Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal EECCIS Journal of Environmental Engineering and Sustainable Technology e-Jurnal Arus Elektro Indonesia Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL
Sholeh Hadi Pramono
Department Of Electrical Engineering, Universitas Brawijaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Implementasi Fuzzy Analytical Hierarchy Process Menggunakan Database Maria DB Anang Aris Widodo; Sholeh Hadi Pramono; Harry Soekotjo Dachlan
Jurnal EECCIS Vol 13, No 2 (2019)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak–-Paper ini berisi tetntang implementasi metode fuzzy Analytical Hierarchy Process (F-AHP) pada database mariaDB. Untuk membuat suatu Decision Support System (DSS) yang dinamis maka metode Analytical Hierarchy Process (AHP) dibuat didalam database, pada penelitian ini menggunakan database mariaDB. Terdapat kelebihan dan kekurangan jika suatu metode diletakkan didalam database. Kelebihannya adalah sistem akan menjadi dinamis karena jika ada perubahan dan penambahan kriteria dan alternatif tidak akan mempengaruhi sistem yang sudah dibangun. Kekurangannya adalah sistem akan menjadi berat karena mengakses data langsung dari server.Kata Kunci—AHP,  DSS, mariaDB, server.
Konsolidasi Beban Kerja Kluster Web Server Dinamis dengan Pendekatan Backpropagation Neural Network Alan Stevrie Balantimuhe; Sholeh Hadi Pramono; Hadi Suyono
Jurnal EECCIS Vol 12, No 2 (2018)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.045 KB)

Abstract

Meningkatnya permintaan pengguna applikasi WWW telah menyebabkan peningkatan yang sepadan dalam penggunaan sumber daya server kluster. Penelitian ini mengkaji tentang penyediaan sumber daya server Web berdasarkan parameter beban kerja server (load average CPU). Data yang digunakan adalah akses terhadap web server yang melayani applikasi Sistem Informasi Akademik Mahasiswa Universitas Brawijaya (SIAM-UB). Penggunaan sumber daya server secara maksimal (beban puncak) terjadi pada periode registrasi mahasiswa, yaitu lebih dari 65000 mahasiswa akan mengakses server SIAM secara bersamaan. Jumlah permintaan yang dilayani server dalam 1 hari dapat mencapai 1.7juta permintaan. Pada penelitian ini, penyediaan sumber daya server diprediksi untuk mendapatkan beban kerja CPU dalam kluster web server yang optimal. Prediksi beban kerja server diklasifikasikan menjadi 3 kelas, yaitu: Min (0-2), Medium (3-6), Maximum >7. Metode backpropagation neural network (BNN) digunakan untuk memprediksi kelas beban kerja server berdasarkan parameter input penggunaan CPU, memory, jaringan (throughput) dan jumlah IP akses. Arsitektur BNN dengan 32 input, 2 hidden layer dengan jumlah neuoron h1 512; h2 32, 3 output, dan learning rate 0.0001, menghasilkan bobot yang mampu melakukan klasifikasi dengan tingkat precision 90%, tingkat sensitivity 0.9, dan tingkat akurasi 93%.
FAULT DETECTION AND PROTECTION METHOD ON LOW VOLTAGE DC MICRO-GRID SYSTEM Sholeh Hadi Pramono; Eka Maulana; Hadi Suyono; Akhmad Zainuri
Journal of Environmental Engineering and Sustainable Technology Vol 5, No 1 (2018)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (764.966 KB) | DOI: 10.21776/10.21776/ub.jeest.2018.005.01.1

