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Published by IAIC Publishing
ISSN : 27745880     EISSN : 27745899     DOI : https://doi.org/10.34306/conferenceseries
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education
IAIC International Conference Series (IICS) managed by Indonesian Association on Informatics and Computing (IAIC) and supported by Alphabet Incubator . All URL of published articles will have a digital object identifier (DOI). The open-access IAIC International Conference Series provides a fast, versatile and cost-effective proceedings publication service for your conference. Proceedings are an important part of the scientific record, documenting and preserving work presented at conferences worldwide. Key publishing subject areas include: Computer Science, Informatics, Electronics Engineering, Communication Network and Information Technologies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 48 Documents
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Forward Chaining Algorithm on Informatics Graduate Job Recommendation System Based on MBTI Test Jhonatan Laurensius Tjahjadi; Yulia Wahyuningsi; Padmavati Darma Putri Tanuwijaya; Ryan Putranda Kristianto
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.641

Abstract

The Myers-Briggs Type Indicator (MBTI) is a method for identifying an individual's personality type based on the psychological theory of Carl Gustav Jung. In the context of computer science students, they often face challenges in planning their academic journey and determining the direction of their career development during their studies, causing confusion when it comes to choosing a career path in the field of computer science in the future. To address these challenges, the researcher has developed a web-based expert system using the PHP programming language. This expert system is designed to make decisions based on a collection of user responses, which are processed using the forward chaining method, ultimately providing the user's personality type along with suitable career choices. The primary objective of the expert system is to assist students in making decisions regarding their studies and future careers. Through this research, the researcher has produced a functioning website capable of efficiently processing user responses and generating decisions regarding personality types and career options. Thus, this study provides a solution to aid computer science students in planning their academic and career paths.
Detecting and Tracking Player in Football Videos Using Two-Stage Mask R-CNN Approach Amir Mahmud Husein; Chalvin; Kalvintirta Ciptady Ciptady; Raymond Suryadi; Mawaddah Harahap
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.643

Abstract

Football is one of the most popular sports worldwide and capable of attracting the attention of millions of fans to a single match in the top leagues. The English Premier League, Spanish LaLiga, German Bundesliga, Italian Serie A, and French Ligue 1 are the five best leagues in the world today. There was an experiment where researchers want to analyze the efficiency and accuracy percentage of tracking and detection using the deep learning method of the Mask R-CNN model in classifying positive and negative X-Ray images in football matches. In this study, we applied Mask R-CNN for the segmentation and detection of football players. This model was based on two different backbones, namely ResNet101 and DenseNet. Both backbones produced accuracy values that were not significantly different, but the DenseNet approach performed better than ResNet101 based on testing results in the validation and testing sets. Based on comprehensive experiment results on the dataset, it has been shown that the Mask R-CNN approach with DenseNet can achieve better results compared to Mask R-CNN with ResNet101. Due to insufficient understanding of the characteristics of image types and the uneven distribution of various types of data sourced from random videos, there was still room for improvement in the trained model.
Customer Segmentation: Transformation from Data to Marketing Strategy Luciana Abednego; Cecilia Esti Nugraheni; Adelia Salsabina
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.645

Abstract

Customer segmentation plays a crucial role in modern business strategies, enabling organizations to effectively target and personalize their marketing efforts and enhance customer relationships. Clustering algorithms have emerged as a powerful tool for segmenting customers based on their similarities and differences. We complement the data with an RFM model to support the clustering results. RFM, which stands for Recency, Frequency, and Monetary, is a model for segmenting customers based on their historical transaction data. This study aims to explore the concept of customer segmentation and the application of the RFM model combined with clustering algorithms in the real customer dataset of a company. It presents an overview of datasets, and introduces the RFM model and its components, emphasizing the significance of recency (how recently a customer made a purchase), frequency (how often a customer makes a purchase), and monetary value (the amount spent by a customer). It highlights the practicality of the RFM model in quantifying customer behavior and categorizing customers into distinct segments. It also explains popular clustering algorithms, analyzes experimental results, and concludes with future remarks on the potential of customer segmentation. We combine unsupervised (K-Means and DBSCAN clustering) and supervised machine learning methods to build customer clusters, label each cluster based on its characteristics, and propose a strategy for each cluster.
Analysis of Information Security Culture at FMIPA Halu Oleo University Using Partial Least Squares-Structural Equation Modeling Method Elsa Julfiana; Natalis Ransi; Gusti Arviana Rahman
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.647

