ILKOM Jurnal Ilmiah
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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<![CDATA[Klasifikasi Topik Tugas Akhir Mahasiswa menggunakan Algoritma Particle Swarm Optimization dan K-Nearest Neighbor ]]>
<![CDATA[Sumarni, Sumarni]]>;
<![CDATA[Rustam, Suhardi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.604.168-175
<![CDATA[Problems the Topic of the final project is a form of scientific writing that contains the results of observations from a study of the problems that occur with the use of methods related to the particular field of science. Every student in every program of study must draw up a final project. However, before embarking on writing the final project, each student must have the topic area as a destination, the step of selection the topic of final project is an initial step before working on the final task. One way to get the final task is to see the value of general courses as well as courses, concentration majors, the value of which dominate the is is decent to scope the research topic. this research is conducted on the application of the method of K-Nearest Neighbor (KNN) for categorization of the value of the courses of concentration for the coverage of the research topic, topic the entire value in the dataset will be classified by KNN and in the optimization with the Particle swarm Optimization algorithm (PSO). The experimental categorization of the final project is built with the training data Mahasiswa Universitas Ichsan Gorontalo that has been classified previously and test data derived from the entire value of the courses is not yet known categories. The results of the experiments, the value of the resulting accuracy of algorithms KNN, namely the value of the best accuracy with K=3, K Folds = 10 has an accuracy that is 72.46% and the Algorithm of KNN-PSO best accuracy with K=3, K Folds = 10 has an accuracy that is 89.86%, shows the accuracy is better by using the optimization algorithm]]>
<![CDATA[Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth ]]>
<![CDATA[Anggreani, Desi]]>;
<![CDATA[Wibawa, Aji Prasetya]]>;
<![CDATA[Purnawansyah, Purnawansyah]]>;
<![CDATA[Herman, Herman]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.538.96-103
<![CDATA[The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.]]>
<![CDATA[Prototipe Alat Pengusir Burung pada Gedung Berbasis Internet of Things menggunakan Sensor RCWL ]]>
<![CDATA[Ali Khumaidi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.602.162-167
<![CDATA[Sound disturbance and bird droppings in buildings are a problem for building managers. Bird droppings are quite difficult to remove and cause damage to the walls and aesthetics, especially the trend in the use of building roofs as a rooftop for productive activities. This study proposes the use of RCWL motion sensors for motion detection and the resulting output is the sound of eagles from speakers and ultrasonic speakers. The tool was developed based on internet of things using an arduino nano ATMega 328 microcontroller, connection and data transmission using SIM800L and GSM modules and power supply using a solar panel power bank. The test results show that the RCWL motion sensor is quite optimal in the detection of more than or equal to 3 birds. Sound output and the resulting waves are able to prevent birds from alighting and nesting.]]>
<![CDATA[Monitoring dan Evaluasi Kinerja Karyawan menggunakan Algoritma Simple Additive Weighting dan Hungarian ]]>
<![CDATA[Wawan Gunawan]]>;
<![CDATA[Muhammad Riski Firmansyah]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.519.87-95
<![CDATA[Monitoring and evaluating the performance of  employees of PT Akulaku Silvrr Indonesia still use microsoft excel application online,   which has some weaknesses, which is an obstacle in monitoring is that  adalah it is unable to give specific job orders   and can not provide due date information,whereas  in determining the provision of salary increase and employee benefits is done randomly and only based on the last education. Tohelp the company succeed in achieving its goals, a system can be needed that can help in making decisions on a large or small scale. The use of Simple Additive Weighting  (SAW) method can be used to determine pay raises and incentivize precisely and accurately  based on the specified criteria, while hungarian method is used to determine who will do the job at a difficult, easy and moderate level. So the rating for the salary increase for the support team is Andi Ansyah, Hangga Bagus, Chikal Aviv and Ray Awaludin. Determining the next task can be determined that Andi Ansyah can do the job easily and difficultly, Hangga Bagus can do the job easily and moderately, Chikal Aviv can do a medium and difficult job. While Ray Awaludin does not have the highest value for each job, so it is necessary to be given training.]]>
<![CDATA[Deteksi Diabetik Retinopati menggunakan Regresi Logistik ]]>
<![CDATA[Raras Tyasnurita]]>;
<![CDATA[Adhi Yoga Muris Pamungkas]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.578.130-135
<![CDATA[Retinopathy diabetic is a disease caused by diabetes mellitus complications that can cause damage to the retina of the eye. It has a direct impact on the disruption of the vision of the patient. Detecting this disease is very important to prevent total blindness on diabetes mellitus patients. One method to do the detection is by using machine learning. This research uses feature extraction data from an image that contains signs of retinopathy diabetic or not. In this research, we focus on retinopathy diabetic classification. We applied logistic regression algorithm for classification. There is four training condition in a machine learning model: using the default parameter, feature standardization, feature selection, and hyper-parameter tuning. The model used a regularization control (C) value of 11.288, iterations 200, and a regularization penalty (l1). The experimental results show that this proposed model with full features produced 80,17% accuracy in data validation.]]>
