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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business JUITA : Jurnal Informatika International conference on Information Technology and Business (ICITB) Annual Research Seminar Journal of Information System JPSriwijaya Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal ULTIMATICS Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Informatik : Jurnal Ilmu Komputer IJID (International Journal on Informatics for Development) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) SEINASI-KESI Jurnal Riset Informatika CSRID (Computer Science Research and Its Development Journal) Jurnal Informatika Global CCIT (Creative Communication and Innovative Technology) Journal JSAI (Journal Scientific and Applied Informatics) Journal of Information Systems and Informatics Mulia International Journal in Science and Technical Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal AbdiMas Nusa Mandiri Jurnal Pendidikan dan Teknologi Indonesia Proceeding of International Conference Health, Science And Technology (ICOHETECH) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Abdimas Maduma JUKEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Aplikasi
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Universitas Sriwijaya
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Journal : Mulia International Journal in Science and Technical

 1 
Expert System Technology in Implementation of K-Means Clustering Algorithm in Patients with Tuberculosis at Cut Meutia Hospitals North Aceh Eva Darnila; Mutammimul Ula; Mauliza; Iwan Pahendra; Ermatita; Hardi, Richki
Mulia International Journal in Science and Technical Vol 2 No 1 (2019): August
Publisher : Universitas Mulia

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Abstract

Technology in detecting potential drop out tuberculosis (TB) in Cut Meutia hospital and Health Office plays a great role and has been very important. This is seen from the increasing number of patients who could not be cured succesfully and who do not care about TB which will have fatal consequences on their health. In addition, the main cause of the increase in the number of potential drop out TB patients is because of the lack of awareness of the community, especially the middle economic level family of the danger of TB disease as seen from the irregular treatment that they have and the continued smoking habit. In this study, an expert system was used to diagnose patients with potential Drop Out tuberculosis who were then diagnosed into the cluster of each TB patient using the K-Means algorithm. The system implementation in the expert system is that the initial symptoms include the question of whether the patient has cough with phlegm for 2-3 weeks or more (yes), has the patient been treated with TB drugs less than 1 month (no), experienced no appetite and nausea. From the results of these symptoms, there are diagnoses of New Patients, Pulmonary BTA (-) / Ro (+), with sub-acute level having moderate severity and duration, the severity can reduce the health status of the patient, the patient is eventually expected to recover and totally recovered the disease does not develop into a chronic disease. The results of this expert system would be entered into the K-Means clustering. The test results of the k-means clustering algorithm with K = 3 (C1, C2, C3). with initial centroid values of m1: C1, 5, 5, 5, 5, 5, 5 and m_2: C2, 3, 3, 3, 3, 3, with patient p1 with the value of each cluster (C1) = 6.928, ( C2) = 2.828, C3 = (4). For the closest cluster value is C2, then the BCV (Between Cluster Variation) calculation value is 19,596, and the WCV (Within cluster Variation) value is 144. Then the ratio value is 0.136. The result of the iteration -3 can be stopped because it does not experience the movement of the clusters and the clusters have been optimal. The results of this system can classify patients for each village and sub-district area so that the Hospital officials and the Health Office can directly monitor potential drop out TB patients and can facilitate the Head of Office/region in handling clustered TB patients using K-Means. Furthermore, in the coming years, it can be used as a tool in taking preventive measures.
Implementation of Clustering K-Means Algorithm classification of the need of Electricity power for each region at PT Lhokseumawe Muhammad Sadli; Wahyu Fuadi; Fajriana; Ermatita; Iwan Pahendra; Mutammimul Ula; Hardi, Richki
Mulia International Journal in Science and Technical Vol 2 No 1 (2019): August
Publisher : Universitas Mulia

