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All Journal Jurnal Teknologi Informasi kawanad
Andri Novandri
Universitas Serambi Mekkah
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pelatihan Internet of Things Berbasis Embedded System untuk Meningkatkan Kompetensi Penelitian Mahasiswa Teknologi Informasi Yuwaldi Away; Andri Novandri; Isyatur Raziah
Kawanad : Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2026): March
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/kjpkm.v5i1.390

Abstract

The rapid advancement of Internet of Things (IoT) technology requires Information Technology students to go beyond theoretical understanding and develop hands-on technical skills in building functional real-world systems. This community service activity aims to strengthen students' research competence through integrated training on Embedded Systems and IoT. The implementation approach encompasses theoretical instruction on foundational concepts, hardware familiarization, and direct practice in programming and system integration. Training materials cover the use of the ESP8266 microcontroller, temperature and humidity sensors (DHT22), voltage and current sensors (INA219), the Node-RED platform, and Cloud Server-based databases. Data transmission was carried out using an internet-based communication protocol, while Node-RED served as the primary platform for data flow management and real-time dashboard visualization. The outcomes demonstrate measurable improvement in participants' ability to design remote monitoring systems, perform real-time data visualization, and apply datalogging techniques. These competencies are expected to support the quality of undergraduate final research projects and technology-based innovation initiatives relevant to current societal needs.  
Pemodelan Daya Photovoltaic Berdasarkan Distribusi Termal Menggunakan Algoritma Support Vector Regression Isyatur Raziah; Andri Novandri; Cut Mutia; Yuwaldi Away
Jurnal Teknologi Informasi Vol 5, No 1 (2026): Mei
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jti.v5i1.14747

Abstract

Kinerja photovoltaic (PV) sangat dipengaruhi oleh karakteristik termal, terutama temperatur yang berdampak langsung terhadap daya keluaran. Pada kondisi nyata, distribusi temperatur pada permukaan panel tidak selalu seragam, sehingga pemodelan berbasis temperatur rata-rata sering kali kurang akurat. Penelitian ini bertujuan untuk memodelkan daya keluaran PV berdasarkan distribusi temperatur menggunakan algoritma Support Vector Regression (SVR). Variabel input yang digunakan meliputi temperatur atas dan bawah panel, irradiance matahari serta kelembapan udara, sementara daya keluaran PV dijadikan sebagai variabel target. Model SVR diterapkan dengan fungsi kernel Radial Basis Function (RBF) untuk menangkap hubungan nonlinier antara variabel input dan output. Hasil pengujian menunjukkan bahwa akurasi model meningkat seiring dengan bertambahnya jumlah dan variasi dataset, dengan performa terbaik diperoleh pada dataset 10 hari yang menghasilkan nilai error rendah serta nilai  dan   yang tinggi. Temuan ini menunjukkan bahwa SVR efektif dan andal dalam memprediksi daya keluaran PV berbasis distribusi temperatur panel.
Co-Authors Cut Mutia Isyatur Raziah Yuwaldi Away