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All Journal Elkom: Jurnal Elektronika dan Komputer Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Technologia: Jurnal Ilmiah Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Infotek : Jurnal Informatika dan Teknologi Teknik: Jurnal Ilmu Teknik dan Informatika 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 Engineering Environmental Science Library & Information Science Other

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Evaluation of Machine Learning Algorithms in Sentiment Analysis of the Satu Sehat Application Marwan Suhendra; Badariatul Lailiah; Yanto Yanto; Lady Agustin Fitriana
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.
Penerapan Model LSTM dan CNN Untuk Klasifikasi Sentimen Pada Ulasan Aplikasi Roblox Lady Agustin Fitriana; Ipin Sugiyarto; Umi Faddillah
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.32848

Abstract

In the rapidly evolving digital era, online gaming platforms like Roblox have transformed into complex interactive social spaces where users interact, create, and co-build virtual experiences. This study aims to compare the performance of two deep learning architectures Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in classifying user reviews of the Roblox app from the Google Play Store into positive, negative, and neutral sentiment categories. The dataset comprises 5,000 Indonesian-language reviews collected via web scraping using the google_play_scraper library. Preprocessing involved text cleaning, case folding, tokenization, normalization of informal words, stopword removal, stemming, and lexicon-based sentiment labeling. Data were converted to numerical representations using Tokenizer and padding, then split into 80% training and 20% testing subsets. CNN achieved superior performance with 89% accuracy, 0.88 precision, 0.81 recall, and 0.83 F1-score, outperforming LSTM (86.60% accuracy, 0.80 precision, 0.82 recall, 0.81 F1-score). CNN effectively extracts spatial patterns in text, while LSTM captures temporal word dependencies. This research affirms CNN's superiority for short Indonesian text sentiment analysis, provides a deep learning benchmark for gaming app reviews, and offers practical implications for Roblox developers to interpret user feedback for feature enhancements.
Implementasi Sistem Informasi Rapor Digital (SIDORAL) Berbasis Web pada SMA Kapuas Pontianak Anggi Basifatul Rahma; Erica Gracila Muntu; Eri Bayu Pratama; Lady Agustin Fitriana
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6940

Abstract

Report card management is a crucial part of the academic process in schools. However, at SMA Swasta Kapuas, the grade recapitulation process is still largely conducted semi-manually using separate spreadsheet applications. This mechanism triggers human error risks, slows down report card compilation, and complicates data retrieval. This study aims to build a web-based Digital Report Card Information System (SIDORAL) to integrate academic data management effectively. The system was developed using the CodeIgniter 4 Framework with Model-View-Controller (MVC) architecture and MySQL database. The software development methodology applied was Extreme Programming (XP), which includes planning, design, coding, and testing stages. The system functionality test was conducted using a structured testing method. The results show that SIDORAL successfully simplifies the integrated management of teacher, student, subject, and grade component data. Based on the testing phase results, all main features of the system were declared 100% valid and functioned properly without program logic errors. The implementation of SIDORAL is able to transform conventional grade data processing at SMA Swasta Kapuas into a secure digital system, thereby increasing distribution time efficiency and facilitating transparent delivery of learning outcomes information to students and parents.
Co-Authors Abdul Latif Abdul Latif Agung Sasongko Ali Mustopa, Ali Anggi Basifatul Rahma Badariatul Lailiah Dahlia , Rizka Debi Handika Eka Herdit Juningsih Elsa Mutia Sari Eri Bayu Pratama Erica Gracila Muntu Fachrurozi, Ahmad Faddillah, Umi Fahmi Julianto, Muhammad Fatmawati, Jeni Fazriansyah, Agung Frizi Olivian Grant Christo Budiyanto Hazael Susanto Hikmawan Alvin Zikirlah Iltavera Paula Iltavera Paula Ipin Sugiyarto Latifah Latifah Marwan Suhendra Mohammad Kamal Reza Muhamad Rizki Pratama Muhammad Fazilla Muhammad Fazilla Muhammad Ifan Rifani Ihsan Muhammad Rifqi Firdaus Nurmalasari Nurmalasari Rabiatus Saadah Rai Markus Panamuan Reza Maulana Rifqi Firdaus , Muhammad Rindiani, Popi Risdiansyah, Deni Riski Annisa Rizka Dahlia Sahrul Bariyah Septian Rheno Widianto Sugiyarto, Ipin Syarah Seimahuira Syarah Seimahuira Umi Faddillah Veronika Agnes Weiskhy Steven Dharmawan Wira Hadi, Sofian Yanto Yanto Zikirlah, Hikmawan Alvin