Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : INFORMAL: Informatics Journal

Implementasi Metode Tabu Search Dalam Penjadwalan Menggunakan Analisa Pieces Made Suci Ariantini; Ayu Manik Dirgayusari
INFORMAL: Informatics Journal Vol 6 No 2 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i2.23811

Abstract

Nowadays, Scheduling subjects is one of the first steps for starting the teaching and learning process in educational institutions. To do so, The role of teachers and school staff is very important and not easy because it takes a long time to compile it. SMK PGRI 4 Denpasar is one of the schools located in the city of Denpasar which is located on Jalan Kebo Iwa No 8, Padangsambian Kaja, Denpasar, Bali. It is a vocational high school that has a tourism expertise and computer engineering study program. Based on current results of observations and interviews, the process of making the subject schedules that run at SMK PGRI 4 Denpasar is still being done using Microsoft Excel, this has resulted in frequent errors in managing schedules such as conflicting schedule and it takes a long time to correct it. Tabu Search is an optimization method based on local search, where the search process moves from one solution to the next by selecting the best solution which is not classified as a prohibited solution. It is a combinatorial optimization problem-solving method that is incorporated into local search methods. This method aims to streamline the process of finding the best solution of a large-scale (np-hard) combinatorial optimization problem. Tabu search method to optimize the process of making the subject schedule and combined using PIECES analysis (Performance, Information, Economic, Control, Efficiency, Services). From this analysis, several problems will be obtained, which in the end can be identified clearly and more specifically, so that we can conclude some suggestions that will help in designing a new and better system. The Tabu Search method can be used to optimize the process of making the subject schedules at SMK PGRI 4 Denpasar, so that the scheduling process will be more easier than using Microsoft Excel.
Analisis Sentimen Opini Publik Terhadap Konser Coldplay Di Jakarta Pada Twitter Menggunakan Metode Support Vector Machine Hidayatulloh, Fachmi; Andika, I Gede; Suryawan, I Wayan Dharma; Ariantini, Made Suci; Sudipa, I Gede Iwan
INFORMAL: Informatics Journal Vol 9 No 2 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i2.46046

Abstract

Advances in information and communication technology are changing the way humans obtain information. One of them is through social media Twitter. Twitter is a social media that is famous as a medium for general public opinion. There are many discussion topics that people respond to on Twitter. One of the topics on Twitter social media that is trending is related to the Coldplay Jakarta concert. The Coldplay Jakarta concert event received various responses from netizens. This is because the public is very enthusiastic about watching this British music group. However, public enthusiasm did not run smoothly due to the emergence of various phenomena and problems that occurred before and after this event was held. Therefore, the sentiments given are very diverse. Starting from negative sentiments to positive sentiments. The sentiment analysis process can find and solve problems based on public sentiment on Twitter. The dataset was obtained through a data crawling process using Google Colabs with the Python programming language. The total dataset obtained was 1,831 raw datasets. After that, the dataset is processed in data cleansing which aims to remove components that are not needed in sentiment analysis. Next, the dataset is labeled negative and positive. Then pre-processing is carried out on the data that has been previously labeled, and a word weighting process is also carried out using Term Frequency - Inverse Document Frequency (TF-IDF). After that, modeling was carried out using the Support Vector Machine classification method and the final process was test evaluation. The number of datasets obtained after going through the cleaning and labeling process is 1,000. The dataset is divided into training data and test data in a ratio of 80:20. The results obtained from the Support Vector Machine method show an accuracy percentage of 80.97%, precision of 81.59% and recall of 79.12. The sentiment test results from the test data were 107 positive and 93 negative