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All Journal Elkom: Jurnal Elektronika dan Komputer Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Technologia: Jurnal Ilmiah Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech JUSTIAN - Jurnal Sistem Informasi Akuntansi Profitabilitas Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Penelitian dan Pengabdian Masyarakat Jotika Jurnal Komputer dan Teknologi (JUKOMTEK) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Indonesian Community Service Journal of Computer Science (IndoComs)
Lady Agustin Fitriana
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Library & Information Science Other

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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.
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Zikirlah, Hikmawan Alvin; Iltavera Paula; Muhammad Fazilla; Riski Annisa; Lady Agustin Fitriana
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

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 indicate 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.
PELATIHAN DASAR LARAVEL UNTUK WEB DEVELOPER PEMULA PADA YAYASAN AL MADANI SYARIF ABDURRAHMAN PONTIANAK Reza Maulana; Deni Risdiansyah; Lady Agustin Fitriana; Mohammad Kamal Reza
Indonesian Community Service Journal of Computer Science Vol. 1 No. 2 (2024): Periode Juli 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v1i2.3811

Abstract

Dalam era digital yang berkembang pesat, keterampilan dalam pengembangan web menjadi semakin vital. Kebutuhan akan web developer yang mumpuni terus meningkat seiring dengan permintaan akan website yang fungsional dan menarik untuk berbagai keperluan seperti bisnis, pendidikan, dan organisasi. Keterampilan web development mencakup sejumlah tugas, termasuk desain UI/UX, pengembangan backend, integrasi database, keamanan web, dan optimisasi kinerja. Mengembangkan kemampuan dalam pengembangan web tidak hanya membuka peluang karir yang luas, tetapi juga memungkinkan ekspresi kreativitas dan inovasi. Laravel, sebagai framework PHP populer, menyediakan lingkungan pengembangan yang ramah dan fitur-fitur kuat yang mudah dipahami, menjadikannya pilihan ideal bagi pemula dalam memulai perjalanan mereka dalam pengembangan web. Selain itu, dengan kemajuan dunia digital, tenaga pendidik juga perlu memiliki keterampilan dalam pengembangan web untuk meningkatkan pengalaman belajar siswa. Dalam rangka merespons kebutuhan tersebut, Universitas Bina Sarana Informatika akan menyelenggarakan kegiatan Pengabdian Masyarakat berupa Pelatihan Dasar Laravel Untuk Web Developer Pemula Pada Yayasan Al Madani Syarif Abdurrahman Pontianak, sebagai bagian dari upaya untuk meningkatkan produktivitas dan kualitas pendidikan di era digital.
PEMODELAN ANALISIS SENTIMEN ROBLOX MENGGUNAKAN ALGORITMA MACHINE LEARNING Agnes, Veronika; Mutia Sari, Elsa; Annisa, Riski; Agustin Fitriana, Lady
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.505

Abstract

The rapid advancement of digital technology has fostered the rise of various interactive online gaming platforms, with Roblox standing out as one of the most prominent. This platform allows users not only to play but also to design and share their own games. As the number of active users increases, the volume of reviews submitted on the Google Play Store also grows. These reviews contain valuable information but require sentiment analysis to automatically understand users’ opinions, satisfaction levels, and complaints. This research aims to conduct sentiment analysis on Roblox user reviews by comparing the performance of three machine learning algorithms—Naïve Bayes, Random Forest, and Decision Tree—to determine which yields the most optimal results. The study follows the Knowledge Discovery in Databases (KDD) framework, which includes several stages: selecting 5,000 reviews, performing text preprocessing (such as cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and labeling), transforming data using word embedding, and evaluating model performance with metrics including Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The experimental findings indicate that the Decision Tree algorithm achieved the best performance, with an accuracy of 85%, precision of 0.847, recall of 0.850, and a weighted F1-score of 0.848. In contrast, Random Forest obtained an accuracy of 83.6% and a macro F1-score of 0.773, while Naïve Bayes recorded the lowest performance with 64.2% accuracy and a macro F1-score of 0.527. Overall, the Decision Tree algorithm demonstrated superior capability and balance in classifying positive, negative, and neutral sentiments in Roblox user reviews, showing more effective text pattern recognition compared to probabilistic-based methods.
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.
Penerapan Metode EDAS dalam Sistem Pendukung Keputusan untuk Pemilihan Software Akuntansi Agustin Fitriana, Lady; Lailiah, Badariatul; Saadah, Rabiatus; Dahlia, Rizka
Jurnal Sistem Informasi Akuntansi Vol 7 No 1 (2026): : Periode Maret 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v7i1.12522

