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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Yuli Astuti
Universitas AMIKOM Yogyakarta, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Firebase Realtime Database Pada Aplikasi Media Informasi dan Pendaftaran Training IT Berbasis Android Angga Arindra Shonta; Laily Nur Hamidah; Muhamad Hasan; Melany Mustika Dewi; Yuli Astuti; Irma Rofni Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4040

Abstract

Jogja IT Training Center (JITC) is a business entity that provides training in the field of information technology. JITC provides various kinds of related training. As a business entity engaged in the service sector, the dissemination of information is very important. Currently, JITC uses social media Facebook and Instagram to convey information to the public, but currently, it is not managed properly due to limited resources so the delivery of information and student registration is hampered. The student registration method is done manually and has to come to the office, so the number of students has decreased and resulting in a decrease in income. The conventional registrar data collection has many shortcomings, namely the data is not well documented, resulting in data loss and the search process takes a lot of time. Based on the existing problems, an android-based information and registration media application was built, this mobile application added the Firebase Realtime Database as a data storage platform for information and registrant data so that the training data can be accessed online and in real-time which can be integrated into one service. Firebase Realtime Database has many features and is accessed for free and has a fairly large data storage limit. The result of this research is an android-based application for information providers and registration services for new students.
Implementasi Metode SMART (Simple Multi Attribute Rating Technique) Pada Sistem Pendukung Keputusan Pemberian Kredit Pinjaman Wildan Muhammad Ardana; Irma Rofni Wulandari; Yuli Astuti; Lilis Dwi Farida; Wiwi Widayani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4333

Abstract

Koperasi Simpan Pinjam (KSP) Tamanmartani Sejahtera is an operational savings and loan cooperative that utilizes funds from members in the form of savings and then flows back to members in the form of credit or loans. KSP Tamanmartani Sejahtera's main problem is arrears or customer credit payment delays. If it is allowed to continue, it will make it difficult for cooperatives to develop. One way to reduce delays in credit payments is to select prospective customers selectively. When choosing prospective customers, managers often experience difficulty making credit request decisions. Managers must consider and analyze the background of the credit application. The provision of credit to prospective customers must meet the standard criteria the cooperative sets. Based on these problems, to provide maximum recognition, it is necessary to have a system to help managers determine customer credit decisions. A Decision Support System (DSS) is a system that can provide information and manipulation data to assist decision-makers. One can use many choices, one of which is the Simple Multi-Attribute Rating Technique (SMART). The SMART method can help solve complex problems based on the difficulties encountered in KSP Tamanmartani Sejahtera. The results of this study are The SMART method was successfully implemented into a website-based system and displayed the ranking of customers who deserved to be given a loan. The test results with Black Box testing show the system can run according to a predetermined design.
Analisis Sentimen Review Produk Skincare Dengan Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO) Tri Astuti; Yuli Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4119

Abstract

Skin care products have become the main needs of all people who are the targets of various brands of skin care products. However, not all skin care products have good quality according to consumer needs. They look for products that have the best quality by looking at reviews from other people, so they have an idea that influences their interest from other people's reviews submitted through various marketplace platforms or social media regarding the results after using these skin care products. Sentiment analysis is one way to analyze and classify reviews into positive opinions and negative opinions regarding the product in question to look for product quality based on public views. The algorithm used in this research is the Naive Bayes Classifier. The Naive Bayes Classifier method was chosen for reasons of ease of implementation, fast and high accuracy. The Naïve Bayes method also has a disadvantage, namely it is sensitive to feature selection, which results in low classification accuracy. Therefore, in this study, the feature selection method, namely Particle Swarm Optimization, was used in order to increase the accuracy of the Naïve Bayes classifier. The dataset used is 800 data reviews and tested using 10-Fold Cross Validation. The results showed an increase in accuracy from 77.96% to 79.85%.
Co-Authors Angga Arindra Shonta Irma Rofni Wulandari Laily Nur Hamidah Lilis Dwi Farida Muhamad Hasan Tri Astuti Wildan Muhammad Ardana Wiwi Widayani