Abstract

ABSTRACTMicro-grid architecture is designed for small scale model in Brawijaya University area in order to change conventional AC-based electricity system previously. Low voltage direct current (LVDC) levels are proposed and charactized to obtain the optimal design of the DC grid system. Some parameters related to the electrical phenomenon of voltage, current and power which occur in distributed-generation, distribution grid, and load sites were also analyzed. Detailed model of photovoltaic (PV) and PMSG was implemented with operational analysis and simulated with study case modes to achieved the power and system efficiency. DC bus is conducted to accommodate the distribution power between PV generation, battery and super capacitor for energy sorage element, distributed-load and other grid utilization.Various condition and operation have been characterized toward stability performance of the voltage and current of 12-36 volts and 0-20 A DC, respectively. This architectural design can be utilized to develop an actual design and small scale implementation of the LVDC smart micro-grid system.Keywords: Fult Detection  Protection  Micro-grid  Low Voltage DC
Optimasi Penataan Base Tranceiver Station GSM dan Penempatan Perangkat Berbasis 3G di Kota Malang Menggunakan Algoritma Genetika Djul Fikry Budiman; Sholeh Hadi Pramono; Onny Setyawati
Jurnal Arus Elektro Indonesia Vol 2 No 1 (2016)
Publisher : Fakultas Teknik, Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ketersediaan Base Transceiver Station (BTS) dengan kapasitas trafik dan perencanaan sel yang tepat merupakan salah satu kebutuhan yang harus tersedia bagi penyedia jasa layanan komunikasi bergerak seluler.peningkatan jumlah BTS di kota Malang dan peningkatan kapasitas yang terus dilakukan terkendala oleh tata ruang kota serta perkembangan jumlah penduduk yang menjadikan beberapa BTS berada pada lokasi pemukiman padat. Penataan BTS merupakan sebuah solusi optimasi yang memungkinkan dalam peningkatan kapasitas tanpa mengurangi kapasitas trafik.Peningkatan kualitas sinyal berhubungan erat dengan nilai propagasi sinyal yang menjamin nilai pathloss dalam cakupan BTS, tidak menghalangi sinyal yang diterima oleh pengguna jaringan seluler. Dari hasil optimasi sinyal pathloss menggunakan algoritma genetika yang dibandingkan dengan nilai pathloss hasil drive test, untuk jarak yang sama didapat penurunan nilai pada masing-masing wilayah. Blimbing dari 193.45 dB menjadi 94.33 dB, Klojen dari 152.14 dB menjadi 97.56 dB, dan Kedungkandang dari 143 dB menjadi 108.2 dB. Frekuensi BTS 39746, dari 936 Mhz (2G) berubah menjadi sekitar 1800 Mhz (3G). Hal yang sama juga untuk  BTS 39744, dari frekuensi 937,6 Mhz (2G), berubah menjadi 1900 Mhz (3G).
Implementation of Deep Learning in Spatial Multiplexing MIMO Communication Mahdin Rohmatillah; Hadi Suyono; Rahmadwati Rahmadwati; Sholeh Hadi Pramono
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp699-705

Abstract

Research in Multiple Input Multiple Output (MIMO) communication system has been developed rapidly in order to improve the effectiveness of communication among users. However, trade-off phenomenon between performance and computational complexity always become the hugest dilemma suffered by researchers. As an alternative solution, this paper proposes an optimization in 3x3 spatial multiplexing MIMO communication system using end-to-end based learning, specifically, it adapts autoencoder based model with the knowledge of Channel State Information (CSI) in the receiver side, make it fairly compared with the baseline method. The proposed models were evaluated in one of the most common channel impairment which is fast Rayleigh fading with additional Additive White Gaussian Noise (AWGN). By appropriately determining hyperparameters and the help of PReLU (Parametric Rectified Linear Unit), the results show that this autoencoder based MIMO communication system results in very promising results by exceeding the baseline methods (methods widely used in conventional MIMO communication) by reaching BER lower than at SNR 22.5 dB.
Optimasi Routing pada Metropolitan Mesh Network Menggunakan Adaptive Mutation Genetic Algorithm Merinda Lestandy; Sholeh Hadi Pramono; Muhammad Aswin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 4: November 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1394.29 KB)

Abstract

In dynamic and wide networks, such as Metropolitan Mesh Network (MMN), routing becomes very complex because a packet can be blocked before it reaches its destination. In addition, users can also log in or log out from network topology. Therefore, a good routing algorithm, which is able to reduce time in network update process or when there is an error in the network, are required. Routing problems can be represented as the shortest path problem to facilitate completion. In this paper, a routing algorithm optimization using Adaptive Mutation Genetic Algorithm (AMGA) on MMN is presented by determining a probability of 0.000005782 at the beginning, with crossover probability of 0.000847, to reduce or avoid premature convergence.
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 Alan Stevrie Balantimuhe Anang Aris Widodo Djul Fikry Budiman, Djul Fikry Harry Soekotjo Dachlan Mahdin Rohmatillah Maulana, Eka Merinda Lestandy Mudjirahardjo, Panca Muhammad Aswin Onny Setyawati Rahmadwati, n/a Samuel Aji Sena Suyono, Hadi Zainuri, Akhmad