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Abstract

PLN (State Electricity Company) is in charge of providing stock of needs for the grouping of electrical power and classification for each region in Lhokseumawe City. The area that were grouped based on the amount of power consists of the four subdistricts, namely Banda Sakti, Blang Mangat, Muara Dua and Muara Satu, each of which is sourced from the village. The importance of clusters is to separate each data between data in the villages that will be input into sub-district data. Furthermore, the K-Means Clustering Classification was used in determining the grouping of electrical power needs in each region in the Lhokseumawe City where this system classify the electricity stock needs in each region categorized into a cluster. In this study, Clustering Classification of K-Means variables include job (V1), overall income (V2), house area (V3), number of rooms (V4), number of electronic equipment (V5) and total of power usage (V6). Results of grouping of C1 system = Subsidy R-1/450 VA, C2 = Subsidy R-1/900 VA, C3 = Non Subsidy R-1/900, C4 = Non Subsidy R-1/1300, C5 = Non Subsidy R- 1/2200 VA. The purpose of this study is to be able to predict the classification of each electric power requirement for each region based on the input data per district. This has an impact on the community and PLN's stock of electricity needs in order to remain stable. It is found out from the Clustering K-Means Classification that there is a new cluster for Banda Sakti. The last step in determining Clustering K- Means stopped at the the iteration 3 until the cluster is optimal. The results of this study are in the form of grouping of PLN Customers from each region displayed in the system in the form of classification of electrical power in each subdistrictdistrict. Furthermore, the grouping can be recommended to predict the power needs of each sub-district and belong to the cluster provided by the PLN.
Co-Authors Abdiansah, Abdiansah Adi Sutrisman Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Sanmorino Aidil Putrasyah Al Farissi Albert Albert Aldin, Moehammad Ali Amran Ali Bardadi Ali Bardardi Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Andini Dwi Lestari Anita Desiani Apriansyah Putra Arnelawati Artika Arista Ayuputri, Niken Bambang Suprihatin Barlian Khasoggi Barlian Khasoggi Belly, Belly Nagustria Budi Prayoga, Muhamad Hafiz Cindo, Mona Dafid Dedik Budianta Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dien Novita Dominica, Alviona Terry Dwi Asa Verano Dwi Lestari, Rizky Dwi Meylitasari Br. Tarigan Dwi Rosa Indah Endah Patimah Endang Lestari Ruskan Endy Suherman Erwin, Erwin Eva Darnila Eva Darnila Fajriana, Fajriana Fajriana, Fajriana Fathiyah, Alyssa Fathoni - Fauza Adelma Syafrizal Fuadi, Wahyu Geovani, Dite Gumay, Naretha Kawadha Pasemah Hadipurnawan Satria Hartini Hartini Hijriani, Nurul Huda Ubaya Huda Ubaya Husnawati Husnawati Ika Oktavianti ina aisyah handayani Indra Maulana Irmanda, Helena Nurramdhani Iwan Pahendra Iwan Pahendra Anto Saputra Jaidan Jauhari Johannes Petrus Joko Purnomo Ken Dhita Tania Khairun Nisak, Novrinda Kurniawan, Rizky Fariz Andry Lovinta Happy Atrinawati M Fariz Januarsyah M. Fariz Januarsyah M. Miftakul Amin Mauliza Mauliza, Mauliza Megah Mulya Mgs Afriyan Firdaus Mira Afrina Mochamad Aryo Aji Kurniawan Mohammed Y. Alzahrani Mona Cindo Monterico Adrian Muhammad Adrezo Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Muhammad Sadli Muhammad Sadli, Muhammad Mutammimul Ula Mutia Fadhila Putri Mutiara Amalia Meizalina Noor Falih Noprisson, Handrie NUNI GOFAR Nurul Chamidah Nurul Mufliha Eka Putri Nurul Mufliha Eka Putri Octaria, Orissa Osvari Arsalan Pacu Putra Pahendra, Iwan Parwito Primanita, Anggina Purwita Sari Purwita Sari, Purwita Rachma nia Rahman, Puti Ayu Andhini Rahmat Budiarto Rahmat Izwan Heroza Rahmat Izwan Heroza Rendra Gustriansyah Reza Alfarezy Reza Firsandaya Malik Richki Hardi Rifkie Primartha Rizka Dhini Kurnia Rizka Dhini Kurnia Rizki Kurniati Royan Dwi Saputra Rudhy Ho Purabaya Ruth Mariana Bunga Wadu Safithri, Selviana Rizki Salamah, Fitri Samsuryadi Samsuryadi Shinta Puspasari Soraya, Atika Suci Destriatania Suci Destriatania Sukemi Sukemi Susan Dwi Saputri Susan Dwi Saputri Sytar, M. Hafizh Terttiaavini, Terttiaavini Tjahjanto, Tjahjanto Tryadriani, Rasqia Nurzulia Verano, Dwi Asa Verlly Puspita Wahyu Fuadi Wahyu Ningsih Yadi Utama Yadi Utama Yudha Pratomo Yudha Pratomo Yudha Pratomo Yundari, Yundari Zalika, Indah Zulkardi