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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Jurnal Hukum Ekonomi Syariah Jurnal Nasional Komputasi dan Teknologi Informasi Al Ghafur : Jurnal Ilmiah Pengabdian Kepada Masyarakat Journal Serambi Engineering (JSE) Jurnal Mahkota Bisnis (Makbis)
Nuzula, Mukhsin
Universitas Jabal Ghafur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Neuroscience Transportation Other

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Arduino-Based IoT Soil pH Monitoring System for Automated Horticultural Fertilization Nuzula, Mukhsin; Salat, Junaidi; Ichsan, Muhammad; Fazira, Zetta; Rahmi, Qairul
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol. 10 No. 2 (2026)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/yqtjsb90

Abstract

Precision fertilization is crucial for enhancing nutrient use efficiency and maintaining optimal soil conditions in horticultural crop cultivation. While IoT-based agricultural automation has been extensively researched, most studies focus on soil moisture-based irrigation, whereas the use of soil pH as a real-time control parameter in automated fertilization systems with remote monitoring remains limited. This limitation hinders accurate nutrient management and timely corrective actions in horticultural production. Therefore, this study aims to design and implement an Arduino-based automated fertilization system integrated with soil pH sensors and IoT technology. The proposed system continuously monitors soil acidity levels and automatically regulates liquid fertilizer application based on predefined pH thresholds. Testing results demonstrate that the soil pH sensor effectively measures acidity levels within the 5.32–6.50 pH range. The system successfully activates fertilizer application when pH values ​​fall below the set threshold, delivering a volume of 150–200 mL per 60-second cycle. The findings indicate that integrating pH-based decision-making with IoT-based monitoring has the potential to support precision fertilization in horticultural cultivation
PENGEMBANGAN KARAKTER MAHASISWA MELALUI PROGRAM KKN BERBASIS KAMPUS MENINGKATKAN KEPEDULIAN LINGKUNGAN DAN KETERAMPILAN SOSIAL DI UNIVERSITAS JABAL GHAFUR Muhammad Yahya; Heri Fajri; Mukhsin Nuzula; Teuku Fadhli
Al Ghafur: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 4, No 2 (2025): Desember
Publisher : Universitas Jabal Ghafur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47647/alghafur.v4i2.3858

Abstract

KKN berbasis kampus merupakan bentuk pengabdian kepada masyarakat yang bertujuan meningkatkan kapasitas mahasiswa melalui keterlibatan langsung dalam pengembangan lingkungan kampus. Pelaksanaan KKN di Universitas Jabal Ghafur meliputi kegiatan sosial, kebersihan lingkungan, pemanfaatan lahan, serta pembangunan fasilitas sederhana. Program ini dirancang untuk menumbuhkan keterampilan komunikasi, kerja sama tim, kepemimpinan, tanggung jawab, serta meningkatkan kepedulian mahasiswa terhadap lingkungan. Metode pelaksanaan dilakukan secara partisipatif dengan melibatkan mahasiswa dalam setiap tahapan kegiatan. Hasil menunjukkan bahwa KKN berbasis kampus efektif membentuk karakter mahasiswa, meningkatkan kesadaran lingkungan, serta menciptakan lingkungan kampus yang lebih bersih dan kondusif sebagai model pembelajaran berbasis pengalaman
PEMBERDAYAAN MASYARAKAT DESA LINGKOK MELALUI PROGRAM KULIAH KERJA NYATA (KKN) DALAM PENGEMBANGAN PENDIDIKAN, SOSIAL, DAN LINGKUNGAN Mukhsin Nuzula; Heri Fajri; Cut Jora Sari; Muhammad Haiqal; Marzuki Marzuki; M Agmar Media
Al Ghafur: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 4, No 2 (2025): Desember
Publisher : Universitas Jabal Ghafur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47647/alghafur.v4i2.3853

Abstract

Kegiatan Kuliah Kerja Nyata (KKN) di Desa Lingkok, Kecamatan Titeu, Kabupaten Pidie, bertujuan untuk meningkatkan kapasitas sosial, pendidikan, dan kepedulian lingkungan masyarakat desa melalui pendekatan partisipatif. Permasalahan utama yang dihadapi masyarakat meliputi perbedaan pendapat dalam pengambilan keputusan bersama, keterbatasan akses pendidikan, serta rendahnya kesadaran terhadap kebersihan dan kelestarian lingkungan. Metode pelaksanaan kegiatan meliputi observasi lapangan, diskusi kelompok, pelatihan, pendampingan, dan aksi sosial secara langsung. Hasil kegiatan menunjukkan peningkatan partisipasi masyarakat, tumbuhnya kesadaran akan pentingnya pendidikan dan lingkungan, serta terbangunnya hubungan sosial yang lebih harmonis antara mahasiswa dan masyarakat. Program KKN ini memberikan dampak positif secara sosial dan edukatif serta dapat menjadi model kegiatan pengabdian masyarakat berbasis kebutuhan lokal.
Learning Rate Tuning of Transfer Learning Models for Fresh and Spoiled Beef Image Classification Tri Mulya Dharma; Mukhsin Nuzula; Aviv Fitria Yulia; Dwi Feriyanto; Ningsiah
Jurnal Serambi Engineering Vol. 11 No. 3 (2026): Juli 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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

Abstract

Beef freshness assessment is a critical aspect of the food industry, where conventional methods relying on visual inspection and olfaction are subjective and prone to human error. This study aims to develop an automated beef freshness classification system utilizing deep learning to enhance the accuracy and objectivity of quality assessments. A total of 2.800 beef images were sourced from an open research data repository, divided equally into fresh and spoiled classes. Three Convolutional Neural Networks (CNN) architectures with transfer learning VGG16, ResNet50-V2, and Inception-V3 were evaluated. The models were systematically tested using learning rate tuning at 0.01, 0.001, and 0.0001 to optimize training convergence. Evaluation results showed that VGG16 outperformed other models in classifying beef freshness. VGG16 achieved a peak testing accuracy of 99.46% at a learning rate of 0.001. The main contribution of this study is the systematic evaluation of transfer learning architectures to establish an optimal baseline for beef quality assessment. By deploying the best performing model into a web based application, this approach offers a practical, objective, and accessible alternative to conventional manual inspection, enabling rapid early detection of beef freshness to improve food safety.
Co-Authors Aviv Fitria Yulia Cut Jora Sari Dwi Feriyanto Fadhli, Teuku Fazira, Zetta Firdaus Firdaus Fitriyani Haiqal, Muhammad Heri Fajri, Heri Junaidi Salat Kahlil, Kahlil M Agmar Media Marzuki Marzuki Marzuki Marzuki Marzuki Marzuki Media, Muhammad Agmar Muhammad Agmar Media Muhammad Haiqal Muhammad Ichsan Muhammad Ichsan Muhammad Yahya Ningsiah Novandri, Andri Rahmi, Qairul Salat, Junaidi Saputra, Teuku Yasman Setiawati, Cut lilis Teuku Fadhli Teuku Yasman Saputra Tri Mulya Dharma Yuwaldi Away