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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Transmisi: Jurnal Ilmiah Teknik Elektro Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Mahasiswa TEUB Jurnal EECCIS Bulletin of Electrical Engineering and Informatics Network Engineering Research Operation [NERO] Indonesian Journal of Electrical Engineering and Computer Science Jurnal INFOTEL
Mudjirahardjo, Panca
Jurusan Teknik Elektro Fakultas Teknik Universitas Brawijaya
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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SISTEM PENDETEKSIAN TANDA PENGENAL DI SEBUAH GEDUNG UNTUK MENENTUKAN SASARAN TEMBAK MUSUH BERBASIS TEMPLATE MATCHING Riza Hasbi Ash Shiddieqy; Rahmadwati Rahmadwati; Panca Mudjirahardjo
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 25, No 1 Januari (2023): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.25.1.32-40

Abstract

Pengenalan dari berbagai sudut pandang atau point of view untuk mendeteksi keberadaan seseorang dengan memanfaatkan pengolahan citra digital dapat dijadikan pembeda dari berbagai karakteristik dalam suatu objek gambar yang ada. Pencarian dan pembuatan database citra adalah salah satu komponen yang berperan sebagai informasi visual. Pengenalan citra dari bentuk dan posisi objek yang akan dideteksi merupakan hasil uji yang akan merepresentasikan seberapa cocok dengan input gambar terdeteksi dengan benar. Objek akan deberikan noise serta halangan atau obstacle contoh ada beberapa orang yang menutupi objek atau gedung dan pohon. Dalam penelitian ini menerapkan template matching merupakan sebuah teknik pengolahan citra digital untuk menemukan bagian kecil dari gambar yang sesuai dengan tempalate gambar. Aplikasi ini dirancang menggunakan matlab 2019b sebagai software pembantu dan membuat database pengolahan citra.
KLASIFIKASI ALZHEIMER PADA CITRA MRI OTAK DENGAN CONVOLUTIONAL NEURAL NETWORK Muhammad Rafi’ Zaidan Maajid; Panca Mudjirahardjo; Akhmad Zainuri
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

In deep learning, Convolutional Neural Network (CNN) is an algorithm from Artificial Neural Network (ANN) which is generally used to analyze visual images. This algorithm can automatically extract important features from each image without human assistance, besides that the CNN algorithm is also more efficient than other neural network methods, especially in memory and complexity. In training, the algorithm will be given training data in the form of images that have been labeled so that the algorithm will be able to recognize the important characteristics of each of the labeled images. After the training stage, the trained algorithm will be given data validation in the form of an unlabeled image to be analyzed and classified. The algorithm will analyze the training and validation data for the specified number of epochs and provide information in the form of the level of accuracy of each epoch that is performed. Some that affect the level of accuracy include the type of optimizer, the pixel size of the input image, and the number of epochs. In this study, the CNN algorithm was used with a layer sequence made personally by the author. The research was conducted in a cloud-based Jupyter notebook environment called Google Colab. The dataset used in this study is the Alzheimer's MRI Preprocessed Dataset which can be accessed by the public on the Kaggle website. The dataset consists of 6400 brain MRI scan images which are divided into four classes, namely: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. As much as 20% of the dataset is used as data validation. In this study, the dataset will be analyzed by the CNN algorithm with several predetermined scenarios, then the accuracy of the training and validation data will be compared with each other to find the most optimal scenario. There are two input image pixel size scenarios to be compared, namely 128 x 128 pixels and 224 x 224 pixels. There are three types of optimizers that will be compared, namely Stochastic Gradient Descent (SGD), Adam, and RMSprop. From the research results, the most optimal type of optimizer to use with the architecture that has been made and the Alzheimer's MRI Preprocessed Dataset is the Adam optimizer. Architectural training with an input size scenario of 224 x 224 pixels, seven epochs, and using the Adam optimizer achieves the most optimal accuracy rate, namely with a training data accuracy rate of 93.01% and a data validation accuracy rate of 94.45%. Architecture training with an input size scenario of 224 x 224 pixels and using the Adam optimizer achieves the most optimal number of epochs, namely achieving an accuracy level above 90% in just five epochs. Keywords: CNN, Alzheimer's, accuracy, optimizer, optimal. Daftar Pustaka [1] Burns, A., & Iliffe, S. (2009). Alzheimer's disease. Bmj-British Medical Journal, 338. [2] Dementia. (2022, 20 September). https://www.who.int/news-room/factsheets/detail/dementia [3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. [4] Khan, S., Barve, K. H., & Kumar, M. S. (2020). Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’sdisease. Current Neuropharmacology, 18(11), 1106-1125. [5] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [6] Mendez, M. F. (2006). The accurate diagnosis of early-onset dementia. The International Journal of Psychiatry in Medicine, 36(4), 401-412. [7] Mortimer, J. A., Borenstein, A. R., Gosche, K. M., & Snowdon, D. A. (2005). Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. Journal of geriatric psychiatry and neurology, 18(4), 218-223. [8] National Institute for Health and Clinical Excellence. (2006, November). Dementia: Quick Reference Guide. Diambil kembali darihttps://web.archive.org/web/20080227161412/http://www.nice.org.uk/nicemedia/pdf/CG042quickrefguide.pdf. [9] Simon, R. P., Aminoff, M. J., & Greenberg, D. A. (2009). Clinical neurology. Lange Medical Books/McGraw-Hill. [10] Smith, M. A. (1998). Alzheimer disease. International review of neurobiology, 42, 1-54. [11] Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and computers in simulation, 177, 232-243.
RANCANG BANGUN DC-DC FLYBACK CONVERTER DENGAN MODE CCM (CONTINUOUS CONDUCTION MODE) UNTUK APLIKASI FUEL CELL Ahmad Syafiq Kanzul Fikri; Waru Djuriatno; Panca Mudjirahardjo
Jurnal Mahasiswa TEUB Vol. 11 No. 3 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

