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All Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Julinar Sari Hutagalung
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Julinar Sari Hutagalung; Rasiban
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.404

Abstract

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.
Sistem Pendaftaran Pasien Secara Online di UPTD Puskesmas Seputih Banyak Berbasis Web Sri Lestari; Aldino Nur Ihsan; Andrian Nur Ihsan; Dionta Dionta; Julinar Sari Hutagalung
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 2 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i2.5164

Abstract

Pelaksanaan implementasi pendaftaran online berbasis website di puskesmas merupakan suatu hal yang positif untuk membantu dalam mempermudah pendaftaran tiket antrian. Adanya pendaftaran tiket online dengan tujuan untuk mempermudah masyarakat mendaftar tiket antrian secara online di wilayah seputih banyak. Namun saat ini pendaftaran tiket antrian masih dilakukan secara manual, seperti pencatatan data pasien yang akan berobat masih dilakukan secara tertulis, Melihat sistem yang berjalan saat ini dinilai masih kurang efektif, untuk mempermudah program pendaftaran tiket antrian penulis mengembangkan sistem pendaftaran tiket antrian berbasis website dengan model Waterfall. Pembuatan website ini menggunakan bahasa pemograman PHP serta Framework codeigniter dengan database menggunakan MySql. Diharapkan dengan adanya sistem pendaftaran tiket antrian online dapat menbantu dan mempermudah dalam pendaftaran di puskesmas seputih banyak
ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Julinar Sari Hutagalung; Rasiban
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.404

Abstract

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.
Co-Authors Aldino Nur Ihsan Andrian Nur Ihsan Dionta Dionta Rasiban Sri Lestari