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All Journal Semesta Teknika Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat
Asroni Asroni
Universitas Muhammadiyah Yogyakarta
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Engineering Medicine & Pharmacology Social Sciences

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Journal : Semesta Teknika

 1 
Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik) Asroni Asroni; Hidayatul Fitri; Eko Prasetyo
Semesta Teknika Vol 21, No 1 (2018): MEI 2018
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.211211

Abstract

The increasing new prospective students in a University to make the stack more and more data, departing from it then conducted a search for new knowledge with data mining. Grouping data for prospective new students will be made by the method Clustering and used the algorithm k-means. In this penmaru there are 5 data attributes are used i.e., hometown, gender, status to qualify for selection, driveways, and majors. This analysis is performed using WEKA software and the source data taken from admissions data (penmaru) in the form of a data warehouse. Class from the use of this method is the attribute of the majors. Iteration performed as many as 3 times and the number of a cluster at the Faculty of medicine and health sciences, i.e. 4 clusters, Faculty of social and political science 3 clusters. Method Clustering can be applied to the classification of data for prospective new students. Another thing that can be analyzed from the results of the grouping candidate data, promotion strategies from each Department to increase the quantity and quality.
Perkiraan Masa Tunggu Alumni Mendapatkan Pekerjaan Menggunakan Metode Prediksi Data Mining Dengan Algoritma Naive Bayes Classifier Asroni Asroni; Nadiyah Maharty Ali; Slamet Riyadi
Semesta Teknika Vol 21, No 2 (2018): NOVEMBER 2018
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.212225

Abstract

Student and Alumni data Universitas Muhammadiyah Yogyakarta is very common, and one of these is the alumni data obtained from work after the completion of undergraduate studies. Former students are given jobs caused or influenced by a range of factors. This research aims to have the grace period Classification or old alumni gain positions by triggering a process of data extraction and using the Bayes naïve classification algorithm. The algorithms used later succeeded in predicting sooner or later to get a job, the predictive results alumni can be used to make decisions to improve the quality of a university. Research on the support system using several parameters, i.e., gender, faculty, GPA, year of graduation, and job status. The data used are as much as 435, including seven years of 2011-2014 volume. The results of this study have the accuracy level of former students having the grace period come to 71% and of the calculated results of the predictions of the former students obtaining a job at Universitas Muhammadiyah Yogyakarta of the year 2011-2014 the Ensure that the work is carried out more quickly with the status of the slow to deliver the work
Penerapan Algoritma C4.5 untuk Klasifikasi Jenis Pekerjaan Alumni di Universitas Muhammadiyah Yogyakarta Asroni Asroni; Badrahini Masajeng Respati; Slamet Riyadi
Semesta Teknika Vol 21, No 2 (2018): NOVEMBER 2018
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.212222

Abstract

The development of education in Indonesia has increased very rapidly. One of the things that have become a benchmark for success in the quality of education at the university is the kind of job getting graduates after graduation. This research aims to identify factors that have an impact on the type of job classification method based on the C 4.5 alumni algorithm. The methodology of this research begins with the study of literature, the identification of a process of data extraction, data selection, data collection, data processing, data testing, and DA conclusion. This research uses some features of the data on a few faculty members, the year of graduation, the annual completion rate, and the strength as a classification performance parameter. Graduates data used up to 259, and consisted of 3 faculties of Economics, medicine and engineering forces from 2001-2013 and graduated from 2011-2016. The research results that have been done is if it comes from the Faculty of Economics, in 2011 and 2012 the majority of work in the private sector has passed, if it comes from the Faculty of Medicine with the years 2011 and 2012 graduated with a cumulative labor rate of between 3 to 3.5 majority working in The private sector, 2012 with a GPA between 3 and 3.5 working in the Private Sector. Finally, the C4.5 algorithm is suitable for the classification of alumni work types.
Sistem Informasi Menajemen Aset (Studi Kasus: Desa Barepan) Haris Setyawan; Asroni Asroni
Semesta Teknika Vol 21, No 2 (2018): NOVEMBER 2018
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.212221

Abstract

Barepan Village is one of the villages in Cawas sub-district, Klaten, Central Java. In 2017 Barepan Village has owned a new building as a means of village administration and obtains assets that support the implementation of government such as computers, tables, chairs, and others. However, asset management is currently not going well and does not have a unique asset database to facilitate the management and tracking of its assets. This caused the difficulty of conducting asset track which made it difficult for asset managers to know the condition of the asset was right, damaged or lost. Therefore, an information system is needed which has the objective to be able to run the business process of asset management to be neat and structured so that asset managers can efficiently manage and track assets. The process of designing an Asset Management Information System using the waterfall model software development method begins with needs analysis, design, coding, testing, and maintenance. From the design stage, it was then created with Code Igniter in the form of a PHP framework with the MVC concept so that a website-based Information System with the MVC concept was produced. Verification and validation are then carried out to determine the suitability of the system design with the Asset Management Information System final results that have been made. Finally, an asset information system can be obtained that fits your needs and archives assets well.
Implementasi Arsitektur Operational Data Store (ODS) dan Dimensional Data Store (DDS) dalam Pembangunan Data Mart Lulusan Rohmana Zulfa Bakhtiar; Slamet Riyadi; Asroni Asroni
Semesta Teknika Vol 18, No 1 (2015): MEI 2015
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v18i1.706

Abstract

Universitas Muhammadiyah Yogyakarta (UMY) is a big and high-grade educational institution. During the period of 1998-2014, UMY has produced about 20550 graduates. But, the big number of graduates is not supported by a good data storage system. Whereas those data are needed in filling up the accreditation form. For that reason, we have to build an integrated data storage system to provide graduates data as needed, that is graduate data mart. The development of graduate data mart uses SDLC Model Waterfall method. This method involves several types, there are requirement analysis, design system, implementation system, testing system, and maintenance system and those must be done sequentially. If there is an error, the process must be repeated from the beginning to fix the error. Development of graduate data mart uses Operational Data Store (ODS) and Dimensional Data Store (DDS) architecture. Those architectures are selected because they support transactional level. By using those architectures, graduate data mart is capable to display the data of graduates on the academic year, GPA, educational years, and the status of the student transfers. As the result, those data are able to help the management of university in filling up the accreditation form.
Co-Authors Abdul Eriawan Nahar Abyiyansyah Meidy Laksono Ade Prasetyo Ramadhan Andhika Putra Setianto Aprilia Kurnianti Aprilia Kurniawati Arifia Kasastra R Asep Setiawan Badrahini Masajeng Respati Bhaskoro Wicaksono Cahya Damarjati Cut Maya Putri Audilla Dita Kurniasari Eko Prasetyo Fadhan Anwarodin Haris Setyawan Haris Setyawan Hidayatul Fitri Lusiana Ning Saputri Marsa Sefta Lesmana Minhajuddin K. Abdurrahim Mufaddal Al Baqir Muhammad Aldi Joko Satria Perdana Muhammad Alfadha Termahadi Mukhtar Hanafi Nadiyah Maharty Ali Nasrul Saefullah Nurfahmi Nurfahmi Pascal Pahlevi Pasha Rammadhany Rammadhany Reza Giga Isnanda Rohmana Zulfa Bakhtiar Ronald Adrian Samsul Arifin Slamet Riyadi Slamet Riyadi Sofran Bahrurozi Suparman Suwadi Suwadi Teuku Syauqi Maulana Yuda Fatah Kurniawan