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All Journal Applied Information Technology and Computer Science (AICOMS)
Felix Gunawan
Universitas Multi Data Palembang
Computer Science & IT

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Analisis Sentimen Komentar Youtube terhadap Kondisi Bursa Saham Indonesia akibat Isu Pengunduran Serempak Dewan BEI Menggunakan IndoBERT Daffa Yudha Musyaffa; Felix Gunawan; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/q27ea163

Abstract

Social media platforms such as YouTube have long served as a primary discussion space for retail investor communities in Indonesia. This study aims to analyze public sentiment in order to understand perception trends and the digital psychology of capital market participants regarding the issue of the simultaneous resignation of the Indonesia Stock Exchange (IDX) board members. The research applies the IndoBERT (Bidirectional Encoder Representations from Transformers for the Indonesian language) deep learning architecture through a fine-tuning process on a dataset of YouTube comments. The textual corpus was cleaned from noise, normalized from stock market slang vocabulary, tokenized, and automatically classified into three sentiment polarities: positive, neutral, and negative. The analysis stage was further continued with dominant keyword extraction using Word Cloud visualization and word frequency trend mapping to identify psychological variables driving market opinions. The model successfully classified the semantic complexity of informal language objectively. Visualization results indicate that communication dynamics were overwhelmingly dominated by negative sentiment (57.5%), reflecting widespread public concern and declining confidence in capital market stability due to the structural crisis. This study demonstrates the effectiveness of local transformer models as instruments for extracting digital market psychology to support real-time automated investment decision-making.
Penerapan Metode Branch and Bound untuk Optimalisasi Rute Wisata Terdekat di Kota Palembang Jaysen Stephanus; Felix Gunawan; Yohannes Yohannes
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/eqadem96

Abstract

This study discusses the application of the Branch and Bound method to optimize the nearest tourist route in Palembang City using the Traveling Salesman Problem (TSP) approach. The problem raised is how to determine the most efficient tourist route from several tourist destinations with minimum travel distance. The study utilizes geographic coordinate data of tourist destinations obtained through OpenStreetMap, then the distance between locations is calculated using the Haversine Formula to obtain an accurate distance estimate based on latitude and longitude. Furthermore, the Branch and Bound Algorithm is used to find the optimal route solution through the process of branching, bounding, and pruning so that the solution search becomes more efficient than the brute force method. The results show that the system successfully produces an optimal circular tourist route with a total minimum distance of 40.47 km and an execution time of 12.84 seconds. The integration of the Haversine Formula and Branch and Bound is proven to be able to provide efficient, accurate, and adaptive tourist route recommendations to help tourists save travel time and transportation costs in Palembang City.
Co-Authors Daffa Yudha Musyaffa Jaysen Stephanus Muhammad Rizky Pribadi Yohannes, Yohannes