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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Artificial Intelligence and Data Mining Conference SENATIK STT Adisutjipto Yogyakarta

Ramdani, Ahmad Luky

Institut Teknologi Sumatera

Author-ID : 3863476

Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Articles

Monitoring system of student performance using data warehouse (Case study: INSTITUT TEKNOLOGI SUMATERA) Ramdani, Ahmad Luky; Hanifah, Raidah; Pilopa, Okta
SENATIK STT Adisutjipto Vol 4 (2018): Transformasi Teknologi untuk Mendukung Ketahanan Nasional [ ISBN 978-602-52742-0-6 ]
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Improving the quality of learning is one of the things that must be achieved in the college academic process. To achieve this, monitoring and evaluation of the results of the learning process is needed, namely by looking at student performance. Based on this, the research aims to develop a university data warehouse with student performance objects that will be used by the board application for the monitoring process. The application was successfully developed with several main features, namely: a) displaying the number of students based on year, region and the entrance to college, b) displaying a comparison of the number of students in each academic year based on student status , d) display student performance every academic year and e) KPI values based on needs analysis. These features have been tested using the blackbox approach and the test results show that the features work properly and produce outputs in corresponding to the test scenario.

Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework Ahmad Luky Ramdani; Taufik Djatna; Heru Sukoco
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.4624

The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced by other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had big size data and rapid changes. The results in identification and selection of user influence exhibited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.

Implementation of AD8232 ECG Signal Classification Using Peak Detection Method For Determining RST Point Martin Clinton Tosima Manullang; Jonathan Simanjuntak; Ahmad Luky Ramdani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

The medical world, especially those related to diseases and management of the heart uses ECG as a measurement tool. ECG has important points determined based on predetermined characteristics. The point is PQRST, where three of them are used as research objects in this paper. AD8232 is used as a research medium where the RST points must be determined in the AD8232 plot results by first determining the R points based on the highest peak. The results obtained were satisfactory wherein from 10 ECG graphic samples, 9 of them obtained RST point measurements which tended to be similar to conventional ECG measurements using millimeter paper as plotting media. Accuracy values reaching more than 90% indicate the reliability of the implementation results.

Clustering Application for UKT Determination Using Pillar K-Means Clustering Algorithm and Flask Web Framework Ahmad Luky Ramdani; Hafiz Budi Firmansyah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 1, No 2 (2018): September 2018
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Clustering is one of technique in data mining which has purpose to group data into a cluster. At the end, a cluster will have different data compared with others. This paper discussed about the implementation of clustering technique in determining UKT (Uang Kuliah Tinggal) / Tuition Fee in Indonesia. UKT is a tuition fee where its amount is determined by considering students purchasing power. Most of University in Indonesia often use manual technique in order to classify UKT’s group for each student. Using web-based application, this paper proposed a new approach to automatise UKT’s grouping which leads to give an reasonable recommendation in determining the UKT’s group. Pillar K-Means algorithm had been implemented to conduct data clustering. This algorithm used pillar algorithm to initiate centroid value in K-means algorithm. By deploying students data at Institut Teknologi Sumatera Lampung as case study, the result illustrated that Pillar K-Means and silhouette coefficient value might be adopted in determining UKT’s group

Co-Authors Firmansyah, Hafiz Budi Heru Sukoco Jonathan Simanjuntak Martin Clinton Tosima Manullang Pilopa, Okta Raidah Hanifah Taufik Djatna

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