Advances in technology and science are rapidly increasing the need for energy. In an effort to meet the increasing demand for energy, a qualified energy source is needed. The biggest source of energy today still comes from fossil fuels which must be reduced dependency to reduce global warming and prevent energy crises in the future. Therefore, a clean and renewable alternative energy source is needed which has great potential to be developed such as fuel cells. As an alternative energy, fuel cells still have problems with their low output DC voltage, so to support fuel cell performance a power electronics device is needed. The flyback topology is a relatively simple power electronics topology compared to other topologies for the low to medium power category. The advantage of the flyback topology is its ability to provide isolation between the input and load sections, with the presence of a transformer (coupled inductor) between the input and output sections. One of the operating modes of the flyback converter is the CCM (Continuous Conduction Mode) operation mode which has the advantage of having a smaller primary current value thereby reducing the conduction losses and switching losses of the flyback converter. This research was carried out by simulating and designing a flyback converter in CCM mode to increase the fuel cell voltage from 24 V to 72 V. In testing the DC-DC flyback converter close loop CCM mode using PI (Proportional Integral) control it has been able to increase the voltage from 24 V to 72 V constantly with an average power efficiency above 80%. The device also always works in CCM mode as indicated by the transformer primary current (Ip) waveform or MOSFET drain-source current (IDS) waveform which is in the shape of a trapezoid. Keywords: Fuel Cell, DC-DC Flyback Converter, Continuous Conduction Mode, Transformator, Coupled inductor, Proportional Integral, Close Loop
KLASIFIKASI PENYAKIT MELANOMA MENGGUNAKAN WHALE OPTIMIZATION ALGORITHM SEBAGAI SELEKSI FITUR Aiman Muhamad Basymeleh; Panca Mudjirahardjo; n/a Rahmadwati
Jurnal Mahasiswa TEUB Vol. 11 No. 4 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

The biggest challenge in melanoma is distinguishing between benign and malignant skin diseases with various problems related to time and patient health to differentiate lesions in patients. To reduce these problems, an image classification system is required using several feature extraction methods on images, namely Gray Level Co-occurrence Matrix, contour features, and HSV image features. In this study, feature selection was also carried out using a metaheuristic algorithm, namely the Whale Optimization Algorithm (WOA), as feature optimization in the next stage, which is the classification stage using the Multilayer Perceptron Neural Network method. However, the results of testing the Multilayer Perceptron Neural Network on these feature extractions showed very good performance, especially in the case of HSV color feature extraction and Gray Level Co-occurrence Matrix (GLCM). In addition, the feature selection also showed the same results from the same feature extraction with a relatively faster prediction time. Keywords : Melanoma, Feature Extraction, Feature Selection, Whale Optimization Algorithm, Classification
EKSTRAKSI CIRI BERDASARKAN KARAKTERISTIK DINAMIS SINYAL MULTISENSOR MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS Muhammad Akbar; Adharul Muttaqin; Panca Mudjirahardjo
Jurnal Mahasiswa TEUB Vol. 11 No. 4 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

