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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding KOMMIT Majalah Ilmiah Matematika Komputer International Journal of Robotics and Control Systems
Busono Soerowirdjo
Gunadarma University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mathematics

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IMPLEMENTASI SANDI HILL DALAM PEMBUATAN PROGRAM APLIKASI PENYANDIAN CITRA Soerowirdjo, Busono; Wibowo, Ardian Adi
Majalah Ilmiah Matematika Komputer 2007: MAJALAH MATEMATIKA KOMPUTER EDISI APRIL
Publisher : Majalah Ilmiah Matematika Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (956.89 KB)

Abstract

Sandi Hill merupakan salah satu ieknik penyandian teks. Dalam penelilian ini, pemakaian sandi Hilldiperluas dari teks menjadi citra 24 bit. Matriks yang dipakai adalah matriks yang berordo 2x2 dan3x3. Hasil percobaan menunjukkan bahwa sandi Hill cocok digunakan untuk enkripsi citra denganvariasi nilai Merah Hijau Biru (MHB) antar piksel berdekatan yang tinggi (seperti foto), tapi tidakcocok untuk citra dengan variasi nilai MHB yang rendah (seperti gambar kartun) karena pola citraasli masih tampak dalam citra sandi. Sandi Hill juga memiliki kelemahan dalam hal tidak tunggalnyamalriks kunci yang dapat dipakai. Akan tetapi untuk pemakaian biasa, dengan pemilihan matrikskunci yang baik, sandi Hill dapat dipakai untuk penyandian karena hanya melibatkan operasimatriks biasa sehingga prosesnya relatif cepat.Kata Kunci : Sandi Hill, Citra, MHB.
DISAIN DAN SIMULASI RANGKAIAN DETEKTOR DETAK JANTUNG JANIN MENGGUNAKAN TEKNOLOGI CMOS 0.35m Salahuddin, Nur Sultan; Sari, Sri Poernomo; Soerowirdjo, Busono
Prosiding KOMMIT 2014
Publisher : Prosiding KOMMIT

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Abstract

Rangkaian detektor detak jantung janin telah berhasil dirancang denganmenggunakan teknologi CMOS 0.35um. Hasil simulasi menunjukan bahwarangkaian detektor ini dapat mendeteksi frekuensi 2 sampai 3 hz. Rangkaian inisiap di realisasikan sebagai detektor detak jantung janin.
Herbal plant leaves classification for traditional medicine using convolutional neural network Fauzi, Alfharizky; Soerowirdjo, Busono; Haryatmi, Emy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3322-3329

Abstract

The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.
Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings Rofii, Ahmad; Soerowirdjo, Busono; Irawan, Rudi; Caesarendra, Wahyu
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1351

Abstract

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.
Integration of strain gauge sensor in biceps muscle movement detection using LabView Kristyawati, Desy; Soerowirdjo, Busono; Christina, Erma Triawati; Harahap, Robby Kurniawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3696-3706

Abstract

Muscle injuries caused by sports can have a serious impact on sportsmen, to avoid injuries during sports can be prevented by detecting the wrong movement using a strain gauge sensor attached to the muscle which in this study is devoted to the biceps muscle. The strain gauge will detect muscle movement, and the output generated at the strain gauge will be converted into the form of voltage and current which will be used to be processed using machine learning to get data patterns so that they can be grouped into data patterns of wrong movements and correct movements. The strain gauge movement pattern here is simulated using LabView by using a gauge resistance of 120 Ω, strain configuration Quarter Bridge 1, gauge factor 2.05, Vex is the excitation voltage given to the Wheatstone bridge is 5 V and the initial voltage -180.08 µV, the strain gauge output pattern is obtained in the form of Excel and with this data can be converted into voltage and current.
Co-Authors Ahmad Rofii Ardian Adi Wibowo Christina, Erma Triawati Emy Haryatmi Fauzi, Alfharizky Harahap, Robby Kurniawan Kristyawati, Desy Nur Sultan Salahuddin Rudi Irawan Sri Poernomo Sari Wahyu Caesarendra