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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Iwan Pahendra
Teknik Elektro, Universitas Sriwijaya, Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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IMPLEMENTASI SISTEM PAKAR PADA PASIEN PENDERITA TUBERKULOSIS POTENTIAL DROP OUT DI RUMAH SAKIT CUT MEUTIA ACEH UTARA Eva Darnila; Mutammimul Ula; Mauliza Mauliza; Ermatita Ermatita; Iwan Pahendra
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v2i1.968

Abstract

The existence of a technology that identifies and controls patients with potential drop out TB disease which is increasingly rapid will be a top priority, especially for the health team in following up the success of treatment. In this study, an expert system was used to diagnose patients with potential Drop Out tuberculosis by using a Case Based Reasoning model to see patients with potential Droup Out. For variable names used are pulmonary smear patients (+), new patients, pulmonary smear (-) / ro (+), new patients, extra pulmonary, relapsed patients, re-treatment, default patients, re-treatment patients, failed patients and others -other. The last detection process is taken from the highest value obtained in the diagnosis of all the symptoms that have been witnessed. Based on the results of the application of the Expert System on Potential Drop Out Tuberculosis Patients at Cut Meutia Hospital in North Aceh based on the case code 31 with a detection system for the AFB (+) Lung Patient with its detection symptoms, the patient coughs with phlegm for 2-3 weeks or more. the results of sputum examination, patients who have been treated with TB drugs less than 1 month and TB patients on sputum examination, patients who have been treated with TB drugs less than 1 month, TB patients stop the treatment and TB patients return to the facility health service facilities with the highest case value of 0.6111 of all detection systems that have been tested.Keywords: Expert system,  CBS, TB
ANALISIS MODEL NAIVE BAYES UNTUK IDENTIFIKASI PENGGOLONGAN DAYA LISTRIK DI KOTA LHOKSUMAWE Muhammad Sadli; Fajriana Fajriana; Wahyu Fuadi; Ermatita Ermatita; Iwan Pahendra
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v2i1.971

Abstract

Electricity subsidy is provided for all 450 VA power household customers and 900 VA power household customers who are poor and disadvantaged. However, there are many facts that household customers with 450 VA power are capable and 900 VA power household customers consist of capable households, boarding houses or luxury rented. Households are able to use more electricity than poor households. This paper describe to the identification of household customers' electrical power in the Lhokseumawe city to facilitate PLN in classifying customer power by using the Naive Bayes method. Naive bayes value variables used in this study are: monthly income, highest diploma, last job, house area, subscription fee and government registered household. The classification of household customer power is grouped into three categories, namely low (450 VA down), medium (900 VA) and high (above 1300 VA).. Based on household customer data that is used as training data, the Naive Bayes method is able to classify the customer data tested. So the Naive Bayes method successfully predicts the magnitude of the probability of household electrical power with an accuracy percentage of 80%.Keywords: Electricity, Naive Bayes,  CBS, low birth weight, subsidy
PENERAPAN MODEL K-NEAREST NEIGHBORS DALAM KLASIFIKASI KEBUTUHAN DAYA LISTRIK UNTUK MASING-MASING DAERAH DI KOTA LHOKSEUMAWE Muhammad Sadli; Fajriana Fajriana; Wahyu Fuadi; Ermatita Ermatita; Iwan Pahendra
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 5 No 2 (2018): Jurnal Ecotipe, Oktober 2018
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/ecotipe.v5i2.646

Abstract

Klasifikasi kebutuhan daya listrik untuk masing-masing daerah sangat diperlukan agar dapat menggambarkan kondisi daya yang dibutuhkan. Hal ini sangat penting untuk pelanggan baru yang ingin mengetahui daya yang diberikan, sebaliknya pelanggan lama juga dapat melihat dan menurunkan daya atau menambah daya sesuai dengan kebutuhan. Adapun variable yang di gunakan pada penelitian ini adalah luas rumah, besaran daya listrik yang akan digunakan dan telah digunakan, pendapatan gabungan orang tua (kotor) / bulan, jumlah daya lampu yang ada dirumah, kemudian dilanjutkan dengan klasifikasi perkiraan daya listrik yang berikan. Klasifikasi yang digunakan adalah penentuan golongan Tarif/Daya R-1/450 VA subsidi, R-1/900 VA subsidi, R-1/900 VA-RTM (Rumah Tangga mampu) non subsidi, R-1/1300 VA non subsidi, dan Tarif/Daya R-1/2200 VA non subsidi. Selanjutnya untuk pengujian menggunakan data training sampel sebanyak 20 data sampel dari masing-masing pelanggan yang akan dilihat pengujiannya dengan tetangga yang paling dekat. Untuk sampel daya terdiri dari variable pengujian dan klasifikasi jenis pengelompokan. Pengujian K-Nearest Neighbors (KNN) untuk luas rumah nilai nya 3, besaran daya 3, pendapatan bernilai 2, jumlah daya keseluruhan, 3 dan konsumsi energi yang digunakan adalah 4. Hasil dari penelitian ini adanya aplikasi teknologi dalam model KNN dalam pengelompokan penentuan kebutuhan daya untuk masing-masing daerah di Kota Lhokseumawe.
Co-Authors Ermatita Ermatita Ermatita Ermatita Eva Darnila Fajriana, Fajriana Mauliza Mauliza Muhammad Sadli Muhammad Sadli, Muhammad Mutammimul Ula Wahyu Fuadi