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All Journal Jurnal Media Infotama JurTI (JURNAL TEKNOLOGI INFORMASI) JSAI (Journal Scientific and Applied Informatics) GATOTKACA Journal (Teknik Sipil, Informatika, Mesin dan Arsitektur) Jurnal Indonesia Raya Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Komputer, Informasi dan Teknologi
Arius Satoni Kurniawansyah
Universitas Dehasen Bengkulu
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Kolaborasi Whatsapp Group, Zoom Cloud Meeting, Dan Google Drive Sebagai Formula Dalam Pelaksanaan Kegiatan Perkuliahan Online Di Masa Pandemi Covid-19 Kurniawansyah, Arius Satoni; siswanto, siswanto
Jurnal Media Infotama Vol 16 No 2 (2020)
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v16i2.1155

Abstract

The world of education is facing big challenges with the Covid-19 pandemic. All lecture activities that are usually face-to-face are now prohibited and activities must be carried out online. Many applications are offered for carrying out online lectures, such as Zoom, Google Meeting, Whatsapp Group, and Google Drive and others. Lecturers and students must be capable and literate in technology, if one cannot utilize technology, it will be difficult to carry out online lecture activities. One of the impacts of the COVID-19 pandemic is the change in the school learning system to online. For most learning schools, many things still have to be adjusted, including how to train students to answer questions without them having to cheat by opening the internet. One technology-based learning media that can be used as a support for existing media is in the form of e-learning (electronic learning). E-learning is an internet application that can connect students and educators in an online learning space. E-learning is designed to overcome limitations between educators and students, especially in terms of time, space, conditions and circumstances. In summary, e-learning can create a digital learning space, where students can access material from various sources without being limited by space and time. During the Covid-19 pandemic, it was very effective to carry out online lectures using the Zoom application, Whatsapp Group and Google Drive.
EXPLAINABLE ARTIFICIAL INTELLIGENCE THEORY IN DECISION MAKING TREATMENT OF ARITHMIA PATIENTS WITH USING DEEP LEARNING MODELS Satoni Kurniawansyah, Arius
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 1 No. 1 (2023): Agustus
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jrsit.v1i1.75

Abstract

In the context of Explainable Artificial Intelligence, there are two important keywords: interpretability and "explainability". Interpretability is the extent to which humans can understand the causes of decisions. The better the interpretability of an AI/ML model, the easier it is for someone to understand why certain decisions or predictions have been made. Some cases of AI/ML implementation may not require explanation, because they are used in a low-risk environment, meaning mistakes will not have serious consequences. The need for interpretability and explainability arises when an AI system is used for certain high-risk problems or tasks, so it is not enough just to get predictive/classification decision outputs, but also needs explanations to convince users that AI (1: Model Explainability) is working the right way and (2: Decision Explainability) has made the right decision (Hotma, 2022). This research provides benefits for the development of knowledge regarding the implementation model of Explainable AI Theory in assisting Doctors' Decision Making for patients with cardiac arrhythmias with the Deep Learning Model in assisting Doctors' Decision Making for patients with cardiac arrhythmias. Knowing the Deep Learning Algorithm can be used in Machine Learning to read EKG Results. Knowing how to improve the results of the accuracy of the Explainable AI Application in Decision Making by Doctors for patients with cardiac arrhythmias. The use of Explanable Artificial Intelligence in the management of arrhythmia patients can provide an interpretation for doctors to be more optimal in treating patients. The results of this AI machine decision can increase doctors' confidence in treating arrhythmia patients optimally, effectively and efficiently. And also treatment will be faster because it is assisted by tools, so that patients can be treated more quickly. Thus it will reduce the mortality rate in arrhythmia patients.
Building an Online-Based Point of Sale Application at the Anton Store Andika Septian Nugraha; Sapri Sapri; Arius Satoni Kurniawansyah
Jurnal Komputer, Informasi dan Teknologi Vol. 1 No. 2 (2021): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v1i2.287

