Pemodelan Pola Hubungan Kemampuan Lulusan Universitas Lancang Kuning Dengan Kebutuhan Dunia USAha Dan Industri

The rapid growth of the data warehouse has created conditions for rich data but poor information. Data mining is the mining or the discovery of new information by looking for certain patterns or rules of a number of large amounts of data that is expected to produce an interesting pattern or important information from the condition. By utilizing the the graduate tracer data that is associated with users of graduates, they are business and industry, are expected to produce information about the pattern of their relationship through data mining techniques, association rule. Category ability of graduates in measuring the level parameter is less necessary, reasonably necessary, needed, and is needed in the world of business and industry. The algorithm used is a priori algorithm, the information displayed in the form of support and confidence values of each category type abilities of graduates

ASSOCIATION RULE MINING IN ANALYSIS OF THE RELATIONSHIP BETWEEN WORK ENGAGEMENT AND WORK LIFE BALANCE ON THE PERFORMANCE OF FEMALE LECTURERS

This study aims to determine the relationship pattern of Work Engagement and Work Life Balance on the performance of female lecturers. And in the context of science and technology, this study aims to develop the field of data mining studies regarding the ability of association rule mining techniques to the field of human resource management. This analysis uses independent variables, namely work engagement and work life balance. The dependent variable is the lecturer performance index. Respondents in this study were female lecturers who taught at Lancang Kuning University. This study uses the Association Rule Mining technique. Association rule mining is one of the techniques in data mining. Association rule mining has received great attention because of its use in a variety of research applications. The data used in data mining is in the form of historical data. The data was collected by means of a questionnaire. The results of this study can trigger the subject to create problem solving in performance management

Analisis Data Lulusan Dengan Data Mining Untuk Mendukung Strategi Promosi Universitas Lancang Kuning

Setiap Perusahaan maupun organisasi yang ingin tetap bertahan perlu untuk menentukan strategi promosi yang tepat. Penentuan strategi promosi yang tepat akan dapat mengurangi biaya promosi dan mencapai sasaran promosi yang tepat. Salah satu cara yang dapat dilakukan untuk penentuan strategi promosi adalah dengan menggunakan teknik data mining. Teknik data mining yang digunakan dalam hal ini adalah dengan menggunakan algoritma Clustering K-Means. Clustering merupakan pengelompokkan record, observasi, atau kasus ke dalam kelas-kelas objek yang mirip. K-Means adalah metode klaster data non-hirarkis yang mencoba untuk membagi data ke dalam satu atau lebih klaster. Penelitian dilakukan dengan mengamati beberapa variabel penelitian yang sering dipertimbangkan oleh perguruan tinggi dalam menentukan sasaran promosinya yaitu asal sekolah, daerah, dan jurusan. Hasil penelitian ini adalah berupa pola menarik hasil data mining yang merupakan informasi penting untuk mendukung strategi promosi yang tepat dalam mendapatkan calon mahasiswa baru.Kata kunci: Data Mining, Clustering, K-Means Each company or organization that wants to survive needs to determine appropriate promotional strategies. Determination of appropriate promotional strategies will be able to reduce costs and achieve the goals the promotion of proper promotion. One way that can be done to determine campaign strategy is to use data mining techniques. Data mining techniques used in this case is to use a K-Means clustering algorithm. Clustering is the grouping of records, observation, or in the case of the object classes that are similar. K-Means is a method of non-hierarchical clustering of data that is trying to divide the data into one or more clusters. The study was conducted by observing some of the variables that are often considered by the college in determining the target of promotion that the school of origin, region, and department. Results of this study are interesting pattern of results in the form of data mining that is important information to support appropriate promotional strategies in getting new students

