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All Journal Jurnal Algoritma Innovative: Journal Of Social Science Research
Sukmana, Dimas
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Other

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Analisis Faktor-Faktor yang Mempengaruhi Dividen Saham Perusahaan Pada Indeks IDXHIDIV20 Sukmana, Dimas; Najib, Farhan; Fikri Yazid, Fuad Hilmi; Aghniya, Muhammad Dhafin; Maesaroh, Syti Sarah
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research (Special Issue)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.11550

Abstract

Penelitian ini bertujuan menganalisis faktor-faktor yang mempengaruhi distribusi dividen perusahaan dalam indeks IDXHIDIV20. Pendekatan kuantitatif digunakan dengan data sekunder dari Bursa Efek Indonesia (BEI) periode 2021-2023. Analisis dilakukan dengan teknik deskriptif, uji asumsi klasik, pengujian hipotesis, dan regresi linier berganda. Hasil penelitian menunjukkan pengaruh signifikan dari variabel Return On Assets, Pertumbuhan, Current Ratio, Free Cash Flow, Ukuran, dan Debt to Equity Ratio terhadap distribusi dividen. Dengan nilai adjusted R-square sebesar 40,8%, penelitian ini menjelaskan sebagian besar variansi dalam distribusi dividen, sambil mengakui adanya faktor lain. Penelitian ini memberikan wawasan penting bagi investor dan pemangku kepentingan pasar dalam membuat keputusan investasi yang tepat.
Peningkatan Akurasi Rekomendasi Film Menggunakan Neural Collaborative Filtering dengan Arsitektur RecommenderNet Sukmana, Dimas; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3013

Abstract

The rapid growth of the film industry and streaming platform users has given rise to the challenge of information overload, where users find it difficult to find films that suit their preferences amid the abundance of content choices. This study aims to develop a Neural Collaborative Filtering (NCF)-based movie recommendation system model with a RecommenderNet architecture to improve prediction accuracy and personal recommendation relevance. The model was evaluated using the Root Mean Square Error (RMSE) metric to assess rating prediction accuracy and Normalized Discounted Cumulative Gain (NDCG@100) to measure recommendation quality and order. The results show that the model achieves an RMSE of 0.1946 and an NDCG@100 of 0.8136, indicating the model's ability to learn user preferences and generate relevant and well-ordered recommendations. This research contributes to the development of more effective and personalized recommendation systems in the digital streaming domain and offers an efficient approach to reducing the impact of information overload and improving the user experience.
Co-Authors Aghniya, Muhammad Dhafin Fikri Yazid, Fuad Hilmi Guntara, Rangga Gelar Maesaroh, Syti Sarah Najib, Farhan Nugraha, Muhammad Rizki