Article Per Year (5 Year)
p-Index From 2021 - 2026
3.582
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Riau Journal of Computer Science Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JISA (Jurnal Informatika dan Sains) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) JURNAL MASYARAKAT NEGERI ROKANIA Mejuajua KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika JOURNAL OF ICT APLICATIONS AND SYSTEM RJOCS (Riau Journal of Computer Science)
Budi Yanto, Budi
Program Studi Teknik Informatika Fakultas Ilmu Komputer Universitas Pasir Pangaraian Jl. Tuanku Tambusai, Kumu Kec. Rambah Hilir Kabupaten Rokan Hulu Telp, 081365929997
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : JOURNAL OF ICT APLICATIONS AND SYSTEM

 1 
Visualization of Covid-19 Data in Indonesia in 2022 through the Google Data Studio Dashboard Putra, Wahyu Eka; Yanto, Budi; Erwis, Fauzi
JOURNAL OF ICT APLICATIONS AND SYSTEM Vol 2 No 2 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i2.238

Abstract

The COVID-19 pandemic has presented significant challenges to governments, researchers and the general public in understanding and monitoring the spread of this disease. In an effort to analyse the spread of COVID-19 disease in Indonesia effectively, this study uses Google Data Studio as a tool for data visualization and better understanding. This review is based on collecting data on the spread of COVID-19 disease in Indonesia which has been collected from various reliable sources. , including the World Health Organization (WHO) and national health agencies. This data is then processed and processed using Google Data Studio to produce informative visualizations. The results of the study show that Google Data Studio can be used effectively to analyse the spread of the COVID-19 disease in Indonesia. Through the use of available features, such as interactive graphs, maps, and tables, researchers can easily describe patterns of disease spread, infection rates, and recovery rates from an area or country. The quality of data collected from different sources may vary, and this can affect the accuracy and reliability of the resulting visualizations. Elements of the Scorecard that displays some important information related to the Covid-19 pandemic from 1 January 2019 to 31 January 2022. Information regarding the Covid-19 displayed on the Scorecard is as follows. The total survivors of the Covid-19 disease are 3,234,336,858 people. This indicates the number of people who have successfully recovered and recovered from infection with the Covid-19 virus during the period in question. The total number of deaths due to Covid-19 is 89,398,496 people. This reflects the number of people who died due to complications caused by the Covid-19 virus in that period.
Visualization of Covid-19 Data in Indonesia in 2022 through the Google Data Studio Dashboard Yanto, Budi; Eka Putra, Wahyu; Erwis, Fauzi
Journal of ICT Applications System Vol 2 No 1 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i1.237

Abstract

Pandemi COVID-19 telah menghadirkan tantangan yang signifikan bagi pemerintah, peneliti, dan masyarakat umum dalam memahami dan memantau penyebaran penyakit ini. Dalam upaya menganalisis penyebaran penyakit COVID-19 di Indonesia secara efektif, penelitian ini menggunakan Google Data Studio sebagai alat visualisasi data dan pemahaman yang lebih baik. Review ini didasarkan pada pengumpulan data penyebaran penyakit COVID-19 di Indonesia yang telah dikumpulkan dari berbagai sumber terpercaya. , termasuk Organisasi Kesehatan Dunia (WHO) dan badan kesehatan nasional. Data ini kemudian diolah dan diproses menggunakan Google Data Studio untuk menghasilkan visualisasi yang informatif. Hasil studi menunjukkan bahwa Google Data Studio dapat digunakan secara efektif untuk menganalisis penyebaran penyakit COVID-19 di Indonesia. Melalui penggunaan fitur-fitur yang tersedia, seperti grafik, peta, dan tabel interaktif, peneliti dapat dengan mudah menggambarkan pola penyebaran penyakit, tingkat infeksi, dan tingkat pemulihan dari suatu daerah atau negara. Kualitas data yang dikumpulkan dari berbagai sumber dapat bervariasi, dan hal ini dapat memengaruhi keakuratan dan keandalan visualisasi yang dihasilkan. Elemen Scorecard yang menampilkan beberapa informasi penting terkait pandemi Covid-19 periode 1 Januari 2019 sampai dengan 31 Januari 2022. Informasi terkait Covid-19 yang ditampilkan pada Scorecard adalah sebagai berikut. Total penyintas penyakit Covid-19 sebanyak 3.234.336.858 orang. Hal tersebut menandakan jumlah orang yang berhasil sembuh dan pulih dari infeksi virus Covid-19 selama periode yang bersangkutan. Total kematian akibat Covid-19 sebanyak 89.398.496 orang. Hal ini mencerminkan jumlah orang yang meninggal akibat komplikasi yang disebabkan oleh virus Covid-19 pada periode tersebut.
Data Visualization Analysis of Waste Production Volume in Every District of Tangerang Regency in 2021 Using Looker Studio and Big Query Platforms Yanto, Budi; Sudaryanto, Aris; Hasri Ainun Pratiwi
Journal of ICT Applications System Vol 2 No 1 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i1.239

