Michael Jovan, was a final year student when he created his startup leveraged by mobile information systems that was implemented as an e-commerce. He was claiming this for his thesis title upon finishing his degree at Business Information Systems program. Currently Michael, according to Bisnis.com:
TaniGroup closing the first round of A series after successfully gathered a total of 10 million USD, this includes investment that comes from Bill and Melinda Gates Foundation.
Tiara is presenting her findings and proposed database Implementation in a Production House. She is using SQL-Server DBMS along with some operation on DDL and DML.
Noer Novario (final year student project)
Name: The implementation of tracking and monitoring systems using Android devices and Google maps API.
Objectives: To develop and implement tracking and monitoring system using an Android device and Google Maps API for businesses which utilized vehicles.
Method: The system was developed using the agile development methodology. There were several iterations which focused on several functionalities. This was done in order to make sure that the developed functionalities worked properly. Testing was done in the form of an alpha and beta test to ensure that there were no major bugs and errors left in the system.
Result: The system developed is able to track client location, get and send location to the server, run on Android and server, send message to the server, client management (add, view , delete), message management(view, delete), view history of client.
Conclusion: From the feedback from the users, the system was easy to use, interactive, there were no major bugs and despite the design flaws, the system was still considered useful to them.
Name: Design and implementation of online quiz in BINUS International for undergraduate student
Objectives: To develop a web based system that helps the lecturer to hold a class quiz.
Method: The whole system was developed with the agile development method. Each iteration of the SDLC included unit testing are to ensure the system works accordingly. After all modules were tested system integration was conducted to integrate every modules.
Results: The requirement that was gathered from potential users has complied by the program includes: reduce time consumption in holding class quiz, help lecturer to analyze student performance, reduce the number of human errors
Conclusion: Based on the user acceptance test the user satisfied with the system. The system considered easy to be used and helpful especially in holding a quiz. However, the design of the system needs an improvement.
Implementation of Computer Vision in Detecting Human Poses
Developing strong core muscles are important for children. Children with strong core muscles allow them to do any kinds of activities that mostly involve physical movement. The work tested on an education organisation that aims to strengthen core muscles by providing yoga-like poses. There are certified trainers that will coach the students along the way. However, mistakes could be made during the coaching because of different trainer’s justification and whether the coaching processes were done correctly. Therefore, a solution is proposed to develop a computerized pose detector package which allows the trainers to improve the coaching with the students. The result is promising where the standardized pose could be implemented and compared to observed students’ poses, however it is found that due to the uniqueness of the poses, it generates several unidentified results.
Mobile Augmented Reality to Enhance Customer Experience while Purchasing Furniture
With the rapid advancement in technology, the younger generations are more inclined to using their gadgets in almost all the activity they do. While buying furniture requires more consideration and is time-consuming, they tend to search for every information that they need on internet first, before taking further action of going to the physical store. Through the field research and interviews, it can be proven that customers in average need two or more trips before finally deciding to purchase. The proposed application is designed to help user find their way to the shop with a sufficient product knowledge and also reducing their consideration time to a faster purchase conversion. Augmented Reality will be used on the mobile application to let user see the color compatibility to the room and allow approximate size measuring. This is done by using a specific target size as a comparison to real life object size. Small size usability testing was conducted to several user, and the final result received great feedback as it was easily understood, self-explanatory and aided user to make decision faster. Eventually, the aim is that when the furniture arrives, customer will feel satisfied as a conclusion of their furniture purchase journey.
Automated Mobile Trip Plan using Simulated Annealing in Microservices Architecture
The work is to provide a better experience on the needs of developing itinerary for travelers. This is achieved through an automated itinerary planning using artificial intelligence of simulated annealing technique. In contrast, planning the itinerary in detail with manual process would be time consuming, disorganized and incomplete which might affect the travel experience negatively. The work presents comparison of manual approach and the proposed automated itinerary on a mobile system application; as well as cloud-architecture that will support the application’s overall functionality, including the cloud-services that is used to support each component of the application. The result of incorporating meta heuristic in building the recommendation system which in return improving the user experience of the leisure travelling industry is promising and will be presented at the end of the paper.
Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language
The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant’s online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model.