Objective: This research aims to analyze the relationship between digital transformation and sustainability, analyze the relationship between digital innovation and sustainability and analyze the relationship between digital knowledge management and sustainability. Method: The research method used in this research is a quantitative method, research data analysis uses the structural equation modelling partial least squares (PLS-SEM) method with tools for processing data using SmartPLS 4.0 software. The population of this research are administrators or managers of SMEs in Indonesia. The respondents of this research were 467 SME managers who were determined using a simple random sampling method. Research data was obtained by distributing online questionnaires via digital platforms or online media. The questionnaire was designed to contain statement indicators using a 5 Likert scale. The independent variables of this research are digital transformation, digital innovation and digital knowledge management, the dependent variable of this research is sustainability. The stages of data analysis are unified validity, reliability testing hypothesis testing or significance testing of relationships between variables. Result: The results of this research are that digital transformation has a positive and significant relationship to sustainability, digital innovation has a positive and significant relationship to sustainability and digital knowledge management has a positive and significant relationship to sustainability. Research Implication: The results of this research provide an idea to SME stakeholders that to improve SME sustainability, competitiveness and performance, SME managers need to adopt digital transformation in SME operational activities. To achieve SME sustainability, SME managers need to cultivate a culture of digital innovation and management. digital knowledge in SME operational activities.

Tumors are caused by uncontrolled growth of abnormal cells. Magnetic Resonance Imaging (MRI) is modality that is widely used to produce highly detailed brain images. In addition, a surgical biopsy of the suspected tissue (tumor) is required to obtain more information about the type of tumor. Biopsy takes 10 to 15 days for laboratory testing. Based on a study conducted by Brady in 2016, errors in radiology practice are common, with an estimated daily error rate of 3-5%. Therefore, using the application of artificial intelligence, is expected to simplify and improve the accuracy of doctor's diagnose.

Aims: With the stick, blind people can not only project their goals well, but also anticipate obstacles from the front, right, left, and unexpected puddles below without physical contact. Study Design: Obstacle Detection Stick for the Visually Impaired Using HC-SR04 Ultrasonic Sensor and Arduino Nano Microcontroller-Based Raindrop Sensor. Methodology: Calibration is performed by comparing the output value of the HC-SR04 ultrasonic sensor and the manual meter. The calibrated parameters are the distance of the obstacle at the front, left and right positions. The relationship between the measured values of the design tool and the manual measuring instrument is determined by the linear regression method to obtain the correction equation and the accuracy of the design tool. Results: Based on the research, the working system of the tool for the blind using an ultrasonic sensor is that when the obstacle is in the left position, the tool will emit a right turn sound and vice versa, when the obstacle is in the front and left position of the sensor, the tool will emit a right turn sound and when the obstacle is in the front and left position of the sensor, the tool will emit a left turn sound, If the obstacle is in the front, left and right positions, the tool will emit a U-turn sound and if the obstacle is in the front position, the tool will emit a U-turn sound, the sound produced comes from the audio card's SD module where the sound was previously recorded and the media used as the obstacle is the card. The percentage level of accuracy obtained by the HC-SR04 ultrasonic sensor in the front position is 96.875%, in the left position is 90.625%, and in the right position is 93.75%. The maximum distance the ultrasonic sensor can detect is 147 cm. In the raindrop sensor, the buzzer sounds when the raindrop sensor detects water if the raindrop sensor reading is more than 5 cm from the bottom of the blind cane, and the buzzer does not sound if the raindrop sensor reading is less than 5 cm. Conclusion: The HC-SR04 ultrasonic sensor works by using the obstacle's ability to reflect back the ultrasonic waves sent by the ultrasonic sensor to detect the distance between the stick and the obstacle.

Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

Aims: To design and manufacture LPG gas leak detection tool that is more efficient, relatively cheaper and can be carried because it has a smaller shape and size compared to tools on the market Study Design: Design of LPG gas leak detection based on Arduino Uno using MQ-2 sensor. Place and Duration of Study: Physics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Udayana, from June 2023 to October 2023. Methodology: The trial and error method is used to calibrate the designed tool with standard tools. Calibration is done by comparing the distances value of the LPG gas leak detection using MQ-2 sensor and the calibration standard tool. The relationship between the measurement values of the design tool and the standard tool is determined using linear regression method to obtain a correction equation. Results: LPG gas leak detection based on Arduino Uno using MQ-2 sensor have been produced. The measurement accuracy obtained 97.97%. The accuracy value indicates that the resulting tool has a good level of accuracy to the standard tool. Conclusion: In this research, has been produced LPG gas leak detection more efficient, relatively cheaper and can be carried because it has a smaller shape and size, low-cost with high accuracy.

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.