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<![CDATA[OTOMATISASI SISTEM PRODUK DALAM PENGOLAHAN DATA PADA PROSES HIRING DI PT.PERTAMINA (PERSERO) ]]>
<![CDATA[Frieyadie, Frieyadie]]>;
<![CDATA[Faldanu, Chaidir Rahman]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2 ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v15i1.162
<![CDATA[One month hiring process of new workers if taken on average reaches 250-300 people, which consists of fresh graduates, experience and shipping workers (Ships). The hiring process is still done manually input into the system, so it is quite a lot of spending and human resources (HR). Reporting data and presenting data related to business processes within the company also need to be made better and informative. Therefore it is necessary to automate the system to be able to support the speed and accuracy in the hiring process. Automation is the initial general form of organizational change in the form of a tool for ease of daily work. The method used to answer, uses the TAM method. The purpose of this research is to find out how much automation on the SAP system can have an effect on working time, which is more than 8 hours a day so that excessive overtime occurs, to find out the system automation in SAP is able to efficiently process data recording that occurs repeatedly and to find out the system automation in SAP can reduce human error. The level of acceptance of SAP users towards Hiring Process Automation at PT. Pertamina (Persero) based on perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use and actual use as a whole reached 87.74%, included in the category of strongly agree.]]>
<![CDATA[PENERAPAN METODE SIMPLE ADDITIVE WEIGHT (SAW) DALAM SISTEM PENDUKUNG KEPUTUSAN PROMOSI KENAIKAN JABATAN ]]>
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 12 No 1 (2016): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2 ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v12i1.257
<![CDATA[HR management of a company greatly affect many aspects of determining the success of the work of the company. One very important process in the Human Resources Department (HRD) a company or entity that is the promotion of a promotion. In general, the promotion was given on the recommendation boss or work unit each based on the old work, the performance assessment and assessment of the behavior of an employee in performing their duties. For that it is necessary appraisal data processing employees who can help facilitate a supervisor and the human resources department to take a decision relating to the promotion of an employee promotions. Currently the company employee appraisal data processing is still performed with computerized excel, so the greater the risk of inputting errors given the number of employees very much and and it takes a relatively long time. It is also still often confusing information regarding the movement of formation of employees. The method used in determining promotion This promotion is Simple Additive Weight (SAW). Where this method is a weighted counting method or methods that provide certain criteria are weighted so that each value of the sum of the weights of the obtained results will be the final decision. Judging from the managerial aspects of the assessment can be developed with other criteria in accordance with the company's needs. Calculations using Simple Additive Weight, with reference to the criteria of employment, performance evaluation, and assessment of employee behavior, then elect an employee who will get promotion.]]>
<![CDATA[WEB SISTEM INFORMASI BERBASIS W2000 UNTUK DUKUNGAN PEMESANAN DAN PENJUALAN PRODUK SAFETY ]]>
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 10 No 1 (2014): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2 ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v10i1.468
<![CDATA[The problem faced by the marketing officer, in a conventional system in which the data collection order for the goods still using paper media carbonates quadruplicate and still handwritten by marketing staff. Documenting ordering goods still using paper media carbonates quadruplicate and still handwritten by marketing staff. Relatively frequent errors in the writing of the carbon and impenetrable in because the writing is less pressured and miscalculations because of misreading the code of goods. Data collection on the availability of goods in the form of name document stock less open. Documenting the availability of goods using Microsoft Excel. The purpose of this study so that sales order (SO) data is not handwritten anymore, but via online order monitoring by employees who are out of the office. Methods of analysis using qualitative methods, and methods W2000, used as an approach for web development information systems]]>
<![CDATA[PENGGUNAAN MODEL RAD UNTUK PEMBANGUNAN SISTEM INFORMASI PENJUALAN TIKET BUS ONLINE ]]>
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 10 No 2 (2014): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v10i2.479
<![CDATA[Daily activities PO Bus today still use the conventional system in booking tickets, the passengers came agencies Sales bus ticket and booked a ticket in accordance with the schedule provided and the number of seats desired or booked over the phone which will then be recorded in book reservations. This manual system, the company must provide a place or an office used to receive and record passengers who will book the tickets and must always be available employees to accept the ticket booking. Companies need a system that is more practical, efficient and accessible to everyone from everywhere to facilitate passengers in ticket reservations. The purpose of this study to make it easier for passengers in booking tickets, and simplify the recording of ticket reservation which in turn can increase the productivity and efficiency of time and place. The development model systems used RAD Model.]]>
<![CDATA[FAKTOR – FAKTOR YANG MEMPENGARUHI MUTU WEB TERHADAP KEPUASAN AKTIVITAS BELAJAR BAGI PENGGUNA WANITA ]]>
<![CDATA[Merlina, Nita]]>;
