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All Journal Jurnal Teknika Jurnal Sains Teknologi dan Sistem Informasi
Sophy Awaliah
Universitas Mulawarman
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Perbandingan Metode Naïve Bayes dan Support Vector Machine dalam Analisis Sentimen Citra Polri berdasarkan Opini pada Platform Twitter/X Sophy Awaliah; Arindra Nurshadrina Ramadini; Najwa Felira Zetti; Anindita Septiarini; Novi Puspitasari
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.4537

Abstract

The Indonesian National Police (Polri) is a law enforcement agency responsible for maintaining security and order in Indonesia. In the digital era, Polri’s image has increasingly been highlighted on social media platforms such as Twitter/X, which serve as a major channel for the public to express opinions and criticism. This study aims to compare the performance of the Naive Bayes method and Support Vector Machine (SVM) in sentiment analysis of public opinion toward Polri. Naive Bayes, known for its probabilistic approach, is compared with SVM, a robust machine learning algorithm capable of classifying data with clear margins between classes. The dataset was divided into 80% training data and 20% testing data with stratification to ensure balanced sentiment proportions. Performance evaluation was conducted using accuracy, precision, recall, and F1-score through a confusion matrix. Results show that SVM achieved the highest accuracy of 90%, while Naive Bayes obtained 83%. In terms of F1-score, SVM reached a macro average of 0.90 with its best performance in the positive category (0.97), while Naive Bayes reached 0.83 with its best in the positive category (0.90). Overall, SVM outperformed Naive Bayes, particularly in classifying neutral sentiment. This study provides insights into the effectiveness of SVM for analyzing informal tweets and can serve as a reference for future research and public opinion monitoring system development.
Klasifikasi Tingkat Penyakit Hipertensi Menggunakan Metode Random Forest Sophy Awaliah; Anindita Septiarini; Novianti Puspitasari
Jurnal Teknika Vol 18 No 2 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jt.v18i2.1660

Abstract

The rising prevalence of hypertension in Indonesia specifically in Samarinda City, which recorded 215,206 cases in 2023 poses challenges for the field of Informatics Engineering regarding the efficiency and consistency of manual disease-level classification in high-patient-volume healthcare facilities. The limitations of conventional approaches create a need for a technology-based system that can classify hypertension severity quickly, accurately, and objectively. This study aims to develop a hypertension severity classification model using the Random Forest algorithm optimized via GridSearchCV and to identify dominant risk factors through Feature Importance analysis. The dataset comprises 5,373 adult patient medical records from the Remaja Community Health Center (Puskesmas) in Samarinda, covering January to December 2024; attributes include gender, age, smoking status, height, weight, systolic and diastolic blood pressure, and a BMI feature derived through feature engineering. Data imbalance was addressed using the SMOTE technique on the training data. Results indicate that the model achieved the highest accuracy of 99.75% with a 70:30 data split, while an 80:20 ratio yielded 99.62% accuracy; both figures exceed the minimum target of 90%. Evaluation using accuracy, precision, recall, and F1-score metrics demonstrates the model's superiority over manual classification. Feature Importance analysis identifies diastolic blood pressure (46.31%) and systolic blood pressure (45.04%) as the most dominant factors, followed by age, BMI, and other attributes. The resulting model can be implemented as a clinical decision support system to assist medical personnel in primary healthcare settings.
Co-Authors Anindita Septiarini, Anindita Arindra Nurshadrina Ramadini Najwa Felira Zetti Novi Puspitasari Puspitasari, Novianti