Abstract

This research aims to analyze the information security culture at FMIPA Halu Oleo University. The results of the analysis show that exogenous latent variables, such as information security awareness, the role of faculty leaders, and information security policies, have a significant positive impact on information security culture. The research results show that the security awareness variable has a positive effect (0.221) on the Information Security Culture variable. Apart from that, the top management variable also has a positive effect (0.185) on the Information Security Culture variable. Likewise, the security policy variable has a significant positive influence (0.233) on the Information Security Culture variable. These findings provide an in-depth understanding of the factors that influence the culture of information security in the FMIPA Halu Oleo University environment, which can be the basis for recommending improvements in increasing information system security at the faculty.
Comparative Analysis of the Performance of the Decision Tree and K-Nearest Neighbors Methods in Classifying Coffee Leaf Diseases Suryadi; Murhaban Murhaban; Rivansyah Suhendra
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.649

Abstract

This study aimed to develop and compare classification models utilizing Decision Tree and K-Nearest Neighbors (KNN) in the detection of diseases in coffee leaf images. The dataset comprises coffee leaf images categorized into four different disease types, namely Nodisease, Miner, Phoma, and Rust. To facilitate model training and testing, the dataset was divided into training and validation data using a cross-validation approach. Both the Decision Tree and KNN models underwent meticulous parameter tuning. The experimental results reveal that the Decision Tree model achieved an accuracy rate of 98.20% on the validation data, while the KNN model achieved an accuracy rate of 75.01%. Furthermore, the Decision Tree model exhibited an AUC of 0.9879, recall of 0.9820, precision of 0.9835, and an F1-score of 0.9819 on the validation data. Conversely, the KNN model achieved an AUC of 0.9465, recall of 0.7501, precision of 0.7569, and an F1-score of 0.7485. These findings suggest that the Decision Tree model surpasses the KNN model in accurately detecting coffee leaf diseases, as demonstrated by higher accuracy and other evaluation metrics. However, the relevance of the KNN model remains contingent on application requirements and modeling preferences. These outcomes may contribute to the development of automated systems for disease detection in coffee plants, ultimately promoting more sustainable agricultural practices.
Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients Okky Putra Barus; Kevil Lauwren; Jefri Junifer Pangaribuan; Romindo
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.651

Abstract

Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.
Integration of Transformer Model Text Summarization and Text-to-Speech in Helping Document Understanding in the Bukudio Application Ivana Lucia Kharisma; Kamdan Kamdan; Anggun Fergina; Tofik Hidayat; Moh. Abd. Aziz Hidayat; Muhamad Muslih; Adhitia Erfina
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.653

Abstract

The need for effective, accurate and precise understanding of information will provide optimization of the decision-making process, increase knowledge and quality of life. Understanding information in relation to the document summarization process, if done manually, sometimes takes quite a long time. Text summarization techniques which are useful as document summarizers have been developed and applied to various things such as summarizing important documents, news texts or customer feedback. In this article, text summarization using the text rank method and transformer modeling integrated with text to speech techniques is developed in the Bukudio application, which is an application that provides audio versions of book documents in the application database. Based on the test results, the evaluation process was carried out using the Rouge method and gave the best results in calculating the Rouge 1 overlap monogram resulting in 0.523 for the F1 Score value, 0.434 for the precision value and 0.659 for the recall value. This research will be developed using other methods so that not only files in PDF document format can be processed, but other EPUB (Electronic Publication) files.
Application System for Setting Values on High Voltage Power Supply Using MCP4725 Module Based on ATmega328P Microcontroller Mohammad Amin; Wahyu Widji Pamungkas; Djoko Harsono
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.655

Abstract

Applications of power supply systems to supply sensors that require high voltage values are widely available in the market in the form of modules. However, in general, setting the voltage value is open by providing a voltage value from the potentiometer or trimmer component which is rotated manually. This becomes less flexible because the operator must always be nearby. The solution option is to implement automatic regulation via a potentiometer attached to an interface component connected to the microcontroller via a serial communication line called I2C. Furthermore, the microcontroller is programmed to receive regulatory commands and monitor the desired voltage value from a computer or mobile phone. This study uses the ATmega328P microcontroller, the MCP4725 DAC module and the CA12P-5TR series HV module from EMCO products. The results of this study are the design, implementation and prototype scheme.

Page 5 of 5 | Total Record : 48

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