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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika
Barlian Henryranu Prasetio
Fakultas Ilmu Komputer, Universitas Brawijaya
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Physics Public Health Social Sciences

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Journal : journal of information technology and computer science

 1 
Early Stunting Detection System for Toddlers Based on Height and Weight Using Backpropagation Neural Network Method Dini Eka Ristanti; Dahnial Syauqy; Barlian Henryranu Prasetio
Journal of Information Technology and Computer Science Vol. 7 No. 3: December 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

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

Abstract

Stunting is a chronic nutritional problem characterized by height and weight problems. Toddlers who have height and weight of more than minus 2 standard deviations are at risk of suffering from stunting and require monitoring for 3 to 6 consecutive months. Currently, the system still measures toddlers' height and weight, then matches it with the World Health Organization(WHO) growth data table. Therefore, we proposed to develop a system to detect height and weight as well as the risk of stunting in toddlers using an ultrasonic sensor, load cell, and backpropagation algorithm. In its implementation, the ultrasonic sensor achieves an accuracy of 99%, and the load cell reaches 93%. The system uses backpropagation neural network method, which achieved an R of 0.99845 using 3 inputs, 16 hidden layers,1 layer for re-weighting, and 1 output layer. The mean squared error reaches 0.01 with 2 prediction classes, low risk, and high riskstunting. Overall, the total system accuracy can reach 97.75%.
Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods Prasetio, Barlian Henryranu; Edita Rosana Widasari; Adi Setiawan; Hanifa Maulani Ramadhan
Journal of Information Technology and Computer Science Vol. 9 No. 3: December 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.93681

Abstract

This study presents a comparative analysis of traditional disk-based and cloud-based Backup and Disaster Recovery (DR) approaches, focusing on the challenges inherent in existing solutions, such as high infrastructure costs, extended recovery times, and operational disruptions during software updates. Conventional disk-based systems often require periodic reboots and manual interventions, which can interrupt ongoing operations. In contrast, cloud-based solutions, particularly Asigra, offer a streamlined alternative by reducing infrastructure dependency, enhancing recovery metrics—specifically Recovery Point Objective (RPO) and Recovery Time Objective (RTO)—and minimizing maintenance downtime through agentless and incremental backups. Cloud backup provides comprehensive upgrades without the need for system reboots, thereby saving time and improving operational effectiveness. This study evaluates the proposed cloud-based approach in a village government organizational environment, analyzing its performance based on Total Cost of Ownership (TCO), RPO, and RTO. Cloud-based configurations are compared with traditional setups to assess improvements in disaster recovery procedures and data storage. Findings demonstrate that cloud-based strategies offer simpler and more efficient DR solutions, providing superior scalability, reliability, and administrative ease tailored to the unique needs of village government data management.
Design of Speaker Verification using Dynamic Time Warping (DTW) on Graphical Programming for Authentication Process Prasetio, Barlian Henryranu; Syauqy, Dahnial
Journal of Information Technology and Computer Science Vol. 2 No. 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.709 KB) | DOI: 10.25126/jitecs.20172124

Abstract

Authentication is generally required on systems which need safety and privacy. In common, typed username and password are used and applied in authentication system. However, this type of authentication has been identified to have many weaknesses. In order to overcome the problem, many proposed authentication system based on voice as unique characteristics of human. We implement Dynamic Time Warping algorithm to compare human voice with reference voice as the authentication process. The testing results show that the system accuracy of the speech recognition average is 86.785%.
Implementation of Lossless Voice Data Communication using Network Streams on Embedded System Prasetio, Barlian Henryranu; Syauqy, Dahnial
Journal of Information Technology and Computer Science Vol. 2 No. 2: November 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (289.623 KB) | DOI: 10.25126/jitecs.20172225

Abstract

Smart home refers to the use of computers and information technology to control home appliances. The smart home system occupies sensors to acquire data for monitoring. One of the most important factors in designing a smart home system was the communication between devices that will be used. It requires high throughput and low latency communication so that monitoring can be done in real time and lossless. The communication network was designed to optimize the stream monitoring for smart home by reviewing the system throughput and latency. Lab-VIEW programming on My-RIO embedded systems was used to acquire accelerometer and voice data readings and send them to personal computer wirelessly. The network communication was designed by implementing network stream protocols between two applications which were planted in the personal computer and My-RIO system. The test results indicate that the network stream protocol did not require normalization of sensor data types while transmitting and receiving. The throughput values were affected by increasing buffer size but on certain conditions saturated throughput were experienced. The value of the latency is always stable with an average value of 13 ms even though the size of the buffer is enlarged.
Hyperparameter Optimization of Extreme Gradient Boosting Using Particle Swarm Optimization For Diabetic Nephropathy Prediction Argaputri, Maulida Khairunisa; Lailil Muflikhah; Prasetio, Barlian Henryranu
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103859

