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Peningkatan ketersediaan N tanah inceptisol dan pengaruhnya terhadap tanaman jagung manis (Zea mays saccharata Sturt) melalui pemberian bokashi jerami padi dan pupuk NPK Perdana, Yogi; Siregar, Chairani; Rambe, Rahmi Dwi Handayani; Mindalisma, Mindalisma
AGRILAND Jurnal Ilmu Pertanian Vol 12, No 1 (2024): AGRILAND: Jurnal Ilmu Pertanian
Publisher : Universitas Islam sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/agr.v12i1.9657

Abstract

Tanaman jagung manis merupakan salah satu tanaman yang respontif terhadap pemupukan. Oleh karena itu, ketersediaan nitrogen yang cukup selama fase pertumbuhannya perlu diperhatikan. Penelitian ini dilaksanakan di Lahan Percobaan Fakultas Pertanian Universitas Islam Sumatera Utara, Jln. Karya Wisata, Gedung Johor Kecamatan Medan Johor Kota Madya Medan, Provinsi Sumatera Utara pada Oktober 2021 sampai Desember 2021. Peneltian ini bertujuan untuk mengetahui pengaruh bokasi jerami padi dan puuk NPK terhadap tanaman jagung manis. Penelitian ini menggunakan Rancangan Acak Kelompok (RAK) Faktorial dengan dua faktor perlakuan yaitu bokhasi jerami padi dan pupuk NPK. Faktor pertama pemberian bokhasi jerami padi yaitu : B0 (kontrol); B1(1,5 kg/plot); B2 (3 kg/plot); B3 (4,5 kg/ plot). Faktor kedua pemberian pupuk NPK yaitu N0 (Kontrol); N1 (15 g/plot; N2 (30 g/ plot); N3 (45 g/ plot). Parameter yang diamati yaitu tinggi tanaman, diameter batang, bobot buah pertanaman, bobot buah perplot, bobot tongkol perplot, dan N-Tanah. Hasil penelitian menunjukkan bahwa pemberian bokashi jerami padi dan pupuk NPK mampu meningkatkan pertumbuhan,  produksi tanaman jagung manis dan ketersediaan N tanah. Interaksi perlakuan terbaik yang menghasilkan pertumbuhan, produksi tanaman jagung manis serta ketersediaan N tanah tertinggi adalah pada perlakuan B3N3 (4.5 kg bokashi/plot dan 45 g pupuk NPK/plot).
Peningkatan ketersediaan N tanah inceptisol dan pengaruhnya terhadap tanaman jagung manis (Zea mays saccharata Sturt) melalui pemberian bokashi jerami padi dan pupuk NPK Perdana, Yogi; Siregar, Chairani; Rambe, Rahmi Dwi Handayani; Mindalisma, Mindalisma
AGRILAND Jurnal Ilmu Pertanian Vol 12, No 1 (2024): AGRILAND: Jurnal Ilmu Pertanian
Publisher : Universitas Islam sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/agr.v12i1.9657

Abstract

Tanaman jagung manis merupakan salah satu tanaman yang respontif terhadap pemupukan. Oleh karena itu, ketersediaan nitrogen yang cukup selama fase pertumbuhannya perlu diperhatikan. Penelitian ini dilaksanakan di Lahan Percobaan Fakultas Pertanian Universitas Islam Sumatera Utara, Jln. Karya Wisata, Gedung Johor Kecamatan Medan Johor Kota Madya Medan, Provinsi Sumatera Utara pada Oktober 2021 sampai Desember 2021. Peneltian ini bertujuan untuk mengetahui pengaruh bokasi jerami padi dan puuk NPK terhadap tanaman jagung manis. Penelitian ini menggunakan Rancangan Acak Kelompok (RAK) Faktorial dengan dua faktor perlakuan yaitu bokhasi jerami padi dan pupuk NPK. Faktor pertama pemberian bokhasi jerami padi yaitu : B0 (kontrol); B1(1,5 kg/plot); B2 (3 kg/plot); B3 (4,5 kg/ plot). Faktor kedua pemberian pupuk NPK yaitu N0 (Kontrol); N1 (15 g/plot; N2 (30 g/ plot); N3 (45 g/ plot). Parameter yang diamati yaitu tinggi tanaman, diameter batang, bobot buah pertanaman, bobot buah perplot, bobot tongkol perplot, dan N-Tanah. Hasil penelitian menunjukkan bahwa pemberian bokashi jerami padi dan pupuk NPK mampu meningkatkan pertumbuhan,  produksi tanaman jagung manis dan ketersediaan N tanah. Interaksi perlakuan terbaik yang menghasilkan pertumbuhan, produksi tanaman jagung manis serta ketersediaan N tanah tertinggi adalah pada perlakuan B3N3 (4.5 kg bokashi/plot dan 45 g pupuk NPK/plot).
Comparative Analysis of Triangulation Methods for Optimal Solutions to the Art Gallery Problem Marzal, Jefri; Niken Rarasati; Waladi, Akhiyar; Perdana, Yogi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10749

