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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal IPTEK Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Teknologi Informasi dan Terapan (J-TIT) JEECS (Journal of Electrical Engineering and Computer Sciences) West Science Social and Humanities Studies Science Information System and Technology Semar: Jurnal Pengabdian Kepada Masyarakat

Eko Prasetyo

Universitas Bhayangkara Surabaya

Author-ID : 861361

Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Evaluation of Understanding of Safety, Health and Safety (K3) Using the Method Cumulative Voting (case Study of PT. Kencar Sukses Investama) Wildansyah Rokhmana Putra; Rani Purbaningtyas; Eko Prasetyo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 5 No. 1 (2020): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Every employee who works must understand safety in order to create a conducive work environment and Zero Accident. This study aims to create an Evaluation System for Understanding Safety, Health and Safety (K3) by using the Cumulative Voting Method so that it can optimize the quality of K3 in the company to be more effective and efficient. From the application trial results obtained the results of the validity test between manual data and application data have a difference in the results because the manual workmanship is calculated with a manual averagewithout any cumulativevalue of each item being tested. Application of K3 Comprehension Evaluation with Cumulative Voting Method can also prevent or minimize user input errors.

Clustering for Searching Type of House Suitable for New Consumer Candidates Using K-Means Clustering Method (case Study of PT. Maxima Jaya Perkasa) Wiwiet Herulambang; Eko Prasetyo; Azziyati Nur
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 4 No. 2 (2019): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

For some Indonesian people, housing is one of the secondary needs, so that in choosing the right housingmust be in accordance with the wishes of consumers. With the existence of PT. Maxima Jaya Perkasa, which waspioneered since 2012, in which the data on housing sales in the company has increased rapidly each year. Then datamining analysis can be done using the K-means Clustering method. K-means Clustering is a method of clustering nonhierarchicaldata which seeks to partition existing data into two or more groups. This method partitioned the data intogroups so that the data with the same characteristics were entered into the same group and the data with differentcharacteristics were grouped into other groups. This study uses data such as salary income, age, status, house pricesand mortgage payments. The results of this study were conducted twice using 12 training data training data and 100training data plus 1 as test data and obtained an accuracy value of 83% and error of 17%.

Development of Nutritional Fulfillment Funds for Pregnant Women Using Web-Based Sugeno Method Case Study: "NURANI" Maternity Clinic and Maternity Hospital Dita Mustika Anggraeni; Eko Prasetyo; M Mahaputra Hidayat
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 4 No. 1 (2019): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

The high rate of malnutrition in various regions and the increasing prevalence of obesity, especially in big cities, is a double burden of nutritional problems in Indonesia. Meanwhile, the utilization of health facilities by those lower classes people in particular is still low and the health service itself is far from optimal. Besides, the pregnant women are also reluctant to consult their nutrients experienced during pregnancy. From the above problems, an application which can help pregnant women in making nutritional decisions at the time of pregnancy without consulting to consult a doctor / nutritionist was finnally made, that is Sugeno Fuzzy method. This method can provide the right nutritional decisions for pregnant women. The purpose of this study is to assist and facilitate pregnant women in the fulfillment of nutrition during pregnancy. Input used in this study is BMI, age of pregnant woman and age of pregnancy. While the output of the end result of this application system is to provide decisions about the nutrients experienced by pregnant women, they are less nutrition, ideal nutrition and excessive or much nutrition. The results of the experiment gives the calculation; the error rate of 62 % and the accuracy of 38 %.

Design of Mental Disorder Consultation System with Decision Tree Method Ahmad Sarif Hikmawan; Eko Prasetyo; Rifki Fahrial Zainal
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 4 No. 1 (2019): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

level or category of disorders suffered by patients, so patients can be dealt with quickly according to the level of the disorder they suffer. Diagnosing the level of mental disorders using the expert system will record the symptoms of the patient and will diagnose the level of the disorder based on the knowledge obtained from an expert, the mental disorder expert system uses the Decision Tree method. in general is a system that seeks to adopt human knowledge to computers, so that computers can solve problems as they are usually done by experts or before consulting a psychologist without reducing the expert role of the psychologist or in other words expert systems are systems that are designed and implemented with help certain programming languages to be able to solve problems as experts do quickly and efficiently. It is hoped that with this system, lay people can be more sensitive in recognizing the level of psychiatry in person. As for the experts of this system can be used as an assistant or supporting the performance of psychologist officers. Based on the results of the system tests that have been done, the accuracy of 97.5% results and system error 2.5% and the percentage of each diagnosis, 32% psychosis, 27% Neurosis, 17% Learning Soldered, 12% Juvenile Delinquency and Growth Flower 10%.

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.

Eye Black Circle of Milkfish Segmentation on Hsv Color Space Eko Prasetyo
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

One of the popular fish consumed by the people is milkfish. The freshness of milkfish can be observed from eyecondition. maka segmentasi lingkatan hitam mata ikan bandeng penting dilakukan we conduct experiment in milkfishimage segmentation to get region of interest in eye circle. Kami mengusulkan frame work untuk segmentasilingkaran mata ikan bandeng menggunakan filter spasial pada komponen Hue dan Value ruang warna HSV. Byusing 10 milikfish images, we get segmentation performance with average of precision 84.04%. But we get badperformance in recall, because achieve recall 43.08%.

