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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika The Indonesian Journal of Computer Science
Wahyudi, Niko
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Safe Security System Using Face Recognition Based on IoT Putra, Ondra Eka; Devita, Retno; Wahyudi, Niko
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12231

Abstract

Face recognition is widely used in various applications, especially in the field of surveillance and security systems. This study aims to design and build a safe security system using face recognition via camera based on internet of things. This system uses the Raspberry Pi 3B and the OpenCV library as face recognition data processing which produces output on the Selenoid to open and close the safe, LCD 16x2 to display system status, IoT-based email delivery when smugglers occur. This study performs face recognition through the face detection stage using the Viola Jones method, feature extraction using the PCA (Principal Component Analysis) method and face recognition, then matched with the existing profile data in the directory. The results of this study indicate that the safe is open when a face is detected and will send a face capture to the e-mail address of the owner’s safe if the detected face is not recognized. Tests carried out on the safe security system using face recognition based on IoT build reach validity 90,25%.
Deteksi Anomali Hasil Pengukuran Penakar Hujan Otomatis Menggunakan Metode Long Short Term Memory Wahyudi, Niko
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4707

Abstract

Perkembangan teknologi memungkinkan Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) untuk melakukan pengamatan curah hujan secara otomatis menggunakan peralatan penakar hujan otomatis. Namun, peralatan ini berpotensi menghasilkan data curah hujan yang tidak valid akibat kerusakan sensor atau gangguan lingkungan. Penelitian ini bertujuan untuk mendeteksi anomali hasil pengukuran penakar hujan otomatis dengan metode Long Short Term Memory (LSTM) untuk memastikan validitas data dan mempercepat perbaikan peralatan yang mengalami malfungsi. Deteksi anomali dilakukan melalui metode quality control (QC) berbasis range dan step check, spatial check, serta error check yang menghasilkan label Total Anomali QC. Label ini kemudian ditransformasikan menggunakan one-hot encoding dan digunakan sebagai input dalam model klasifikasi berbasis LSTM. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan anomali data dengan akurasi lebih dari 90% pada kluster barat, timur, dan pesisir, sehingga memungkinkan deteksi anomali yang lebih akurat dan efisien. Hasil penelitian ini berkontribusi dalam meningkatkan keandalan pengukuran curah hujan otomatis dan mendukung upaya BMKG dalam menjaga kualitas data cuaca dan iklim.
Anomaly Detection of Automatic Rain Gauge Measurement Using Artificial Neural Network Long Short Term Memory Method Wahyudi, Niko
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.13858

Abstract

Purpose: The purpose of this research is to accurately detect anomalies in the results of automatic rain gauge measurements using the Long Short Term Memory (LSTM) method, so that measurement errors can be immediately identified and the equipment can be repaired immediately.  Design/methodology/approach: Detection of anomalies from rain gauge measurements is carried out using quality control (QC) methods based on range and step check, spatial check and error check which produce anomaly labels which are totaled to become Total Anomaly QC. Total Anomaly QC is transformed via one-hot encoding and then the results of the Total QC data transformation are used to build an anomaly detection classification model using the LSTM algorithm.Findings/result: The model performance was tested with a confusion matrix. LSTM is able to classify data anomalies in the western, eastern and coastal clusters quite well. The accuracy value of these clusters is more than 0.9, so that >90% of the anomalies are classified correctly. The results of this research can improve BMKG's ability to detect rainfall measurement anomalies from automatic rain gauges and assist in maintaining the validity of rainfall data so that equipment maintenance is carried out on time.Originality/value/state of the art: This research uses different methods and parameters from previous research. The results obtained are quite satisfactory as shown by an accuracy above 0.9.
Co-Authors Devita, Retno Putra, Ondra Eka