Abstract

Selecting appropriate accounting software is a significant challenge for organizations due to diverse functional attributes. Suboptimal choices can adversely affect operational efficiency and financial management quality. This study develops a Decision Support System (DSS) using the Evaluation Based on Distance from Average Solution (EDAS) method to determine the optimal software choice among five alternatives: Zahir, Accurate, Mekari Jurnal, SAP Business One, and Kledo. Data were collected via questionnaires from 30 respondents, including accounting practitioners and active users. The evaluation focused on five key criteria: software pricing, ease of use, feature completeness, system integration, and technical support. The EDAS method evaluated these alternatives by calculating their deviation from the average solution through Positive Distance from Average (PDA) and Negative Distance from Average (NDA), resulting in a final Appraisal Score for ranking. The results show that EDAS produces a clear, discriminative ranking. Zahir achieved the highest score (0.798), followed by Mekari Jurnal (0.779), SAP Business One (0.553), Kledo (0.500), and Accurate (0.444). These findings demonstrate that the EDAS approach effectively supports objective multi-criteria decision-making and possesses strong potential for implementation in digital-based recommendation systems for accounting software selection, ensuring businesses make data-driven, efficient choices.
Performance Evaluation of the BERT Model in Sentiment Analysis of DANA Application User Reviews Hazael Susanto; Weiskhy Steven Dharmawan; Riski Annisa; Lady Agustin Fitriana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2359

Abstract

The rapid growth of digital wallets in Indonesia generates a large volume of user reviews on platforms such as the Google Play Store that cannot be efficiently analyzed manually. This study aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) model in sentiment classification tasks on a dataset of DANA application user reviews collected from the Google Play Store. The BERT model is fine-tuned using labeled Indonesian-language data with three sentiment classes: positive, negative, and neutral. Specialized preprocessing strategies are applied to handle the characteristics of informal text, abbreviations, and code-switching phenomena prevalent in Indonesian user reviews. Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the fine-tuned IndoBERT model achieves an accuracy of 91.24% with a weighted F1-score of 0.91 on a test dataset of 6,106 samples. The Negative class achieves the highest performance with an F1-score of 0.95, followed by the Positive class (0.88) and Neutral class (0.84). This study provides empirical evidence of the effectiveness of the IndoBERT Transformer architecture for sentiment analysis in the Indonesian-language fintech domain and can serve as a reference for developing deep learning-based NLP systems in similar contexts.
Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews Frizi Olivian; Sahrul Bariyah; Grant Christo Budiyanto; Riski Annisa; Lady Agustin Fitriana; Weiskhy Steven Dharmawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2362

Abstract

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.
Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method Rai Markus Panamuan; Debi Handika; Muhamad Rizki Pratama; Weiskhy Steven Dharmawan; Lady Agustin Fitriana; Riski Annisa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2364

Abstract

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.
Co-Authors Abdul Latif Abdul Latif Agnes, Veronika Agung Sasongko Ali Mustopa, Ali Alvin Zikirlah, Hikmawan Badariatul Lailiah Dahlia , Rizka Debi Handika Fachrurozi, Ahmad Faddillah, Umi Fahmi Julianto, Muhammad Fatmawati, Jeni Fazilla, Muhammad Fazriansyah, Agung Frizi Olivian Grant Christo Budiyanto Hazael Susanto Iltavera Paula Latifah Latifah Mohammad Kamal Reza Muhamad Rizki Pratama Muhammad Fazilla Muhammad Ifan Rifani Ihsan Muhammad Rifqi Firdaus Mutia Sari, Elsa Nurmalasari Nurmalasari Paula, Iltavera Rai Markus Panamuan Reza Maulana Rifqi Firdaus , Muhammad Rindiani, Popi Risdiansyah, Deni Riski Annisa Rizka Dahlia Saadah, Rabiatus Sahrul Bariyah Septian Rheno Widianto Sugiyarto, Ipin Suhendra, Marwan Syarah Seimahuira Syarah Seimahuira Weiskhy Steven Dharmawan Wira Hadi, Sofian YANTO YANTO Zikirlah, Hikmawan Alvin