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International Journal of Computer Science and Humanitarian AI
Published by Universitas Bina Nusantara
ISSN : 30644372     EISSN : -     DOI : https://doi.org/10.21512/ijcshai.v2i2.14418
Core Subject : Humanities, Science, Engineering,
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
International Journal of Computer Science and Humanitarian AI (IJCSHAI) is an international journal published biannually in February and October. The Journal focuses on various issues: Computer Science, Artificial Intelligence (AI), Fuzzy Systems, Expert Systems, Geo-AI, Machine Learning, Deep Learning, Humanitarian AI, Data Science, Computer Vision, Natural Language Processing (NLP), Information Systems, Psychoinformatics, Computational Intelligence, Recommender Systems, Robotics, Robot Vision and Control Systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 20 Documents
 1   2 
Development of Telegram-Based Home Automation and Data Acquisition System Widodo Budiharto; Heri Ngarianto
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12030

Abstract

Home automation and data acquisition system using Telegram application makes us able to automate tasks related to control and monitoring through a system installed at home or building. In this research, we propose an algorithm and architecture used for Telegram-based home automation and data acquisition with environmental sensors such has temperature, humidity, CO2, and Volatile Organic Compounds (VOC) measurement (air quality) as indicator of good air quality. Based on experiment, we can detect the condition of environment, control the relay and the system able to give information about the quality of air from smartphone.
User Requirement Analysis on Sales Information System at PT. MITRA INDOLINK Mochamad Naufal Akbar; Deddy Purba Pratama; Kevin Alexander; Suzanna Suzanna
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12135

Abstract

The era is developing very quickly and dynamically, creating many changes that result in tight competition in the business world. The business world needs to follow the progress of information systems. Analysis of user needs in the business world needs to be done in order to find out the various needs of the company. The purpose of this study is to find out the results of the analysis of user requirements that are needed and that can support sales operations in the sales information system of PT. Mitra Indolink Grosir supports the company's sales operations. The research method used is by interviewing users of the information system which is then processed using the System Usability Scale (SUS) technique. The results of the analysis show that the sales information system with the TikTok shop application can be used efficiently and meets user needs (user requirements) with a value of 69.65 which is included in grade C.
Assessing University Website Performance: A Comparative Analysis Using GTmetrix Davin Nayaka Pandya; Doddy Suryadharma; Lili Ayu Wulandhari; Islam Nur Alam
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12152

Abstract

As the reliance on websites to disseminate information increases, universities are no exception, using websites as an important platform for their information systems. However, ensuring optimal website performance is imperative, as slow or unresponsive websites can lead to decreased user satisfaction and affect the university's reputation. This study aims to analyze and compare the performance of university information system websites from Indonesia’s top five universities against the world’s leading institutions in Computer Science and Information Systems based on QS World University Rankings by Subject 2023. Utilizing GTmetrix, a comprehensive performance assessment tool, key performance metrics such as First Contentful Paint (FCP), Speed Index (SI), Largest Contentful Paint (LCP), Time to Interactive (TTI), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS) were evaluated. The findings reveal a significant performance gap between the top universities globally and in Indonesia. While global universities demonstrate good performance across various metrics, Indonesian universities exhibit areas in need of improvement, particularly in metrics like FCP, SI, LCP, TTI, and CLS. Nevertheless, Indonesian universities excel in blocking time, suggesting strategic strengths that can be leveraged for overall performance enhancement. This study underscores the importance of regular attention to website performance to enhance user experience and maintain the university's reputation within the academic community.
Implementation of IoT Edge Computing for Control and Monitoring System of Hydroponic Plant Water Quality Using Raspberry Pi Cahya Lukito; Rony Baskoro Lukito; Endang Ernawati
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12153

Abstract

Hydroponics involves cultivating plants using a water-based medium mixed with mineral nutrients, continuously supplied to the roots 24/7. Factors such as water reserve height, temperature, nutrient content, and pH are crucial considerations in hydroponic farming. Connectivity issues to the internet-based cloud system can disrupt the monitoring and control system. To ensure the effective operation of the hydroponic plant control and monitoring system, IoT edge computing within the Local Area Network is necessary as an extension of the cloud system. Periodically, the system will transmit calculation results from water quality sensors to the cloud-based system through IoT edge computing, enabling decision-making within the Local Area Network and subsequent transmission to Internet of Things devices within the hydroponic system for optimal plant growth.
A Systematic Literature Review: Cyber Attack: Phishing Environments, Techniques, and Detection Mechanism Cindy Natasya; Irvin Irvin; Alexander Agung Santoso Gunawan
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12155

Abstract

In this digital era, phishing has attacked many platforms such as email, website, message, link form. Phishing is an act of creating a website that is exactly like the original website that is used to take someone's personal data. Phishing causes loss of customer confidence to use any application or website. Most of the victims of phishing are people who do not understand phishing or an organization. This kind of cyber-attacks consist of various types and countermeasures that need to be considered for the public user to prevent phishing based on phishing techniques, educate individuals about these attacks, and encourage the use of phishing prevention techniques. This paper consists of types of phishing and awareness to wary of phishing to overcome them. Therefore, the goal of this study is to identify the most typical environments for phishing attacks in order to ascertain the most popular media and technique. The authors of this study plan to conduct a Systematic Literature Review (SLR) of studies that have been done on the subject that was just described. The authors come to the overall conclusion that a website is the ideal option for phishing attacks using social engineering techniques. Additionally, the authors offer numerous suggestions for preventing phishing with various techniques. However, the most effective defense against phishing attacks is identification of phishing attempts through education and training.
Comparison of Machine Learning Classification Models in Predicting The Titanic Survival Rate Andika Elok Amalia; Cindy Rahayu
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.12163

