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All Journal Jurnal Simetris Buletin Ilmiah Sarjana Teknik Elektro
Nugroho, Herminarto
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Computer Science & IT Electrical & Electronics Engineering

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Penerapan Metode Particle Swarm Optimization (PSO) Dan Genetic Algorithm (GA) Pada Sistem Optimasi Visible Light Communication (VLC) Untuk Menentukan Posisi Robot Istighfarin, Nanda Febri; Rahmastati, Regina Anisa; Nugroho, Herminarto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.539 KB) | DOI: 10.24176/simet.v11i1.4052

Abstract

Visible Light Communication (VLC) adalah suatu teknologi komunikasi yang memanfaatkan pancaran cahaya tampak untuk pengiriman dan penerimaan sinyal. Perkembangan VLC merupakan sebuah inovasi dalam sistem navigasi untuk melacak posisi dari sebuah objek dengan komunikasi cahaya tampak LED. LED digunakan untuk menentukan titik koordinat dari sebuah objek. Pada penelitian ini digunakan metode Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA) untuk menentukan posisi robot terhadap posisi LED. Dari dua metode tersebut dapat diketahui keefisienan masing-masing metode serta mengetahui tingkat keakuratan posisi robot dalam menerima informasi data dari LED. Hasil analisis menunjukan jika nilai path loss yang diterima receiver semakin besar, maka nilai squared error semakin besar pula. Saat nilai squared error semakin besar, tingkat keakuratan penentuan posisi robot semakin kecil karena posisi robot semakin jauh dari titik referensi yaitu posisi LED. Dengan menggunakan metode PSO dan GA diketahui jika hasil pencarian nilai minimum error relatif sama baiknya. Perbedaan pada kedua metode terlihat pada proses komputasi, yaitu waktu komputasi serta banyaknya partikel atau generasi yang dihitung.
Gray Level Co-Occurrence Matrix (GLCM)-based Feature Extraction for Rice Leaf Diseases Classification Nugroho, Herminarto; Pramudito, Wahyu Agung; Laksono, Handoyo Suryo
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.9286

Abstract

In this paper, we propose Gray Level Co-Occurrence Matrix (GLCM) based Feature Extraction to identify and classify rice leaf diseases. An Artificial Neural Network (ANN) algorithm is used to train a classification model. Various statistical features such as energy, contrast, homogeneity, and correlation are extracted from the GLCM matrix to describe the image texture features. After feature removal, an ANN classification model was trained using a dataset consisting of images of healthy and diseased rice leaves. The ANN training process involves optimizing weights and bias using backpropagation to achieve accurate classification. After training, the ANN model is tested using split test data to measure classification performance. The experimental results show that the GLCM method is effective in helping improve accuracy, validation of accuracy, loss, validation of loss, precision, and recall.
DEEP LEARNING IMPLEMENTATION FOR EMPLOYEE ATTENDANCE SYSTEM IN UNIVERSITAS PERTAMINA Susanty, Meredita; Sahrul, Sahrul; Setiawan, Erwin; Nugroho, Herminarto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 2 (2020): JURNAL SIMETRIS VOLUME 11 NO 2 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i2.4605

Abstract

Attendance recording with RFID tags scan, make human resource (HR) staff’s task more effective and efficient because it is saving time and effort in performing manual recording and recapitulation that must be performed by the HR staff in a company. However, the number of cases where employees forget to bring their identification cards, which has an RFID tag increases the workload of human resource staff. This study proposes a facial recognition prototype as an alternative way to record employee attendance. The model used in this study uses artificial neural networks that have more than one hidden layer and uses a supervised learning approach. The results of the study show that when a high-resolution image provided for the training data, the prototype able to make an accurate prediction. However, some further study is needed before replacing existing attendance recordings with face recognition to address several problems such as distance between camera and object and accessories that affect the essential features in a face like glasses, headscarves, and mask. Further research should find the maximum distance between the object(s) and the camera and the position (angle) of the object towards the camera.
Optical Ring Resonators (ORR) Minimalisasi Rugi Daya Menggunakan Metode SA Dan PSO Nuryusuf, M. Faizal; Hapid, Lutvi; Nugroho, Herminarto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i1.4071

Abstract

Penyelesaian permasalahan yang terjadi pada industri penerbangan ataupun transfer data dapat diselesaikan dengan berbagai aplikasi teknik. Beberapa metode telah dikembangkan untuk menyelesaikan sistem persamaan ini dan metode Simulated Annealing (SA) merupakan salah satu metode yang dapat digunakan untuk menyelesaikan persamaan ini. Namun, metode ini memerlukan waktu yang lama untuk mendapatkan nilai optimum karena sistemnya akan terus mencari nilai global minimum tanpa memperhatikan jumlah iterasi yang telah dilakukan. Pada makalah ini, dibuktikan bahwa pada permasalahan ini untuk mendapatkan nilai global minimum dari daya yang diinginkan, metode Partical Swarm Optimization (PSO) lebih efektif dan efisien daripada metode SA. Hal ini karena PSO lebih cepat menemukan nilai global minimum serta hasil dari setiap iterasi yang dilakukan cenderung stabil daripada metode SA. Dari lima sampel yang dilakukan dengan metode PSO, nilai global minimum yang didapatkan lebih baik dengan nilai yang besar dan waktu yang lebih cepat daripada metode SA.
OPTICAL CHARACTER RECOGNITION IMPLEMENTATION FOR ADMISSION SYSTEM IN UNIVERSITAS PERTAMINA Susanty, Meredita; Nugroho, Herminarto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1159.422 KB) | DOI: 10.24176/simet.v11i1.3838

Abstract

Starting in 2019, prospective college students require to take Computer-Based Writing Exam (UTBK) to register for the state universities in Indonesia. Some private university also adopts this exam as a requirement for admission. One of the private university that adopts it is Universitas Pertamina. UTBK consist of several exam group score printed in a digital certificate in image format (jpg). The university admission team must download the UTBK certificate that has uploaded by applicants, read and record the score for each exam group then make a calculation to make a decision whether the applicant is accepted in a certain school in the university. This research proposes to replace the manual process performed by the admission team with optical character recognition (OCR). The OCR engine will extract text from an image. Some information from the extracted text is calculated to provide an acceptance decision. The research shows that OCR cannot accurately convert text from an image when there is a grayscale background in the image. However, image preprocessing can improve overall accuracy. Lastly, Tesseract performs better in converting black text with white-background than white text with a black background.
Co-Authors Erwin Setiawan, Erwin Hapid, Lutvi Istighfarin, Nanda Febri Laksono, Handoyo Suryo Nuryusuf, M. Faizal Pramudito, Wahyu Agung Rahmastati, Regina Anisa Sahrul, Sahrul Susanty, Meredita