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All Journal Jurnal Ilmiah Informatika Komputer UG Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Media Jurnal Informatika Jurnal Profesi Insinyur Universitas Lampung Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Desti Riminarsih
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Evaluasi Performa Algoritma Supervised Learning untuk Prediksi Risiko Serangan Jantung: Pendekatan Rekayasa Sistem Cerdas Riminarsih, Desti; Nugraha, Alfin
Jurnal Profesi Insinyur Universitas Lampung Vol. 6 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpi.v6n1.169

Abstract

Serangan jantung merupakan salah satu penyebab kematian tertinggi di negara berkembang. Penyakit ini terjadi akibat tersumbatnya aliran darah ke otot jantung akibat penyempitan atau penyumbatan arteri koroner. Faktor risiko serangan jantung terdiri atas faktor yang dapat diubah, seperti pola hidup, dan faktor yang tidak dapat diubah, seperti usia dan riwayat keluarga. Deteksi dini terhadap risiko serangan jantung sangat penting untuk meminimalkan angka kematian. Penelitian ini bertujuan untuk membangun dan mengevaluasi model prediksi risiko serangan jantung dengan mengelompokkan pasien ke dalam dua kategori, yaitu risiko rendah dan risiko tinggi, menggunakan pendekatan rekayasa sistem cerdas berbasis supervised learning. Proses penelitian mencakup tahap pengumpulan data, eksplorasi data, pemilihan fitur, pra-pemrosesan data, pelatihan model, serta pengujian model untuk evaluasi performa model menggunakan metrik akurasi. Model klasifikasi dibangun dengan empat algoritma supervised learning yaitu Gradient Boosting, Random Forest, Naive Bayes, dan Logistic Regression. Pada pelatihan model digunakan 10-fold cross validation untuk melihat akurasi dan konsistensi model. Hasil pengujian menunjukkan bahwa algoritma Naive Bayes memiliki akurasi tertinggi sebesar 90%, diikuti oleh Logistic Regression dan Random Forest (88%), dan Gradient Boosting. Model Naive Bayes tetap menunjukkan performa terbaik tidak hanya akurasi tertinggi tetapi juga nilai precision dan F1-score tertinggi untuk kelas positif, serta recall tertinggi untuk kelas negatif. Oleh karena itu, Naive Bayes dinilai sebagai model yang paling andal dan direkomendasikan untuk prediksi risiko serangan jantung secara dini, karena mampu memberikan klasifikasi yang seimbang dan akurat dalam konteks aplikasi medis.
Evaluating Machine Learning Models Across Feature Extraction and Data Balancing Scenarios for Coretax Sentiment Analysis Syah Putra, Subhan; Riminarsih, Desti
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5968

Abstract

The implementation of the Core Tax Administration System (Coretax) by the Indonesian Directorate General of Taxes has generated diverse public responses on social media, particularly on platform X, making sentiment analysis a relevant approach to assess public perception of this policy. This study aims to evaluate the performance of machine learning classifiers across different feature extraction and data balancing scenarios. Three machine learning classifiers, namely Multinomial Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression were evaluated under four experimental scenarios combining two feature extraction methods, namely Term Frequency–Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), with original and balanced data distributions. A dataset of more than 50,000 Coretax-related posts collected from platform X was preprocessed and automatically labeled into positive, negative, and neutral sentiment classes using a pretrained IndoBERT sentiment model. A brief manual inspection of a random subset indicates moderate agreement between automatic and manual labels, highlighting potential noise while supporting the use of automatic labeling for comparative analysis. The results show that performance is shaped by the combined effects of representation and data distribution rather than algorithm choice alone. Logistic Regression consistently achieved the most stable and competitive performance across all scenarios, with accuracy values ranging from approximately 0.80 to 0.83 and macro F1-scores around 0.72–0.73. TF-IDF generally provided more stable performance, while data balancing improved prediction fairness for minority sentiment classes despite a slight decrease in overall accuracy. These findings demonstrate that Logistic Regression is the most robust model for Coretax sentiment analysis across varying feature extraction and data balancing conditions and provide practical insights into the influence of data representation and distribution on sentiment classification performance.
Co-Authors Achmad Benny Mutiara Achmad Fahrurozi Anwar, Gusti Alfahmi Bambang Wahyudi Cut Maisyarah Karyati Dina Indarti Ernastuti Ernastuti Fachry Abda El Rahman Ilmiyati Sari Nadya Putri Ekananda Nugraha, Alfin Nurma Nugraha Rifki Kosasih Saraswati, Mediana Syah Putra, Subhan