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Engineering, Mathematics and Computer Science Journal (EMACS)
Published by Universitas Bina Nusantara
ISSN : -     EISSN : 26862573     DOI : https://doi.org/10.21512/emacs
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics
Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol. 3 No. 1 (2021): EMACS" : 6 Documents clear
Klasifikasi Status Desa/Kelurahan DIY (Yogyakarta) Menggunakan Model Decision Tree (Studi Kasus Data Praktik Kerja Lapangan Politeknik Statistika STIS Tahun 2020) Apriliansyah Mahmud; Ana Pangestika; Annisa Putri Ramadhanty; Galang Madya Putra; Galuh Sri Natungga Dewi Susilo Putri; Rani Nooraeni
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6787

Abstract

Status desa/kelurahan menjadi sebuah hal yang penting guna mengetahui perkembangan pembangunan yang ada pada desa/kelurahan tersebut serta dalam melakukan evaluasi terkait kebijakan yang telah dibuat mengenai infrastruktur. Badan Pusat Statistik (BPS) telah melakukan proses klasifikasi dengan metode skoring. Oleh karena itu pada penelitian ini akan mengimplementasikan model decision tree dikarenakan indicator klasifikasi status desa/kalurahan yang digunakan BPS belum mengikuti perkembangan zaman. Dalam penelitian ini menggunakan data hasil Praktik Kerja Lapangan (PKL) Politeknik Statistika STIS tahun 2020 yang dilaksanakan di D.I.Yogyakarta. Diharapkan penelitian ini dapat menjadi metode alternatif untuk mengganti metode yang sudah ada. Hasil penelitian menunjukkan bahwa dari 438 desa/kelurahan model decision tree mampu mengklasifikasi secara benar 392 desa/kelurahan sesuai dengan status desa/kelurahan sebelumnya. Model ini memiliki tingkat kebaikan model (specificity) sebesar 90.32%, presisi model (precision) sebesar 87.5%, sensitivitas model (recall) sebesar 88.42%, serta F1 Score sebesar 87.95%.
Tableau Business Intelligence Using the 9 Steps of Kimball’s Data Warehouse & Extract Transform Loading of the Pentaho Data Integration Process Approach in Higher Education Indrabudhi Lokaadinugroho; Abba Suganda Girsang; Burhanudin Burhanudin
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6816

Abstract

This paper discusses about how to build a data warehouse (DW) in business intelligence (BI) for a typical marketing division in a university. This study uses a descriptive method that attempts to describe the object or subject under study as it is, with the aim of systematically describing the facts and characteristics of the object under study precisely. In the elaboration of the methodology, there are four phases that include the identification and source data collection phase, the analysis phase, the design phase, and then the results phase of each detail in accordance with the nine steps of Kimball’s data warehouse and the Pentaho Data Integration (PDI). The result is a tableau as a tool of BI that does not have complete ETL tools. So, the process approach in combining PDI and DW as a data source certainly makes a tableau as a BI tool more useful in presenting data thus minimizing the time needed to obtain strategic data from 2-3 weeks to 77 minutes.
Gamification for Increasing Learning Motivation of College Student Agnes Kurniati; Francisco Maruli Panggabean; Nadia Nadia; Thomas Galih Satria
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6843

Abstract

The purpose of this research is to build an attractive gamification system that could motivate college students as the user target by customized challenges with adjustable difficulty-scaled reward systems. The methods used for the research consists of problem identification, collecting and analyzing the data, problem formulation, solution and design creation, product implementation, and evaluation. StudyGO is a mobile application that has 2 main features that have gamification aspects which are focused and scheduled study. The application evaluation is done with questionnaire evaluation based on 5 measurable human factors. The majority results from the application feels very motivated and rewarded enough from this gamification system.
Analysis Inventory Cost Jona Shop with EOQ Model Abigail Vania; Hanni Yolina
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6847

Abstract

Jona Shop is located in Indonesia, Jakarta is currently having a problem. The problem is the shop’s owner thinks that the inventory costs are too big especially for a powdered drink which brand is “Nutrisari”. The author finishes an EOQ (Economic Order Quantity) model for minimize the inventory cost. EOQ model is an old model but a valid model which still used now. Even EOQ model is an old model, many researchers used EOQ model to minimize inventory cost until 50% or more than 50%. But the EOQ model has some assumptions and Jona Shop fulfilled all the assumptions in the EOQ model. The assumptions of EOQ model are demand is known and constant, the lead time is constant and known, only one product can be estimated, every order is accepted in one-time delivery and can be used right away, there is no backorder because run out stock, no discount, and the holding cost per year and the ordering cost per year are constant. The result of the EOQ model can save up to almost 90%.
Laplacian Integral of Particular Steiner System Alfi Yusrotis Zakiyyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6883

Abstract

The notion of a hypergraph is motivated by a graph. In graph, every edge contains of two vertices. However, a hypergraph edges contains more than two vertices. In this article use hyperedge to mention edge of hypergraph. A finite projective plane of order n, denoted by , is a linear intersecting hypergraph. In this research finite projective plane order is Laplacian integral.
Forecasting the Items Consumption in the Hotel Storage with the Autoregressive Integrated Moving Average Method Christopher Chandra; Alfannisa Annurrullah Fajrin; Cosmas Eko Suharyanto
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6979

Abstract

In this era, hotel has storage as a storing space for every kind of items. Items stored in the storage are items being used for the needs of the staffs, also for the needs of hotel’s operational. The item consumption is running smoothly with resupply. However, there are often mistakes in resupplying the items. For preventing those several mistakes, a reference is needed to be used for controlling the amount of items arrival (monthly) with minding the amount of items in the storage should be. The reference to be used is the forecast of the item consumption every month. Forecasting was being done with Autoregressive Integrated Moving Average (ARIMA) method. There are five steps needed to build the ARIMA model, such as plot identification, model identification, model estimation, choosing the best model, and prediction (forecast). The input variable to be used in this research is the rime series from the data of storage’s item consumption starts from January 2018 until October 2020, and the output variable is the result of the prediction of item consumption in the next period, such as in November to December 2020. The results is subtracted with the number of items left in storage to obtain the minimum amount of item to be entered for the month.

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