Abstract

The waste problem is a problem that continues to increase every year in Indonesia. Tangerang Regency as one of the regencies in Banten Province has the same problem related to the volume of waste production. Therefore, this study aims to analyze and visualize data on the volume of waste production in each sub-district in Tangerang Regency in 2021 using the Looker Studio and Big Query platforms. The method used in this research is descriptive method with a quantitative approach. The data used is secondary data obtained from the Tangerang Regency Environmental Service. The results showed that there were significant differences in the volume of waste production between one sub-district and another. Data visualization using the Looker Studio and Big Query platforms makes it easy to understand patterns and trends in the volume of waste production in each district. This research is expected to provide input for the Tangerang Regency government in making policies related to waste management in the area. This study employs data processing and analysis methods utilizing two platforms, namely Looker Studio and Big Query. The decision to adopt these platforms is backed by previous research, such as the study conducted by, which demonstrated that utilizing Looker Studio can lead to expedited and more effective business decision-making. Furthermore, the effectiveness of Big Query in processing large and intricate datasets has also been substantiated by various studies. By leveraging these platforms, the study aims to enhance the efficiency and accuracy of data processing and analysis for better-informed business decisions.
Data Visualization Using Google Data Studio: A Case Study of the 2019 Presidential Indonesia Election Results Bangkit Habiburrohman; Yanto, Budi; Muhammad Arif
Journal of ICT Applications System Vol 2 No 2 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i2.396

Abstract

The 2019 Indonesian Presidential Election generated a significant amount of data requiring effective visualization for analysis and comprehension. This study demonstrates the application of Google Data Studio, a data visualization tool, for creating interactive dashboards based on election results. The dataset was sourced from the official Bureau of Statistics and processed to produce visualizations such as scorecards, bar charts, and pie charts, facilitating detailed insights into regional and candidate-specific voting patterns. The methodology includes data collection, processing, and visualization to construct a comprehensive dashboard. The findings illustrate the potential of Google Data Studio in enhancing data interpretability and decision-making through interactive visual representations. This research provides a practical guide for leveraging Google Data Studio in electoral data analysis
Analisis Optimasi Algoritma Backpropagation Momentum Dalam Memprediksi Jenis Tingkat Kejahatan Di Kecamatan Tambusai Utara Yanto, Budi; Hendri; almadison; Hutagaol, Ramses; Rahman, Ripatullah
Journal of ICT Applications System Vol 1 No 1 (2022): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.429 KB) | DOI: 10.56313/jictas.v1i1.165

