E-Report is developed based on the web to make it easier in preparing students’ assessment reports in the form of rating reports, competency achievement reports, and ledgers. Even, many high schools/vocational schools in Bali have not implemented the E-Report. Implementation of cognitive walkthrough techniques, heuristic evaluation, and users’ experience questionnaire in this research aimed to find out the results of evaluating user experience from the aspects of effectiveness, efficiency, user satisfaction, and recommendations for improvement. Effectiveness and efficiency were calculated by Cognitive Walkthrough techniques (CW), user satisfaction by user experience questionnaire techniques (UEQ) and recommendations for improvement, obtained from experts who discuss aspects of usability with Heuristic Evaluation techniques and Cognitive Walkthrough data. Based on the results obtained in terms of users, E-Report has been ineffective and inefficient. While in terms of user satisfaction, respondents were satisfied with Applications E-Report. This is because the effectiveness of a task that has a very large error and failure in one of the features as well as the clarity of results that poor user satisfaction can affect processing time. Therefore, evaluation of user experience based on aspects of usability in the E-Report has not met the criteria for products with good usability. Improvements made on the start page, page values, page value results were done through a photo frame. Therefore, identifying the problems from these three sources can further optimize the usability and improve user experience on E-Report.

This study developed an application for official letter management in Sukasada 1 Public High School. The development of the application aimed to contribute to government programs in improving public services, especially in correspondence. In the letter classification process, the researcher implemented the TF-IDF method whose working procedures included conversion, Optical Character Recognition (OCR), filtering, tokenizing, and classification. Conversion is to convert official mail to digital form (jpg). OCR is to get the text of the letter then carries out the official letter in the form of an image and then filtering and tokenizing are carried out. Classification is the process of grouping a letter into its category by calculating the cosine similarity value between the letter being tested and the letter in the system. To test the accuracy of the classification results, a confusion matrix is used. The results of the study are in the form of web-based and mobile-based applications. Operators and admins used the web application to manage official letters at SMAN 1 Sukasada. The mobile application served to facilitate the principal in accessing mail data from a smartphone. The system was able to group letters with 78% accuracy (good), precision 77% and recall 77%.

Balinese script writing, as one of Balinese cultural richness, is going to extinct because of its decreasing use. This research is a way to preserve it through collaboration between Computer Science and Language discipline, that focused on accuracy comparison of Latin-to-Balinese script transliteration method on mobile application as a ubiquitous learning media. From few research in this area, there are only two existing methods to be compared, i.e. each on Android mobile application that were called Belajar Aksara Bali (BAB), and Transliterasi Aksara Bali (TAB). The comparison was based on The Balinese Alphabet writing rules and examples document by Sudewa. Through the experiment, TAB has outperformed BAB since TAB has passed over 68% (103 of 151) cases, while BAB has passed over only 39% (59 of 151) cases. This research contributes on a comprehensive accuracy comparison analysis of Latin-to-Balinese script transliteration method, specifically on mobile application, since there is no such study. This research also contributes on those methods improvement possibility. In the future, this research can be used as a reference for improvement of any Latin-to-Balinese script transliteration method by taking care on thirteen kind of special words that were found during this comparison study.

