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All Journal JMM (Jurnal Masyarakat Mandiri) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Komtekinfo Jurnal Pengabdian Masyarakat Dharma Andalas CSRID Culture education and technology research
Halifia Hendri
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Public Health Social Sciences Other

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Rancang Bangun Prototipe Pengukuran dan Pemantauan Suhu, Kelembapan Serta Cahaya Secara Otomatis Berbasis IOT Pada Rumah Jamur Merang Ifvan Yurisvi; Okta Andrica Putra; Halifia Hendri
Culture education and technology research (Cetera) Vol. 3 No. 1 (2026): Vol.3 No.1 2026
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i4.218

Abstract

Rancang bangun prototipe ini bertujuan menghasilkan sistem pengukuran dan pemantauan kondisi lingkungan rumah jamur merang yang bekerja otomatis dan dapat dipantau jarak jauh berbasis Internet of Things (IoT). Sistem memanfaatkan ESP8266 sebagai penghubung perangkat ke internet sehingga seluruh data sensor dapat dikirim dan ditampilkan secara real-time pada dashboard aplikasi Blynk. Untuk aspek keamanan akses, prototipe dilengkapi RFID sehingga hanya pengguna berwenang yang dapat mengakses sistem serta data hasil pembacaan sensor. Parameter yang dipantau meliputi suhu dan kelembapan udara menggunakan sensor DHT11, kelembapan tanah menggunakan sensor soil moisture, tingkat keasaman tanah menggunakan sensor pH tanah, serta intensitas cahaya menggunakan sensor LDR, di mana seluruh nilai pengukuran ditampilkan pada antarmuka Blynk. Mekanisme kendali otomatis diterapkan pada dua kondisi utama: ketika suhu/kelembapan tanah mendekati nilai yang tidak mendukung pertumbuhan jamur merang, waterpump akan mengalirkan air untuk menjaga kelembapan tanah tetap sesuai; dan ketika intensitas cahaya terlalu rendah atau terlalu tinggi, motor servo akan mengatur pencahayaan melalui bilik pintu rumah jamur. Dengan integrasi pemantauan dan aktuasi tersebut, prototipe diharapkan meningkatkan efisiensi pengelolaan lingkungan budidaya, mempercepat respons terhadap perubahan kondisi, serta mempermudah pengawasan rumah jamur merang secara berkelanjutan.
Perancangan Gazebo Sehat dengan Atap Otomatis Berbasis IoT dan Notifikasi Blynk Haniva Budiana Deswary; Retno Devita; Halifia Hendri
Culture education and technology research (Cetera) Vol. 3 No. 1 (2026): Vol.3 No.1 2026
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i4.222

Abstract

Perancangan gazebo sehat dengan atap otomatis berbasis Internet of Things (IoT) dan notifikasi Blynk bertujuan untuk meningkatkan kenyamanan serta mendukung aspek kesehatan pengguna di ruang terbuka melalui sistem yang adaptif terhadap perubahan cuaca. Di wilayah tropis seperti Indonesia, cuaca yang tidak menentu dan paparan sinar matahari berlebihan dapat memengaruhi aktivitas serta kondisi fisik masyarakat. Paparan sinar matahari pagi pada pukul 06.00 hingga 10.00 diketahui membantu proses pembentukan vitamin D yang berperan penting bagi kesehatan tulang dan daya tahan tubuh. Oleh karena itu, sistem dirancang mempertahankan atap tetap terbuka pada rentang waktu optimal tersebut. Sistem ini menggunakan sensor hujan, sensor suhu (DHT), sensor cahaya (LDR), dan sensor ultrasonik yang terhubung dengan mikrokontroler ESP32 untuk membaca kondisi lingkungan secara real-time serta mengontrol mekanisme buka tutup atap secara otomatis sesuai parameter yang ditetapkan. Ketika terdeteksi hujan, peningkatan suhu, atau perubahan intensitas cahaya di luar batas aman, atap akan menutup sebagai bentuk perlindungan terhadap cuaca ekstrem dan menjaga ketahanan bangunan. Aplikasi Blynk diintegrasikan sebagai media pengontrolan jarak jauh dan sistem notifikasi berbasis internet. Hasil pengujian menunjukkan sistem bekerja responsif, stabil, dan sesuai perancangan.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Halifia Hendri; Larissa Navia Rani; Sofika Enggari; Agung Ramadhanu; Febri Hadi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1298

Abstract

Concrete surface defect detection is a critical aspect of maintaining the integrity and safety of infrastructure in civil engineering. Traditional manual inspection methods are time-consuming, prone to human subjectivity, and often limited by physical accessibility, necessitating the development of robust automated solutions. This paper presents an automated pixel-level concrete surface defect detection system utilizing the U-Net deep learning architecture. The primary contribution and novelty of our approach lie in optimizing the network's encoder-decoder structure with skip connections to effectively capture both broad contextual features and precise spatial localization. This overcomes the critical limitations of existing traditional methods, which frequently struggle with complex concrete background textures, inherent noise, and uneven illumination. To validate our approach, the proposed U-Net model is systematically compared against a widely used baseline method, K-Means clustering combined with Gray-Level Co-occurrence Matrix (GLCM) texture analysis. The evaluation was conducted using a comprehensive dataset consisting of 1000 high-resolution concrete images. Experimental results reveal that the deep learning architecture vastly outperforms the traditional baseline. Specifically, the U-Net model achieved an outstanding F1-Score of 92.47%, a precision of 93.18%, and a mean Intersection over Union (mIoU) of 86.55%. In stark contrast, the K-Means and GLCM approach only yielded an F1-Score of 69.83% and an mIoU of 54.21%. These quantitative findings demonstrate that the proposed U-Net-based system not only successfully minimizes false segmentations but also provides a highly reliable, efficient, and scalable computational framework. Ultimately, this research delivers a practical solution that can be seamlessly integrated into continuous automated structural health monitoring systems, paving the way for safer and more proactive civil infrastructure management.
Co-Authors Aggy Pramana Gusman Awal, Hasri Chan, Fajri Rinaldi Dendi Novrizal Devita, Retno Devita, Retno Dicky Wiransyah Emil Salim Enggari, Sofika Febri Hadi Haniva Budiana Deswary Harkamsyah Andrianof Hasri Awal Husna Arsyah, Rahmatul Ifvan Yurisvi Larissa Navia Rani Mardison Mardison Mardison Mardison Mardison Muhammad Hafizh Muhammad Pondrinal Negoro, Wahyu Saptha Okta Andrica Putra Ramadhanu, Agung - Rianti, Eva Riska Robianto Riska Robianto Romi Wijaya Sarjon Defit Silfia Andini, Silfia Sofika Enggari Yasmin, Nabilla Yuhandri Yuhandri Yusvi Diana