Jurnal Informatika: Jurnal Pengembangan IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
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<![CDATA[Optimalisasi Infrastruktur Jaringan Menggunakan Link Aggregation Control Protocol Dengan Device Cloud Router Switch ]]>
<![CDATA[Subandri Subandri]]>;
<![CDATA[Zaenal Mutaqin Subekti]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1866
<![CDATA[The development of data in a company is getting bigger and requires fast access time. the existence of a system that uses computer networks and internet access to support the activities of the company becomes an important part in the use of infrastructure in the company. Computer networks are accessed by all staff and employees in the building and there are servers and databases that are accessed all the time. In the building part consists of several work processes that simultaneously access to the same network and server. Then each staff and employee will access the same network infrastructure, where the data transfer burden is delegated to only one (1) switch port to connect. This can result in a large burden and network access can be slowed. By providing a configuration to optimize the switch network that adopts the link agro-protocol protocol (LACP) method on the use of network infrastructure so that it can be a solution for overcoming large network loads and slow access. By applying PPDIOO to the infrastructure development method so that the expectations can be achieved in accordance with the desired results]]>
<![CDATA[Rancang Bangun Wireless Access Point dengan Capsman dan Mac Mask Access list ]]>
<![CDATA[Zaenal Mutaqin Subekti]]>;
<![CDATA[Subandri Subandri]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1878
<![CDATA[In the era of millennial needs using the internet became the primary needs of everyone, to connect to the internet one of them by connecting through an access point. The problem that occurs is the difficulty of getting legal internet access that is already connected to an access point then moving place or room. Because the number of access point devices installed with SSID (Service Set Identifier) is different for each device that the user needs to log in again to get internet access, and there is no filtering of users who are entitled to use hotspot facilities based on certain devices. With the number of access points being hotspots, this becomes a consideration for network managers to set hotspot passwords or other settings, this is a new challenge for network managers. Therefore, to overcome this problem a wireless access point management system was built that uses centrally using CAPsMAN (Controller Access Point System Manager) and MAC Mask Access Lists as filtering. By using the NDLC (Network Development Life Cycle) method in floating network design.]]>
<![CDATA[Penerapan Artificial Intelligence (AI) Pada Sistem Informasi Optimasi Jadwal Kuliah Menggunakan Algoritma Genetika ]]>
<![CDATA[Dedi Prasetio]]>;
<![CDATA[Wawan Hermawan Syah]]>;
<![CDATA[Budi Budi]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1867
<![CDATA[Scheduling lectures is one important aspect to support the implementation of lectures. With the scheduling of lectures, lecture time can be arranged, so that the room can be used effectively. To arrange lecture scheduling, it is of course needed the right way so that arranging the lecture schedules becomes easier and faster. At the College of XXXX, lecture schedules is still done in the conventional way that is arranging the schedule manually using Microsoft Excel software so that it takes a long time in preparing the scheduling of lectures. To simplify and accelerate the process of preparing lecture schedules optimally, the Lecture Scheduling Information System is one of the right solutions. Genetic Algorithm is one way to make logical and systematic steps so that the Information System Scheduling lectures can provide solutions that are easy, faster and more optimal. The genetic algorithm is one of the advances in the field of information technology in the field of Artificial Intelligence (AI) that can solve optimization problems. So that the process of making a schedule using genetic algorithms prepares lecture schedules faster, more precise and optimal. The results obtained from the modeling of genetic algorithms in the lecture schedules system include reguler morning class schedules, reguler evening class schedules, and extention class schedules in all majors at XXXX Higher Education.]]>
<![CDATA[Strategi Marketing Penerimaan Mahasiswa Baru Menggunakan Machine Learning dengan Teknik Clustering ]]>
<![CDATA[Raditya Danar Dana]]>;
<![CDATA[Cep Lukman Rohmat]]>;
<![CDATA[Ade Rizki Rinaldi]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1879
<![CDATA[The marketing activity of new student admissions is one of the efforts undertaken by a university to maintain its existence in order to remain known and gain interest from the wider community. From the results of observations made at the research location, marketing activities carried out so far are still carried out in the same way from year to year without distinguishing the characteristics of the target prospective registrants, so the marketing pattern undertaken is not necessarily effective for all prospective applicants who have different characteristics - different . Therefore, it is necessary to make an effort to target target applicants based on certain characteristics to facilitate the determination of strategies and marketing patterns for new student admissions. The aim of this research is to group students' spread data using Machine Learning Technology approach using Clustering technique. This research resulted in the grouping of registrants in the admission activities of new students divided into 3 cluster groups, namely cluster 1 by 11%, cluster 2 by 56% and cluster 3 by 33%.]]>
<![CDATA[Penerapan Intelligence System berbasis Case Base Reasioning dan Metode K-Nearest Neighbor Untuk Identifikasi Gangguan IT Support ]]>
<![CDATA[Muhamad Dedi Suryadi]]>;
<![CDATA[Sahlan Sahlan]]>;
<![CDATA[Ndaru Ruseno]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1868