SISTEM PENDUKUNG KEPUTUSAN UNIVERSITAS FAKULTAS TERBAIK UNIVERSITAS LANCANG KUNING MENGGUNAKAN METODE SMART DAN MOORA

Decision Support System (SPK) is a system that can help the leader / owner in the decision making process by using a calculation method based on assessment criteria. DSS is not a decision making tool but only a comparison tool in decision making. The Best Faculty Decision at Lancang Kuning University is a process of evaluating faculties from several aspects of criteria that can help leaders / quality assurance bodies in assessments with the aim of creating competition among faculties of assessment. The research will be carried out using comparison of assessment results using Smart and Moora methods. The results of the decisions of each method will be seen effectiveness in the assessment process to become a decision. In each method using the same or different criteria, it depends on the weight value of each criterion that is assessed. This result can be a reference for Lancang Kuning University in evaluating the assessment of the Best Faculty based on aspects of assessment.Sistem Pendukung Keputusan (SPK) adalah sebuah system yang dapat membantu pimpinan / owner dalam proses pengambilan keputusan dengan menggunakan metode perhitungan berdasarkan kriteria-kriteria penilaian. SPK bukan sebuah tool pengambil keputusan tetapi hanya sebuah alat perbandingan dalam pengambilan keputusan. Keputusan Fakultas Terbaik di Universitas Lancang Kuning adalah proses penilaian fakultas dari beberapa aspek kriteria yang dapat membantu pimpinan / badan penjamin mutu dalam penilaian dengan tujuan agar terciptanya persaingan antar fakultas aspek penilaian. Penelitian yang akan dilakukan denga menggunakan perbandingan hasil penilaian dengan menggunakan metode Smart dan Moora. Hasil keputusan dari masing-masing metode akan dilihat keefektivitasan dalam proses penilaian hingga menjadi sebuah keputusan. Didalam masing-masing metode menggunakan kriteria sama atau berbeda, hal itu tergantung dari pada nilai bobot dari masing-masing kriteria yang dinilai. Hasil ini bisa menjadi acuan bagi Universitas Lancang Kuning dalam mengevaluasi penilaian Fakultas Terbaik berdasarkan aspek penilaian

PEMODELAN POLA HUBUNGAN KEMAMPUAN LULUSAN UNIVERSITAS LANCANG KUNING DENGAN KEBUTUHAN DUNIA USAHA DAN INDUSTRI

AbstrakPertumbuhan yang pesat dari gudang data telah menciptakan kondisi kaya akan data tapi miskin informasi. Data mining merupakan penambangan atau penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar yang diharapkan dapat menghasilkan pola yang menarik atau informasi penting dari kondisi tersebut. Dengan memanfaatkan data tracer lulusan yang dihubungkan dengan pengguna lulusan yakni dunia usaha dan industri, diharapkan dapat menghasilkan informasi tentang pola hubungan keduanya melalui teknik data mining, association rule. Kategori kemampuan lulusan di ukur dengan parameter tingkat kurang diperlukan, cukup diperlukan, diperlukan, dan sangat diperlukan dalam dunia usaha dan industri. Algoritma yang digunakan adalah algoritma apriori, informasi yang ditampilkan berupa nilai support dan confidence dari masing-masing kategori jenis kemampuan lulusan.Kata kunci: data mining, association rule, tracer lulusan, algoritma aprioriAbstractThe rapid growth of the data warehouse has created conditions for rich data but poor information. Data mining is the mining or the discovery of new information by looking for certain patterns or rules of a number of large amounts of data that is expected to produce an interesting pattern or important information from the condition. By utilizing the the graduate tracer data that is associated with users of graduates, they are business and industry, are expected to produce information about the pattern of their relationship through data mining techniques, association rule. Category ability of graduates in measuring the level parameter is less necessary, reasonably necessary, needed, and is needed in the world of business and industry. The algorithm used is a priori algorithm, the information displayed in the form of support and confidence values of each category type abilities of graduates.Keywords: data mining, association rule, graduate tracer, apriori algorithm</p

Penerapan Data Mining Untuk Menggali Informasi Tersembunyi Dalam Big Data Nilai Mata Kuliah

