Recommender systems are essential engines to deliver product recommendations for e‐commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF‐IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF‐IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e‐commerce recommender systems with sparse data issues.

Application-based transport or online ojek has been main trends in Indonesia. It gives solution to public in rush hours especially in urban area. Even though they are needed but many customers have complaint about their services. The purposes of this study are observing the operator effort to maintain customer satisfaction and loyalty. It evaluated customer response to determine the cause of the satisfaction and loyalty especially for the construct of service quality and online servicescape. In addition, it brings new construct as mediation variable of perceived value to strengthen the relationship among the variables. This study found the new relationship among service quality and online servicescape to strengthen customer satisfaction and loyalty. By collecting the customers from Jakarta region as respondent’s, this study tested their perception about the online service delivery and their effect to the loyalty. Some suggestion and direction also given to expand the research as discussion topic.

Several efforts have been conducted to handle sparse product rating in e-commerce recommender system. One of them is the inclusion of texts such as product review, abstract, product description, and synopsis. Later, it converted to become rating value. Previous researches have tried to extract these texts based on bag of word and word order. However, this approach was given misunderstanding of text description of products. This research proposes a novel Dynamic Convolutional Neural Network (DCNN) to improve meaning accuracy of product review on a collaborative filtering recommender system. DCNN was used to eliminate item sparse data on text product review while the accuracy level was measured by Root Mean Squared Error (RMSE). The result shows that DCNN has outperformed the other previous methods.

In analyzing customer or consumer satisfaction with company services, it is essential for companies to find service deficiencies and to know user expectations for the company. This study aims to build sentiment analysis on the Shoppe application on the Google Playstore. The method used includes TF-IDF as text vectorization, Random Forest as a classification model, and Evaluation Matrix as an evaluation model, providing accuracy, precision, recall, and F1-Score. Based on the results of this study, the model we used achieved an accuracy rate of 94%, a precision of 91%, a recall of 91%, and an F1-Score of 93%. The limitations of this study are identifying words in English and regional languages because the corpus module we use is literature, a special Indonesian corpus. In future research, we will try to build a new engine/algorithm and try to add datasets in the hope that the level of accuracy will be even better.

Since early 2020, coronavirus has spread extensively throughout the globe. It was first detected in Wuhan, a province in China. Many researchers have proposed various models to solve problems related to COVID-19 detection. As traditional medical approaches take a lot of time to detect the virus and require specific laboratory tests, the adoption of artificial intelligence (AI), including machine learning, might play an important role in handling the problem. A great deal of research has seen the adoption of AI succeed in the early detection of COVID-19 using X-ray images. Unfortunately, the majority of deep learning adoption for COVID-19 detection has the shortcomings of high error detection and high computation costs. In this study, we employed a hybrid model using an auto-encoder (AE) and a convolutional neural network (CNN) (named AMIKOMNET) with a small number of layers and parameters. We implemented an ensemble learning mechanism in the AMIKOMNET model using Adaboost with the aim of reducing error detection in COVID-19 classification tasks. The experimental results for the binary class show that our model achieved high effectiveness, with 96.90% accuracy, 95.06% recall, 94.67% F1-score, and 96.03% precision. The experimental result for the multiclass achieved 95.13% accuracy, 94.93% recall, 95.75% F1-score, and 96.19% precision. The adoption of Adaboost in AMIKOMNET for the binary class increased the effectiveness of the model to 98.45% accuracy, 96.16% recall, 95.70% F1-score, and 96.87% precision. The adoption of Adaboost in AMIKOMNET in the multiclass classification task also saw an increase in performance, with an accuracy of 96.65%, a recall of 94.93%, an F1-score of 95.76%, and a precision of 96.19%. The implementation of AE to handle image feature extraction combined with a CNN used to handle dimensional image feature reduction achieved outstanding performance when compared to previous work using a deep learning platform. Exploiting Adaboost also increased the effectiveness of the AMIKOMNET model in detecting COVID-19.

During the world-changing year of covid 19, social media commerce grew fast. The prolonged use of social media encourages users to make online purchases via social media. TikTok, the most downloaded social media app, offers its users a social media commerce experience, TikTok Shop. The TikTok shop provided a new option for business expansion. Business owners may optimize the potential use of TikTok shops by learning more about TikTok Shop. The purpose of this study is to use sentiment analysis to evaluate the business potential of TikTok Shop. The data from Google Play reviews is analysed using the LSTM algorithm. Based on the results of research conducted using a confusion matrix, the LSTM algorithm method using word2vec has an accuracy of 74%. This study found that the business prospects of TikTok shops may be challenging.