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All Journal Jurnal Inotera TIN: TERAPAN INFORMATIKA NUSANTARA Jambi Accounting Review (JAR) Tensile : Jurnal Pengabdian Kepada Masyarakat JRIIN :Jurnal Riset Informatika dan Inovasi APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer dan Teknik Informatika
Andri Fahmi
Universitas Pamulang
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Comparison of LSTM and Naïve Bayes in Google Play Store App Review Sentiment Analysis Endar Nirmala; Andri Fahmi
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID653

Abstract

The development of mobile application technology has driven increased user interaction through digital reviews on the Google Play Store platform. The review contains opinions that reflect the user's level of satisfaction, experience, and complaints about the app. However, the large number of reviews and variations in language expressions make manual analysis inefficient and potentially subjective. The main problem in this study is how to determine the most effective sentiment classification model to accurately identify users emotional tendencies. This study aims to compare the performance of the Naive Bayes method as a conventional machine learning model with Long Short Term Memory (LSTM) as a deep learning model based on word order in analyzing the sentiment of user reviews of Google Play Store applications. The dataset used comes from Google Play Store Reviews and goes through a pre-process process that includes text cleanup, tokenization, stopword removal, and sentiment labeling based on rating scales. The Naive Bayes model is trained using the TF-IDF representation, while the LSTM model uses an embedding sequence with standardized input padding. Evaluation uses accuracy metrics and F1-score with a ratio of 80 : 20 to train and test data distribution. The test results showed that the Naïve Bayes model achieved an accuracy of 65.78% with an F1 score of 0.5589, while the LSTM only achieved an accuracy of 45.26% with an F1-score of 0.2077. Thus, Naive Bayes was established as the best model in this study
A Comparative Analysis of XGBoost and Random Forest for Time Series Based Stock Price Prediction with Directional Movement Evaluation Fahmi, Andri; Rofiq, Nur
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9197

Abstract

Stock price prediction remains a complex task due to the dynamic nature of financial time series and the difficulty of extracting informative patterns from historical price movements. This study addresses the need to better understand whether the choice of model or the design of time series features plays a more dominant role in prediction performance. The objective of this research is to comparatively evaluate Extreme Gradient Boosting (XGBoost) and Random Forest for stock price prediction using engineered time series features, while also assessing their ability to capture directional price movements. The proposed approach applies a structured pipeline involving data preprocessing, extraction of time series features (lag, moving average, and volatility), and evaluation using a time-aware data split to preserve temporal order. Unlike conventional studies that focus solely on prediction accuracy, this research integrates both regression-based evaluation (RMSE, MAE, and R²) and directional movement analysis using confusion matrix, along with feature importance interpretation to understand model behavior. The experimental results, based on 1,258 daily stock price records, show that XGBoost achieved an RMSE of 457.97, MAE of 345.28, and R² of 0.884, while Random Forest obtained an RMSE of 462.01, MAE of 351.02, and R² of 0.882. The difference in R² (0.002 or 0.2%) indicates that both models perform comparably, with no substantial performance gap. Directional evaluation further reveals that both models are more accurate in predicting upward trends than downward movements. These findings suggest that feature engineering plays a more critical role than model selection in this context, providing a practical contribution to the development of stock prediction systems.
Penguatan Literasi Kecerdasan Buatan Melalui Pengenalan dan Pelatihan Pembuatan AI Agent bagi Siswa SMK Budi Santoso; Andri Fahmi; Yudi Permana Wiyadi
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2026): APPA : Jurnal Pengabdian kepada Masyarakat 
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan pesat kecerdasan buatan (AI) belum diimbangi dengan tingkat literasi AI di kalangan siswa sekolah menengah atas, sehingga banyak siswa berperan sebagai pengguna pasif tanpa kemampuan kritis dan produktif. Berdasarkan survei awal pada siswa kelas 12 MAN 1 Kota Tangerang Selatan, lebih dari 75% siswa belum memahami konsep dasar, cara kerja, dan potensi pemanfaatan AI. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan literasi AI melalui penyuluhan edukatif dan pelatihan praktis pembuatan AI agent berbasis platform ramah pemula. Metode yang digunakan meliputi ceramah interaktif, diskusi, demonstrasi, dan praktik terbimbing dengan melibatkan 40 siswa. Hasil yang ditargetkan mencakup peningkatan literasi AI minimal 40% berdasarkan pre-test dan post-test, pengembangan produk AI sederhana oleh siswa, penyusunan modul pelatihan berkelanjutan, serta publikasi ilmiah. Kegiatan ini diharapkan menjadi langkah konkret dalam mempersiapkan generasi muda yang adaptif dan kompeten di era digital.
Co-Authors Abdul Halim Ari Putra Ari Putra Ari Putra, Budi Santoso Endar Nirmala Fanza Yovian Fredy Olimsar Galuh Oka Safitri Hairia Sarfan Mudiah Salsabella Perdiyanto Raihan Pratama Rofiq, Nur Sabilah Endah Warni Safitri, Galuh Yudi Permana Wiyadi