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Implementasi RAD Pada Pengembangan Website Halo Gamers SOPHYA HADINI MARPAUNG
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7405

Abstract

The presence of Key Opinion Leaders (KOL) is increasingly important in the business world, especially in increasing consumer purchasing intentions. KOLs are now widely used in various sectors, including the gaming industry. One of them is the presence of Halo Gamers, a YouTube channel that focuses on gaming product reviews. Since 2017, Halo Gamers has targeted the Indonesian gaming market, driven by government projections to control 50% of the gaming industry market share. With more than 2,700 subscribers and collaboration with 16 brands as of November 2024, Halo Gamers acts as a successful KOL in promoting gaming products. Obstacles arise, especially in managing all content portfolios owned by channel owners and information regarding collaboration with brands that are spread across various platforms without centralized media that makes it difficult for channel owners to collaborate with related parties. To overcome this, the author developed a website with the RAD method (a common and popular method in system development that starts from the requirement planning, user design, construction to implementation stages) which functions as a centralized container for Halo Gamers, allowing brands to access portfolios and published content, as well as expanding opportunities for future collaboration at the address halogamers.info.
Urgensi Sistem ERP (SAP) dalam Kurikulum Marpaung, Sophya Hadini; Riche, Riche
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.36342

Abstract

Perkembangan transformasi digital di dunia usaha dan dunia industri masa kini menuntut lulusan Sistem Informasi (SI) memiliki kompetensi yang menyatukan perspektif bisnis dan teknologi dengan standar praktik terbaik. SAP sebagai perangkat lunak Enterprise Resource Planning (ERP) yang dominan secara global menawarkan ekosistem modul yang menyeluruh dan luar biasa besar, dimulai dari pengantar ERP, manajemen rantai pasok, hubungan pelanggan dan pemasok, akuntansi keuangan, pengelolaan sumber daya manusia, penjualan dan distribusi, hingga pengadaan dan berbagai modul lainnya yang relevan dengan kebutuhan DUDI (dunia usaha dunia industri) yang secara langsung menautkan proses bisnis end-to-end. Penulis mengulas urgensi integrasi SAP dalam kurikulum prodi sistem informasi, nilai jual (value proposition) lulusan sistem informasi bagi industri masa kini, serta rancangan keterkaitan modul SAP dalam mata kuliah untuk membangun kemampuan analitik, kepatuhan, dan pengambilan keputusan berbasis data. Kajian ini disusun dengan pendekatan akademik, memaparkan landasan teoritik, peta kompetensi, desain pembelajaran, studi kasus, dan indikator capaian pembelajaran (CPL) yang diukur.
Optimizing Rice Planting Schedules Based on Rainfall Prediction Using a BiLSTM Network Fandi Presly Simamora; Khairul Hawani Rambe; Sophya Hadini Marpaung
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1340

Abstract

This study addresses the critical challenge of optimizing rice planting schedules in Indonesia, where unpredictable rainfall threatens national and regional food security. To tackle this issue, a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed to accurately predict rainfall patterns, with a specific focus on Deli Serdang Regency in North Sumatra. Utilizing a comprehensive weather dataset from 2013 to 2022 sourced from BMKG, a feature selection process was conducted to identify the 10 most influential features for rainfall. The BiLSTM model was then developed through several experimental scenarios, varying the data duration and architectural complexity. The best-performing model, achieved in a scenario using a double BiLSTM architecture and 10 years of data, yielded a Mean Absolute Error (MAE) of 11.2382 mm and a Root Mean Squared Error (RMSE) of 19.5650 mm. The resulting predictive capability provides a data-driven framework for optimizing planting schedules. Crucially, the study also reveals the limitations of current planting criteria, which can be misleading in regions prone to intense, short-duration rainfall, highlighting the need for more adaptive, region-specific guidelines. This work contributes to mitigating crop failure risks, enhancing crop resilience, and ensuring long-term regional food security.