This study aims to facilitate learning, therefore all existing technologies and educational technology products must be selected and built on the needs analysis of a particular learning environment. E-health learning media is an Androidbased Application. As we know that Android is one of the most widely used mobile platforms in the world with operating system based on open source. E-Health or Electronic Health is the use of information and communication technologies including electronics, telecommunications, computers and informatics to process various types of medical information, to perform clinical services (diagnosis or therapy), administration and education. As an education provider in the field of health, Vocational School of Health (SMK Kesehatan) feels obliged to participate in equipping their graduates with life skills in integrity, which combine generic and specific potency, to solve and overcome life problem. Life skills possessed by each graduate will include: Personal Knowledge, Rational Thinking Skills (Vocational Skills) and Vocational Skills. Implementation of digital literacy in SMK is expected to encourage students. One of the learning media that can be used through digital literacy is through the utilization of Androidbased applications. Results of Media Assessment of E-health application obtained average score 4.47 (Very Good).

A measuring instrument plays important role in the biomedical measurement since the biological process in living organism generates very weak signal. Therefore, a reliable and sensitive measuring instrument is needed. In this study, a lock-in amplifier was analysed and tested. This paper presents an experiment to investigate the lock-in amplifier for biomedical measurement. An experiment using RC (resistor capacitor) tissue model to measure the voltage change related to impedance change was performed using a lock-in amplifier to evaluate the accuracy of the lock-in amplifier. Three different values of the capacitor in the RC tissue model were applied regarding to simulate small impedance changes. The measurement results were compared with the theoretical calculation and an impedance measurement system. An error analysis was conducted to investigate the accuracy of the measurement. The comparison result showed that impedance measurement using lock-in amplifier is an effective technique, which could able to measure very small voltage regarding impedance change in the RC tissue model.

Abstract. This study aims to extract the essential features of Photoplethysmography (PPG) signal of men and women in healthy subjects using Power Spectral Density (PSD) and Detrended Fluctuation Analysis (DFA). A PPG instrument was used to obtain the PPG signal of 15 men and 15 women. Using PSD, four frequency bands were selected to divide the spectral component. The areas within the frequency bands relative to the total area were computed as features of the signals. Furthermore, using DFA, the average fluctuation F(w) was computed. The feature extraction using this technique produced 4 features from different windows. Hurst exponent was calculated to analyse the characteristics of the time series. For comparing the feature extraction techniques, Heart Rate (HR) and Peak to Peak Interval (PPI) were computed. Additionally, F and T tests for all techniques were computed to determine the differences between man and woman features that have been gathered using these two techniques. The results indicate that the features of PPG signals of men and women using PSD and DFA were significantly different. In order to evaluate the results, a clustering analysis was applied to the results using K-means clustering technique. The clustering plots show that the features were well distributed into the two groups.

The importance of Information and Communication Technology has been recognized but often undervalued by small businesses. Furthermore, best practices of ICT use in large companies do not always translate to a small business environment. Few studies have been done at the intersection of ICT and Micro and Small Enterprises (MSEs) in a ‘holistic’ manner taking into consideration various aspects of ICT adoption and use. The LIAISE framework provides guidelines for investigating various aspects of ICT adoption and use, including ICT literacy, information, and content, access, infrastructure and support, and evaluation. The framework was originally developed for the non-profit sector and has been applied in the small business context by examining MSEs in developing countries. Using a case study in Indonesia, this paper will use LIAISE as an analytical framework to investigate mobile phone adoption and use by micro-entrepreneurs.

h i g h l i g h t s g r a p h i c a l a b s t r a c t• Facilitating health care in remote and rural area using mobile phone infrastructure.• Prediction of disease based on data sends by patient using machine learning (Support Vector Machine).• Characterise the proposed model with different type of kernels and parameters to find optimum classification.

Fault detection is considered an important and challenging task to be incorporated in many industrial applications. It has gained interest in recent years, and many techniques have been proposed for developing an effective fault detection approach due to its significant importance in everyday life. This study presents an automated intelligent fault detection technique incorporating image processing and fuzzy logic. Image processing is the first step where features such as entropy estimation, color-based segmentation and depth estimation from gradients are obtained. The extracted features (number of {blobs, minima, maxima}, and estimated entropy) act as input to the fuzzy logic. The subsequent step incorporates fuzzy logic; the four inputs are fed to fuzzy which extract the fault and acts as knowledge rule-based tool and final step, i.e. the output generation, classifies it accordingly into four categories of faults (rust, bumps, hole, wrinkles/roller marks). The proposed method is compared with Linear Vector Quantization, and Multivariate Discriminant Function approaches. The method is tested on a database of 150 images. The proposed method demonstrated its significance and effectiveness with performance accuracy of 99%, 98%, 96.8% and 97.6% for rust, bumps, holes and wrinkles/roller marks respectively.

This research aims to determine the growth response of corn plants (Zea mays) given zeolite coated urea fertilizer as a nitrogen slow-release fertilizer. This research uses Randomized Group Design (RGD) with 3 repeats. The observation parameters are. Corn plant growth including corn cob weight (grams), corn cob length (cm) and corn kernel weight (grams) which are measured after harvest. The research results showed that there was interaction between zeolite coated urea fertilizer and the corn plant growth results with different results than other treatments namely negative control (only Mono Potassium Phosphate fertilizer) and tending to be better than positive control (urea fertilizer and Mono Potassium Phosphate).

Determination of nitrogen levels in plants is essential for variable rate fertilizer application in precision agriculture. In the past, several techniques have been developed for nitrogen concentration estimation in plants and crops employing vision system, however, they are computationally expensive and hence requires a considerable amount of time to produce accurate results. The technique developed in this work the determination of nitrogen levels in plants could be achieved effectively in real-time time by advance image processing techniques, machine visions and support vector machine (SVM) with MATLAB. The developed technique processes leaf's colored image via examining it Red, Green and Blue (RGB) values and compares them with standard intensity levels. The experimental results show effectiveness of the developed technique and accurately detect low or high concentration levels in corn. In addition, this method depends on two techniques for a final result, i.e. color intensity and SVM. If the answer is not similar between the two techniques the process will be repeated until the detection is similar. This study could be applied to a variety of crops, since this technique does not require large collection of data for training and special expertise for its on-field application.