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All Journal Jurnal Ilmu Komputer dan Sistem Informasi Riau Jurnal Teknik Informatika
Dede Eko Saputro
Universitas Pamulang
Computer Science & IT Library & Information Science

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Optimalisasi Random Forest untuk Sentimen Bahasa Indonesia dengan GridSearch dan SMOTE Ahmad Fauzi; Agus Heri Yunial; Dede Eko Saputro; Reza Saputra
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.
Analisis Keputusan Pembelian Mobil Menggunakan Metode MOORA dalam Sistem Pendukung Keputusan Dede Eko Saputro; Herwis Gultom
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3269

Abstract

The purpose of this study is to analyze car purchase decisions using the Multi-Objective Optimization based on Ratio Analysis (MOORA) method. This method was chosen because it is able to handle various conflicting decision-making criteria. The study evaluated fifteen car alternatives based on seven criteria: price, fuel consumption, engine capacity, safety features, comfort, resale value, and CO2 emissions. Price and CO2 emission criteria were considered the most important, while other criteria were considered the most important. The analysis process begins with data collection and the application of criteria for value normalization for each alternative. Then, the final score is obtained by summing the normalization value of the maximized criteria and subtracting the normalization value of the minimized criteria. The results of the analysis show that the car that receives the highest score is the most suitable to buy. This research shows how the MOORA method functions in decision support systems and provides useful knowledge to consumers on how to make more informational and data-driven purchasing decisions.
Co-Authors Agus Heri Yunial Ahmad Fauzi Herwis Gultom Reza Saputra