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Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Published by Universitas Respati Yogyakarta
ISSN : 29856914     EISSN : -     DOI : https://doi.org/10.35842/icostec
Core Subject : Science,
Computer Science & IT
ICoSTEC is an annual forum for international researchers and students to exchange ideas on current studies and research topics. The international conference will discuss several sub-topics, including innovation in information science and technology and leveraging globalization.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 57 Documents
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Comparison Analysis of Fuzzy Sugeno & Fuzzy Mamdani for Household Lights Miftah Alfian Firdausy; Ema Utami; Anggit Dwi Hartanto
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.1

Abstract

The rapid growth of knowledge and technology in the IoT field encourages scientists to make discoveries in facilitating daily activities by utilizing artificial intelligence, one of which is the Smart Home. Smart home technology is needed that has better advantages over existing building materials, one of which is home lighting or the use of lights. If all this time, houses still use manual control to turn the lights on and off, it will potentially cause the lights to turn on still even though they are not needed, for that we need a method used in the implementation of automatic light control. From several studies, the Fuzzy method is widely used in this case. This method has several models, but the author uses the Fuzzy Mamdani method and the Sugeno method to apply in this study. The variable at the input is the LDR sensor, while the output is a lamp. From the trial results of the two methods, and accuracy test was sought as a parameter for the better ana method. It can be concluded that the Sugeno method has a better accuracy rate of 88.25%, compared to Mamdani's, which is only 84.5%.
Deep Learning Approach For Modelling The Spread of Covid-19 Riah Ukur Ginting; Muhammad Zarlis; Poltak Sihombing; Syahril Efendi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.2

Abstract

In March 2020, the Covid-19 outbreak in Indonesia, where the symptoms of the corona virus made the Indonesian people worry and experience depression. It has been almost two years that Covid-19 has not known what causes it, let alone a person's body condition is not good which can result in being attacked by the virus. Covid-19 first appeared in the city of Wuhan, part of China, where it spread very quickly and was deadly. Its spread through direct physical contact with humans is transmitted through the mouth, nose and eyes, therefore a model is needed for the spread of the corona virus. The spread of COVID-19 affects the pattern of interaction between susceptible (susceptible) and infected (infectious) individuals, where human social contact is very heterogeneous and in groups. To influence the impact of the spread of COVID-19 using deep learning approach that is modeled on the spread of COVID-19, individuals are exposed, infected, recover and die. The purpose of this research is to produce good predictions with a deep learning approach for modeling the spread of COVID-19. The results of the deep learning approach for the COVID-19 spread model carried out the 400 time iteration with an MSE achievement of 0.021112.
Evaluation of Naive Bayes, Random Forest and Stochastic Gradient Boosting Algorithm on DDoS Attack Detection Ricki Firmansyah; Ema Utami; Eko Pramono
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.3

Abstract

The Internet has given unlimited access to every user through the network used. Vulnerabilities in a network can also be caused by increasing knowledge about hacking and cracking. This is the reason why information and network security are so important. The dataset used in this study uses a dataset from CIC (Canadian Institute Cybersecurity), which covers 7 different attack scenarios, including brute-force, heartbleed, botnet, dos, DDoS, web attacks, and network infiltration from within. The existing attack documents will be extracted. Feature extraction is a process to find the feature values contained in documents for the text mining process. Based on this explanation, this DDoS attack will generate a log where the attack log will be processed and processed into a CSV file for the classification process using Naive Bayes, Random Forest, and Stochastic Gradient Boosting. In this study, researchers used the Naive Bayes, Random Forest, and Stochastic Gradient Boosting algorithms to generate a classification comparison of DDoS attack data so that researchers can find out which algorithm is the best in generating classifications for DDoS attack cases. The results of this study can be concluded that the average accuracy generated by Naive Bayes is 82.45%, the average accuracy generated by the Random Forest algorithm is 99.78%, and the average accuracy generated by the Stochastic Gradient Boosting algorithm is 100%, so that the SGB algorithm is better than Naive Bayes and Random Forest algorithms in classifying DDoS attacks.
Waste Classification using CNN Algorithm Mohammad Diqi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.4

Abstract

One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.
Sentiment Analysis on Jurassic Park Development in the Komodo Conservation Area Annisa Septiana Sani; Indra Budi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.5

Abstract

Rinca Island is included in one of the islands in NTT Province, which is used as a National Park to protect Komodo dragons. According to the government's agenda, in June 2021, Rinca Island will build a world-class premium tourist destination project for tourists who want to see Komodo, which is called Jurassic Park Komodo. The existence of tourism is expected to be able to attract tourists and investment. However, the news about project development that is not in accordance with the conservation value has created public sentiment towards the project development process. The purpose of this study is to analyze public sentiment related to the classification of support & against the development of Jurassic Park in the Komodo Conservation Area and to measure the accuracy using two methods, namely Naïve Bayes and Decision Tree.
Project Management Methodologies for Engineering KMS based on PMBOK Approach: A Systematic Literature Review Oki Priyadi; Dana Indra Sensuse
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.6

