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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTECOMS: Journal of Information Technology and Computer Science Jurnal Kridatama Sains dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science (CO-SCIENCE) Jurnal Sistem Informasi dan Bisnis Cerdas ILTEK : Jurnal Teknologi Scientica: Jurnal Ilmiah Sains dan Teknologi
Afandi, Mohamad Irwan
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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RANCANG BANGUN APLIKASI MARKETPLACE PANGKAS RAMBUT BERBASIS ANDROID Daniswara, Mohammad Chevalier; Afandi, Mohamad Irwan; Wibowo, Nur Cahyo
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.5017

Abstract

Barbershop adalah pilihan populer untuk menarik pelanggan, namun banyak bisnis yang tidak memiliki potensi marketplace digital. Penggunaan marketplace dapat meningkatkan pertumbuhan bisnis, mengoptimalkan kinerja bisnis, dan mempercepat proses sehari-hari. Penelitian ini menggunakan metode pengembangan Waterfall, bahasa pemrograman Java, database Firebase, pengujian Blackbox, dan seluruh skenario berhasil diuji.
ASPECT-BASED SENTIMENT ANALYSIS PADA DATA MULTIMODAL TWITTER UNTUK BENCHMARK PERFORMA PRODUK STUDI KASUS SMARTPHONE Simbolon, Tegar Oktavianto; Wayuni, Eka Dyar; Afandi, Mohamad Irwan
ILTEK : Jurnal Teknologi Vol. 20 No. 02 (2025): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v20i02.239

Abstract

The growing public opinions about smartphones on social media have driven the need for an informative and comprehensive sentiment analysis system. This study aims to develop a smartphone benchmark dashboard based on Aspect-Based Sentiment Analysis (ABSA) using multimodal data. The data consists of tweet texts and image URLs listed in a CSV file. The process involves text preprocessing (case folding, tokenization, stopword removal, and stemming) and Optical Character Recognition (OCR) to extract text from images. The tweet texts and OCR results are then combined and classified using the Support Vector Machine (SVM) algorithm to predict sentiment for each product aspect, such as camera, performance, and others. The results show that the SVM model performs well in predicting neutral and negative sentiments, although the identification of positive sentiment still needs improvement. The developed dashboard assists consumers in comparing products and serves as a reference for producers in improving product quality and marketing strategies.
Co-Authors Abdul Rezha Efrat Najaf Al Rahma, Aghni Qisthina Amalia Anjani Arifiyanti Daniswara, Mohammad Chevalier Fitri, Anindo Saka Fitri, Maulidya Firdausy Al Islami, Muhammad Chairuladnan Syaifullah Purnaningsih, Elwis Ghaitza Putra, Agung Brastama Ridwandono, Doddy Saputra, Mohammad Rangga Simbolon, Tegar Oktavianto Tiowardhana, Alvin Wahyuni, Eka Dyar Wayuni, Eka Dyar Wibowo, Nur Cahyo