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All Journal International Journal Of Science, Technology & Management (IJSTM) RENATA Jurnal Pengabdian Masyarakat Kita Semua Journal of Digital Market and Digital Currency
Stephanus, Alphin
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation Other

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Pemanfaatan Pembangkit Listrik Tenaga Surya (PLTS) Untuk Kantor Negeri Rutong, Kecamatan Leitimur Selatan, Kota Ambon Mbitu, Elisabeth Tansiana; Stephanus, Alphin; Wattimena, Sefnath Johanis; Suatkab, Syukri Gazali; Salamoni, Thenny Daus
RENATA: Jurnal Pengabdian Masyarakat Kita Semua Vol. 3 No. 2 (2025): Renata - Agustus 2025
Publisher : PT Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/1.renata.192

Abstract

Listrik merupakan salah satu kebutuhan dasar masyarakat baik di rumah, perkantoran, fasilitas pendidikan, layanan kesehatan, maupun sektor industri, karena mendukung hampir seluruh aktivitas sehari-hari dan operasional berbagai layanan publik serta kegiatan ekonomi. Demikian pula halnya dengan kantor negeri atau desa Rutong, kota Ambon. Sumber energi listrik utama untuk menjalankan administrasi pemerintahan negeri Rutong adalah yang berasal dari Perusahaan Listrik Negara. Permasalahan yang terjadi saat ini adalah ketika terjadi pemadaman maka pelayanan publik yang membutuhkan suplai listrik tidak dapat digunakan. Solusi untuk mengatasi permasalahan ini adalah dengan memanfaatkan energi matahari sebagai sumber energi terbarukan, yang dikenal sebagai Pembangkit Listrik Tenaga Surya (PLTS). Oleh karena itu, tujuan kegiatan ini adalah mendesain dan mengimplementasikan sistem kelistrikan PLTS di kantor negeri Rutong. Metode yang digunakan meliputi survei kebutuhan beban listrik, analisis potensi energi surya berdasarkan data radiasi matahari lokal, perhitungan kapasitas sistem PLTS yang diperlukan, dan pemasangan perangkat PLTS di kantor negeri Rutong. Hasil kegiatan menunjukkan bahwa sistem PLTS yang dibangun dapat memenuhi kebutuhan dasar operasional kantor, seperti pencahayaan, perangkat komputer, dan peralatan elektronik lainnya. Sistem yang telah dikembangkan ini merupakan solusi yang berkelanjutan dan ramah lingkungan. Selain itu, implementasi sistem ini tidak hanya meningkatkan kemandirian energi, tetapi juga mendukung agenda pembangunan negeri untuk mengurangi ketergantungan terhadap pasokan listrik konvensional
ESP32-Based Real-Time Blood Typing Detection for Emergency and Rural Use Stephanus, Alphin; Tansiana Mbitu, Elisabeth; Gazali Suatkab, Syukri; Daus Salamoni, Thenny
International Journal of Science, Technology & Management Vol. 6 No. 4 (2025): July 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i4.1340

Abstract

Accurate blood group identification is essential in emergency medical care, especially in rural or remote areas with limited access to laboratory facilities. This paper presents the design and development of a low-cost, real-time blood typing detection device focused on the ABO blood group system. The device utilizes an ESP32 microcontroller to process optical input signals based on agglutination reactions between blood samples and antisera. Measurement results are displayed directly on an LCD screen, providing immediate and easy-to-read feedback without the need for internet connectivity or external devices. The system is compact, portable, and user-friendly, making it ideal for use in field operations, mobile clinics, or basic healthcare units. Initial testing shows the device is capable of accurately determining blood types A, B, AB, and O in real-time, supporting rapid decision-making in critical situations.
Enhancing Short-Term Price Prediction of TON-IRT Using LSTM Neural Networks: A Machine Learning Approach in Blockchain Trading Analytics Stephanus, Alphin; Mbitu, Elisabeth Tansiana
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.43

Abstract

This study explores the application of Long Short-Term Memory (LSTM) neural networks for predicting short-term price movements of the TON-IRT trading pair in the cryptocurrency market. Given the high volatility and complexity of cryptocurrency prices, traditional models like Linear Regression and ARIMA often fail to capture the underlying non-linear and temporal dependencies. To address this, we implemented an LSTM model, a type of recurrent neural network specifically designed for sequential data. The model was trained on historical hourly data, utilizing various technical indicators and lagged features to improve prediction accuracy. Our results demonstrated that the LSTM model significantly outperformed traditional methods, achieving a Mean Absolute Error (MAE) of 0.0274, a Root Mean Squared Error (RMSE) of 0.0321, and an R-squared (R²) value of 0.8743, which indicated that the model captured over 87% of the variance in the actual price data. Visual analysis of predicted versus actual prices revealed a strong alignment, though some lag in predictions during high-volatility periods was observed. The model also showed a tendency to underestimate price peaks, highlighting areas for further refinement. This study contributes to the field of blockchain trading analytics by demonstrating the effectiveness of LSTM models in addressing the unique challenges of cryptocurrency price prediction. Practical implications for traders and investors include the ability to enhance trading strategies, optimize entry and exit points, and improve risk management. Future research could integrate additional external factors, such as market sentiment and news events, or explore advanced architectures like Transformer models. By doing so, the predictive capabilities of LSTM models in volatile markets like cryptocurrency could be further refined, leading to more robust and accurate forecasting tools for financial decision-making.
Co-Authors Daus Salamoni, Thenny Gazali Suatkab, Syukri Mbitu, Elisabeth Tansiana Salamoni, Thenny Daus Suatkab, Syukri Gazali Wattimena, Sefnath Johanis