Abstract

Anton Bengkulu shop is a shop that sells goods and food for daily needs. So far, the processing process at the Anton Bengkulu Store has not used a computer for sales transactions, all of these processes are still using manual notebooks, and manual notes (data attached). This makes it difficult for the Anton Bengkulu Store to find out the availability of stock of goods and reports on the sale of goods at the Anton Bengkulu Store, because they have to calculate one by one the notes that have been issued and because there are also sales that do not use notes, so that it can cause losses and the stock of goods does not match the needs. which exists. The Online-Based Point Of Sale application at the Anton Store is an application that was built to make it easier for consumers to view and order products and shop at the Anton Store. This point of sale application can be accessed via the online link http://andika.vadapp.my.id so that it can be opened anytime and anywhere. This Point Of Sale application is made using the PHP programming language and MySQL database. This application displays information on products sold at the Anton Store, and there are 2 access rights applied to this application, namely administrator access rights and consumer access rights which have different functions. Based on the tests that have been carried out, the results show that the functionality of the Point Of Sale Application at the Anton Store runs as expected.
Application of Tsukamoto's Fuzzy Logic in Determining the Level of Service Quality at the Bengkulu Province Library and Archives Service Wiwin Purnama Sari; Mesterjon Mesterjon; Arius Satoni Kurniawansyah
Jurnal Komputer, Informasi dan Teknologi Vol. 1 No. 2 (2021): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v1i2.289

Abstract

Fuzzy logic is part of an artificial intelligence system that emulates human ability to think into algorithms which are then run by machines. At the Bengkulu Province Library and Archives Service, there is no application that can help to determine the quality of service based on the value given by the respondent. Service Quality Level Application at the Bengkulu Provincial Library and Archives Service is an application built to facilitate library members in providing value to the quality of services provided by the Bengkulu Provincial Library and Archives Service, and this application applies the Tsukamoto Fuzzy Logic method where the level of service quality is divided into 5 levels, namely Very Good, Good, Fairly Good, Less Good and Not Good. Based on the results of the tests that have been carried out, the functionality of the application has run as expected, and the Service Quality Level Application at the Bengkulu Provincial Library and Archives Service for 2021 is Very Good and this application is able to display the level of service quality every year through the Tsukamoto Fuzzy Logic Method approach.
Application of Parking Retribution Income Forecasting at PT Bumi Daya Plaza Bengkulu City Using Trend Method Non Linear Squadratic Model Fido Rama Nugraha; Jusuf Wahyudi; Arius Satoni Kurniawansyah
Jurnal Komputer, Informasi dan Teknologi Vol. 2 No. 1 (2022): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v2i1.783

Abstract

The calculation of parking retribution income which has been managed by PT. Bumi Daya Plaza Bengkulu City used Microsoft Excel application. Data processing is only limited to revenue recapitulation. Then a forecasting application will be made that will be used to forecast future income, for the benefit of the company by using Trend Method Non-Linear Quadratic Model. Trend Method NonLinear Quadratic Model is a trend in which the value of the dependent variable increases or decreases linearly or a parabola occurs when the data is made a scatter plot (the relationship between the dependent and independent variables is quadratic) tend to be in one direction (up or down), such trends generally include movements lasting about 10 periods or more. This motion reflects the nature of continuity or a continuous state from time to time over a certain period of time, because of the nature of this continuity, the trend is considered a stable motion so that in interpreting it a mathematical model can be used, according to the circumstances and the time series data itself. Trend can be a straight line (regression/linear trend) or non-straight (regression/non-linear trend). The results obtained are derived from the processing of income for the last 12 months using Trend Method Non-Linear Quadratic Model. With data that has been processed from January 2020 to December 2020, it produces income forecasting at the T001 parking point of Rp. 467,272 and at the T002 parking point of Rp. 15,814,697 in January 2021. By using Trend Method Non-Linear Quadratic Model can determine forecasting/ prediction of parking retribution income in the future using preexisting data.
Building And Analyzing The Use Of Network Systems In Diskless-Based Intro Internet Café Abid Rusidi; Khairil; Arius Satoni Kurniawansyah
Jurnal Komputer, Informasi dan Teknologi Vol. 4 No. 1 (2024): June
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v4i1.1755

