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All Journal Jurnal Masyarakat Informatika Jurnal Simetris Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Abdimasku : Jurnal Pengabdian Masyarakat Educenter: Jurnal Ilmiah Pendidikan EDUCENTER JURNAL PENDIDIKAN
Ningrum, Novita Kurnia
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Perbandingan Kinerja Model Prediksi Cuaca: Random Forest, Support Vector Regression, dan XGBoost Syahreza, Ahmad; Ningrum, Novita Kurnia; Syahrazy, Muhammad Anjas
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27640

Abstract

Accurate weather predictions are essential to mitigate the impacts of weather changes and support better planning in sectors such as agriculture, transportation, and tourism. Indonesia often faces unpredictable weather, such as sudden rains and long droughts, which can cause huge losses. This study aims to compare the performance of three machine learning algorithms Random Forest, Support Vector Regression (SVR), and XGBoost in predicting weather using meteorological data (minimum temperature, maximum temperature, rainfall, wind direction, average humidity) as well as IoT data totaling 1650 data per variable. The variables used in this study include minimum temperature, maximum temperature, rainfall, wind direction, and average humidity. Data analysis techniques were performed using three main evaluation metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). The results showed that XGBoost gave the best performance with MAE 0.3744, MSE 0.2278, and R² 0.8183. Random Forest and SVR also produced good predictions, with MAE values of 0.3869 and 0.3820, MSE 0.2422 and 0.2524, and R² 0.8068 and 0.7987, respectively. The results show XGBoost is the best model for weather prediction, which can help improve accuracy in agricultural planning and weather-related disaster risk mitigation.
Analisis Sentimen Ulasan Game dengan KNN: Perbandingan Rating dan Kamus Sentimen Sunyaruri, Wisesa Sat; Ningrum, Novita Kurnia
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30133

Abstract

The growth of the global gaming industry makes sentiment analysis of user reviews a crucial tool for understanding satisfaction and identifying technical issues. This study aims to evaluate three labelling methods (rating-based, Sentiwords_id, and InSet) for classifying the sentiment of Indonesian-language reviews for the game Zenless Zone Zero (ZZZ) using the K-Nearest Neighbor (KNN) algorithm. The study analyzes 4,282 reviews from the Google Play Store, which underwent a Data Preprocessing stage, including Null Handling, Cleaning, Case Folding, Tokenization, Stopword Removal, and Stemming. The KNN's performance for each labelling method was evaluated using accuracy, precision, recall, and F1-score metrics on 80:20 train-test split. The labelling results reveal different sentiment perceptions: the rating-based method tends toward positive, InSet toward negative, while Sentiwords_id is dominated by the positive and neutral classes. The KNN performance evaluation shows that rating-based labelling achieved the highest accuracy (72%), excelling on the positive class (86% recall) but performing poorly on the neutral class (9% recall). Conversely, the lexicon-based labelling methods (both 69% accuracy) have specific strengths: InSet in negative detection (81% recall) and Sentiwords_id in recognizing the neutral class (83% recall). Main challenges of this study include the lexicon's limitations in handling slang and game-specific terms, as well as the inconsistency between ratings and text. This study is expected to provide empirical evidence on performance trade-offs among automatic labelling methods to aid in identifying player satisfaction and advancing the quality of game development.
Co-Authors Agustina, Feri Ahmad Syahreza Ajib Susanto Astuti, Yani Parti Azkia, Naila Nur Dwitama, Reiza Hersa Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Ibnu Utomo WM Ivan Putra Pratama Karis Widyatmoko Kurniawan, Defri Kusumawati, Yupie Liya Umaroh, Liya Muhammad Naufal, Muhammad Mulyono, Ibnu Utomo Wahyu Musyaffa, Muhammad Syihabuddin Nabila, Isyeh Salma Bilqis Nareswari, Andrea Keysa Nugroho, Aulia Jilan Pramesti, Nadya Arum Qomariyah, Dania Saputra, Muhammad Alwi Nanda Sasmita, Tomy Elen Sembiring, Rinawati Septian Enggar Sukmana, Septian Enggar Setyawan, Aditya Rendy SUDARYANTO SUDARYANTO Sukamto, Titien Suhartini Sunyaruri, Wisesa Sat Syahrazy, Muhammad Anjas Syahreza, Ahmad Tangkas Surya Wibawa Taqi, Muhamad Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Wicaksono, Aldo Anggara Zahrotul Umami, Zahrotul Zaidan, Alfa Hikma