Abstract

The tragic sinking of the Titanic in 1912 has been a subject of great interest, particularly in analyzing the factors that influenced passenger survival rates. This study applies machine learning techniques to predict the survival of Titanic passengers based on various attributes. The dataset used includes demographic details and passenger-specific features such as age, gender, ticket class, number of siblings/spouses, number of parents/children traveling, ticket fare, and departure location. An exploratory data analysis is conducted to understand patterns within the dataset, followed by data preprocessing steps, including handling missing values and encoding categorical variables. To develop the predictive model, multiple machine learning algorithms are implemented, including Logistic Regression, Random Forest, Extra Trees, Decision Tree, LGBM Classifier, and XGBoost Classifier. The results indicate that the Random Forest model achieves the highest accuracy at 0.815, while the LGBM Classifier attains the highest cross-validation score of 0.821. Feature importance analysis highlights gender and ticket class as the most significant factors affecting survival probability. This study demonstrates the effectiveness of machine learning classification techniques in analyzing historical data and predicting binary outcomes. The insights gained from this research can be applied to other domains involving historical data analysis and classification tasks, such as risk assessment, medical prognosis, and social science research. By leveraging machine learning, this approach provides a data-driven perspective on historical events, enabling better decision-making in similar predictive modeling scenarios.
Editorial, Foreword, and Table of Content Widodo Budiharto
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Two-Dimensional Segmentation to Reconstruct Three-Dimensional Covid-19 Patient’s Lung CT Using Active Contour Zaki Ambadar; Tri Arief Sardjono; Nada Fitrieyatul Hikmah
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.12417

Abstract

Beginning in December 2019, SArS-CoV-2, also referred to as COVID-19, quickly spread over the world. With two recurrent waves and a 3.3% fatality rate, COVID-19 has caused over 4 million cases in Indonesia. RT-PCR, antigen, and RT-LAMP are currently the main techniques for COVID-19 detection and diagnosis. A CT scan is usually used for additional diagnosis when RT-PCR results are uncertain, but extra confirmation is required. The need to inform patients about the effects of COVID-19 on the lungs is increasing as the number of cases of the virus keeps rising and diagnosis and first aid techniques advance. The severity of COVID-19-induced pneumonia, which shows up as ground-glass opacities (GGO), which are gray patches in the lung cavity, may be seen on a single-slice CT scan. The degree of lung injury can be measured using image processing techniques. In this study, two- and three-dimensional representations of the lungs were created utilizing a multi-slice CT scan and image processing techniques like active contour and marching cubes. The suggested approach produced an average volume difference of 5% and an accuracy of 62% based on intersection over union (IoU).
Smoker Melanosis Classification Using Oral Photographic Feature Extraction Based On K-Nearest Neighbor I Gede Maha Prastya Budi Dharma; Nada Fitrieyatul Hikmah; Tri Arief Sardjono
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.12418

Abstract

Smoking is one of the causes of various diseases in the body. Smoking can also cause abnormal conditions that are pathological and physiological in the oral cavity, one of which is smoker melanosis. The clinical picture of pigmentation smoker melanosis is the presence of scattered brown spots with a diameter of less than 1 cm and is most often located on the gingiva. The data was taken using the oral photograph image capture method using a 12MP resolution camera, provided that the object distance from the camera was 6 cm and the flash was on. This analysis utilized the Gingiva Pigmentation Index (GPI) classification system proposed by Hedin, which compares the pigmented area, and Dummett's Oral Colour Index (DOPI), which assesses the density of pigmentation. In this study, the classification process was carried out with the KNN algorithm using features from digital image processing in the segmentation area, the average value of the red, green, and blue colour levels. The classification process uses the nearest neighbour value of 3 and a p-value of 2 to measure the distance to the nearest neighbor using the Minkowski distance formula. The results of the test data accuracy (1.0) with F1 scores for each class for test data DOPI 0 = 1.0, DOPI 1 = 1.0, DOPI 2 = 1.0, DOPI 3 = 1.0. Meanwhile, the classification process can use more up-to-date methods, such as CNN to improve classification accuracy.
Systematic Literature Review of The Use of Music Information Retrieval in Music Genre Classification M. Aqila Budyputra; Achmad Reyfanza; Alexander Agung Santoso Gunawan; Muhammad Edo Syahputra
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.13019

Abstract

Emphasizing deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this article explores the application of Music Information Retrieval (MIR) techniques in music genre categorization. These algorithms outperform traditional methods in capturing complex audio patterns, showcasing their potential in advancing music classification tasks. Accurate genre classification critically depends on key features such as spectral, temporal, and timbral characteristics, which play a pivotal role in distinguishing musical styles. However, the performance of these models is heavily influenced by the quality and diversity of the training datasets. Additionally, challenges like model interpretability and reliance on large datasets are addressed. This research utilized a Systematic Literature Review (SLR) to investigate the capabilities of advanced MIR techniques in enhancing music categorization systems, particularly for educational applications and personalized music recommendations. The findings reveal that analyzing the importance of spectral, temporal, and timbral features—key components of MIR—can significantly boost the accuracy and reliability of music genre classification.

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