Abstract

The use of computers and internet has become an integral part of daily life. A popular use is online gaming, where access convenience is often lower through smartphones compared to computers, leading many gamers to continue using their computers or visit internet cafes to meet their gaming needs. The success of internet cafes also depends on internet speed and computer device maintenance. The process of updating games on each client computer also affects overall internet speed. To address this issue, Intro internet cafe has built a diskless-based system, which centralizes data on the server so that game updates only need to be done on the server, allowing users to access the latest version without manual updates on each client computer, while also having sufficient security. This research aims to build and analyze the use of network systems in Diskless-Based Intro internet cafe. With a focus on configuration, performance, and efficiency, this research addresses several issues that arise with the use of diskless systems. The results show that the diskless system provides many benefits, such as reducing hardware costs, easier management, and increased data security. This system allows client computers to boot via the network and efficiently run the operating system. It is expected that the implementation of a diskless system in the operations of Intro internet cafe will provide efficiency and significant benefits.
Implementasi Metode Saw Pada Sistem Pendukung Keputusan Penerima Bantuan Langsung Tunai Dana Desa (BLT-DD) Muhammad Wahyu Rizky; Indra Kanedi; Ila Yati Beti; Arius Satoni Kurniawansyah
Jurnal Media Infotama Vol 21 No 2 (2025): Oktober
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v21i2.9165

Abstract

In determining the recipients of direct cash assistance for village funds (BLT-DD), Karya Pelita Village still uses a manual method with village meetings. In its application, village meetings are considered less efficient and prone to conflicts of interest. This needs to be overcome with a system that can help the decision-making process quickly, accurately and fairly. The decision support system for direct cash assistance recipients of village funds uses the Simple Additive Weighting (SAW) method. The process involves determining criteria, giving weights, and normalizing data to produce a preference value that is the basis for decisions. This system is designed with PHP programming language and MySQL database. This research produces a system that is able to increase efficiency and accuracy in determining recipients of direct cash assistance for village funds, and reduce conflicts of interest. Based on the results of data testing as many as 14 alternative recipient candidates, the final results showed that 10 alternatives had the highest score and were eligible to receive assistance, and 4 other residents were declared ineligible for BLT-DD assistance.
Analisis Hasil Imputasi Menggunakan Arsitektur Imputasi Autoencoder Arius Satoni Kurniawansyah; Liza Yulianti; Indra Kanedi
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10940

Abstract

Missing values in multivariate time series data are a critical issue in many domains, especially in healthcare datasets such as MIMIC-IV. This study aims to analyze the performance of imputation results using an Autoencoder-based architecture. Autoencoder is a deep learning model capable of learning data representations and reconstructing missing values through latent feature extraction. The research methodology includes data preprocessing, missing value simulation, model training, and evaluation using metrics such as MAE, RMSE, and R². The results show that Autoencoder-based imputation provides competitive performance in reconstructing missing values, particularly in nonlinear and complex patterns. However, the model's performance depends on the proportion of missing data and network architecture design. This study contributes to understanding the effectiveness of Autoencoder in multivariate time series imputation and provides a baseline for further development using hybrid models.
Co-Authors Abid Rusidi Agil Setiawan Andika Septian Nugraha Fido Rama Nugraha Herlina Latipa Sari Ila Yati Beti Indra Utama Julita, Rina Jusuf Wahyudi Kanedi, Indra Kanedi Khairil Khairil Khairil Khairil Leni Natalia Zulita Liza Yulianti Mesterjon Mesterjon Muhammad Wahyu Rizky Novrizam Hari Renal Pujangga Rina Julita Rizka Tri Alinse S Siswanto Sapri Sapri Siswanto Siswanto Wiwin Purnama Sari