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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

Data Benchmark pada Google BigQuery dan Elasticsearch Nisrina Akbar Rizky Putri; Widyawan; Teguh Bharata Adji
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 3: Agustus 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1334.058 KB) | DOI: 10.22146/jnteti.v10i3.1745

Abstract

Nowadays,the cloud is not only a data storage medium but can be used as a medium for managing or analyzing data. Google offers Google BigQueryas a platform capable of managing and analyzing data,while Elasticsearch itself is a search and analysis engine that can be used to analyze data using Kibana. Using a dataset in the form of tweets crawled through http://netlytic.org/,containing the hashtags #COVID19 and #coronavirus, the data will be analyzed and used to compare its performance with benchmarks. Benchmark is a process used to measure and compare performance against an activity so that the desired level of performance is achieved. Data benchmark is performed on both platforms to generate or determine the workload of the platforms. The result obtained in this study is that Google BigQueryhas superior results, both from the upload container for larger datasets than Elasticsearch and with two query testing models.The query management time on Google BigQueryis also shorter and faster than Elasticsearch. Meanwhile, the visualization results from these two platforms have the same percentage amount.
Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia SYAIFULLOH AMIEN PANDEGA PERDANA; Teguh Bharata Aji; Ridi Ferdiana
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 3: Agustus 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1241.472 KB) | DOI: 10.22146/jnteti.v10i3.1819

Abstract

Customer reviews are opinions on the quality of goods or services that consumers perceive. Customer reviews contain useful information for both consumers and providers of goods or services. The availability of a large number of customer reviews on the websiterequires a framework for extracting sentiment automatically. A customer review often contains many aspects, so the Aspect Based Sentiment Analysis (ABSA) should be used to determine the polarity of each aspect. One of the important tasks in ABSA is Aspect Category Detection. The application of Machine Learning Methods for Aspect Category Detection has been mostly done in the English language domain, but in the Indonesian language domain,there are still a few. This study compares the performance of three machine learning algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM),and Random Forest (RF),on Indonesian language customer reviews using Term Frequency-Inverse Document Frequency (TF-IDF) as term weighting. The results showthat RFperformsthe best,compared to NB and SVM,in three different domains, namely restaurants, hotels,and e-commerce,with the f1-scoresfor each domainare84.3%, 85.7%, and 89.3%.