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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem Cerdas
HAFIZAH, IDA
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Digital Democracy: Analyzing Political Sentiments through Multinomial Naive Bayes in Election Campaign Ads DIQI, MOHAMMAD; RAHMAYANTI, DIAN RHESA; HISWATI, MARSELINA ENDAH; ORDIYASA, I WAYAN; HAFIZAH, IDA
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.379

Abstract

This research delves into sentiment analysis for digital election campaign advertisements using the Multinomial Naive Bayes approach. The study addresses the limitations of standard sentiment analysis methodologies in capturing the intricacies of public sentiments toward political ads. The dataset, sourced from Kaggle, encompasses 3000 records with sentiments categorized as positive, neutral, and negative. The Multinomial Naive Bayes model demonstrated a substantial accuracy increase from 92% to 96%, outperforming the standard Naive Bayes model. Precision, recall, and F1-score metrics consistently improved across sentiment categories. While dataset representativeness and cultural specificity pose limitations, the research contributes significantly to sentiment analysis methodologies in politically charged digital environments. Future research recommendations include exploring advanced NLP techniques, incorporating real-time data from diverse social media platforms, and addressing ethical considerations in political sentiment analysis. The outcomes emphasize the importance of tailored methodologies for enhanced accuracy in understanding sentiments expressed in digital election campaign advertisements.
Log-Scale Correlation Classifier for Mushroom Identification in Agricultural Internet of Things Systems Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Rahmayanti, Dian Rhesa; Basuki, Umar; Hafizah, Ida
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6841

Abstract

Classifying edible and poisonous mushrooms is crucial to food safety, as misidentification can pose severe toxicological risks. Conventional probabilistic classifiers, such as Naïve Bayes and Logistic Regression, often underperform on categorical datasets with correlated attributes and skewed distributions. This study introduces the Log-Scale Feature Correlation Classifier, a novel probabilistic framework that integrates logarithmic transformation and correlation-weighted probability estimation to address these challenges. Using the UCI Mushroom dataset and a 10-fold cross-validation scheme, LSFCC was benchmarked against standard models. The results demonstrate that LSFCC achieved consistently superior accuracy (0.99), precision, and recall, significantly outperforming both Logistic Regression and Naïve Bayes, as confirmed by statistical tests (p<0.01). Its lightweight design and interpretability make it highly suitable for real-time deployment on resource-constrained IoT devices, particularly within Agricultural IoT systems for autonomous mushroom identification. Future research will explore LSFCC’s adaptability to noisy, multimodal data and hybrid architectures, ensuring broader applicability in real-world bioinformatics and food safety domains.
Co-Authors Basuki, Umar Dian Rhesa Rahmayanti Marselina Endah Hiswati Mohammad Diqi Ordiyasa, I Wayan