Ulfi Andayani

Bio: Ulfi Andayani is an academic researcher from University of North Sumatra. The author has contributed to research in topics: Augmented reality & Probabilistic neural network. The author has an hindex of 9, co-authored 64 publications receiving 261 citations.

Electrical appliances control prototype by using GSM module and Arduino

Abstract: The development of remote control technology has grown rapidly along with the development of communication technology nowadays. The simplest communication technology available is by using GSM protocol. In this paper, a prototype of electric appliance control tool via SMS by using GSM is proposed. GSM protocol was chosen because it does not depend on mobile devices' platform. GSM SIM 900 and Arduino for controlling a relay module were utilized here. Relay module worked in accordance with orders given through SMS and the mobile device then received the feedback of the command. For testing purpose, ten (10) different types of input string as a command control was proceed. Relay worked according to orders sent from the input string submitted and feedback messages from the command given previously was provided.

Comparison of Exponential Smoothing Methods in Forecasting Palm Oil Real Production

Abstract: Palm oil has important role for the plantation subsector. Forecasting of the real palm oil production in certain period is needed by plantation companies to maintain their strategic management. This study compared several methods based on exponential smoothing (ES) technique such as single ES, double exponential smoothing holt, triple exponential smoothing, triple exponential smoothing additive and multiplicative to predict the palm oil production. We examined the accuracy of forecasting models of production data and analyzed the characteristics of the models. Programming language R was used with selected constants for double ES (α and β) and triple ES (α, β, and γ) evaluated by the technique of minimizing the root mean squared prediction error (RMSE). Our result showed that triple ES additives had lowest error rate compared to the other models with RMSE of 0.10 with a combination of parameters α = 0.6, β = 0.02, and γ = 0.02.

Remote monitoring system for hydroponic planting media

Abstract: Hydroponics is a method of planting or cultivating plants without using soil but uses water, nutrients, and oxygen It has advantages such as higher quantity and quality of production, cleaner, more efficient use of fertilizers and water, and also easier in pest and disease control Hydroponics system requires precision, patience, and regular monitoring which is quite a challenge to conduct Here we propose a monitoring system designed with PH sensor, Electro Conductivity Sensor, water temperature sensor, air temperature, Light Sensor, GSM / GPRS, Open Garden Shield, Open Garden Hydroponic, and Arduino Uno as main board or microcontroller We found the results of number of leaves and plant height during the test for each plant as follows: on the lettuce it has 6 leaves and 36 cm tall, for red spinach plants it has 6 leaves and 38 cm high, while in mustard plant pak choy, the number of leaves obtained is about 6 strands with height 42 cm Two weeks of testing on lettuce, red spinach, and mustard pak choy indicated that the sensor and the system were operating well

Classification of stroke disease using convolutional neural network

Abstract: Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.

Hypertensive retinopathy identification through retinal fundus image using backpropagation neural network

Abstract: Hypertension or high blood pressure can cause damage of blood vessels in the retina of eye called hypertensive retinopathy (HR). In the event Hypertension, it will cause swelling blood vessels and a decrese in retina performance. To detect HR in patients body, it is usually performed through physical examination of opthalmoscope which is still conducted manually by an ophthalmologist. Certainly, in such a manual manner, takes a ong time for a doctor to detetct HR on aa patient based on retina fundus iamge. To overcome ths problem, a method is needed to identify the image of retinal fundus automatically. In this research, backpropagation neural network was used as a method for retinal fundus identification. The steps performed prior to identification were pre-processing (green channel, contrast limited adapative histogram qualization (CLAHE), morphological close, background exclusion, thresholding and connected component analysis), feature extraction using zoning. The results show that the proposed method is able to identify retinal fundus with an accuracy of 95% with maximum epoch of 1500.

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

Abstract: An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development of an extremely effective model for the prediction of palm oil yields with the most minimal computational difficulty.