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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
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Articles 16 Documents
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Search results for , issue "Vol 13, No 3 (2021)" : 16 Documents clear
Grouping the spread of the covid-19 virus based on the positive case number, population and area width using the k-means clustering method Asep Muhidin; Muhtajudin Danny; Elkin Rivali
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.1011.314-321

Abstract

The spread of the Covid-19 virus began to enter Indonesia in early 2020. Until now, the spread of the virus is still occurring in several parts of Indonesia, although it has begun to decline in many areas. ). The possibility of direct human contact depends on the number of confirmed positives, the population and the area. Therefore, this study looks for the characteristics of the city/district area group based on the population, area and number of residents who are confirmed positive for the Covid-19 virus using the K-Means Clustering method. The clustering process begins with finding the best number of clusters (K) using the elbow method and testing cluster members using the silhouette and davies-bouldin index methods. The best K value results obtained by the elbow method are 3 clusters (K=3). The results of K-Means Clustering with 3 clusters show that in cluster 1, a city or district with a small area with a large population, the number of positive COVID-19 residents is large. In cluster 2, a city or district with a small area, with a moderate population, the number of people who are positive for COVID-19 is small. And in cluster 3, a city or district with a large area, with a very small population, the number of positive COVID-19 residents tends to be moderate.
Augmented reality application on the tourism orientation sign digital system at the 'Bawah Langit' museum Zuhriyah, Sitti; William Asrul, Billy Eden; Jura, Suwatri
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.857.216-225

Abstract

One of the most visited tourism icons in Makassar is the Losari Beach Pavilion which provides many choices ranging from beautiful sunsets, typical banana epe cuisine, floating mosques, and rows of statues of heroes and icons of Makassar City which are spread over four platforms, namely Makassar Pavilion, Bugis Pavilion, Mandar and Toraja which is commonly called the Museum Under the Sky. In supporting the attraction, it is necessary to be equipped with an accurate explanation of the statues in the attraction. In the new normal that limits interaction with other people, digital and virtual explanations will be one solution to provide attractive and up-to-date information. This study aims to create a Digital Tourism Orientation Sign System, which is an information system that provides accurate explanations and descriptions of the hero statues in the Underground Sky Museum. With this application, the Losari Pavilion is one of the futuristic tourist attractions with local wisdom. This application has been tested using Alpha testing which includes distance testing with test results a minimum distance of 10 cm and a maximum distance of 1 m marker will be detected; light testing with object test results will appear from a room brightness level greater than 10 Lux.
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction Shoffan Saifullah; Andiko Putro Suryotomo
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.937.285-293

Abstract

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.
Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor Zulfrianto Yusrin Lamasigi; Andi Bode
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.1025.322-333

Abstract

Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data and image testing of batik motifs, the K-Nearest Neighbor classification method will be used based on texture feature extraction value obtained. In this experiment, 5 experiments will be carried out based on angles 0degrees, 45degrees, 90degrees, 135degrees, and 180degreesusing the values of k is1, 3, 5, and 7. The confusion matrix will be used to calculate the accuracy level of the K-Nearest Neighbor classification. From the results of experiments carried out using training data as many as 607 images and testing as many as 344 images in five classes used with angles of 0 degrees, 45degrees, 90degrees, 135degrees, 180degrees, and values of k are 1, 3, 5, and 7, getting the highest accuracy results is at an angle of 135degreesand 180degreeswith a value of k is 1 of 89.24% and the lowest is at an angle of 90degreeswith a value of k is 3 of 67.44%. This shows that the Gray level co-occurrence matrix method is good for extracting the texture features of batik motifs from five major islands in Indonesia, it is evidenced by the results of the average accuracy of the classification obtained.
Usability testing of intensive course mobile application using the usability scale system Manda Rohandi; Nurlaila Husain; Indri Wirahmi Bay
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.821.252-258

Abstract

The mobile intensive course (MIC) application version 2 is an application created to assist the learning process of the English intensive course. Measurement of the usability of the MIC version 2 application has never been done before, so the effectiveness, efficiency, and user satisfaction for this application have not been measured. In addition, usability measurement can be used as a reference for the level of user loyalty, whether including net promoters, passive users, or detractors. One of the usability measuring tools that are easy to use, the calculation is simple and can be used on a small sample, but still valid and reliable is the System Usability Scale (SUS). The purpose of this study was to test the usability of the MIC version 2 application to determine the quality of this application using the SUS questionnaire. This research was conducted in four steps, namely 1) piloting the MIC version 2 application to the respondents; 2) distributing SUS questionnaires to respondents to be filled out manually or online; 3) calculating the average SUS score; 4) analyzing the mean SUS score. This study involved 37 respondents consisting of students and lecturers of EIC. The results of this study indicate that the usability of the MIC version 2 application can be accepted by users with an average SUS score of 70.61 and get category C based on the CGS assessment. When viewed from the level of user loyalty, the average SUS score for this application only results in passive users. The average value of the contribution to learnability obtained is still low, which is 1.9 from the maximum value of 4. Improvements are needed in future applications in terms of application learnability, such as simplifying the appearance of features and functions in the application, thus allowing users to be more familiar with applications and potential as net promoters.
Geographic information system in determining flood and safe zone for flood mitigation Nur Khaerat Nur; Muhammad Chaerul; Abdul Azis
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.942.294-299