This research aims to perform feature extraction on the dynamic characteristics on dynamic characteristic data on multisensor data. The training data consists of aroma recordings from 6 different species of mint at the Botanical Institute of Karlsruhe Institute of Technology (KIT), Germany, recorded using 12 different sensors. The dataset consists of 28,746 data points collected over a period of 175.52 minutes. The data underwent preprocessing to address spiking issues and standardize the data. Three testing stages were conducted, which are Performance testing of Feature Extraction using raw data, Performance testing of Feature Extraction using Piecewise Linear Regression (PLR) Data, and Performance testing of a Classification Model using the k-Nearest Neighbor (k-NN) algorithm. Feature Extraction was performed using the PCA technique to obtain Principal Component (PC) values of the reduced-dimensional data. For the Feature Extraction using raw data, PC1 had a value of 96.35% and PC2 had a value of 1.84%. Meanwhile, for the Feature Extraction using 12 PLR data, PC1 had a value of 95.95% and PC2 had a value of 1.88%. And for the Feature Extraction using 24 PLR data, PC1 had a value of 95.77% and PC2 had a value of 1.75%. The evaluation testing of the Feature Extraction results employed a machine learning model with the k-NN method. The training results showed that the k-NN model using raw data before PCA Feature Extraction achieved an Accuracy of 95.67% with a computation time of 0.3 seconds, and after PCA Feature Extraction, it achieved an Accuracy of 98.7% with a computation time of 0.19 seconds. In contrast, the k-NN model with 12 PLR Pre-processing before PCA Feature Extraction obtained an Accuracy of 56.67% with a computation time of 0.049 seconds, and after PCA Feature Extraction, it achieved an Accuracy of 85.83% with a computation time of 0.041seconds. Similarly, the k-NN model with 24 PLR Pre-processing before PCA Feature Extraction obtained an Accuracy of 61.67% with a computation time of 0.071 seconds, and after PCA Feature Extraction, it achieved an Accuracy of 90.21% with a computation time of 0.063 seconds. These results indicate that PCA Feature Extraction successfully improved the Accuracy of the prediction model, even when the data dimensions were reduced. The development of this system can serve as an alternative for various data analysis and machine learning algorithms. Keywords: Multisensor, Quartz Cristal Microbalance (QCM), Principal Component Analysis (PCA)
IDENTIFIKASI JENIS GAS BERDASARKAN DATA MULTISENSOR DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) Bagus Esa Pramudya; Adharul Muttaqin; Panca Mudjirahardjo
Jurnal Mahasiswa TEUB Vol. 11 No. 4 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

This research aims to develop a method for identifying gas types based on multisensor data using Recurrent Neural Network (RNN) in the context of Electronic Nose (E-Nose) application. The method utilizes Quartz Crystal Microbalance (QCM) sensors that respond to changes in oscillation frequency to detect gases. The data used in this study were obtained from QCM sensor measurements on six species of mint at the Botanical Institute of Karlsruhe Institute of Technology (KIT), Germany, recorded by Shalih Okur. Through the training process using RNN models with ReLU and LeakyReLU activation functions, training accuracies of 98.84% with a computation time of 326 seconds (ReLU) and 97.78% with a computation time of 267 seconds (LeakyReLU) were achieved. Furthermore, in the identification phase, the RNN model achieved accuracies of 79% with a computation time of 10 seconds (ReLU) and 85% with a computation time of 4 seconds (LeakyReLU). These findings indicate the potential of the RNN method for gas type identification based on multisensor data, with a focus on QCM sensor usage. Thus, the results of this study demonstrate the effectiveness of the RNN method in identifying gas types based on multisensor data, particularly when utilizing QCM sensors. Keywords: Multisensor, Recurrent Neural Network (RNN), Gas identification
EKSTRAKSI CIRI BERDASARKAN KARAKTERISTIK DINAMIS SINYAL MULTISENSOR MENGGUNAKAN LINEAR DISCRIMINANT ANALYSIS Reinato Teguh Santoso; Adharul Muttaqin; Panca Mudjirahardjo
Jurnal Mahasiswa TEUB Vol. 11 No. 5 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

