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All Journal Telematika JURNAL SIMETRIK Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Jurnal Pengabdian Masyarakat Iron (sipil, elektro,mesin) International Journal Of Science, Technology & Management (IJSTM) Jurnal ELKO (Elektrikal dan Komputer) Journal of Health, Education, Economics, Science, and Technology (J-HEST) RENATA Jurnal Pengabdian Masyarakat Kita Semua EBAMUKAI; JURNAL PENGABDIAN ILMU PENGETAHUAN DAN TEKNOLOGI Journal of Digital Market and Digital Currency
Mbitu, Elisabeth Tansiana
Politeknik Negeri Ambon
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 Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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.
RANCANG BANGUN PROTOTIPE ALAT MONITORING TEGANGAN, ARUS, DAYA, DAN SUHU PADA TRANSFORMATOR DISTRIBUSI DENGAN BERBASIS INTERNET OF THINGS (IOT) Siti Fatmalia Tuanany; Mbitu, Elisabeth Tansiana; Jamlaay, Marselin
JURNAL SIMETRIK Vol 14 No 1 (2024): Jurnal Simetrik (Sipil, Mesin, Listrik)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31959/js.v14i1.1822

Abstract

Transformator memiliki peran yang sangat penting dalam penyaluran energi listrik hal ini dikarenakan transformator berfungsi untuk menaikkan dan menurunkan tegangan. Oleh karena itu perlu dilakukan pemeliharaan berupa pemantauan dan pengukuran pada transformator distribusi hal ini dilakukan untuk menjaga kinerja transformator. Namun, pada kondisi tertentu gangguan pada transformator terlambat untuk dideteksi dikarenakan posisi transformator kadang sulit dijangkau. Berdasarkan keterbatasan tersebut maka diperlukan adanya monitoring pada transformator dari jarak jauh untuk meningkatkan efisiensi serta dapat mengetahui kondisi transformator secara real-time. Tujuan dari penelitian ini untuk merancang dan membangun sebuah prototipe alat yang dapat memonitoring tegangan, arus, daya , dan suhu pada transformator distribusi secara real-time dengan memanfaatkan IOT. Metode penelitian ini adalah metode eksperimental dengan tahapan penelitian yaitu studi literatur dan observasi, perancangan prototipe, pengumpulan alat dan bahan, pembuatan alat dan program, dan pengujian prototipe. Prototipe alat monitoring juga dilengkapi dengan sensor suhu yang akan memberikan notifikasi bahaya apabila suhu melebihi 400C. Nilai pembacaan alat monitoring akan ditampilkan pada LCD alat dan aplikasi Blynk pada smartphone. Hasil pengujian menunjukan bahwa prototipe alat monitoring yang dibuat dapat bekerja dengan baik. Perbandingan dengan alat ukur digital memperlihatkan nilai perbedaan pengukuran tegangan sebesar 0.02%,  pengukuran arus sebesar 0.04% dan pengukuran daya yaitu 0.03%.
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.
Development of a Multi-point IoT Based Ship Draft Monitoring System for Barge Mounted Power Plants Elisabeth Tansiana Mbitu; Marceau A. F. Haurissa; Fatima Zahra
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3414

Abstract

Continuous monitoring of ship draft is essential for maintaining vessel stability, operational safetakay, and fuel distribution efficiency, particularly in Barge Mounted Power Plants (BMPP), where load variations directly influence the vessel’s balance. At the PLTMG BMPP Nusantara-1, draft measurements are currently performed through manual visual inspection of draft marks, making the process susceptible to human error and unsuitable for continuous real time monitoring. This study proposes the development of a multipoint Internet of Things (IoT) based ship draft monitoring system employing six waterproof JSN-SR04T ultrasonic sensors integrated with an Arduino Mega, ESP32 communication module, RTC DS3231, and Arduino IoT Cloud platform. The proposed system continuously measures draft values at the port and starboard sides along the bow, midship, and stern, enabling comprehensive monitoring of vessel trim and heel conditions. Measurement data are transmitted in real time to a cloud dashboard and automatically stored in Google Sheets for historical data logging. Ten latency trials produced a mean end to end response time of 30.00 s. In addition, the system incorporates an automatic warning mechanism that detects overdraft, minimum draft, and vessel imbalance conditions based on predefined operational thresholds. Experimental evaluation using a laboratory scale prototype of the BMPP Nusantara-1 demonstrates that the proposed system successfully performs continuous draft monitoring, provides real time visualization with an observed 30 s response interval, records historical measurement data, and generates timely warning notifications under various operating scenarios, including overdraft, heel, and minimum draft conditions. The developed system offers a practical IoT based solution for improving operational safety and supporting digital monitoring of floating power plants.
Co-Authors Angel Merlyn Pattimahu Annisa Qurraisyn Qurraisyn Daus Salamoni, Thenny Deny Ismail Pellu Dirk Berly Tehuayo Fatima Zahra Gazali Suatkab, Syukri Gelvan Tuapetel Hamles Latupeirissa Johanis Tupalessy Marceau A. F. Haurissa Marselin Jamlaay Mey Chyntia Yesaya Parera, Lory Marcus Persulessy, Sylvia Irene Sahusilawane, Ferdiaztha Rendy Salamoni, Thenny Daus Salamony, Theny Daus Siti Fatmalia Tuanany Stephanus, Alphin Suatkab, Syukri Gazali Thenny Daus Salamonit Wattimena, Sefnath J. Wattimena, Sefnath Johanis Widianto .