Instant Cement Forming Using Holt-Winter (case Study: CV Trijaya Abadi) Devit Hari Firmanto; Eko Prasetyo; Mas Nurul Hamidah
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

CV.Trijaya Abadi is an industry that produces cement, and make various innovations by producing instant cement. Itis often the case with errors in doing the forecasting is if the amount of production is produced too much while thedemand is small it will cause losses for the company as well as vice versa if the demand a lot while the productionwill be a bit disappointment of consumers resulting in the company losing konsakuya. know the amount of instantcement production in the next period. The method used for forecasting in this research is Exponential SmoothingHolt-Winters method with multiplyative seasonal method and additive seasonal method. The alpha, beta and gammavalues used are 0.9, 0.1, and 0.1. With the value of these parameters are able to produce the value of MSEamounting to 52347.63 and MAPE value of 6,649 is forecasting in 2016 for multiplyative seasonal method. Foradditive seasonal method, the value of MSE is 50560.88 and MAPE value of 6,619 forecasting in 2016. So it isconcluded that it is more accurate to use the Holt-Winters additive seasonal method in 2016 forecasting of instantcement.

Clasification System of Library Book Based on Similarity of The Book Title Using K-Means Method (case Study Library of Bhayangkara Surabaya) Arif Mardi Waluyo; Eko Prasetyo; Arif Arizal
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

In the grouping of book data in the library of Universitas Bhayangkara Surabaya at this time, the grouping is stillbased on the title and the existing field. So that resulted in the laying of some books whose title is not in accordancewith the field of place. To facilitate the grouping of library books, in this research will provide a solution by doingthe grouping of books based on the similarity of the title using K-Means method with the distance dissmilarity. Thedata are grouped a number of 500 titles in the library of Bhayangkara University Surabaya. The data will beprocessed through the Pre-processing process first of each book title by using the Information Retrieval Systemwhich results in the basic word. The basic word that will be used as a feature in the process of grouping so that canbe known similarity. The result of the research is that it can be concluded that the application of Library BookGrouping System Based on Similarity of Book Title Using K-Means Method (Case Study of Bhayangkara LibrarySurabaya) is suitable for data that has been specified on each title. And some processes there are clusters that arealways consistent in putting the book data in accordance with the similarity. Of all test results that have the bestsilhouette value is on using the value of K = 7, ie in the process to 1 with the value of silhouette = 0.2221

Implementation of Naive Bayes Method in Classification of Breast Cancer Disease Alamsyah; Eko Prasetyo; R Dimas Prasetyo
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

Less knowledge of early symptoms of breast cancer and how to deal with it early and the number of specialist doctorswho are still limited is one factor contributors because of the increasing number of people affected by breast cancerdisease. The development of breast cancer disease classification system aims to predict the early diagnosis of breastcancer disease in users or patients into two categories of malignant or benign. The initial diagnoses of this systemprediction variable include Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion,Single Epithelial Cell Size (Single Epithelial Cell) Size), Bare nuclei, Bland Chromatin, Normal nucleoli, Mitosis Usingthe naive bayes method to process diagnostic data in patients, the results of this system test show that the system isable to predict and classify breast cancer disease into two categories (malignant or benign) with the amount of datatesting of 500 data. With the output of malignant or YA and benign or NO, the system is able to predict with an accuracyvalue of 98%.

System Prediction Production PT.Vico Indonesia Using Method Holt Winters Riyan Sukma Sasongko; Eko Prasetyo; 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

Problem that Taken in this study is the process of forecasting oil and gas production in accordance so that companiescan know the prediction of the amount of oil and gas in the future. The method used to determine production predictionis Holt-Winters forecasting method. In testing the system will do the comparison of alpha, beta and gamma. Using thealpha value = 0.2, beta = 0.1 and gamma 0.5 to get better multiplicative forecast for oil and gas data. And to get thesmaller error difference compared to the smaller alpha (α), beta (β) and gamma (γ) then the smaller the differencewill be. The Multiplexative Spring Method and the Seasonal Additive Method are good enough for oil and gasproduction data

Co-Authors Achmad Irfan Ferdiansyah Achmad Kiki Qushayri Wahyu Kusuma Adiananda Adiananda Afif Nuzia Al-Asadi Agus Mahmudi Ahmad Sarif Hikmawan Alamsyah Amirullah, Amirullah Anis Suryaningrum Ari Tria Widagda Arif Arizal Arif Mardi Waluyo Azziyati Nur Bambang Teguh Wicaksono Budi Endarto Chastine Fatichah Devit Hari Firmanto Diana Rapitasari Dita Mustika Anggraeni Efilah Risqi Maulana Erwin Dwi Riyanto Fardanto Setyatama Haryono Haryono Ida Lailatul Qurnia Indah Epriliati M Mahaputra Hidayat M. Febryansyah Hadi Putra M. Mahaputra Hidayat Mas Nurul Hamidah Mochammad Muchid Nanik Suciati R Dimas Adityo R Dimas Prasetyo R. Dimas Adityo Rahmawati Febrifyaning Tias Rahmawati Febriyaning Tias Rani Pubaningtyas Rani Purbaningtyas Rifki Fahrial Zainal Riyan Sukma Sasongko Rizka Ardiana Fitrianti Sona Ahmad Susanto Sutrisno Syariful Alim Wahyu Dyah Rizki Septiana Wildansyah Rokhmana Putra Wiwiet Herulambang

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