This research was conducted to develop a feature extraction system based on the dynamic characteristics of multisensor output data. The Quartz Crystal Microbalance (QCM) sensor was utilized as one type of multisensor that responds to changes in oscillation frequency for gas detection. The data used in this study were measurements of 6 types of mint species using the QCM sensor, taken at the Botanical Institute of Karlsruhe Institute of Technology (KIT) in Germany. The data underwent preprocessing to enhance the system's efficiency, followed by feature extraction using the Linear Discriminant Analysis (LDA) method. The feature extraction testing involved various data variations, resulting in a 58.33% reduction in the number of features from 12 to 5. Subsequently, classification testing was performed using three types of classifier models: k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Decision Tree (DT). Four data variations were used for classification, which included frequency response data before and after LDA, as well as dynamic characteristic data extracted using Piecewise Linear Regression (PLR) before and after LDA. For the k-NN classification, the accuracies obtained were 96.05%, 98.40%, 93.75%, and 95% for each of the four data variations, with computation times of 0.402 s, 0.208 s, 0.027 s, and 0.023 s, respectively. For SVM classification, the accuracies obtained were 52.25%, 94.94%, 75.21%, and 87.92% for the four data variations, with computation times of 38.93 s, 3.749 s, 0.695 s, and 0.168 s, respectively. Lastly, for DT classification, the accuracies obtained were 97.95%, 96.09%, 86.25%, and 92.08% for the four data variations, with computation times of 0.647 s, 0.236 s, 0.066 s, and 0.023 s, respectively. Keywords: Quartz Crystal Microbalance, Feature Extraction, Linear Discriminant Analysis
Sistem Layanan Informasi dan Pemesanan Nomor Antrian Menggunakan Media SMS Berbasis Komunikasi Serial Asinkron Multipoint Standar RS-485 Danny Kurnianto; Panca Mudjirahardjo; M. Julius St Julius St
JURNAL INFOTEL Vol 6 No 2 (2014): November 2014
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v6i2.19

Abstract

Sistem layanan informasi dan pemesanan nomor antrian terpusat melalui media handphone dapat dijadikan sebagai  salah satu solusi untuk mempermudah masyarakat dalam melakukan antrian sehingga aktivitas mereka bisa berjalan dengan baik dan waktu mereka tidak terbuang terlalu lama. Dengan menggunakan sistem ini, nasabah dapat dengan mudah melihat kondisi antrian saat ini dan memesan nomor antrian, yaitu dengan mengirimkan SMS berupa kata “daftar” untuk memesan nomor antrian dan kata “info” untuk mengetahui kondisi antrian ke handphone server. Personal komputer digunakan sebagai pusat pengendalian yang berfungsi untuk mengirim dan menerima data dari hanphone dan dari mikrokontroler pada unit slave. Komunikasi data antara komputer sentral dengan mikrokontroler berjalan dengan menggunakan komunikasi serial asinkron multipoint dengan baudrate 57600 bps. Komunikasi serial antara komputer sentral dengan handphone berjalan dengan baudrate 19200 bps. Dari hasil pengujian menunjukkan bahwa sistem layanan informasi dan pemesanan nomor antrian dapat bekerja dengan baik. Informasi yang diberikan saat nasabah mendaftar nomor antrian melalui handphone berupa nomor antrian dan password. Informasi yang diberikan komputer sentral saat nasabah meminta informasi kondisi antrian berupa berupa jumlah nasabah yang terdaftar pada sistem antrian saat ini, nomor antrian yang sedang dilayani pada masing-masing loket, waktu tutup antrian.
Designing an Optimization of Orientation System toward Moving Object in 3-Dimensional Space Using Genetic Algorithm Feishal Reza; Panca Mudjirahardjo; Erni Yudaningtyas
JURNAL INFOTEL Vol 10 No 4 (2018): November 2018
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v10i4.408