Abstract

Crime is conduct that disobeys the law and contravenes social norms in a way that society finds objectionable. There is no system that can forecast the kind and quantity of crimes that will happen in the future as a guide in the process of preventing crime, according to the North Tambusai Police. However, the public service process in the form of complaints has been done digitally. Backpropagation is an iterative method that works well even with complex and convoluted data. Backpropagation is an artificial neural network with several levels (multi-layer). Data processing is done on the types and numbers of crimes that took place in North Tambusai District between 2015 and 2020. The first step in the data processing procedure is to normalize the data and choose the network training parameters. Crime data and levels were used in the artificial neural network research, which used a 5-5-1 design. The test results reveal that the average prediction accuracy rate is 92.66%, with the greatest prediction accuracy rate being 99.6% and the lowest forecast accuracy rate being 90.01 percent. Theft had the highest weighting (Curat) of crimes the next year with 15 cases, while fraud, crime, and extortion/threats each had the lowest weighting (1 case). The prediction findings exhibit a sufficiently high level of accuracy to serve as a basis for evaluation.
Optimized Detection of Red Devil Fish in Low-Quality Underwater Images from Lake Toba Using a Hybrid CNN and Transfer Learning Approach Enda Ribka Meganta P; Yanto, Budi
Journal of ICT Applications System Vol 4 No 1 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i1.429

Abstract

The detection of freshwater fish in turbid underwater environments presents significant challenges due to poor image quality caused by low lighting, suspended particles, and visual noise. This study proposes an optimized detection model for Amphilophus labiatus (Red Devil fish) in the murky waters of Lake Toba, Indonesia, using a hybrid Convolutional Neural Network (CNN) integrated with transfer learning and visual enhancement techniques. The proposed architecture combines MobileNetV2 and ResNet50 backbones with CLAHE (Contrast Limited Adaptive Histogram Equalization) and median filtering to improve image clarity and feature extraction. A custom dataset comprising 3,500 annotated underwater images was used to train and evaluate the model. The hybrid model achieved a detection accuracy of 96.1%, a precision of 95.6%, a recall of 94.8%, and a mean Average Precision (mAP@0.5) of 0.941—outperforming baseline models such as YOLOv5 and Faster R-CNN. Visual diagnostics and Grad-CAM attention maps confirm the model's ability to focus on key anatomical features under varying image conditions. The architecture is optimized for real-time deployment on edge-AI devices, supporting conservation efforts and biodiversity monitoring in freshwater ecosystems
Co-Authors -, Basorudin Adyanata Lubis Afdol, Pangeran agung setiawan Aini, Yulfita Akhmad Zulkifli Ali Djamhuri Alifiansyah, Eldi Almadison Alvito Dian Deva Amri, Rafelina Andriani Andriani Anjeli, Riri Arfandi, Zirhan ARIF KURNIAWAN Arif Mukti, Muhammad Asmen, Faisal B. Herawan Hayadi Bakti, Imam Rangga Bangkit Habiburrohman Basorudin, Basorudin Crisdianto, Ahlul Cut Nayla Husin Dede Fadhillah Dian Rahayu Dimas Sasongko EDI SAPUTRA Eka Putra, Wahyu Enda Ribka Meganta P ENDRI ENDRI Erni Rouza, Erni Erwis, Fauzi Ezra Ariendy Widodo Fasha, Ulya Fatimah Firmansyah Firdaus Hasri Ainun Pratiwi Hidayatullah, Riski Hutagaol, Ramses Inal, Inal Indah Wahyuni Irawan Zai, Feri Iskandar Zulkarnain, Wisnu Ismi Asmita Jufri -, Jufri Khairul Sabri Kiki Yasdomi Kridoyono, Agung Listio, Bayu Luth Fimawahib Meisaroh Melani, Tasya Muhammad Arif Muhammad Hafis Maulana Muhammad Wali, Muhammad Nasution, Yandi Ni Putu Nina Eka Lestari Ogidia Bella, Novica Parabi, Faisal Pratama, Rinanda Rizki Putra, Wahyu Eka Rahmad Rusli, Miltri Rahmadani, Dwi Rahman, Ripatullah Ramadani, Hendrizal Riski Mai Candra Sabri, Khairul Samsul Bahri Sari, Rika Perma Sastra, Amner Satria Riki Mustafa, Satria Riki Sholehatun, Euis Silvia Wulandari, Silvia Siregar, Intan Sudaryanto, Aris Sulastri, Ika Supriyanto, Asep Syarif Hasyim Zahara Zalzabila, Dea