INFOKUM
The INFOKUM a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Software Engineering. Image Processing Datamining Artificial Neural Networks
Articles
615 Documents
<![CDATA[IMPROVEMENT OF DIGITAL IMAGE WITH HISTOGRAM EQUALIZATION METHOD ]]>
<![CDATA[Muhammad Ullila Fahry]]>;
<![CDATA[Darlena Darlena]]>
<![CDATA[ INFOKUM Vol. 8 No. 1, Desembe (2019): Data Mining,Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Digital image has become an inherent thing in everyday life. This is supported by the increasingly sophisticated technology of today where communication devices such as mobile phones have been able to use the role of replacing analog cameras to take pictures or even record videos. Capture the moments of happiness or just want to record an event becomes very easy to do, but sometimes the resulting photo is less satisfactory because of the mobile phone's specifications and other factors such as poor lighting (dark). Efforts to improve become very necessary, but because the application to do this only exists on the computer and not too many are found on mobile phones, this becomes difficult to do. One method for image enhancement is the Histogram Equalization method. This method can be used to improve image quality related to lighting by maintaining color constancy. The use of the histogram equalization method is considered easy because of its simplicity and relatively better performance on almost all types of images. The operation of HE (Histogram Equalization) is carried out by remapping the gray level of the image based on the probability distribution of the gray input level. This flattens and dynamically stretches the various histogram images and results in an overall increase in contrast.]]>
<![CDATA[DATA MINING FOR DETERMINING BOOK LOAN PATTERNS IN-LIBRARY USING APRIORI ALGORITHM ]]>
<![CDATA[Preddy Marpaung]]>
<![CDATA[ INFOKUM Vol. 8 No. 1, Desembe (2019): Data Mining,Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Data mining is the process of exploring the added value of knowledge so far not manually known from a data set using these techniques or methods. Data mining is a new science that has its roots in various fields of science such as artificial intelligence, machine learning, statistics, and databases. In the process of borrowing books, of course raw data will be processed by dividing it into different pieces of data. Among the lending data tables processed are general lending tables, 2-itemset candidate tables, lending tabular tables, support value tables, confidence value tables.]]>
<![CDATA[IMPLEMENTATION OF GRAY LEVEL TRANSFORMATION METHOD FOR SHARPING 2D IMAGES ]]>
<![CDATA[Jonhariono Sihotang]]>
<![CDATA[ INFOKUM Vol. 8 No. 1, Desembe (2019): Data Mining,Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[At present image processing plays an important role, in which the processing does not only provide effects that make an image more artistic but can also improve the quality of the image itself. In the field of photography and film that is used in making animated advertisements on television, or creating effects from dangerous scenes that cannot be done by real humans. Not all digital images have a visual appearance that satisfies the human eye. Dissatisfaction can arise due to interference or lack of maximization of the image quality, such as spots appearing caused by the process of capturing imperfect images, lack of image sharpness due to uneven lighting and resulting in non-uniform intensity, image contrast is too low so objects are difficult separated from the background or interference caused by dirt that adheres to the image.]]>
<![CDATA[IMPLEMENTATION OF FOURIER PHASE ONLY SYNTHETIS METHOD FOR 3D IMAGE PLANNING ]]>
<![CDATA[Yuda Perwira]]>;
<![CDATA[Wira Apriani]]>
<![CDATA[ INFOKUM Vol. 8 No. 1, Desembe (2019): Data Mining,Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[In carrying out the process of the Fourier Phase Only Synthetic method on two images, the observer must get the impression as if seeing the image changes its form to an intermediate form before turning into a destination image. These changes must occur regularly and consistently to achieve the goal image. This sharpening system is one of the systems that aim to process this transformation in many forms used in applications in the fields of entertainment, computer animation, scientific visualization, and education. The sharpening system on the 3-dimensional image-side aims to identify image patterns. The image quality is good if it has good contrast and can describe the structure of ridges and valleys clearly. From the implementation of the Fourier Phase Only Synthesis Analysis, using the main parameters of Ridge Orientation Image, it has been successfully obtained the results of image improvement. Improvement of this side of the image will greatly help to improve the quality of the extraction of 3-dimensional images, by determining the value of constants to get the best results.]]>
<![CDATA[ANALYSIS OF IMPROVING DIGITAL IMAGE QUALITY USING ARITHMETIC MEAN FILTER ALGORITHM ]]>
<![CDATA[Mamed Rofendi]]>;
<![CDATA[Kristian Siregar]]>
<![CDATA[ INFOKUM Vol. 8 No. 1, Desembe (2019): Data Mining,Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Image is a combination of points, lines, fields, and colors to create an imitation of a physical or human object. Digital imagery consists of square elements called pixels. An example of an image is an image. However, sometimes the image can also experience a decrease in quality (degradation). Images or pixels that have decreased image quality in image processing are called noise. This research discusses the enhancement of digital image quality using the Median filter technique to reduce noise. In this study using color image data (RGB) as test data and then converted into grayscale images to determine the gray degree of the image. The Grayscale image is stored in the database. Then noise is generated by using random numbers. The type of noise used is Salt & Pepper. Noise salt & pepper is a type of noise that has a value of 0 and 255 spread. To reduce noise salt & Pepper, an Arithmetic Mean Filter method or technique is used.]]>
