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Herdiatmoko, Hendrik Fery
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Computer Science & IT Economics, Econometrics & Finance

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Prediksi Kualitas Udara DKI Jakarta Menggunakan Algoritma Random Forest Berbasis Time-Lag Feature Prasetya, Heronimus Diego; Pratama, Jeremia Sandy; Khoirunnisaa, Alifah; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3933

Abstract

The volatility of air quality in Jakarta, which often deteriorates abruptly, demands a proactive early warning mechanism rather than mere real-time monitoring. A major limitation in environmental datasets is the class imbalance, where extreme hazardous conditions are recorded much less frequently than normal conditions, causing them to be overlooked by standard prediction models. This study aims to develop an H+1 (next-day) air quality prediction system by integrating the Random Forest algorithm with the SMOTE (Synthetic Minority Over-sampling Technique) data balancing technique. A Time-Lag feature engineering approach was applied to transform historical data from 2010-2025 into future predictive variables. Experimental results demonstrate that the application of SMOTE successfully improved the model's sensitivity in recognizing 'Unhealthy' categories that were previously difficult to detect. Feature analysis revealed that the accumulation of surface Ozone (O3) and Particulate Matter (PM10) serve as the most dominant indicators triggering air status changes for the following day. This system is intended to serve as a health mitigation reference for the public prior to outdoor activities.
Optimasi Support Vector Machine Menggunakan Feature Selection Chi-Square untuk Klasifikasi Sentimen Program Makan Siang Gratis Darmawan, I Komang; Prayogo, Aji; Chan, Steven; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3943

Abstract

The Free Nutritious Lunch program policy has triggered massive public discourse on social media X, reflecting diverse public perceptions toward government policy effectiveness. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm by implementing Chi-Square Feature Selection to address high data dimensionality and noise challenges in social media text. A dataset of 10,524 tweets was acquired and processed through preprocessing, TF-IDF weighting, and lexicon-based automatic labeling. The results show that Chi-Square feature selection integration successfully reduced dimensions from 16,394 to the 1,000 best features without degrading accuracy. The linear kernel SVM model achieved an optimal accuracy rate of 91.12%. However, this study identifies that this high accuracy is heavily influenced by the dominance of the positive class, whereas performance on the negative and neutral classes remains limited due to data imbalance. Overall, feature optimization proved to increase computational efficiency while maintaining accuracy stability in mapping public responses to strategic national policies.
Analisis Sentimen Ulasan Aplikasi Bibit Menggunakan TF-IDF dan Support Vector Machine Adeodatus, Marselinus Dewadaru Bayu; Nopitasari, Sepiyana; Meivia, Wirda Arta; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

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

Abstract

The rapid growth of financial technology (fintech) applications has increased the number of user reviews on digital platforms. These reviews contain valuable information regarding application quality, yet they are unstructured and difficult to analyze manually. This study aims to classify user review sentiments of the Bibit investment application into positive and negative categories using the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm. The dataset was obtained from Kaggle, consisting of user reviews of the Bibit application collected from Google Play Store. The data were processed through several preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and classification was conducted using SVM with a linear kernel. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the combination of TF-IDF and SVM provides good performance in classifying the sentiment of Bibit application user reviews.
Co-Authors Adeodatus, Marselinus Dewadaru Bayu Chan, Steven Darmawan, I Komang Khoirunnisaa, Alifah Meivia, Wirda Arta Nopitasari, Sepiyana Prasetya, Heronimus Diego Pratama, Jeremia Sandy Prayogo, Aji