EVALUASI APLIKASI SEMI-IMMERSIVE VIRTUAL REALITY PADA BIDANG PENDIDIKAN MENURUT ASPEK HEURISTIK DAN PEMBELAJARAN

In this paper, the implementation of the Benders decomposition method to solve the Adjustable Robust Counterpart for Internet Shopping Online Problem (ARC-ISOP) is discussed. Since the ARC-ISOP is a mixed-integer linear programming (MILP) model, the discussion begins by identifying the linear variables in the form of continuous variables and nonlinear variables in the form of integer variables. In terms of Benders decomposition, the ARC-ISOP model can be solved by partitioning them into smaller subproblems (the master problem and inner problem), which makes it easier for computational calculations. Pseudo-codes in Python programming language are presented in this paper. An example case is presented for the ARC-ISOP to determine the optimal total cost (including product price and shipping cost) and delivery time. Numerical simulations were carried out using Python programming language with case studies in the form of five products purchased from six shops.

Nutmeg is an Indonesian agricultural commodity that has been recognized and interested by the world import market. In the last decade, several cases of export of nutmeg from Indonesia have been rejected due to post-harvest handling contaminated with Aflatoxin. One of the causes is the process of sorting and guaranteeing the quality of products that are done traditionally and not yet perfect. This paper presented the prototype development of image acquisition station using two cameras to capture nutmeg surface in smart sorting machine. In addition, the ultraviolet light was added to ease the detection of aflatoxin using colour segmentation method. Moreover, based on SNI 01-0006-1993 and SNI 01-0007-1993, there are some criteria to categorize the quality of nutmeg such as shape, texture and colour. The experimental study of image analysis showed that our proposed sorting system could classify nutmeg based on SNI and calculate contamination area of aflatoxin.

Line segmentation can be a useful process for further text segmentation. There are some certain line segmentation framework that use binarization method as an initial step. But binarization process is still facing a major challenge, especially on old document palm-leaf manuscripts. As the quality of the image has varying degrees of noises in the non-text region. Seam Carving method, one of line segmentation methods that uses binarization-free approach, can be an alternative solution. However, this method can separate the incorrect text line on small element text located at the bottom or at the top of a main character contour. Therefore, an improvement on line segmentation framework is proposed by using hybrid binarization and its implemented on the smallest energy function to separate out the text-lines. The proposed framework have been evaluated on 44 Sundanese old manuscript images that consist of true color and binary images. The evaluation matrix shows that this framework can improve Niblack binarization process up to 50%. In addition, our framework does not only generate the number of text-lines to come near to the number of target lines, but it also can separate the text-lines well on small element text. Overall, the expected result can in the end be produced from the proposed line segmentation framework.

The use of mobile-assisted virtual reality in fear of darkness therapy

This paper presents the method for enhancing quality of degraded images which is essential for information automatic extraction from the digitized ancient manuscript known as lontar. The degraded lontar images have circular non-uniform illumination characteristics that are represented by more brightness on inner center of images and darker in the outer ellipse-like area of the images. The purpose of enhanced image processing is to leveling the distribution of pixels so the images have similar characteristics. The proposed method is developed based on DEWA Framework, a novel and optimized method for object detection, combined with background subtraction technique, which is implemented in several color spaces. Background images are estimated using morphological mathematics with specific type and size of structuring element. This estimation process has to be done due to the unavailability of exact background image captured during image acquisition step. Theoretical foundations of the proposed method along with experimental results are reported in this paper. The proposed method has been tested on 12 Sundanese lontar images which shows this approach is different and unique from most of the existing methods in this field of study. Experiment demonstrated the successful results of the method which is represented by displaying profile characteristics of each images on row 128.