Abstract

Triangulation is the process of breaking down an n-sided polygon into triangles and it is necessary in deciding the optimal count and the position of guards in the Art Gallery Problem (AGP) There is a theoretical limit that has been established which states that the number of required guards needed to keep an eye on such a polygon is ⌊n/3⌋ and this research considers this as the limit. Among various triangulation methods, Ear Clipping and Minimum Weight are two primary approaches frequently used to achieve optimal solutions. Nonetheless, its comparison with other methods, more particularly the amount of guards required for the maximum theoretical figure, is still a gap in literature. The aim of this research is to create an AGP simulation program and test it against the theoretical upper bound, determining the number of guards required. 228 simple polygons with vertices varying between 10 and 110 were utilized in this research. The polygons were classified into three groups based on the ratio of convex to concave vertices: less concave vertices, equal amount of concave and convex vertices and vice versa. Result study shows that the Ear Clipping method is significantly superior to Minimum Weight in reducing guard requirements. Practically speaking, these advancements are important for the design of engineering systems such as surveillance systems and the surveillance of public spaces. In the context of building security system design and monitoring of large areas, these conclusions are of utmost importance.
Implementation of the Design Thinking Method in Designing a Laboratory Room Scheduling System Nindy Raisa Hanum; Hasanatul Iftitah; Yogi Perdana
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Laboratories are essential facilities in higher education institutions, particularly in supporting practical learning and research activities. However, manual management often leads to scheduling conflicts, inefficient room utilization, and limited access to real-time information. This study aims to develop a web-based, integrated laboratory room scheduling system for the Faculty of Science and Technology at the University of Jambi. The research adopts the Design Thinking methodology, which consists of five stages: Empathize, Define, Ideate, Prototype, and Test. Stakeholder needs were identified through interviews with lecturers, students, lab staff, and laboratory heads. The primary issue identified was the lack of a centralized scheduling system. A prototype was designed using Figma, incorporating features such as real-time schedule viewing, room booking management, and usage reporting. Usability testing with five respondents revealed high satisfaction in terms of learnability (84%), memorability (85%), and efficiency (87%). The results confirm that the system meets user needs and improves laboratory management. This study contributes to the digital transformation of academic services by offering a user-centered, context-aware solution tailored to the needs of the Faculty of Science and Technology.
ANALISIS PERBANDINGAN MODEL GRU DAN LSTM UNTUK PREDIKSI HARGA SAHAM BANK RAKYAT INDONESIA: Deep Learning, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), Stock Price Prediction Perdana, Yogi; Raisa Hanum, Nindy; Rabiula, Andre; Anzari, Yandi
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1692

Abstract

This research implements and compares two deep learning architectures, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for predicting the stock price of Bank Rakyat Indonesia (BRI) using historical data from February 2023 to October 2024. Through systematic hyperparameter tuning and comprehensive evaluation, the study finds that GRU consistently outperforms LSTM across all regression metrics, with a 10.7% improvement in R² and an 18.5% reduction in MAPE. The optimal GRU configuration (100 units, 100 epochs, batch size 32, learning rate 0.001) achieves an MSE of 6517.5 and MAPE of 1.3764%. Visual analysis confirms GRU's superior ability to capture stock price fluctuations and adapt more quickly to trend changes. The simpler architecture of GRU with fewer parameters proves more effective for handling the high-noise characteristics and varying volatility of stock price data. While both models face challenges in predicting extreme market events, GRU demonstrates better resilience and faster recovery after such occurrences. This research contributes to the understanding of recurrent neural network applications in financial time series forecasting and provides practical insights for developing more accurate stock price prediction systems.