Abstract

The process of creating, sharing, discovering, and capturing knowledge becomes an intangible asset for an organization. As a valuable asset, Knowledge needs to be managed. There is a tool called Knowledge Management System (KMS). The latest technology and social or structural mechanisms are components to develop KMS. But developing an IT project has a high risk of failure, so it should be managed using a project management approach. A project is a temporary endeavor to make a product or service. It means that a project has a start and an end project phase. Some methodologies which exist do not offer complete stages of development KMS. They do not cover feasibility study activities, lack of clear specification, and lack of closing project phase. The study gap is expected to be loaded up and well explained in this paper through a systematic review of the published literature over the last six years using the Kitchenham method. The results show that most researchers used a predictive approach compared to an iterative, incremental, or adaptive development approach to build KMS. The main result of this study we found 36 processes in the developing KMS. The novelty of this research, we generated a table to map the processes into the model, method, artifact and presented it by group process based on the Project Management Body of Knowledge (PMBOK). The table can help the organization develop the KMS project to improve its effectiveness and competitiveness.
Auto-level System on 3D Printer Bed Using Arduino and 3D Touch Probe Sensor Alexander Ariantono
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.7

Abstract

The development of technology today has emerged the latest technology in three-dimensional (3D) printer machines. Problems faced by 3D printers include the position of the printer bed that is not horizontal. The position of the printer bed that is not horizontal will cause the 3D print to be skewed. To overcome the problem of tilting the bed of a 3D printer, the auto-level system is needed, which is a system that can correct the position of the base so that it becomes horizontal. In this study, an auto-leveling system was designed and made for four 3D printing base support positions. The drive for the printing press uses a stepper motor with a worm gear transmission. The controller uses an Arduino board and tested various position sensors to determine the slope of the base. The sensor tested is a 3D touch probe sensor. From the test results, the 3D touch probe sensor has a height value difference of not more than 0.05mm from each measurement position. The print base slope reference point is at the center point of the print base. The position of the other corner of the base will be adjusted in height with a stepper motor drive
The Impact of Online Community on Farmer Empowerment: A Technology-to-Performance Chain Approach Anton Susanto; Ali Agus; Jangkung Handoyo Mulyo; Hakimul Ikhwan
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.8

Abstract

This research provides empirical evidence for the benefit of social media in the online farmer community. It uses the case study analysis in exploring the ICT usage in Empowerment of Laying Hens Farmer in East and Central Java implementing the multi-feed and additive supplement. The research develops The Technology-toPerformance Chain (TPC) approach and farmer empowerment model. This research use farmer empowerment as the performance impact of the online community. Meanwhile, it also predicts some factors that affect the performance impact. Those are farmer characteristics, task-technology fit, and facilitating conditions. The empowerment impacts are influenced significantly by task-technology fit with a path coefficient of 0,704. It means that utilization of social media as the online media of community will encourage empowerment if the information and knowledge sharing activities inside are related to the task or farmer's livelihood. The farmer's characteristics and the facilitating condition affect the performance insignificantly. However, the facilitating condition determines the tasktechnology fit with the path value 0,868. It means to endorse the ICT becoming fit with the task/livelihood of the farmer need the facilitating condition as the underlying factors. These factors are the internet accessibility of farmers, the innovation of the multi-nutrient and additive supplement product from socio-entrepreneurs, and the assistance/consultancy from the extension agent.
THE FUTURE OF ACCOUNTING WITH ARTIFICIAL INTELLIGENCE: OPPORTUNITIES AND CHALLENGES Khaula Lutfiati Rohmah; Aditya Arisudhana; Tri Septa Nurhantoro
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.9

Abstract

Artificial intelligence (AI) is one of the remarkable discoveries in the twentieth century. AI is a science related to the creation of machine intelligence capable of performing tasks that previously can only be done by people (Simarmata et al, 2021). It can be said that AI is a breakthrough to create machines that have higher intelligence than machines in general. The current development of AI is aimed at improving learning and problemsolving abilities (Dongre et al, 2020). Accounting is a field that is very suitable for utilizing AI in every part of its information system. AI is expected to be able to help accounting practitioners to improve their performance and develop services they provide. During the development of AI, accounting practices that already adopted AI have resulted in more qualified and diverse outcomes. Various problems that become accounting limitations can be solved with the help of AI, so that the accounting profession can work on a wider area of accounting services, including forensic accounting and financial services and digital investments
Predicting Startup Success, a Literature Review Harjo Baskoro; Harjanto Prabowo; Meyliana Meyliana; Ford Lumban Gaol
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.10

Abstract

The development and growth of startups around the world nowadays have become a global phenomenon. Startups have become an essential element of innovation and economic growth in many countries. But literature shows that the failure rate of a startup is around 90%. Therefore it is crucial for investors, financial advisors, and the government to spot the 10% which eventually will generate higher return rates, bring in greater revenue and ensure economic growth. This research aim is to study what are the critical factors of the startup’s success that can be used to make a predictive model using a machinelearning algorithm to predict the success of a startup.

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