Abstract

Computer-based technology has pervaded practically every aspect of human life in the modern era. Through diverse information systems, various fields have employed computer-based technology to build theories and their applications. Geographic Information System (GIS) technology is one type of computer-based system that is widely employed. Various disciplines can use Geographic Information System (GIS) technology to research and map flood zones. The research approach employed is the employment of ARC GIS 10.5 system technology to determine the flood zone's features. Pampang, Tamamaung, Sinri Jala, and Karuwisi Utara are four villages that are flood-prone locations that are included in the warning zone, according to the results of data processing using GIS software and data analysis illustrated from mapping the flood area in Panakkukang District. There are 1,985 hectares in the Rappocini sub-district, which is included in a vulnerable region, namely Bantabantaeng Village, and 145,709 hectares in Karunrung Village, Gunung Sari, Bontomakkio, Tidung, Kassi-Kassi, Mappala. Parts of the Manggala, Bangkala, and Borong regions, as well as sub-districts of Manggala, are included in the vulnerable areas. Tamalanrea Indah, Tamalanrea Jaya, and Tamalanrea sub-districts are the Tamalanrea sub-districts that have been designated as vulnerable zones.
Classification model of Toraja arabica coffee fruit ripeness levels using convolution neural network approach Aryo Michael; Melki Garonga
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.861.226-234

Abstract

The purpose of this study is to design a CNN deep learning algorithm model that can classify the maturity level of Arabica coffee fruit based on image, the resulting model can be applied to a coffee bean sorting device based on artificial intelligence so that problems that exist in the process of sorting arabica coffee fruit that meets the standards can be avoided, to improve the quality of arabica Toraja coffee products. The research began from the collection of data in the form of raw Arabica coffee image Toraja as many as 4000 images of arabica coffee fruit with 4 categories, half-cooked, perfectly ripe, and mature old. CNN basic architecture is created using images with a size of 128x128 pixels, 4 convolution layers using 3x3 filters opening 32, 64, 128, and 256 with ReLU activation, followed by a poll layer with a 2x2 filter. The full connected layer uses 2 hidden layers with dropout layers. The training model was conducted with a 5-fold cross-validation method using epoch 100, 'adam' optimization algorithm with a learning rate of 0.0001, and batch size 10. The success of a model is seen based on the calculation of the confusion matrix. The test results showed that the accuracy rate of the third model using a combination of max polling and average polling performed best with an introduction accuracy of 98.75%, the first model used max polling with an accuracy of 98.25% while the lowest accuracy on the second model used average polling with an accuracy of 97.75%.
Classification of cendrawasih birds using convolutional neural network (CNN) keras recognition Warnia Nengsih; Ardiyanto Ardiyanto; Ayu Putri Lestari
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.865.259-265

Abstract

Classification is part of predictive modeling and supervised learning. This method is used to determine the data class based on the previous value. In solving certain cases, there are various classification methods with varying degrees of accuracy. Convolutional Neural Network (CNN) is part of the Multilayer Perceptron (MLP) for processing two-dimensional data. CNN is also part of the Deep Neural Network and is applied to image objects. From several sources, it is stated that the classification process using images is not properly implemented in this MLP. Of course, this will result in the accuracy of the method in handling certain cases. In this study, the object classification process uses hard recognition to determine the accuracy value of the method using the object of the bird of paradise. From the results of this study, a training model was conducted using 10 ephocs with an accuracy value of 0.0850 while a loss value of 2.5658. So these results indicate that MLP can successfully complete the classification process using images.
Image processing analysis to determine fajr time using the imagej application Arif Septianto; Rosalina Rosalina; Harry Ramza
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.954.300-306

Abstract

The issue of determining the time of prayer is very fundamental because it is one of the pillars so that the worship of a Muslim is accepted. The government has set a standard for carrying out the dawn prayer, namely by determining the degree of appearance of the dawn of shodiq at -20 degrees. This study aims to compare the initial determination of the government's dawn time using different sensors, namely drones as image sensors, drones were chosen because they have several advantages. The resulting data is in the form of photos and then the photo data is processed using digital image processing software, namely imagej. The data from imagej processing are in the form of mean and standard deviation, all data are then recapitulated using Microsoft excel and plotted so as to form data which is then carried out by a polynomial approach to determine the cutoff point as an early indicator of the entry of dawn. The method used in this study is using a qualitative analysis method with a polynomial 5 approach. The conclusion obtained in this study is that the government's dawn time is 21 minutes faster, the standard used in this study is a DIP of -13.95 degrees and unlike the 2D SQM. 3D drone data results in more accurate data analysis and is not easy to manipulate because it can be verified with photo data.
K-means algorithm for clustering system of plant seeds specialization areas in east Aceh Rozzi Kesuma Dinata; Novia Hasdyna; Sujacka Retno; Muhammad Nurfahmi
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.863.235-243

Abstract

The number of regions and types of plants in East Aceh Regency requires a data clustering process in order to easily find out which areas are most in-demand based on the type of plants. This study applies the k-means algorithm to classify the data. The data used in this study were obtained from the Department of Agriculture, Food Crops and Horticulture, East Aceh Regency. Based on the test results with k-means, the average number of iterations in the 2015-2019 data is 8,7,6,4,3 iterations for each commodity. The test results can show areas of interest for plant seeds with clusters of high demand, attractive, and less desirable. The system in this study was built based on the web using the PHP programming language.

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