<![CDATA[DESIGN OF DIGITAL IMAGE EDGE DETECTION APPLICATIONS USING FREI-CHEN ALGORITHM ]]>
<![CDATA[Leonardus Sitinjak]]>
<![CDATA[ INFOKUM Vol. 8 No. 2, Juni (2020): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[One image processing technique used is edge detection. Edge detection is a common thing in digital image processing because it is one of the first steps in image segmentation, which aims to present the objects contained in the image. Edge detection functions to identify the boundary lines of an object against overlapping backgrounds. Currently several methods can be used for edge detection, for example the Sobel, Canny, Prewitt, Frei-Chen, and SUSAN methods. In this research, 1 method is taken, namely the Frei-Chen algorithm. The results of this study indicate that this operator is successful in detecting edges in an image. When detecting edges in images containing noise the Frei-Chen algorithm is better at edge detection.]]>
<![CDATA[IMAGE EDGE DETECTION USING ROBINSON OPERATOR ]]>
<![CDATA[PANDI BARITA NAULI SIMANGSONG]]>
<![CDATA[ INFOKUM Vol. 8 No. 2, Juni (2020): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Image processing is now widely used in almost all fields including medicine, industry, agriculture, geology, marine, and so on. One of the main things in image processing is the process of detecting the edge of an image, wherewith this process the boundary edge of an object with its background can be well determined. Edge detection is a method that can be used to detect the edge of an image that aims to improve the appearance of the boundaries of an area or objects in the image so that objects or boundaries of an area in the image are more easily recognized by humans and machines. Edge detection in digital images aims to recognize a pattern contained in the image itself. With the recognition of patterns in an image will be easily obtained information in an image. Determining the location of the edge of an image is easy if the condition of the image is clear and sharp, but the accuracy of the existence of the edge becomes difficult to determine if there is interference in the image, such as noise. But often it also results in an unsustainable image border where the results are influenced by the type of method used.]]>
<![CDATA[CANNY OPERATOR'S IMPLEMENTATION OF IMAGE SEGMENTATION ]]>
<![CDATA[Pristiwanto Pristiwanto]]>
<![CDATA[ INFOKUM Vol. 8 No. 2, Juni (2020): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[The process of segmentation in digital images that separates an object from the background or background can be obtained from the RGB value of each pixel in the digital image so that the object can be processed for other purposes. As technology develops in applications that process digital images, segmentation is becoming increasingly necessary. The results of segmentation must also be more accurate because if the results of segmentation are inaccurate it will affect the results of the next process. In general, the segmentation process is divided into three parts based on classification, by edge, and by region. the process starts with inputting a digital image and then the grayscale process is carried out. Next, choose the method then do the edge detection process with the Canny or Laplacian operator and finally the dilation procccess]]>
<![CDATA[IMPLEMENTATION OF APRIORI ALGORITHM IN DETERMINING THE LEVEL OF PRINTING NEEDS ]]>
<![CDATA[R. Mahdalena Simanjorang]]>
<![CDATA[ INFOKUM Vol. 8 No. 2, Juni (2020): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Competition in the business world, especially in the increasingly difficult printing world, requires developers to find strategies to increase orders for printed products ordered. An increasing number of order data every day can be used to develop marketing strategies if processed correctly. A priori algorithms include the type of association rules in data mining. One of the stages of association analysis that attracts many researchers to produce efficient algorithms is the analysis of high-frequency patterns (frequent pattern mining). The importance of an association can be known by two benchmarks, namely: support and confidence. Support (support value) is the percentage of the combination of these items in the database, while confidence (certainty value) is the strength of the relationship between items in association rules.]]>
<![CDATA[ANALYSIS OF SERVICE SATISFACTION LEVEL USING ROUGH SET ALGORITHM ]]>
<![CDATA[Jonhariono Sihotang]]>
<![CDATA[ INFOKUM Vol. 8 No. 2, Juni (2020): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Data mining Is a technique that combines traditional data analysis techniques with algorithms for processing large amounts of data. Data mining can be used to perform data analysis and find important patterns in data. Data mining will be a benchmark or reference for making data mining processing decisions that can be done with the Rough Set method. Rough Set Method is one of the methods above that allows us to make decisions in hotel services because in this method there are formulations or stages of problem mechanics and a Result (decision) of a combination that may occur from the criteria above. From the results (decisions) derived from the processed data mining, it can be used as a reference for decision making. The Rought Set Method is a mathematical technique developed since 1980.]]>
