cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Putra Wanda
Contact Email
wpwawan@gmail.com
Phone
+62274-488781
Journal Mail Official
icostec@respati.ac.id
Editorial Address
Faculty of Science and Technology, Universitas Respati Yogyakarta Yogyakarta, Indonesia Phone: 0274-488781 Email: ijicom@respati.ac.id
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
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
 2   3   4   5   6 
Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition Yuni Franciska br Tarigan; Teddy Surya Gunawan; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.51

Abstract

Javanese script is one of Indonesia's cultural heritages that are increasingly rarely used today. The difficulty of recognizing the shapes of letters, let alone writing them, is the main obstacle in using the Hanacaraka script. This research offers an alternative to Hanacaraka script recognition using a combination of image feature extraction and machine learning, where we utilize a pre-trained SquzeeNet model and Multilayer Backpropagation algorithm. Of the 18 models built using ReLu, Sigmoid, and Tanh activation functions, we found that the Tanh activation function, using the combination of 50-50-100 neuron configuration and 25 epochs, was the most optimal function used to classify the training data with accuracy, precision, and recall values of 93.8%. Meanwhile, the Tanh activation function, using the 50-100-50 neuron configuration and 50 epochs, is the most optimal function to classify the testing data, with accuracy, precision, and recall values of 89.1%, 89.5%, and 89.5%. All built models show a training and testing performance ratio below 10%. From this result, we conclude that all models have good reliability in the training and testing classification process.
Detection of Book and Borrower Communities Based on Book Borrowing Records in the Library Using Complex Network Analysis Tedy Setiadi; Rina Ratih; Nanik Arkiyah; Gretha Prestisia
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.52

Abstract

To improve user services, libraries need to have deep insight into users and the books they manage. This study aims to identify the community of users and borrowed books based on book borrowing records using complex network analysis. It starts with collecting book borrowing data, then converting it into a bipartite book-borrowing network using python programming and visualizing it with Gephi. Network analysis is performed to investigate network properties, and to use the BIMLPA method to find communities. The results of the investigation show that the structure of the book borrowing network is divided into two separate components. One main component of the network represents the optimal process of borrowing books, and the rest consists of many small components representing suboptimal borrowing. Community detection on the main component found 217 borrowing communities and 141 book communities. Most of the communities are small, where the book community is 2-4 members, while the borrowing community is in the form of individual borrowers. This research also produces the top 5 book communities, and borrower communities, the most popular books and the most active borrowers. The characteristics of the users and the books found can be used as a library reference for a more effective and efficient book development policy strategy, as well as book recommendations for more targeted users.
Data Mining in Auditing: Challenges and Opportunities Aditya Arisudhana; Khaula Lutfiati Rohmah
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.53

Abstract

Audit is a process of evaluation of a subject matter with a view to express an opinion on whether the subject matter is fairly presented carried out by an independent party. Assessment of the fairness of financial statements is very important because of the conflict of interest between the preparers of financial reports and stakeholders. The audit usually uses sampling to obtain data related to the fairness assessment. The auditor uses sampling because of time and cost constraints. Sampling also has a risk that can lead to errors in assessing the fairness of financial statements. Therefore we need a method that can improve accuracy in data collection and processing, for example data mining. Data mining is a method that can be used to collect and process data more quickly and accurately. The use of data mining techniques that may develop in the future can have an impact on the audit process. Data mining in audits will be both an opportunity and a challenge for auditors in the future
Predicting Children's Talent Based On Hobby Using C4.5 Algorithm And Random Forest Sugeng Riyadi; Hartono Hartono; Wanayumini Wanayumini
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.54

Abstract

A person's talent is closely related to intelligence, hobbies, and interests. These factors are the best features to be used in a dataset to predict a children's talent, such as in an academy, arts, or sports. This research uses the C4.5 and random forest algorithms in 8 different models to predict a children's talent based on a dataset gained from a survey involving 1601 parents. Each model contains four training-testing data ratios, such as 50:50, 60:40, 70:30, and 80:20. We calculate each model prediction performance using 10-fold and 20-fold crossvalidation, with the accuracy, f-score, precision, and recall values as a comparison. The best result for the training evaluation we get is 91.5% for each comparison value from the random forest model (70:30 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 92.7%, 92.8%, 92.8%, and 92.7% from the random forest model (50:50 ratio). The worst testing evaluation we get is 81.7% for each comparison value from the C4.5 model (50:50 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 89.2%, 89.2%, 89.3%, and 89.2% from the C4.5 model (50:50 ratio).
Predicting Non-Performing Loan's Risk Level Using KMeans Clustering and K-Nearest Neighbors Muhammad Mizan Siregar; Roslina Roslina; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.55

Abstract

In data mining, clustering is an unsupervised learning technique often used to group data by similarity. Clustering, especially the K-means clustering algorithm, is a feasible tool for expanding a dataset label by increasing the cluster's number according to the label's categories. This research extends the credit loan label data set from two categories (non-performing and performing loans) to four risk levels (high risk, medium risk, low risk, and no risk). The combination of three K-nearest neighbor’s distance metrics, Euclidean, Manhattan, and Chebyshev distance, with four different K values (K = 3, K = 5, K = 7, and K = 9) produced the best model with accuracy, precision, and recall values of 90%, 90.53571%, and 90%, from the model using the Euclidean distance with K = 9
TEXT MINING IN ONLINE TRANSPORTATION USER SENTIMENT ANALYSIS ON SOCIAL MEDIA TWITTER USING THE MULTINOMIAL NAIVE BAYESIAN CLASSIFIER METHOD AND K-NEAREST NEIGHBOOR METHOD Sartika Mandasari; Roslina Roslina; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.56

Abstract

Text mining is the process of detecting information or something new and researching large information. Text mining can also usually perform an analysis of unstructured text. Social media users in Indonesia, which currently almost reach 200 million users, have resulted in a flood of data. This condition makes text mining a solution to extract knowledge from the flood of data [1] . In exploring knowledge, there are various techniques or methods that can be adopted including the Multinomial Naive Bayesian Clasifier and K-Nearest Neighbor methods. Both of these methods have several phases that are able to explore the potential knowledge of a flood of supervised and unsupervised learning data. It is hoped that the combination of these two methods will help analyze public sentiment or perception towards online motorcycle taxi users in Indonesia
Model of MSME Digital Marketing through for Biopharmaceutical Products Marselina Endah Hiswati; Putra Wanda; I Wayan Ordiyasa; Lila Retnani Utami; Supardi RS; Rainbow Tambunan
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): 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.v2i1.57

Abstract

Sleman Regency has more than 50 traditional markets and also a variety of MSME business and there are more than 18,293 accommodation, food and beverage business sectors that are developing. Mobile-based information technology is urgently needed as a medium that supports efforts to promote and market MSME products, especially traditional culinary products, in this case processed products of Biopharmaca plants. The existence of a social restriction policy due to the COVID-19 pandemic requires the public to recognize technology as a medium of socialization towards digitalization. Thus, a mobile-based application is needed as a meeting place for sellers and buyers specifically for local Sleman products. Digital innovation has an impact on increasing the income and economy of MSME actors in Sleman Regency, Special Region of Yogyakarta

Page 6 of 6 | Total Record : 57

 2   3   4   5   6 

Filter by Year

2022 2023


Reset
Filter By Issues
All Issue Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In