Article Per Year (5 Year)
p-Index From 2021 - 2026
2.188
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Fundamental Mathematics and Applications (JFMA)
Priyo Sidik Sasongko
The Informatics Departement, Faculty Of Mathemathics And Sciences, Diponegoro University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Hybrid ERC20 Ethereum Blockchain Multisignature Wallet 3of3 with Withdrawal Pattern, External Effects, and Mutex as Single Key and Reentrancy Mitigation. Jason Al Hilal Sabda Dewa; Indra Waspada; Priyo Sidik Sasongko
Jurnal Masyarakat Informatika Vol 15, No 1 (2024): May 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.1.62835

Abstract

In the rapidly evolving era of Decentralized Finance (DeFi), the convergence of Blockchain technology with intermediary-free financial services has forged a revolutionary landscape. However, this progress has been accompanied by critical challenges, notably the Single Key Risk and reentrancy attack threats against ERC20 smart contracts in private Ethereum Blockchain. This research formulated a proactive approach and implemented an innovative solution by embodying Reliable Decentralized Finance through the deployment of a 3-of-3 Hybrid Multisignature Wallet system with Withdrawal Pattern, External Effects, and Mutual Exclusion in the form of a Decentralized Application (DApps). The system not only applied withdrawal patterns but also integrated external effects and the principle of mutual exclusion to enhance the security of smart contracts. The system development methodology was executed comprehensively using Agile Software Engineering, encompassing the development of both smart contracts and external applications (decentralized applications). Testing was conducted using Ganache EVM (Ethereum Virtual Machine) connected to the Hot Wallet Metamask as an Externally Owned Account (EOA) for transaction signing. Valid results were obtained from comprehensive testing against the system's functional requirements, affirming the system's success in managing Single Key Risk and preventing reentrancy attacks, providing a reliable and concrete solution
Klasifikasi Status Gizi Balita Menggunakan Metode Backpropagation Dengan Algoritma Levenberg-Marquardt dan Inisialisasi Nguyen Widrow Wildan Azka Adzani; Priyo Sidik Sasongko
Jurnal Masyarakat Informatika Vol 12, No 1 (2021): May 2021
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.12.1.41020

Abstract

Banyaknya  Kasus  Gizi  Buruk  pada  anak-anak  usia  di  bawah  lima  tahun  umumnya  ditemukan  akibat ketidaktahuan terhadap permasalahan gizi pada anak. Klasifikasi Status Gizi Balita merupakan upaya yang dilakukan untuk mengetahui status  gizi pada balita. Penelitian ini bertujuan untuk membangun sebuah sistem Klasifikasi status gizi balita berbasis web menggunakan jaringan syaraf tiruan Backpropagation dengan Algoritma Levenberg-Marquardt dan inisialisasi Nguyen-Widrow. Variabel yang digunakan dalam penelitian ini merupakan data antropometri sebanyak 4 variabel. Seluruh data penelitian diambil dari POSYANDU RW 08 Kelurahan Sambiroto Kecamatan Tembalang, Semarang, Jawa Tengah. Data yang diambil sebanyak 100 data dengan pembagian data latih dan data uji menggunakan K-Fold Cross Validation. Hasil penelitian menunjukkan arsitektur terbaik untuk melakukan Klasifikasi didapat pada kombinasi parameter hidden neuron 12, parameter Levenberg-Marquardt (µ)  0.01,  maksimum  epoch  1000  dan  target  error  0.001  yang  menghasilkan  MSE 0.000064
Indonesian continuous speech recognition optimization with convolution bidirectional long short-term memory architecture Sukmawati Nur Endah; Rismiyati Rismiyati; Priyo Sidik Sasongko; Anwar Petrus F Noiborhu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.24994

Abstract

Speech recognition can be defined as converting voice signals into text or lines of words by using algorithms implemented in computer programs. There are several types of speech recognition, including recognition for isolated word speech, continuous speech, spontaneous speech, and conversational speech. Research on continuous speech recognition, especially in Indonesian, has been developed using both stochastic methods such as Hidden Markov model (HMM) and deep learning methods. Currently, deep learning approaches are more widely used in speech recognition applications. This research optimizes Indonesian speech recognition by adding convolution layers to the bidirectional long short-term memory (Bi-LSTM) architecture. The goal of this research is to find the best architecture so that better Indonesian continuous speech recognition results can be obtained. The dataset used in this research was created by the intelligent systems research group in the Department of Informatics at Universitas Diponegoro. All speakers who participated in this dataset came from five ethnic groups in Indonesia, representing the dialects of their respective ethnic groups. The research results show that by adding a convolution layer to the Bi-LSTM architecture, speech recognition performance increases significantly with an average word error rate (WER) reduction of 15.56% compared to using only the Bi-LSTM architecture.
Co-Authors Anwar Petrus F Noiborhu Eko Adi Sarwoko Fajar Agung Nugroho Helmie Arif Wibawa Iffa Zainan Nisa Indra Waspada Indriyati Indriyati Jason Al Hilal Sabda Dewa Khadijah Khadijah Khadijah, Khadijah Mahendra, Ida Bagus Satya Muhamad, Nida Nisa, Iffa Zainan Nurdin Bahtiar Okky Widya Arditya Prasetyo, Syalwa Dea Putri Rachmat Gernowo Rachmawati Soewono, Rachmawati Ragil Saputra Redemtus Heru Tjahjana Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Satriyo Adhy Satya Sandika Putra Sirait, Dheo Ronaldo Sukmawati Nur Endah Sukmawati Nur Endah Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno, Sutikno Sutimin Sutimin Tatik Widiharih Triatmaja, Yudha Kusuma Vebriana, Melanie Safira Widowati, Widowati Wildan Azka Adzani