<![CDATA[Klasifikasi Topik Tugas Akhir Mahasiswa menggunakan Algoritma Particle Swarm Optimization dan K-Nearest Neighbor ]]>
<![CDATA[Sumarni Sumarni]]>;
<![CDATA[Suhardi Rustam]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.604.168-175
<![CDATA[Problems the Topic of the final project is a form of scientific writing that contains the results of observations from a study of the problems that occur with the use of methods related to the particular field of science. Every student in every program of study must draw up a final project. However, before embarking on writing the final project, each student must have the topic area as a destination, the step of selection the topic of final project is an initial step before working on the final task. One way to get the final task is to see the value of general courses as well as courses, concentration majors, the value of which dominate the is is decent to scope the research topic. this research is conducted on the application of the method of K-Nearest Neighbor (KNN) for categorization of the value of the courses of concentration for the coverage of the research topic, topic the entire value in the dataset will be classified by KNN and in the optimization with the Particle swarm Optimization algorithm (PSO). The experimental categorization of the final project is built with the training data Mahasiswa Universitas Ichsan Gorontalo that has been classified previously and test data derived from the entire value of the courses is not yet known categories. The results of the experiments, the value of the resulting accuracy of algorithms KNN, namely the value of the best accuracy with K=3, K Folds = 10 has an accuracy that is 72.46% and the Algorithm of KNN-PSO best accuracy with K=3, K Folds = 10 has an accuracy that is 89.86%, shows the accuracy is better by using the optimization algorithm]]>
<![CDATA[Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth ]]>
<![CDATA[Desi Anggreani]]>;
<![CDATA[Aji Prasetya Wibawa]]>;
<![CDATA[Purnawansyah Purnawansyah]]>;
<![CDATA[Herman Herman]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.538.96-103
<![CDATA[The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.]]>
<![CDATA[Simple Additive Weighting untuk Front-end Framework Terbaik ]]>
<![CDATA[Lestari Yusuf]]>;
<![CDATA[Taufik Hidayatulloh]]>;
<![CDATA[Dini Nurlaela]]>;
<![CDATA[Lila Dini Utami]]>;
<![CDATA[Fuad Nur Hasan]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.593.136-142
<![CDATA[Web applications that used to run on a desktop in recent years have received huge demand for this area to be more sophisticated and complex, not only that, but users also want web applications to run on mobile devices. Web appearance that is only designed for computer devices will make users difficult when opening a web page display on a different device. By using the CSS framework library, web developers will be greatly helped in making the program more responsive and can also be run on a variety of Open Source both Windows, iOS, and Android. Decision-making system that can determine the best front-end Framework can be an alternative solution for web developers to determine which front-end framework is easier and more convenient to use. Simple Additive Weighting is used to analyze and decide which the best alternative with calculations that take five main criteria in this research that is Preprocessor, Responsive, Browser Support, Easy to Use, and Template. In this study the highest prefects were obtained by Bootstrap 1,000 while for foundation and bulma get a large prefensie s 0.868 and 0.820.]]>
<![CDATA[Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus ]]>
<![CDATA[Rozzi Kesuma Dinata]]>;
<![CDATA[Hafizal Akbar]]>;
<![CDATA[Novia Hasdyna]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.539.104-111
<![CDATA[K-Nearest Neighbor is a data mining algorithm that can be used to classify data. K-Nearest Neighbor works based on the closest distance. This research using the Euclidean and Manhattan distances to calculate the distance of Lhokseumawe-Medan bus transportation. Data that used in this research was obtained from the Organisasi Angkutan Darat Kota Lhokseumawe. The results of the test with k = 3 has obtained the percentage of 44.94% for Precision, 37.06% Recall, and 81.96% Accuracy for the performance of K-NN with Euclidean Distance. Whereas by using Manhattan Distance the result obtained was 45.49% for Precision, 36.39% Recall, and 84.00% Accuracy. The result shown that Manhattan Distance obtained the highest accuracy, with the difference of 2.04% higher than Euclidean Distance. It indicates that Manhattan Distance is more accurate than Euclidean Distance to classify the bus transportation.]]>
<![CDATA[Anti-WebShell PHP Backdoor Scanner pada Linux Server ]]>
<![CDATA[Christian Ronaldo Sopaheluwakan]]>;
<![CDATA[Dian Widiyanto Chandra]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 12, No 2 (2020) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v12i2.596.143-153
<![CDATA[Backdoor or commonly also known as web shell is one of the malicious software that hackers use to maintain access systems that they have entered. Relatively few programs like Anti Web-Shell, PHP Backdoor Scanner circulating on the Internet, and can be obtained free of charge to deal with the issues above. But most of these programs have no actual database of signature behavior to deal with PHP backdoor / Shell nowadays. Then comes the contemporary Anti Web-Shell program that can deal with today's backdoor shell. This study uses an experimental method concerning previous similar studies and is implemented directly into the world of cyber security professional industries. By enriching the Regex dictionary signature and String Array Matching the actualized Anti Web-Shell program can detect more backdoor than similar programs that have existed in the past. The results of this study are in the form of a web application software in PHP extension. The application can minimize 100% of false positives and is twice as fast in scanning files because it is more specific in heuristic analysis scan.]]>