Balinese script writing, as one of Balinese cultural richness, is going to extinct because of its decreasing use. This research is one of the ways to preserve Balinese script writing using technological approach. Through collaboration between Computer Science and Balinese Language discipline, this research focused on the development of a Latin-to-Balinese script transliteration robotic system that was called LBtrans-Bot. LBtrans-Bot can be used as a learning system to give the transliteration knowledge as one aspect of Balinese script writing. In this research area, LBtrans-Bot was known as the first system that utilize Noto Sans Balinese font and was developed based on the identified seventeen kinds of special word. LBtrans-Bot consists of the transliterator web application, the transceiver console application, and the robotic arm with its GUI controller application. The transliterator used the Model-View-Controller architectural pattern, where each of them was implemented by using MySQL database (as the repository for the words belong to the seventeen kinds of special word), HTML, PHP, CSS, and Bootstrap (mostly for the User Interface responsive design), and JavaScript (mostly for the transliteration algorithm and as the controller between the Model and the View). Dictionary data structure was used in the transliterator memory as a place to hold data (words) from the Model. The transceiver used batch script and AutoIt script to receive and trasmit data from the transliterator to the GUI controller, which control the Balinese script writing of the robotic arm. The robotic arm with its GUI controller used open-source mDrawBot Arduino Robot Building platform. Through the experiment, LBtrans-Bot has been able to write the 34-pixel font size of the Noto Sans Balinese font from HTML 5 canvas that has been setup with additional 10-pixel length of the width and the height of the Balinese script writing area. Its transliterator gave the accuracy result up to 91% (138 of 151) testing cases of The Balinese Alphabet writing rules and examples document by Sudewa. This transliterator result outperformed the best result of the known existing transliterator based on Bali Simbar font, i.e. Transliterasi Aksara Bali, that only has accuracy up to 68% (103 of 151) cases of the same testing document. In the future work, LBtrans-Bot could be improved by: 1) Accommodating more complex Balinese script with trade off to the limited writing area of robotic system; 2) Enhancing its transliterator to accommodating the rules and/or examples from the testing document that recently cannot be handled or gave incorrect transliteration result; enriching the database consists of words belong to the seventeen kinds of special word; and implementing semantic relation transliteration.

Balinese script is a part of Balinese culture is rarely used today. The Provincial Government of Bali with the Governor Regulation number 80 of 2018 is trying to preserve the Balinese language and script. This study aimed at preserving the Balinese script through a mobile technology approach which is the recent trend with worldwide coverage for supporting ubiquitous learning. This research integrated the Android application to recognize Balinese characters in the form of images into text with Tesseract open source Optical Character Recognition (OCR) engine. The input of this application is a Balinese script image captured by a mobile camera or from a Balinese script image. The application recognized input image into text that can be further processed based on training data available in the application. The new Balinese script training data was created based on eighteen Balinese script’s basic syllables and numbers only. This application can be operated offline with mobile hardware that supports camera functions. The result for testing for 50-word, recognition was 62% obtained in good quality image-based Bali-Simbar font. This application can be further developed to recognize other character repertoire i.e., vowels (Akśara Suara), semi vowels (Arda Suara), additional syllables (Akśara Şwalalita), and sound killers (Pangangge Tengenan).

Bali is a world tourism destination known for its cultural riches. Balinese culture is a legacy of the world's ritches that must be preserved. In the development of the world of education and technology today, there have been many efforts to preserve Balinese culture such as researches used to document the Balinese culture so this culture can still be maintained. One study for the preservation of Balinese culture in the field of technology is the application of Balinese script authors. Balinese script is one of the traditional Indonesian archipelago that developed on Bali. Because its use is limited to a narrow scope, so in everyday use, most Balinese script has been replaced with Latin letters. Therefore this research is focused to make conservation effort to Balinese script with robot technology. Robot is a tool that is a combination of mechanical circuits and electronics circuits. The robot that developed in this research is a robot that can write balinese script with design resembles human arm. In this research for connectivity using USB and on construction platform using Makeblock and mDraw application is a graphics program developed based on open source python. By developing the transliteration application of the script, it can be converted from .png generated into the form .svg or vector as input mDraw and give the command to the robot to write the transliteration result on a piece of paper. In this paper a new idea has been proposed to implement the robot on cultural preservation and Balinese script education. This research has developed a robotics system that can write Balinese Script. This robotic arm was successfully tested with 60% (21 glyph) categorized as very good, 37.14% (13 glyph) categorized as good, and 2.86% (1 glyph) enough. This research was developed on accuracy analysis of transliteration result and result of writing robot to paper. Through the experiment, there was an accuracy of 40.72% for the correct test count and 59.28% for the number of false tests of 1137 words on thirteen types of special words.