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Rifki Fahrial Zainal
Universitas Bhayangkara Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification of Diabetes Disease Using Naive Bayes Case Study : Siti Khadijah Hospital Ida Lailatul Qurnia; Eko Prasetyo; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 2 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.573 KB) | DOI: 10.54732/jeecs.v1i2.177

Abstract

Less knowledge about symptoms and how to treat the disease of diabetes mellitus as well as a number of specialist diabetes mellitus which is still limited is one of the causes of the growing number of people affected by the disease. Diabetes disease classification system development aims to predict the type of diabetes patient or user who already suffer from diabetes mellitus. Therefore this system is made to diagnose the type of diabetes through laboratory test results, namely in the form of gender, age, disease history, family history, systolic, diastolic tensi tensi, temperature, pulse, blood sugar, fasting blood sugar JPP and Random blood sugar. That is by using the method of naive bayes as a method to process data on the patient's diagnosis. Test results of this system indicates that the system is able to predict the type of diabetes in patients, from the amount of data as much as 200 patient data, with an output that is the form of Diabetes Without Complications, Diabetes Type II and Normal but obtained the lowest accuracy rating of 39% and the value of the highest accuracy of 80%.
Forecasting the Number of Brick Production Using the Method of Exponential Smoothing Holt-Winter (case Study: PT Sik Krian) Afif Nuzia Al-Asadi; Eko Prasetyo; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 2 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.953 KB) | DOI: 10.54732/jeecs.v1i2.178

Abstract

PT. SIK is an industry that produces a light brick type of brick. At a certain period, some companies are rising and the decline in demand which is quite significant. This research aims to know the condition of the company to overcome the overstock in the warehouse. The methods used to conduct forecasting in this research is a method of Exponential Smoothing Holt-Winter with seasonal multiplicative component and the addition of seasonal. The value of alpha, beta and gamma used is 0.6, 0.1, and0.5. With the value of the parameter is capable of producing the best MSE values with the value 1 in forecasting the year 2011 in October for seasonal multiplicative component, and the value of 0.006 in MAPE and the same month. For the addition of a seasonal best MSE values obtained on forecasting in 2013 in February with the value and worth of 5.016 MSE MAPE 0.013. The results of this research, the company was able to reduce the buildup of inventory and maximizing production for the coming period without having to fear a shortage of stock and overstocking.
Application of Certainty Factor Method to Web-based Expert System for Chicken Disease Diagnosis Adam Ridwan; M Mahaputra Hidayat; Rifki Fahrial Zainal; Rahmawati Febrifyaning Tias; Rangsang Purnama; Akhmad Najmul Irfani; Noer Firda Yuana Ridhawaty
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 1 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i1.5

Abstract

This research aims to design an expert system to diagnose diseases in chickens. This system is designed to assist farmers in identifying diseases in chickens accurately and quickly. This expert system was built using the Certainty Factor method. Chicken disease data is collected from trusted sources, and rules are made to support the diagnostic process. This application is used to assist users in identifying chicken diseases based on the symptoms they input. This expert system is tested to see its ability to provide accurate and useful diagnosis for users. Therefore, this expert system of chicken disease diagnosis can be a useful solution in the field of animal husbandry.
Co-Authors Achmad Irfan Ferdiansyah Achmad Kiki Qushayri Wahyu Kusuma Adam Ridwan Afif Nuzia Al-Asadi Ahmad Sarif Hikmawan Akhmad Najmul Irfani Allexandro Billy Sukarta Andriano Lukas Ani Dijah Rahajoe Arif Arizal Bachtiar Azhari Deddy Gita A.P Dina Zahrotul Fadhilah Eko Prasetyo Erwin Dwi Riyanto Fajrul Islam Fardanto Setyatama Febriardi Mahendra Ida Lailatul Qurnia Indah Sri Rahayu M Mahaputra Hidayat M. Mahaputra Hidayat Mas Nurul Hamidah Muhammad Rosyid Kurniawan Muhammad Saiful Irawan Mustofa Noer Firda Yuana Ridhawaty R Dimas Adityo Rahmawati Febrifyaning Tias Rahmawati Febriyaning Tias Rangsang Purnama Rani Pubaningtyas Rani Purbaningtyas Rizky Yudha Pramudhika Shagi Hisyam Al Fathony Syariful Alim Tommy Ferdiansyah Wiwiet Herulambang