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All Journal Jurnal Informatika Universitas Pamulang Jurnal Ilmu Komputer dan Sistem Informasi
Yunial, Agus Heri
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Computer Science & IT Library & Information Science

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Analisa Perbandingan Algoritma Bubble Sort, Insert Sort dan Selection Sort Yunial, Agus Heri
Jurnal Informatika Universitas Pamulang Vol 10 No 1 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

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Abstract

The algorithm in a program describes the flow or sequence of the program. A good program must have a good algorithm, where an algorithm is said to be good, one of which is the best use of time in processing the program. A sorting algorithm is an algorithm that carries out the sorting process in the program. Some existing sorting programs include bubble sort, insert sort and selection sort. In this research, the processing time of the three algorithms will be compared in sorting 100-10,000 random data both ascending and descending which are arranged in 15 combinations of data sequences. The sorting process is carried out using the C++ programming language. From the test results it was found that for fully random data the bubble sort algorithm performs the longest sorting process and the selection sort algorithm performs the process with the fastest average time and for almost sorted data the bubble sort algorithm performs the longest sorting process and the insert sort algorithm performs the process with the fastest average time
Optimalisasi Random Forest untuk Sentimen Bahasa Indonesia dengan GridSearch dan SMOTE Fauzi, Ahmad; Yunial, Agus Heri; Saputro, Dede Eko; Saputra, Reza
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 2 (2025): Mei 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i2.207

Abstract

This research focuses on optimizing the Random Forest algorithm for sentiment analysis of social media x in Indonesian using TextBlob as a labeling tool, followed by the SMOTE data balancing technique and hyperparameter optimization with GridSearch. The data used was taken from 611 tweets with the keyword ukt (single tuition). Sentiment labeling using TextBlob produces 438 negative sentiments and 173 positive sentiments. The SMOTE method is used to balance the data by first dividing the data into 75% training data and 25% test data. Data vectorization using tf-idf. The Random Forest algorithm model was evaluated with an initial accuracy using split data of 73%, and cross validation evaluation with 10 k-folds produced an accuracy value of 75%. Optimization carried out with GridSearch hyperparameters succeeded in increasing the accuracy value to 74%, while cross validation evaluation using 10 k-fold accuracy was 89%. In this research, the SMOTE method was effective in balancing unbalanced data, and gridsearch hyperparameter optimization succeeded in increasing the accuracy value of the Random Forest algorithm in classifying social media sentiment x in Indonesian with automatic texblob labeling.
Co-Authors Fauzi Ahmad Muda Reza Saputra, Reza Saputro, Dede Eko