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Jurnal CoreIT
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 2460738X     EISSN : 25993321     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi published by Informatics Engineering Department – Universitas Islam Negeri Sultan Syarif Kasim Riau with Registration Number: Print ISSN 2460-738X | Online ISSN 2599-3321. This journal is published 2 (two) times a year (June and December) containing the results of research on Computer Science and Information Technology.
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Articles 5 Documents
 1 
Search results for , issue "Vol 8, No 1 (2022): June 2022" : 5 Documents clear
Determination of Discounts Using K-Means Clustering with RFM Models in Retail Business Zebua, Rina Sisca; Heroza, Rahmat Izwan; Adrian, Monterico; Atrinawati, Lovinta Happy
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (835.8 KB) | DOI: 10.24014/coreit.v8i1.14695

Abstract

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.
Computer Vision for Identifying and Classifying Green Coffee Beans: A Review Ligar, Bonang Waspadadi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.141 KB) | DOI: 10.24014/coreit.v8i1.17450

Abstract

Coffee is widely consumed around the world, also considered one of the most important beverages today.  Factors contributing to the quality of coffee beans such as color, texture, size, aroma, etc. and other processes along the production chain such as plant, roasting, and grinding. Those processes will be worthless if the quality of the coffee bean is low. It is important to only use the best quality coffee beans. Therefore, the challenge is to develop a system that uses computer vision to either identify high quality beans or classify them by their species to ease the effort needed by all actors in the supply chain. Providing information for end customers is a defining factor to push forward the coffee industry. This paper aims to review literatures within the topic of using computer vision for coffee beans. After reviewing a selected number of studies which corresponds with the topic chosen in our paper, computer vision techniques were used for two main reasons, identification and classification. Researches on this topic are still limited. Hence, it can be concluded that there are still plenty of room for study on this topic. This study also aims to help provide research material for future researchers.
Implementation of Google Translate Application Programming Interface (API) as a Text and Audio Translator Nabila, Zhara; Ayu, Humairoh Ratu; Surtono, Arif
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.517 KB) | DOI: 10.24014/coreit.v8i1.15629

Abstract

Through this translator program, it is craved that it can avail the general public to understand foreign language videos, can be useful in the world of education and technology, and can avail the persons with disabilities be up to communicate.The method used is a classification method that functions to detect the flow of shapes to instruct the class attribute as the task of the input attribute by generating automatic output through three stages, namely Machine Learning, Natural Language Processing, and Speech.The results showed that 90.38% of videos were successfully translated into text and audio, 9.62% of videos failed to be translated because the owner limited public interaction, and 89%-97% synchronization between text and audio.In this research, a text and audio translator program has been created using the Application Programming Interface (API). This program is a configuration of deep learning, machine translation, and text-to-speech designed using the high-level programming language python. The system used is a predictive system in which the system tries to predict the output equally the wishes of the user. 
Web-Based General Affair Information System Using Prototyping Method Amrulloh, Arif; Saintika, Yudha
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.505 KB) | DOI: 10.24014/coreit.v8i1.17029

Abstract

Abstract. Operations are one of the main activities in the company; almost all companies have a General Affairs (GA) section that takes care of all household and operational matters. The bigger the company, the more complex the problems faced. To overcome these problems, the role of technology is needed. In today's digital era, many technologies can be used to assist companies in administrative activities, one of which is a website-based application. Website-based applications have advantages over desktop applications because users only need a browser to access the application. This research will build a web-based general affairs administration system using the prototype method. The prototype is a method that requires the developer's interaction with the client so that it can overcome the incompatibility between the system developer and the client. Tests are carried out using black-box testing techniques focusing on checking system functionality. The results of tests conducted by 26 respondents show that the system built is 100% feasible and meets expectations. 
Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company Jayanti, Luh Putu Saraswati Devia; Wasesa, Meditya
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.211 KB) | DOI: 10.24014/coreit.v8i1.16915

Abstract

Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes

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