19 research outputs found

    COMPARISON OF ACCURACY LEVELS OF SVM, DECISION TREE AND RANDOM FOREST ALGORITHMS IN SENTIMENT ANALYSIS OF USER RESPONSES OF THE GOPAY APPLICATION

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    The development of technology from time to time makes all work or activities easier, one of which is online money transactions which are called e-wallets or digital wallets. One of the digital wallet applications that is often used is GoPay, which is a platform and tool created for making digital payments. Not long ago, GoPay was separated into one application, which previously existed in the Gojek application. However, every application certainly has a negative side, such as GoPay, where to use the application you have to be connected to the internet, which creates dependence on smartphones. Based on this problem, the company needs to know the response of users of the GoPay application which has been launched using the SVM, Decision Tree and Random algorithms. Forest. Therefore, the aim of this research is to carry out sentiment analysis on the responses of GoPay application users after being separated from Gojek and to find out the comparison of evaluation results or accuracy produced by the three algorithms. The results of this research show that of the three algorithms used, Positive sentiment is more than Negative sentiment, where in SVM Positive 89% and Negative 85%, Decision Tree class Positive 89% and Negative 76% while in Random Forest class positive 93% and Negative 86 %. Apart from that, the Random Forest algorithm has a high level of accuracy, namely 90%, then the SVM algorithm 88% and the Decision Tree algorithm 84%

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    ANALYSIS OF THE LAPAK HIJAU BUSINESS MODEL IN THE SALE OF FURNITURE GOODS THROUGH THE E-COMMERCE PLATFORM

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    The problems researched concern how Lapak Hijau can optimize its operations, increase customer accessibility, and maximize profits through the integration of second-hand furniture sales in an e-commerce environment including business model analysis in the context of second-hand furniture sales through an e-commerce platform. Lapak Hijau is a company engaged in buying used goods from various sources, including hotels, restaurants, and factories and reselling them at a higher price. This research aims to analyze Lapak Hijau's business model in the sale of used furniture through an e-commerce platform focusing on business model changes that allow integrating the sale of used furniture through an e-commerce platform. The research method combines business model analysis with a literature review on e-commerce and stock management to identify the optimal strategy for Lapak Hijau in integrating second-hand furniture sales in an e-commerce environment. The results of this research provide an in-depth insight into effective business strategies for Lapak Hijau in taking on the challenges and opportunities in the e-commerce era. The conclusion of this research will outline practical recommendations for Lapak Hijau and similar businesses in enhancing their success and growth in the digital environment

    KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MENENTUKAN KUALITAS UDARA DI PROVINSI DKI JAKARTA

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    Air quality in DKI Jakarta Province refers to the state and cleanliness of the air in the area at a given time. The type and concentration of air pollutants are among the indicators used to assess air quality. DKI Jakarta's air pollution is severe, causing respiratory irritation, respiratory illnesses, and long-term health issues such as cardiovascular disease and lung cancer. Air pollution can also harm the environment by limiting visibility and harming ecosystems. The problem with the research is that no appropriate and relevant features for predicting air quality were used. The goal of this study is to identify and compare algorithms with the highest accuracy between decision trees. In determining air quality, naive Bayes and k-nearest neighbor are used. According to the findings of the K-5fold evaluation process performed with the RapidMiner tool, the accuracy of the Decision Tree algorithm was 95.89%, the accuracy of the Nave Bayes algorithm was 93.15%, and the accuracy of the K-NN algorithm was 91.78%. Based on these findings, the decision tree method has the greatest or best accuracy when compared to the Nave Bayes and K-NN algorithms

    PEMILIHAN KARYAWAN TELADAN MENGGUNAKAN METODE SAW DAN TOPSIS

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    Perkembangan teknologi yang pesat khususnya di bidang teknologi informasi telah memberi banyak dampak positif dan kemudahan dalam berbagai aspek. Pegawai dalam sebuah perusahaan haruslah memiliki dan memenuhi standar yang ditetapkan oleh perusahaannya. Maka kualitas karyawan didalam perusahaan juga harus diperhatikan, baik dari segi efektifitas karyawan dalam bekerja maupun evaluasi yang harus dilakukan untuk masa depan. Permasalahan yang terjadi dalam pemilihan karyawan teladan pada PT. Aerotrans Services Indonesia adalah belum adanya suatu aplikasi untuk pemilihan karyawan teladan. Oleh karena itu dibutuhkan suatu aplikasi sistem pendukung keputusan yang dapat mengangkat suatu kasus untuk menilai kinerja karyawan berdasarkan kriteria yang telah ditentukan perusahaan. Rumusan masalah yang akan dibahas adalah belum ada rekomendasi pendukung untuk sistem pendukung keputusan yang dijadikan sebagai alat bantu Manager HRD dalam menentukan pemilihan karyawan teladan. Metode yang digunakan untuk pemilihan karyawan teladan adalah metode Simple Additive Weighting (SAW) dan Technique for Order by Similarity to Ideal Solution (TOPSIS). Tujuan dari penelitian ini adalah bagaimana mengembangkan model Sistem Penunjang Keputusan dengan metode SAW dan TOPSIS terhadap penentuan karyawan teladan. Dari hasil uji sistem oleh user untuk 4 (empat) karakteristik yaitu Functionality, Reability, Usability, dan Efficiency. Rata-rata nilai menunjukkan 78,02%

    SISTEM PAKAR DETEKSI KERUSAKAN JARINGAN LOCAL AREA NETWORK (LAN) MENGGUNAKAN METODE BECKWARD CHAINING BERBASIS WEB

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    The form of computer network connection can be via cable or wireless such as fiber optic, microwave, wireless, or satellite. One type of computer network that is often used to connect personal computers and workstations in an office or an organization, company or factory for the use of shared resources is a local area network. The purpose of this research is to analyze, design and create an application that can detect damage to Local Area Network (LAN) networks. The research method used is backward chaining. The results of this study are applications that can detect damage to local area networks using the web-based backward chaining method. With this expert system application, it can speed up and make it easier to detect damage to Local Area Network networks
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