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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.35 KB) | DOI: 10.24014/ijaidm.v1i2.5126

Abstract

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
Application of Linear Regression Analysis Model on Early Warning System for Inefficiency of Electricity Usage Rahman Indra Kesuma; Hafiz Budi Firmansyah; Mahardika Yoga Darmawan
SENATIK STT Adisutjipto Vol 4 (2018): Transformasi Teknologi untuk Mendukung Ketahanan Nasional [ ISBN 978-602-52742-0-6 ]
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v4i0.258

Abstract

Recently the Indonesian people often get inefficiency of electricity usage. On the other side, in Indonesia, the electricity is mostly produced from steam power plant, which require fuel from non-renewable natural resources. So the highness of demand and the occurrence of inefficiency the electricity usage can increase the consumption of natural resource and the air pollution. Therefore, an early warning system are proposed in this study, become one of the various solution than can increase awareness of the people in efficiency of electricity usage. This system requires the input data of electricity usage in the last 6 months, that will be formed the electricity usage trend from each user using linear regression analysis. Furthermore, this trend will predict the electricity usage for next month, this is used as the limit to give the warning from the system. The outcome from this study is the system that can provide a warning to users if their electricity usage run over the certain limits.
PEMBANGUNAN SISTEM PAKAR UNTUK DIAGNOSIS PENYAKIT TULANG DAN SENDI Hafiz Budi Firmansyah
Journal of Science and Applicative Technology Vol 1 No 2 (2017): Journal of Science and Applicative Technology December Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (988.316 KB) | DOI: 10.35472/281487

Abstract

Abstract—In Indonesia the numbers of orthopaedic surgeons are still not able to cover all demand. Badan Pusat Statistik (2015) reported that there are merely two orthopaedic surgeons in Lampung province having to serve about 8.117.268 people. Meanwhile, based on Ministry of Health of Republic of Indonesia, the ideal ratio between doctor and people is 12.2 doctor for each 100.000 people. Besides that, sometimes the surgeons need to serve more than one medical facility. Consequently, the patients might feel unpleasant. The lack of human resources becomes the main reason for developing expert system for diagnosing orthopaedic diseases. The expert system is able to diagnose orthopaedic diseases as well as fracture, dislocation, osteoarthritis and osteoporosis. The system is developed by using forward chaining methodology. This methodology is suitable for identifying a disease based on their symptoms. The result shows that the expert can answer 45 % of questions by identifying 6 main symptoms without continuing to following symptom questions.