<![CDATA[Delay in handling and solving IT problems both hardware / software greatly affects the company's business processes. Sometimes, IT helpdesk personnel still difficult to find solutions and make decisions to solve IT problems. This is because there is currently no system that can help IT Helpdesk personnel in finding solutions to be able to solve the problems being faced quickly and accurately. When going to look for solutions to the problems being faced can not display solutions that are appropriate or close to the existing historical data. In identifying IT problems, the authors use case-based reasoning or Case-Based Reasoning (CBR) by carrying out the process of finding the case with the highest proximity and proximity measurement using the K-Nearest Neighbor (k-NN) algorithm. This study aims to explain the application of Case-Based Reactioning (CBR) and K-Nearest Neighbor (k-NN) Algorithm to identify IT problem disturbances. The results showed that the application of Case-Based Reasioning and K-Nearest Neighbor (k-NN) Algorithm after being tested by 5 Experts got accurate results.]]>
<![CDATA[E-Learning Satisfaction Menggunakan Metode Auto Model ]]>
<![CDATA[Arif Rinaldi Dikananda]]>;
<![CDATA[Fidya Arie Pratama]]>;
<![CDATA[Ade Rizki Rinaldi]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1864
<![CDATA[E-Learning just like learning media in general need to be evaluated to find out and measure how much effectiveness, efficiency and user satisfaction with the quality of the overall learning process. One effort that can be done to find out and evaluate the quality of a learning is to use satisfaction evaluation. Measurement of satisfaction requires data derived from questionnaires that are presented using a Likert scale. The data illustrates the perception of users who have uncertainty because it is very subjective so that it has the potential to cause misinterpretation. The auto model method can be used to evaluate e-Learning satisfaction because the auto model method has the advantage of solving a problem with the various models produced, which in this case are in accordance with the context of the satisfaction problem that is often presented in natural language that has uncertainty, such as "how satisfied? "," How efficient? "And" how much is user satisfaction. Based on the auto model method, the results of the satisfaction scores of each respondent, shown in the table above, are summed and the average is calculated. With the auto model, the results show that SVM is the best performance method with an acceleration rate of 90% and best gains with a value of 38.]]>
<![CDATA[Pengelompokkan Tingkat Pemahaman Kurikulum Berbasis KKNI Menggunakan Metode X-Means Clustering ]]>
<![CDATA[Saeful Anwar]]>;
<![CDATA[Nisa Dieanwati Nuris]]>;
<![CDATA[Yudhistira Arie Wijaya]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1869
<![CDATA[In Law Number 12 of 2012 concerning Higher Education in Article 35 Paragraph 2 explains that the development of PT curriculum refers to the National Higher Education Standards for every study program that develops noble, intellectual intelligence and skills. KKNI or the Indonesian National Qualification Framework brings together, equates, and combines the field of education with the field of training and work experience and conceptually the Indonesian National Qualification Framework (KKNI) is composed of six (6) main parameters consisting of science, knowledge, know-how, skills, affection, competency. From the results of clustering analysis or grouping using the x-means algorithm it is found that the parameters of science (A), knowledge (B), skill (D), and competency (F) respondents are more likely to answer understanding and are very understanding. In the know-how parameter (C) the tendency of respondents to answer is well understood, whereas for the affection parameter (E) respondents tend to answer between understanding and quite understand but is in contrast between cluster 0 and cluster 1.]]>
<![CDATA[Identifikasi Visual Cacat Produk Menggunakan Neural Network Model Backpropagation (Studi Kasus: PT. Panasonic Gobel Eco Solution) ]]>
<![CDATA[Muhammad Nur]]>;
<![CDATA[Sjaeful Irwan]]>;
<![CDATA[Danang Santosa]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1865
<![CDATA[Product defects are common in the production process. Visual identification of product defects is first carried out when the product is produced. Identification of vague defects in very small shapes with different sizes and positions is difficult to do with ordinary eye sight, so that often results in decisions about the status of the product that is not right. Product defects in visual form can be identified by patterns such as shape, size and position on the product image. In this study, we will apply a neural network with the backpropagation model as a classification of the pattern. Product images will be processed using image processing by converting the RGB pixel value of the image into a numeric value. Data in numerical form will be input for training values in the backpropagation model. Training results are used to identify identified product defects and produce product status decisions. The results show that the backpropagation neural network model is able to recognize product patterns with an accuracy of 99.24% and based on simulation test data with the final weight and bias of training results, able to identify product defects with success up to 91%.]]>
<![CDATA[Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning ]]>
<![CDATA[Martanto Martanto]]>;
<![CDATA[Irfan Ali]]>;
<![CDATA[Mulyawan Mulyawan]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v4i2-2.1877
<![CDATA[The graduation rate of students on time at the Informatics Engineering study program STMIK IKMI Cirebon greatly affects the accreditation assessment. Graduation prediction is difficult to do, but many have done predictions using a variety of methods. Graduation prediction is needed in order to determine preventive policies for students who graduate not on time. The method used in this research is Machine learning with deep learning techniques. The data set used as many as 405 data of students who graduated on time or who were not on time. The research attributes used are the Nim attribute, the GPA value of students who have graduated and the status of graduating or not graduating. The results of this study are the level of accuracy using Machine Learning by 72.84%.]]>