Abstract

Diabetic Nephropathy (DN) is a critical complication with a mortality rate 20-40 times higher than in non-diabetic nephropathy patients, necessitating precise machine learning models to determine whether a patient has nephropathy. Extreme Gradient Boosting (XGBoost) has emerged as a prominent machine learning model for medical diagnostics, with several studies validating its superiority in medical classification. Nevertheless, a significant limitation of XGBoost lies in the complexity of manual hyperparameter tuning. To address this limitation, an automated optimization algorithm is requisite to systematically identify the optimal hyperparameter configuration. This study focuses on optimizing Extreme Gradient Boosting (XGBoost) hyperparameters using Particle Swarm Optimization (PSO), with the F1-Score as its fitness function. To evaluate its effectiveness, the performance of this hybrid XGBoost-PSO model was compared against the baseline XGBoost model. The results showed that the hybrid model outperformed the baseline model, achieving a consistent improvement of 0.02 (2%) across all evaluation metrics. Notably, the F1-Score increased from 0.91 to 0.93, while the Recall metric improved from 0.93 to 0.95. Furthermore, the PSO algorithm efficiently identified the Global Best (GBest) hyperparameters at the 9th iteration. In conclusion, the XGBoost-PSO model provides a robust medical diagnostic tool that maintains a stable performance to enhance clinical judgment.
Co-Authors Achmad Ridok Adharul Muttaqin Adi Setiawan Adven Edo Prasetya Adven Edo Prasetya, Adven Edo Agra Firmansyah Ahmad Afif Supianto Aldi Jayadi Ali, Zidane Allaam, Fakhrul Argaputri, Maulida Khairunisa Arief Kurniawan Aryo Pinandito Ash-Shadiq, Aqsath Muhammad Aswin Suharsono, Aswin Atmojo Pamungkas, Handoko Bagus Ayu Astina Sari, Ni Made Baariu, Rahagi Abdu Bagus Priyo Pangestu Brylliano Maza Putra Budi Darma Setiawan Budy Prakoso, Khrisna Shane Chatarina Umbul Wahyuni Dahnial Syauqy Dayat, Fauzi Syarifulloh Defri Alif Raihan Denny Sagita Rusdianto Dhimas Arfian Lazzuardhy Dini Eka Ristanti Dini Ismawati Dwiki Ilham Bagaskara Dwinanda Romolo Edita Rosana Widasari Edita Rosana Widasari Edita Rosana Widasari, Edita Rosana Eko Setiawan Eko Setiawan Eko Setiawan Fabiana, Ryzaldi Ananda Fachry Ananta Fadhilah, Khairian Fadhillah, Muhammad Galih Faisal Natanael Lubis Faviansyah Arianda Pallas Faza Gustaf Marrera Fitra Abdurrachman Bachtiar Fitriyah, Hurriyatul Gembong Edhi Setiawan Gembong Edhi Setyawan Gembong Edhi Setyawan Ghifari, Ahmad Hafidz Abdillah Masruri Hanifa Maulani Ramadhan Haqyah, Saprina Hani Heru Nurwarsito Hilal Imtiyaz I Wayan Boby Astagina Naghi Imam Cholissodin Iqbal Maulana Susanto Irfan Muzakky Nurrizqy Irwanda Adhi Firmantara Isnandar, Muhammad Fawwaz Dynoeputra Ivan Rafli Adipratama Iwasawa, Takeru Jevandika Joan Chandra Kustijono Julisya Thana Khriswanti Kamal, Attar Syifa Kusuma, Lindhu Parang La Ode Adriyan Hazmar Lailil Muflikhah Lavanna Indanus Ramadhan M. Hannats Hanafi Ichsan M. Ihsan An-Nashir Mahardika, Aryanta Seta Manurung, Manfredy Patarida Hasiholan Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Muchlas Mughniy Muflih, Aufada Muhammad Fatikh Hidayat Muhammad Ghifari, Muhammad Muhammad Habib Jufah Alhamdani Muhammad Nabil Aljufri Muhammad Rizki Chairurrafi Nadi Rahmat Endrawan Nashrullah, Ega Rasendriya Nashrullah, Ikbar Razan Naviaddin, Arsal Wildan Ngulandoro, Mochammad Giri Wiwaha Nobel Edgar Novaria Elsari Ryzkiansyah Novea, Leisha Nur, Farhan Marwandi Nurrizqy, Irfan Muzakky Nurul Hidayat Ovriawan Aldo Pribadi Putra Paleva, Haidar Rheza Panggabean, Riki Boy Parja, Mujianto Anda Perkasa, Septiyo Budi Permana, Galih Pierl Kritzenger Sinaga Prawironegoro, Abdul Harris Putera, Thariq Andhita Putra Pamungkas, Dimas Resha Putra, Brylliano Maza Putra, Ravelino Adhianto Surya Raden Galih Paramananda Rahmawan, Muhammad Fuad Rajasa, Mohammad Fariq Rakhmadhany Primananda, Rakhmadhany Ramadhan, Dimas Ramadhan, Muhammad Fitrah Ramadhani, Aryo Sheva Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Reza Hastuti Riyad Febrian Rizal Maulana Rizal Maulana, Rizal Rosyana Lencie Mampioper Ryan Anggito Priono Sabriansyah Rizkiqa Akbar Sabriansyah Rizqika Akbar Sabriansyah Rizqika Akbar Septiyo Budi Perkasa Shabiyya, Syifa’ Hukma Sifaunnufus Ms, Fi Imanur Sigit Priyo Jatmiko Subianto, Aflah Fadhlurrahman Syahrul Chilmi, Syahrul Tampubolon, Jeremya Tiara Mahardika Tibyani Tibyani Utaminingrum, Fitri Valensiyah Rozika Widasari, Edita Rosana Wijaya Kurniawan Wijaya Kurniawan Yasa Palaguna Umar Yosia Nindra Kristiantya Yudhistira, Gevan Putra Yunan Alamsyah Nasution Yusril Dewantara Yusuf, Delfi Olivia