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 8 No 2 (2012): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septembe ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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<![CDATA[This study aims to determine the factors that affect the quality of the web to satisfaction of learning activities for female users. Final model obtained in this study approached the research UTAUT (Unified Theory Of Aceptual and Use Of Technology) with data analysis using Structural Equation Modelling (SEM) on the software Analysis of Moment Structure (AMOS) version 6.0, a causal relationship between these factors which affect the quality of web-user satisfaction for women adalahVariabel beajar Performance Expectancy effect on Symbolic Adoption means the higher the student achievement expectations tehadap the greater web of learning opportunities to receive an online learning web mentally, Variable Effort Expectancy effect on Attitude Toward Technology means the higher expectations tehadap student effort, the greater web of learning attitude to receive online learning web, Social Influence Variables no effect on the Attitude Toward Technology college student means that studying the web with online learning medium was not influenced by others but their own consciousness to be able to learn the web, Variable Faciliting Condition effect on Symbolic Adoption means the student will receive an online learning web fasilatas when supported by adequate, Variable Attitude Toward Technology effect on Symbolic Adoption means the better the level of technology acceptance more likely to receive an online learning web mentally.]]>
<![CDATA[PENGGUNAAN METODE PROFILE MATCHING UNTUK PEMILIHAN EOSH CAPTAIN TERBAIK PADA PT.COCA-COLA INDONESIA ]]>
<![CDATA[Mashyur, Riduan Syaiful]]>;
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v15i2.767
<![CDATA[Environment, Occupational, Safety and Health (EOSH) Captain is an activity created by the Safety Officer at PT. Coca-Cola Indonesia. To determine the best EOSH Captain for the first time and is still influenced by the Subjective factor in assessing the prospective EOSH Captain, the safety officer is sometimes difficult to determine the best EOSH Captain, due to the lack of criteria so that the inaccuracy of assessment results in errors in determining the selection of EOSH Captain, constraints Another factor is the search for supporting data for the EOSH captain selection criteria is hampered, because the safety officer must focus on the job desk of his work, resulting in the length of the decision-making process. In this study, to overcome the above constraints, a method called the Profile Matching method is used. This Profile Matching method can process and compare the actual data value of a profile to be assessed with the expected profile value so that competency differences can be known. The purpose of this research is to accelerate the decision-making process. The assessment process will be more accurate, resulting in the determination of the selection of EOSH to be more precise and correct. The assessment process will be more accurate, resulting in the determination of the selection of EOSH to be more precise and correct. The results of calculations using the Profile Matching method above have obtained the greatest value and become the best EOSH captain is employee 17.]]>
<![CDATA[IMPLEMENTATION OF THE SAW METHOD AS A DECISION SUPPORT FOR GIVING FEASIBILITY OF KUR ON BANK MANDIRI DRAMAGA BOGOR ]]>
<![CDATA[Frieyadie, Frieyadie]]>;
<![CDATA[Setiyawan, Riki]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v16i1.1302
<![CDATA[Currently, the public's interest is very high to get KUR, but it makes it difficult for banks to determine who is eligible to receive the KUR and in the process of giving credit using the "LOS" system but this system is still quite a time consuming to analyze customer data and the process requires consideration and good analysis from the leader, due to the high number of problem loans. The SAW method used in this study. The SAW method is able to simplify and accelerate the results of credit lending recommendations. The calculation results obtained by debtors who are very worthy given credit as much as 1 debtor (4%), decent debtors with low risk as many as 16 debtors (70%), and worthy of being given with high risk as much as 6 debtors (26%) The purpose of this study to know the process and requirements for granting business credit at Bank Mandiri Dramaga Bogor.]]>
<![CDATA[COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION ]]>
<![CDATA[Setiyorini, Tyas]]>;
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v16i2.1459
<![CDATA[Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions. This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.]]>
<![CDATA[COMPARISON OF APPLE IMAGE SEGMENTATION USING BINARY CONVERSION AND K-MEANS CLUSTERING METHODS ]]>
<![CDATA[Nurdiani, Siti]]>;
<![CDATA[Rezki, Muhammad]]>;
<![CDATA[Dahlia, Rizka]]>;
<![CDATA[Ihsan, Muhammad Ifan Rifani]]>;
<![CDATA[Frieyadie, Frieyadie]]>;
<![CDATA[Fauziah, Siti]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v17i1.2256
<![CDATA[Apples are quite popular consumption among the community and have different kinds of shapes and colors. Apples themselves have many nutrients and various vitamins including fat, as well as energy, carbohydrates, protein, vitamin C, vitamin A, vitamin B2, vitamin B1, and many more. Because of the variety of types of apples, it is difficult for people to distinguish between these types of apples. However, with the development of technology and sophistication, it is now possible to classify the types of apples using digital images. This study aims to segment the image of apples by comparing 2 methods at once to find out which method is the best. This process is an initial stage that must be done before classifying. From the comparison results of apple image segmentation with binary conversion methods and k-means clustering, it can be concluded that the best method is k-means clustering. Because it can segment the image of apples almost perfectly.]]>
<![CDATA[APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET ]]>
<![CDATA[amalia, Hilda]]>;
<![CDATA[Puspita, Ari]]>;
<![CDATA[Lestari, Ade Fitria]]>;
<![CDATA[Frieyadie, Frieyadie]]>
<![CDATA[ Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/pilar.v18i1.2714
<![CDATA[Student performance is the ability of students to deal with the entire academic series taken during school. Student performance produces two labels, namely successful and unsuccessful students. Successful students can graduate with excellent, excellent, and suitable performance labels. At the same time, students who have a label on average are students who get poor performance. Measurement of student performance is needed for every educational institution to take strategic steps to improve student performance. This study aimed to obtain a data mining method that worked well on student performance datasets. In this study, student performance datasets were processed, which had 11 indicators with one result label. Student performance datasets are processed using data mining methods, namely decision tree and nave Bayes, while the tool used for dataset processing is WEKA. The research results from processing student performance datasets obtained that the accuracy value for the decision tree method was 94.3132%, and the accuracy produced by the naive Bayes method was 84.8052%.]]>
