Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Jurnal Pengabdian kepada Masyarakat
Stephanie Christina Yolanda Pardede
Politeknik Negeri Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Literasi Artificial Intelligence (AI) bagi Pemuda/i Gereja: Peluang, Tantangan, dan Etika di Era Digital Joel Panjaitan; Arnold Pakpahan; Regina Sirait; Yunni Rianawati; Stephanie Christina Yolanda Pardede; Thea Fitri Astarani; Moh Muchlishiin
Karya Unggul: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2026): Edisi Juni
Publisher : Karya Unggul: Jurnal Pengabdian Kepada Masyarakat

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

Abstract

Perkembangan Artificial Intelligence (AI) telah membawa perubahan besar dalam berbagai aspek kehidupan, termasuk pendidikan, pekerjaan, pelayanan gereja, dan aktivitas sosial masyarakat. Namun demikian, masih banyak kalangan pemuda gereja yang belum memiliki pemahaman yang memadai mengenai pemanfaatan AI secara bijaksana dan bertanggung jawab. Oleh karena itu, tim dosen Akademi Teknik Deli Serdang berkolaborasi dengan dosen Politeknik Negeri Medan melaksanakan kegiatan Pengabdian kepada Masyarakat (PkM) bertema “Literasi Artificial Intelligence (AI) bagi Pemuda/i Gereja: Peluang, Tantangan, dan Etika di Era Digital” di GPdI Maranatha Matiti, Desa Matiti, Kecamatan Doloksanggul, Kabupaten Humbang Hasundutan pada tanggal 28 Juni 2026. Metode pelaksanaan kegiatan meliputi penyampaian materi, demonstrasi penggunaan berbagai aplikasi AI, diskusi interaktif, tanya jawab, serta evaluasi pemahaman peserta melalui pre-test dan post-test sederhana. Materi yang diberikan mencakup pengenalan konsep AI, peluang pemanfaatan AI dalam pendidikan dan pelayanan gereja, risiko penyalahgunaan AI, serta etika penggunaan AI berdasarkan nilai-nilai moral dan tanggung jawab sosial. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta terhadap konsep AI, manfaat AI dalam kehidupan sehari-hari, serta kesadaran mengenai pentingnya penggunaan AI secara etis. Peserta juga menunjukkan antusiasme tinggi selama kegiatan berlangsung dan mampu mengidentifikasi berbagai bentuk penggunaan AI yang positif maupun negatif. Kegiatan ini diharapkan dapat menjadi langkah awal dalam meningkatkan literasi digital masyarakat, khususnya generasi muda gereja, sehingga mampu memanfaatkan teknologi AI secara produktif, bertanggung jawab, dan sesuai dengan nilai-nilai etika.
Sistem Pendukung Keputusan Berbasis IoT untuk Monitoring Kinerja Panel Surya dan Pemeliharaan Prediktif Stephanie Christina Yolanda Pardede; Thea Fitri Astarani; Surya Hardi; Aprima Anugerah Matondang; Regina Sirait
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 4 (2026): Agustus 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i4.9169

Abstract

Solar energy is a critical component of renewable energy strategies; however, photovoltaic (PV) system performance is frequently degraded by environmental factors such as dust accumulation, shading, and component failures. These issues lead to reduced energy yield and increased operational costs. This research develops an IoT-based decision support system (DSS) for real-time performance monitoring and predictive maintenance scheduling of solar panels. The system integrates sensor data on voltage, current, irradiance, and temperature, transmitted via a wireless network to a cloud-based analytics platform. A fuzzy logic algorithm evaluates panel health and triggers maintenance recommendations when performance deviations exceed pre-defined thresholds. The system was tested under simulated real-world conditions. Results demonstrate that the proposed DSS effectively detects performance degradation due to soiling and module-level faults, achieving a detection accuracy of 94% as indicated by the correlation coefficient between predicted and actual maintenance urgency, enabling timely maintenance interventions. This approach contributes to the field of intelligent renewable energy monitoring by providing a reliable, data-driven tool that reduces unnecessary maintenance costs and maximizes the operational lifespan of PV installations.
Expert System for Electric Vehicle Charging Infrastructure Planning: A Scenario-Based Decision Support Framework for Two-Wheeler and Bus Fleets Thea Fitri Astarani; Stephanie Christina Yolanda Pardede; Aprima Anugerah Matondang; Junaidi Junaidi
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 4 (2026): Agustus 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i4.9209

Abstract

The rapid adoption of electric vehicles (EVs) presents significant challenges in charging infrastructure planning, particularly for diverse vehicle fleets in developing regions. This study proposes an expert system-based decision support framework for EV charging infrastructure planning, specifically addressing two-wheeler and bus fleet scenarios. The framework integrates scenario-based demand characterization with rule-based inference to provide location recommendations and capacity planning decisions. Using data from Indonesian urban transportation contexts, the expert system evaluates multiple criteria including demand patterns, infrastructure availability, and energy consumption profiles. Results indicate that the proposed framework successfully identifies optimal charging locations with 87.5% accuracy compared to expert validation, while reducing planning time by approximately 40% compared to conventional methods. The system provides decision-makers with actionable recommendations for prioritizing infrastructure investments across different fleet types. This research contributes to the growing body of knowledge on AI-based decision support systems for sustainable transportation infrastructure, with implications for policy formulation and urban planning in developing economies.
Co-Authors Aprima Matondang Arnold Pakpahan Hardi, Surya Joel Panjaitan Junaidi Junaidi Moh Muchlishiin Regina Sirait - Thea Fitri Astarani Yunni Rianawati