracy value is 77%, precision is 77%, recall is 77%, and f1-score is 77%.

SARA is a sensitive issue based on sentiments about self-identity regarding ancestry, religion, nationality or ethnicity. The impact of the issue of SARA is conflict between groups that leads to hatred and division. SARA issues are widely spread through social media, especially Twitter. To overcome the problem of SARA, it is necessary to develop an effective method to filter negative SARA. This study aims to analyze Indonesian-language tweets and determine whether the tweet contains positive or negative SARA or does not contain SARA (neutral). Machine learning (i.e., SVM) and lexicon-based method (i.e., LIWC) were compared based on 450 tweet data to determine the best approach for each sentiment (positive, negative, and neutral). The best evaluation results are shown in the negative SARA classification using SVM with λ = 3 and γ = 0.1, where Precision = 0.9, Recall = 0.6, and F1-Score = 0.72. The best results from the positive SARA classification were shown in the LIWC method, where Precision = 0.6, Recall = 0.8, and F1-Score = 0.69. The best evaluation results for neutral classification are shown in SVM with λ = 3 and γ = 0.1, with Precision = 0.52, Recall = 0.87, and F1-Score = 0.65.

In gamelan, one of the most important instruments is trompong. Trompong is an idiphones instrument that has 10 rows of round shaped metal called pencon. Every pencon has its own sound. As a traditional music instrument, of course gamelan especialy trompong must be preserved continuously. But unfortunately, playing Balinese gamelan with real instrument is hard to do because the difficulty to finding gamelan in the real world. By using technolgy such as Augmented Reality, playing trompong possible to do even without having the real instrument.  Augmented Reality will be develop using Unity 3D software along with Vuforia SDK, and also this application using Android smartphone as a base of Augmented Reality application. This Augmented Reality application called TrompongAR and will be marker based Augmented Reality, by using a target marker will help Augmented Reality to place where the 3-dimensional trompong will placed. The 3-dimensional trompong will have 10 pencon that can played by tapping the pencon, the touched pencon will produce sound like the real instrument.

The world is being hit by a pandemic due to the COVID-19 virus outbreak. The changes brought by this virus are huge, one of which is school activities that are transformed into online learning. Online learning causes students to not do their own classroom learning. This causes students to become un familiar with their school properly, such as the layout of classrooms and school facilities. By using Augmented Reality the problem can be solved. Augmented Reality (AR) is the merging of real and virtual objects in a real environment with interactive results and presented in real time. AR can be used to modeling the entire shape of the school, making it easier for users to get information about the building from the school instead of walking manually. Users only need to install the app on their smarthphone and scan the specified QR code in order to be able to bring up the building object along with the information. The result of this research is an AR application which can provide information about the rooms and buildings at SDN 1 Padangsambian.

<p>Jati belanda (<em>Guazuma ulmifolia</em>) adalah salah satu tanaman yang berkhasiat sebagai antioksidan karena pengaruh senyawa aktif yang terkandung di dalamnya. Cahaya pantulan (<em>reflectance</em>) dapat digunakan untuk mengetahui kualitas senyawa aktif pada daun jati belanda. Penelitian ini membahas tentang estimasi spektrum <em>reflectance</em> citra digital daun jati belanda menggunakan model <em>reflectance</em> daun tanaman obat dengan menerapkan transformasi <em>wavelet.</em> Bahan yang digunakan adalah daun tanaman obat dan daun jati belanda. Transformasi <em>wavelet</em> digunakan untuk merepresentasikan <em>reflectance</em> daun tanaman obat. Model polinomial diterapkan untuk mengekspansi ciri citra digital. Model <em>reflectance</em> terbaik dari penerapan transformasi <em>wavelet</em> dan model polinomial digunakan untuk mengestimasi <em>reflectance</em> dari daun jati belanda. Evaluasi spektrum <em>reflectance</em> asli dengan spektrum keluaran model estimasi <em>reflectance</em> menggunakan kriteria kesalahan terkecil dan kemiripan terbesar.</p><p>Kata kunci: jati belanda, model polinomial, <em>reflectance, wavelet</em></p>