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Rifki Fahrial Zainal

Universitas Bhayangkara Surabaya

Author-ID : 5987353

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Realtime Portable Music's Genre Classificator with The Kohonen (SOM) Methods Using Raspberry PI Wiwiet Herulambang; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 2 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Music genre is one of the digital music data that is determined to classify music based on all the characterequations of each type. The characteristics in question are usually seen from the frequency of music, rhythmicstructure, instrumentation structure, and harmony content that the music has. Classification of music genres inrealtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because itis done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quiteconcisely used as a portable media and is quite powerful for realtime data processing. Raspberry Pi is used as asound processing unit, music genre identifier, and information on the results of the introduction of the musicgenre. This system input is in the form of music sound (realtime), while the system output is information (text)about the music genre. Whereas for the process of recognizing the music genre, the Self Organizing Maps (SOM)type Neural Neural Network (JOM) method is also used, or also known as the Kohonen ANN Network. Thefeature extraction stage uses the Music Genre Recognition by Analysis of Text (MUGRAT) method, with ninefeatures related to the spectral surface of music, and six features related to beat / rhythm of music. MelFrequency Cepstral Coefficients (MFCCs) feature extraction process was carried out as input from theclassification process using the Self Organizing Map (SOM) method. The classification results using the SOMmethod give an accuracy value of 74.75%. Accuracy of classification results using training data as many as 400pieces which are divided into 4 musical genres amounting to 74.75%.

Shoes Sales Forecasting Using Autoregressive Integrated Moving Average (arima) (case Study UD.Wardana Mojokerto) Achmad Kiki Qushayri Wahyu Kusuma; Eko Prasetyo; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 2 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Shoes sales is increase of day by day along with growing trend in the society. This makes shoemanufacturers demand to fulfill the customer needs. UD. Ward as one of the shoe manufacturers in Mojokertocity trying to fulfill the customer needs efficiently in order that the make sales fit with production. To predictsales of shoes used Autoregressive Integrated Moving Average (ARIMA) method. ARIMA forecasting method isone of methods that According to historical data. Before go into the forecasting stage, differentiated the salesdata per day during the year 2015-2016 ACF and PACF formula used Whose function is to Determine the valueof p and q coefficient of the which will later be used in forecasting models in every formula that is AR , MA andARMA. Result of this research shows that for the marching band category Obtained the best models that is MAwith forecasting the result at the last period of 95.6432 and MSE of 472.4514. Obtained fashion category for thebest models of forecasting that is AR with the result at the last period of 57.1872 and MSE of 304.8306. Obtainedcategory for the best wedding that is AR models with forecasting the result at the last period of 21.4206 and MSEof 118.0681.

Real Time Monitoring System Eggtray Production Process PT.Sinar Era Box Gresik Muhammad Rosyid Kurniawan; Rifki Fahrial Zainal; R Dimas Adityo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 3 No. 1 (2018): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

PT. Sinar Era Box Gresik does not yet have a production process information system, so that all activities of production process process have not been well accommodated, for example production data file such as raw material mixing data, HPP (Cost of Production), production cost, raw material stock, production stock. Coordinator in data entry all aspects also not terakomodir well, and also data entry is still done manually and still have to meet with aspect concerned. System design is done by modeling language DFD (Data Flow Diagram) with system testing apply black-box testing with functional testing and error handling testing. While the programming language that is in use is PHP with framework CI (Codeigniter) and using database MySQL and the final result of the design is generating. RealTime Monitoring System Production Process Eggtray PT. Sinar Era Box Gresik is very helpful because it simplifies the employees and also the owner in monitoring the production process.

Forecasting for Book Classified on A Library by Using Single Exponential Smoothing (case Study : Library of Bhayangkara Surabaya University) Deddy Gita A.P; Rifki Fahrial Zainal; M Mahaputra Hidayat
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 2 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

To facilitate the addition of library collections of UBHARA libraries, in this study will provide a solution to fieldmanuals based on forecasting using MSE single exponential smoothing formula errors and RMSE errors. Data isforecast from 2012 to 2016, with the value of each field of economics, law, socio-political, and engineering. The datawill be processed through the pre-processing process before preparing the data to be forecast. In the calculationexample, the program uses data in 2012 and 2015, alpha value = 0.1 and is calculated from month 1 to month to 3months so it is estimated to 4. The result of the data obtained is borrowed book which has the highest data isEconomy. Because in every data the number of loan books looks more dominant economic data. In 2015 thecalculation shows the value of MSE error and RMSE error. The error value to determine whether the errorforecasting results is better or not. For 2015 forecast data to be displayed at the value of the error.

Classification of Scout Skills Using Naive Bayes Algorithm Rizky Yudha Pramudhika; Rifki Fahrial Zainal; Rani Purbaningtyas
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 2 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

In the development of skills and skills of students Scout often finds the problem is the difficulty of developing theskills of learners caused by mistakes in determining the skills dominated by learners. How you can do it to solve thatproblem is by making a class determination or classification of areas of expertise controlled by the learner. Thisstudy aims to build a system to determine the inner class Scout skills area using Naïve Bayes algorithm for ScoutCoach to determine the area of expertise of learners. So Scout Coach can develop the skills of learners inaccordance with the areas of expertise that are owned optimally. Assessment criteria used are the values of GeneralKnowledge, Scout Knowledge, Sign Language (Password, Morse, Semaphore), Node Bond, Pioneering, and HastaKarya. All assessment criteria are used numerically. The resulting classification is the students who are included inthe class Intelligence (Intelligence), Physical (Strength), or Creativity. The results of this study is a program that canperform calculations to determine skill class scout field. The Scouting Skill Classification Program using the NaïveBayes Algorithm has been tested and its accuracy or accuracy is 100%.