AbstrakJarang sekali perguruan tinggi melihat kompetensi lulusannya sebelum dilepas ke dunia nyata.  Salah satu variabel yang bisa digunakan adalah nilai matakuliah yang telah diperoleh mahasiswa atau calon lulusan. Kemudian memetakan nilai matakuliah yang telah diperoleh tiap mahasiswa atau calon lulusan pada aspek kompetensi dasar lulusan Strata satu Informatika yang disusun oleh asosiasi perguruan tinggi komputer (APTIKOM) dengan menggunakan teknik data mining. Pemetaan dilakukan berdasarkan nilai matakuliah yang telah ditempuh oleh mahasiswa atau calon lulusan, dalam hal ini objek penelitian adalah mahasiswa angkatan 2012 s/d 2015 yang telah mencapai 120 sks. Daftar aspek kompetensi dasar yang digunakan adalah aspek kompetensi yang disusun oleh APTIKOM berdasarkan ACM/IEEE 2013. Kemudian dilakukan penentuan kelompok matakuliah pada tiap kompetensi tersebut. Topik-topik yang dikaji antara lain meliputi : database, data mining, association rule, apriori dan beberapa algoritma lain yang mungkin dapat digunakan, serta perangkat lunak yang digunakan untuk proses mining. Pengolahan data yang telah disiapkan menggunakan beberapa perangkat lunak bantu seperti Excel, dan Tanagra. Mining data yang telah dilakukan menghasilkan informasi mengenai kompetensi dari calon lulusan yang dapat digunakan sebagai bahan analisa untuk pengambilan keputusan. Kata kunci : kompetensi, informasi, nilai mata kuliah AbstractRarely college graduates look competence before being released into the real world. One of the variables that can be used is the value of the courses that have been acquired or prospective graduate students. Then mapping the value of the courses that have been taken by each student or graduate candidates on the basis of competence of graduates Strata aspects of the Information compiled by the association of colleges computer (APTIKOM) using data mining techniques. Mapping is done based on the value of the courses that have been taken by students or prospective graduates, in this case the object of study is the student of 2012 s / d in 2015 which has achieved 120 credits. List aspects of basic competencies that are used are compiled by the competence aspect APTIKOM based ACM / IEEE 2013. Then is the determination of subjects in each group that competency. Topics to be studied include: databases, data mining, association rule, a priori and some other algorithm that may be used, as well as the software used to process mining. Processing of the data which has been prepared using some assistive software such as Excel, and Tanagra. Data mining has been done to produce information concerning the competence of prospective graduates who can be used as material analysis for decision making. Keywords: competence, information, mark</p

SOSIALISASI PENGISIAN SISTER BAGI DOSEN PESERTA SERTFIKASI DOSEN TAHUN 2020

This activity is to provide assistance to the group of Lancang Kuning University lecturers, especially to the lecturers participating in the Phase I lecturer certification in 2020. The service team will explain the techniques and methods of collection starting with explaining the functions of the Sister application (www.sister.unilak.ac.id ) for lecturers both lecturer certification participants and certified lecturers. Certification participants must enter their personal data, educational history, functional position history, education implementation history, research history, service history and other supporting data. All participants explained the rules in the process of inputting data, especially the maximum size of data that can be input up to a quick trick in order to be able to input data themselves on the application. Of the 10 faculties at Lancang Kuning University, 72 lecturers can continue at stage D3 to stage D4. Whereas at stage D5 only 51 lecturers were able to proceed because of the selection process for the assessment of the system. The dedication team made WAG in guiding the data system input process in the framework of the certification process. This is done so that they can monitor and assist if there are obstacles in the certification process

Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus images to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created and it is unknown whether one is best suited for use with images of the retinal fundus. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. The contrast-limited adaptive histogram equalization (CLAHE) method, the gray-level slicing method, the median filtering method, and the low light method are image improvement methods used to enhance images of the retinal fundus. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Sains University Hospital (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best