cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Purwanto
Contact Email
garuda@apji.org
Phone
+62895395733773
Journal Mail Official
fatqurizki@apji.org
Editorial Address
Perum Cluster G11 Nomor 17 Jl. Plamongan Indah, Kadungwringin, Pedurungan, Semarang, Provinsi Jawa Tengah, 50195
Location
Kota semarang,
Jawa tengah
INDONESIA
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 33 Documents
 1   2   3   4 
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 : 33

 1   2   3   4 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science Vol. 1 No. 4 (2024): November : International Journal of Information Engineering and Science