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International Journal of Information Engineering and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481902     EISSN : 30481953     DOI : 10.62951
Core Subject : Engineering,
Engineering
The scope of the this Journal covers the fields of Information Engineering and Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 38 Documents
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Humanist Librarians in the AI Era : Maintaining Human Values in Information Services Azwar Azwar
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.327

Abstract

The development of artificial intelligence (AI) technology has brought about a major transformation in the world of libraries and information services. Automation, data analysis, and AI-based recommendation systems have increased the efficiency and accessibility of information for users. These advances also pose new challenges for librarians, particularly in maintaining human values ​​in the service process. Humanist librarians in the AI ​​era are required not only to understand technology but also to maintain an ethical, empathetic, and communicative role in interactions with users. This research uses a literature review to address the questions raised. Librarians, as mediators between technology and humans, act as bridges between digital literacy and ethical information, maintaining warmth and empathy in library services. By prioritizing human values ​​such as empathy, responsibility, and information justice, librarians can ensure that the application of AI in libraries remains oriented toward human needs and does not diminish the essence of civilized service.
Satisfaction Level Analysis QRIS Users Based on Experience and Perception Twitter Users/X Using Naive Baiyes Veri Arinal; Satria Wira Yudha; Muhammad Joko Umbaran Kharis Bahrudin; Dessyanti Ryantina
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.53

Abstract

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.
Sentiment Analysis of the Kabur Aja Dulu Trend on X as a Basis for Designing a Public Sentiment Monitoring System Using Naïve Bayes and SVM Sutisna Sutisna; Tri Wahyudi; Dwi Swasono Rachmad; Fachrur Rozi
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.79

Abstract

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.
Design of a Web-Based Instagram Content Management System to Support Brand Awareness for SR12 Herbal Cosmetics Products Untung Surapati; Agus Tanti Rahayu; Tatinia Arda Rizqi Amalia; Lusi Noviani
International Journal of Information Engineering and Science Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.83

Abstract

PT. SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 by Toni Firmansyah, S. Farm., Apt. and Asrianty Salam, S. Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.
Sentiment Analysis of the Performance of the Legal System in Indonesia Based on Twitter Comments Using the Naïve Bayes Algorithm Rasiban Rasiban; Dadang Iskandar Mulyana; Muhammad Joko Umbaran Kharis Bahrudin; Nicola Marthy
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.84

Abstract

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in Indonesia
Scalable Big Data Analytics and Fare Prediction for NYC Taxi Trips Using Distributed Computing and Machine Learning Kumar , Brian Shimmer Bino Deva; Hetharion, Sthania
International Journal of Information Engineering and Science Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v3i1.97

Abstract

This study develops a scalable big data analytics framework to process and analyze the New York City (NYC) Taxi Trip dataset using distributed computing and machine learning techniques. The objective of the research is to generate operational insights from large-scale transportation data and to build an accurate predictive model for total fare estimation. The dataset consists of integrated Green Taxi and Yellow Taxi trip records containing temporal, spatial, and financial transaction attributes. Data preprocessing was conducted through cleaning, schema harmonization, anomaly filtering, and enrichment using taxi zone lookup information. Descriptive analytics was performed to examine demand trends, trip behavior, revenue concentration, tipping patterns, and trip efficiency. The results show that monthly demand peaked during 2014–2016 with more than 16 million trips per month, followed by gradual decline after 2017 and a major disruption in 2020 during the COVID-19 period. Taxi activity was highly concentrated in Manhattan and during afternoon-to-evening peak hours. Revenue was largely dominated by a small number of strategic pickup–dropoff borough pairs, particularly Manhattan-centered routes. Tipping behavior remained significant, with 62.96% of trips including gratuities. In addition, trips lasting 30–60 minutes provided the best balance between income opportunity and operational efficiency for drivers. For predictive analytics, a streaming batch training approach was implemented to handle more than 970 million trip records. Two incremental learning models, ElasticNet and Passive Aggressive Regressor, were evaluated using Root Mean Square Error (RMSE). The results indicate substantial improvement over the baseline model, reducing RMSE from 25.05 to 13.03 and 13.04, respectively. This represents an error reduction of approximately 48%. Overall, the findings demonstrate that combining big data platforms with online machine learning methods can effectively support urban mobility analysis, fare prediction, and data-driven transportation decision-making. The proposed framework is also adaptable for other smart city applications involving massive real-world datasets.
Social Media Sentiment Analysis of Instagram Use by Early Childhood Education Information System Development Based on Naïve Bayes Yuma Akbar; Sugiyono Sugiyono; Dedi Gunawan; Salsabila Putri Wibowo
International Journal of Information Engineering and Science Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i1.341

Abstract

This study employs the Naïve Bayes method to analyze social media sentiment regarding the use of Instagram by early childhood users. The primary objective of this research is to understand public perceptions of the positive and negative impacts of Instagram usage among young children, particularly in relation to their social, psychological, and digital behavioral development. Sentiment analysis is carried out using data from various social media platforms, which are then classified into positive, negative, and neutral opinions. The classification results form the basis for developing an integrated educational information system designed to provide guidance for parents, educators, and children in using Instagram safely, healthily, and responsibly. The system also emphasizes the importance of age-appropriate content education, privacy settings, and strategies to minimize the risks of exposure to inappropriate content and the negative effects of excessive usage. This research is expected to support the creation of a more positive, safe, and beneficial digital environment for early childhood users while also serving as a reference in formulating effective policies in the social media era.
Implementation of the YOLO Algorithm for Detecting Bullying Behavior at Pesantren Bisnis SMK Skill Village Islamic School Jonggol Bogor Sutisna Sutisna; Rizki Ananda Pratama; Nandang Sutisna; Jundi Kariman Husni
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.346

Abstract

Bullying is a serious problem that can disrupt the learning process and mental development of students, including in Islamic boarding schools. Early detection of bullying is essential to creating a safe and conducive learning environment. This study aims to apply the You Only Look Once (YOLO) algorithm to automatically detect bullying through video recordings in the environment of the SMK Skill Village Islamic School Business Boarding School. The method used involves collecting a video dataset representing various types of bullying behavior, labeling the data, and training an object detection model using the YOLOv5 algorithm. The developed system is capable of detecting and classifying bullying behavior in real- time with detection accuracy reaching [accuracy value if known]. The implementation of this system is expected to assist school authorities and boarding school administrators in monitoring, preventing, and addressing bullying incidents more quickly and effectively, while also serving as an initial step in leveraging artificial intelligence technology to create a safer and more comfortable educational environment.

Page 4 of 4 | Total Record : 38

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