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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Fitriana, Lady Agustin
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Application of the Finite State Automata (FSA) Method in Indonesian Stemming using the Nazief & Adriani Algorithm fitriana, lady agustin; Mustopa, Ali; Firdaus, Muhammad Rifqi; Dahlia, Rizka
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4038

Abstract

Language is a communication tool commonly used in everyday life. Each country has a different language with predetermined rules. For instance, in the Indonesian language, there are approximately 35 official affixes mentioned in the Big Indonesian Dictionary. These affixes include prefixes (prefixes), infixes (insertions), suffixes (suffixes), and confixes (a combination of prefixes and suffixes). In Information Retrieval, there is a stemming process, which is the process of converting a word form into a base word or the process of transforming variant words into their base form. The theory of language and automata is the foundation of the computer science field that provides the basis for ideas and models of computer systems. In the implementation of the research, several stages were carried out, such as explaining the Nazief & Adriani stemming algorithm, finite state automata, creating pseudocode, and testing using a web-based system, resulting in affixed words becoming the correct base words with 20 affixed words. The results obtained from reading this web-based system, the base word "cinta" (love) used as a test yielded accurate results in accordance with the concept of the Nazief & Adriani stemming algorithm. There are some weaknesses in stemming from suffixes, and the solution is to perform stemming from the prefix position (Prefix).
Penerapan Machine Learning untuk Analisis Sentimen Agoda dengan Algoritma KNN, Naive Bayes, dan SVM Rindiani, Popi; Fatmawati, Jeni; Wira Hadi, Sofian; Fazriansyah, Agung; Fitriana, Lady Agustin
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 4 No 2 (2025): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v4i2.232

Abstract

The rapid advancement of digital technology has significantly transformed the tourism industry, particularly in online hotel booking services such as Agoda. The large volume of user reviews available on this platform serves as a valuable data source for analyzing customer satisfaction and perceptions. This study aims to conduct sentiment analysis on 5,000 Indonesian-language user reviews from the Agoda mobile application by comparing the performance of three machine learning algorithms: K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM). Data were collected using a web scraping technique from Google Play Store and processed through several preprocessing stages, including cleaning, case folding, tokenization, word normalization, stopword removal, and stemming. Text representation was performed using the CountVectorizer method, with an 80:20 ratio of training and testing datasets. The experimental results show that the SVM algorithm achieved the highest performance with an accuracy of 84.1%, outperforming Naïve Bayes (65.3%) and KNN (61.7%). These findings indicate that SVM demonstrates superior capability in classifying positive, negative, and neutral sentiments in Indonesian text. The results of this research are expected to contribute to the development of sentiment analysis models and support service quality improvement based on user feedback.
Evaluation of Machine Learning Algorithms in Sentiment Analysis of the Satu Sehat Application Suhendra, Marwan; Lailiah, Badariatul; Yanto, Yanto; Fitriana, Lady Agustin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1816

Abstract

This study aims to analyze and compare the performance of three sentiment classification algorithms—Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN)—in classifying user reviews of the Satu Sehat application. The data preprocessing stage involves several steps, including text cleaning through normalization, removal of punctuation, numbers, and irrelevant characters, as well as the elimination of stopwords. Subsequently, stemming is performed to reduce words to their root forms. Feature extraction is conducted using the CountVectorizer method with a bag-of-words approach, which converts textual data into numerical representations. The dataset is then divided into training and testing subsets using an 80:20 train-test split ratio. Model performance is evaluated through a confusion matrix, producing key evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the results of testing 9,192 user reviews, the SVM algorithm with a linear kernel demonstrated the best overall performance compared to NB and K-NN, as indicated by the highest accuracy score. These findings suggest that SVM is more effective in handling high-dimensional textual features, making it a highly suitable algorithm for sentiment analysis of digital health application reviews, particularly those related to Satu Sehat.
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Alvin Zikirlah, Hikmawan; Fazilla, Muhammad; Paula, Iltavera; Annisa, Riski; Fitriana, Lady Agustin
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp228-235

Abstract

The rapid advancement of information and communication technology has significantly increased the popularity of online games in Indonesia, one of which is Mobile Legends: Bang Bang (MLBB) with millions of active users. The abundance of user reviews on digital platforms provides valuable data for analysis using text mining and natural language processing (NLP) approaches. Sentiment analysis is applied to classify user opinions into positive, negative, and neutral categories, offering insights into player satisfaction and perceptions of game quality. This study compares the performance of three classification algorithms Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) in analyzing sentiment from Mobile Legends user reviews on the Google Play Store. A total of 5,000 reviews were collected using the web scraping technique and processed through the Knowledge Discovery in Databases (KDD) framework, which includes cleaning, case folding, tokenization, normalization, and stopword removal. Sentiment labeling was performed using a lexicon-based approach with the InSet sentiment lexicon. The dataset was divided into training and testing sets with an 80:20 ratio and evaluated using accuracy, precision, recall, and f1-score metrics. The results show that the SVM algorithm achieved the highest accuracy of 88.1%, followed by KNN at 65.1% and NB at 62.6%. Thus, SVM is recommended as the most effective model for sentiment analysis of Mobile Legends user reviews.
Co-Authors Ali Mustopa, Ali Alvin Zikirlah, Hikmawan Badariatul Lailiah Fatmawati, Jeni Fazilla, Muhammad Fazriansyah, Agung Firdaus, Muhammad Rifqi Paula, Iltavera Rindiani, Popi Riski Annisa Rizka Dahlia Suhendra, Marwan Wira Hadi, Sofian YANTO YANTO