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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

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

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

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

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

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.

Pemanfaatan Mathverse untuk Peningkatan Pemahaman dan Motivasi Belajar Matematika Siswa SMP/MTs Di Desa Simbur Naik Gustiningsi, Tria; Pasaribu, Feri Tiona; Perdana, Yogi; Sainuddin, Syamsir; Nusantara, Duano Sapta; Sutrimo, Sutrimo
Jurnal Pengabdian Kepada Masyarakat Patikala Vol. 5 No. 2 (2025): Jurnal PkM PATIKALA
Publisher : Pusat Pengembangan Pendidikan dan Bakat Indonesia/Education and Talent Development Center of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/patikala.v5i2.3876

Mathematics is a fundamental subject for junior high school students, yet numeracy achievement in Indonesia, including in Simbur Naik Village, remains low. This community service activity aimed to improve students’ conceptual understanding and learning motivation through the implementation of the MathVerse application, which integrates Augmented Reality and Artificial Intelligence. The program involved 53 students and 6 mathematics teachers, carried out in three sessions consisting of socialization, training, and classroom implementation. The results indicated a significant improvement in cognitive achievement. The average test score increased from 49.15 to 70.67, while mastery rose from 33% to 70%. In terms of motivation, there was an increase in the indicators of effort and perceived relevance of mathematics, although interest and self-efficacy slightly declined. Student responses were highly positive, with the highest scores on the indicators of understanding and learning (82.94%) and application navigation (93.52%). Teachers also expressed consistently positive responses, with the highest rating on interest and adoption (90.63%). Documentation highlighted students’ enthusiasm in using AR and AI features, as well as teachers’ active role in facilitating the process. This activity demonstrates that MathVerse not only significantly enhances students’ conceptual understanding of mathematics but also fosters learning interest and strengthens teachers’ roles in integrating educational technology. The novelty of this program lies in the direct application of AR- and AI-based learning tools in rural schools, which are rarely reached by digital innovations. Therefore, this activity provides tangible benefits for the local education community and contributes theoretically to the effectiveness of multimedia learning and technology acceptance in mathematics education.

Pemberdayaan Siswa SMK melalui Pelatihan IoT untuk Sistem Monitoring Tanaman dalam Mendukung Smart Farming Khaira, Ulfa; Saputra, Edi; Waladi, Akhiyar; Perdana, Yogi; Hanum, Nindy Raisa; Iftitah, Hasanatul; Ashar, Rahmad; Rozi, Syamsyida
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari (In Progress)
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/9xvxt587

Provinsi Jambi memiliki potensi besar di sektor pertanian, namun praktik konvensional masih menghadapi kendala terkait efisiensi, produktivitas, dan keberlanjutan. Smart farming berbasis Internet of Things (IoT) hadir sebagai solusi inovatif, namun implementasinya memerlukan sumber daya manusia yang kompeten. Kegiatan Pengabdian kepada Masyarakat (PPM) ini melibatkan sebanyak 20 orang siswa SMK, serta guru SMKN 1 Muaro Jambi, dengan tujuan meningkatkan kompetensi peserta dalam merakit, memprogram, dan memanfaatkan sistem monitoring tanaman berbasis IoT. Program dilaksanakan dalam empat sesi pelatihan yang meliputi pengenalan konsep smart farming dan IoT, pengenalan komponen, praktik perakitan sistem, serta dasar pemrograman mikrokontroler. Hasil evaluasi menunjukkan peningkatan signifikan pemahaman peserta, yang ditunjukkan oleh kenaikan nilai rata-rata post-test sebesar 26 poin (dari 37,1 menjadi 63,1), serta keberhasilan peserta dalam merakit prototipe sistem monitoring tanaman yang fungsional. Temuan ini menegaskan bahwa pelatihan berbasis praktik mampu menjembatani kesenjangan kompetensi siswa vokasi terhadap kebutuhan teknologi pertanian modern. Keberlanjutan program ini diharapkan dapat memperkuat peran sekolah vokasi dalam mendukung penerapan pertanian cerdas sekaligus membuka peluang karier dan kewirausahaan di bidang agroteknologi.

Comparative Analysis of Random Forest and XGBoost for Detecting Phishing Websites: A Machine Learning Approach Perdana, Yogi
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1933

Phishing attacks represent one of the most significant cybersecurity threats in the digital era, with over 300,000 complaints reported globally in 2023. In Indonesia, the National Cyber and Crypto Agency reported anomalous traffic related to phishing reaching 47,231,390 incidents in 2023, making it one of the greatest threats to the national digital ecosystem. The complexity of increasingly sophisticated modern phishing attacks requires machine learning-based automatic detection approaches to overcome the limitations of ineffective manual detection methods. This study presents a comparative analysis of Random Forest and XGBoost algorithms for automatic phishing website detection using machine learning techniques. Although both algorithms have proven effective in the cybersecurity domain, comprehensive comparisons considering aspects of performance, interpretability, and computational efficiency in the context of phishing detection remain limited, creating a research gap that needs to be filled to optimize national phishing detection systems. The research methodology includes data collection, preprocessing, model implementation, hyperparameter optimization using randomized search with 5-fold stratified cross-validation, and comparative analysis. Experimental results demonstrate that optimized XGBoost delivers the best performance with 97.78% accuracy and 73% faster training time, while Random Forest offers interpretability advantages with 97.65% accuracy. Feature importance analysis reveals SSL certificate status and anchor URL characteristics as the most critical discriminative features. This study concludes that optimized XGBoost is the more optimal choice for production deployment of real-time phishing detection systems, while Random Forest is more suitable for scenarios requiring model transparency. These findings contribute to the development of national phishing detection systems that support the Indonesian government's digitalization program and protect the public from increasing cybersecurity threats.

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