Decision Support System for New Employee Placement on An Office Department by Using Saw Fuzzy (case Study: CV.Kencana Abadi) Indah Sri Rahayu; Syariful Alim; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 2 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

CV. KENCANA ABADI is a company engage for sale Castrol Oil in which there are several departments of HRD,accounting, finance & tax (tax), lubricant general manager and IT. During this time to determine the placement ofnew employees to the appropriate department still using the manual system. So needed a system needs that can be adecision support system for placement of new employees to appropriate departments that help the HRD and GA inorder to decide the right position placement for new employees. The model used in this decision support system isFuzzy Multiple Attribute Decision Making (FMADM). This SAW method is chosen because it determines the weightvalue for each attribute, followed by a ranking process that will select the best alternative from a number ofalternatives. Based on the results of testing HRD departments are eligible received Fajar Ferdhina, the test resultsof the Finance department, Accounting, Tax Budi Setyawan eligible, the results of testing IT departments eligibleGatot Nugroho, and the results of testing department Lubrican General Manager eligible Imelda Oktavia F.

Prediction for Total Number of Lab Participants by Fuzzy Time Series Method (case Study: Information Engineering of Bhayangkara Surabaya University ) Febriardi Mahendra; Rifki Fahrial Zainal; Syariful Alim
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 2 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Forecasting is a way to estimate a future value with using past data. One method of forecasting is the fuzzy methodtime series. The purpose of this study is to predict the number of students practitioners follow Department ofInformatics University Bhayangkara Surabaya by using fuzzy method time series. The created app can be used topredict the next 1 year. If the actual data in the year predicted inputted, the application can predict the next yearagain. The prediction error rate is calculated using Mean Absolute Percentage Error (MAPE). From the test resultsin predicting the number of students followers 7 courses Practicum Informatics Engineering Bhayangkara Universityof Surabaya in 2010-2012 using the method proposed in this thesis for practicum PTI obtained MAPE value of20.50%, Practical ANP obtained MAPE value of 0.50%, Network Computer practicum obtained MAPE value at8.50%, practicum Database obtained MAPE value of 0.50%,Managemen Network Computer practicum obtainedMAPE value of 14.50%, practicum PKG obtained MAPE value of 0.84% and practicum PBO obtained MAPE valueof 0.21%. Based on the results of testing the data it can be concluded that the fuzzy time series method when used onmore data many, it will get the accuracy of better and precise forecasting values.

Decision Supplier Package System Using Fuzzy Subjective and Objective Integrated Weights Method (case Study: PT Garudafood Putra Putri Jaya) Muhammad Saiful Irawan; Rifki Fahrial Zainal; Syariful Alim
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 1 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

The purchasing department is the part that plays an important and influential role in a company even can be said mostof the business process comes from this section. Because of its nature as a procurement of goods and services then oneof its duties and responsibilities is to choose suppliers of procurement of goods and services for production operationalprocesses within the company. In this case PT Garudafood Putra - Putri Jaya often have difficulty in determining thebest product packaging supplier because of the performance instability of each supplier. For that the company needsa system whose purpose is to help decide the best supplier determination. Decision support system is a computer-basedinformation system that generates various decision alternatives to assist management in handling various structuredor unstructured problems using data or models. There are many methods used in a decision support system such asFuzzy Subjective And Objective Integrated Weights. Fuzzy can solve the problem of uncertainty in determining theweight of each supplier's criteria. The result of the implementation of this decision support system resulted in thesupplier of Mandhara Adhitama Utama as the best supplier. Where the results of calculations with Fuzzy SubjectiveAnd Objective Integrated Weights this supplier obtained the highest value with the number 1.742.

Grouping of Books Type Based by Time Borrowing (months) Using Fuzzy C-Means Algorithm. (case Study: Surabaya Public Library) Bachtiar Azhari; Rifki Fahrial Zainal; Rani Purbaningtyas
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 2 No. 1 (2017): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Book lending transactions are the main activities that take place in the Library. With the method of grouping, it willget what kind of books are most often borrowed. The method used is Fuzzy C-Means. This thesis discusses theapplication of Fuzzy C-Means method to classify book data based on lending per month and measure the effectivenessof the use of methods in the process. This application has been tested by producing 3 clusters and classify the time(month) where the frequency of borrowing done by visitors in the Library.

Item Arrangement Pattern in Warehouse Using Apriori Algorithm (giant Kapasan Case Study) Rifki Fahrial Zainal; Fardanto Setyatama
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

Giant is a retail company with supermarket format. Giant supermarkets should understand what are the items actually needed by their customers, particularly in easiness of choosing shop items. One of the method that can be used to analyze customer shopping behaviour pattern is analysis using the help of apriori algorithm. The analysis result, rules for item procurement are succesfully obtained. Rule that can be formed with minimum support and minimum confident highest values shows that the produced rule is {Sedap Mie Rasa Ayam Spc 69g  Cleo Air Minum Extra Oxygen 550 ml}. Based on the result, therefore Giant Kapasan should provide item Cleo Air Minum Extra Oxygen 550 ml when it sells Sedap Mie Rasa Ayam Spc 69g.

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

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