Abstract

This research offers a scheme of orientation system toward moving object in 3-dimensional space that using Stereo Vision Camera. The system benefits in giving an alternative solution in projecting practically without manual identification by conventional measuring device. The result of the projection in the system is in the form of coordinate position information (x, y, z), the length, the width, and the height of the object detected. The output displayed in the real-time digital image with 3-dimensional modeling. In the process of the object identification, there was a stage when an image was converted from colored image to binary image. But the conversion used the threshold method which was considered less efficient when an object moved. As consequence, the new adaptive method in solving the problem was needed. Genetic Algorithm was proposed as the optimization method because it was considered suitable with the emerging problems. In the optimization process, genetic algorithm was in a task of searching process and determining the threshold value as the process of creating binary image. The result shows an increased accuracy in the identification process after the system had been optimized by the Genetic Algorithm (GA).
Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss Samuel Aji Sena; Panca Mudjirahardjo; Sholeh Hadi Pramono
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.430

Abstract

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.
Co-Authors Abdul Goffar Ricky Mahendra Achmad Basuki Adharul Muttaqin Ahmad Syafiq Kanzul Fikri Aiman Muhamad Basymeleh Airlangga, Daniar Putri Alkafi Dimitri Sukmana Andy Kurnia Santoso Angger Abdul Razak Anthony Wijoyo Arafah, Ghifari Raihan Bagus Esa Pramudya Bidin Yuniar Hamzah Bima Feridhan Nugraha Bimasena, Muhammad Farrel Brahmana, Nigel Shidqy Razendriya Chandra Halim Harahap Dachlan, Hary Soekotjo Danny Kurnianto Doni Juli Wiranata Eka Maulana Erni Yudaningtyas Esa Ilham Akbar Faradisa , Annisa' Illah Farihah Hedar Fatchur Rozi Al Fitrah Fauzi, Maher Feishal Reza Firmansyah, Vicky Gilang Luih Pinandita Haidar Taqy Hartono, Rafendra Ariwardana Hary Soekotjo Dachlan Hasdi Sasandi Ismail Musirin Ismail Musirin Ita Dwi Purnamasari Izanati, Nazuha Juan Mora Michael Marbun Juli Arianes Leonard Dimas Prakoso Lilik J. Awalin Lukman Gumelar M Fauzan Edy Purnomo M. Hanif Azhary M. Julius St Julius St M. Julius St Julius St, M. Julius St Machfud Firmansyah Manerep Luis Fernando Purba Marco Gunawan Maulana, Eka Miladina Rizka Aziza Mohammad Alif Robby Gani Mohammad Ilhammudin Toiyib Monifa Arini Muhammad Akbar Muhammad Aziz Muslim Muhammad Ikhsan Muhammad Ivan Fadillah Muhammad Rafi’ Zaidan Maajid n/a Soeprapto Nanang Sulistiyanto Nathanael, Indra Notario Pramudita Nugraha, Dimas Aji Nurus Sa'adah Octarudin Mahendra Oky Risky Dwi Santoso Pangemanan, Christofel Panjaitan, Gian Amadea Pebrianto, Wahyu Permatasari, Alissa Dyah Ayu Ponco Siwindarto Pratolo Rahardjo R. A. Setyawan Rachmawati, Luthfiyah Raden Arief Setyawan Rahmadwati Rahmadwati Rahmadwati, n/a Rahmadwati, n/a Rahmadwati, Rahmadwati Rauf, Daru Adiyatma Reinato Teguh Santoso Reza, Feishal Ricky Insyani Santosa P. P. Ridho Herasmara Rif'an, Mochammad Rifqa Asruroh Efnif Rini Nur Hasanah Riza Hasbi Ash Shiddieqy Rizky Aiman Haniffalah Harijanto Robbith Qosath Al Auhi Rohman, Muhammad Ariefur Samuel Aji Sena Sena, Samuel Aji Septi Uliyani Sholeh Hadi Pramono Sirojul Hadi Sofyan Andika Yusuf Sultoni Sultoni Sultoni, Sultoni Surya Agung Kurnia Suyono, Hadi Syarifah, Naily Tri Nurwati Vira Zafarin Waru Djuriatno Waru Djuriatno Wuri Roro Indraswari yuliana diah pristanti Zainuri, Akhmad