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

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v2i2.158

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v2i1.165

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v2i1.168

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v1i2.175

Abstract

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.
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 | 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 | 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.
Educational Data Mining for Mapping Student Ability Based on School Location Using Apriori Method Case Study : SMK YPM Sidoarjo M. Mahaputra Hidayat; Rifki Fahrial Zainal; Andi Alfian Efendi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 2 (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.v8i2.6

Abstract

With the advancement of information technology today, the need for accurate information is needed in everyday life, so that information will become an important element in the development of society today and in the future. However, the high need for information is sometimes not matched by the presentation of adequate information, often the information still has to be re-excavated from very large amounts of data. Traditional methods of analyzing existing data, cannot handle large amounts of data. Basically Senior High School is programmed for those who continue to a higher level, while the provision of skills can be said to be non-existent. Vocational High Schools can produce quality graduates in terms of work skills, therefore currently many companies require graduates from Vocational Schools. The purpose of this study is to create an application to obtain useful information about mapping the value of subjects, especially English at YPM Vocational School in Sidoarjo with data mining techniques and Apriori method. From the results of system testing, it shows that there is still a lack of National Examination Scores for English in most YPM Vocational Schools in Sidoarjo.
Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Mas Nurul Hamidah; Rifki Fahrial Zainal; Rahmawati Febrifyaning Tias; Tio Kukuh Ardiansyah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Abstract

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.
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 Andi Alfian Efendi 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 Tio Kukuh Ardiansyah Tommy Ferdiansyah Wiwiet Herulambang