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Jay Idoan Sihotang
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jurnal.teika@unai.edu
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Universitas Advent Indonesia, Jalan Kolonel Masturi 288, Kec. Parongpong, Kab. Bandung Barat
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Teika
Published by Universitas Advent Indonesia
ISSN : 19796439     EISSN : 23385979     DOI : https://doi.org/10.36342/teika
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media
TeIKa (Teknologi Informasi dan Komunikasi) Journal invites scholars, researchers, and students to contribute the result of their studies and researches in the areas related to Information and Communication Technology work which covers Information System, Computer Networks, Computer Security, Database, Programming, Artificial Inteligence, Datamining, Multimedia Technology. It also seeks to provide a forum for an open exchange of ideas, based on research and serious professional reflection on both the theoretical underpinning of practice and practical applications of theory. TeIKa welcomes contributions from all Information Technology Lectures nationwide as well as fresh voices with something new to contribute to the community. Scope - Information System - Computer Networks - Computer Security - Database - Programming - Artificial Inteligence - Operating System - Multimedia - Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 225 Documents
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Design of a Web-Based Attendance System Using Face Recognition and Liveness Detection with Integrated Email Notifications Budy Satria; Sandi Fadilah; Muhammad Al Ashari; Tomy Nanda Putra; Abdussalam Al Akbar
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/9717b994

Abstract

Efficiency, identity verification, and the possibility of duplicate attendance records are still issues with traditional attendance systems. The goal of this project is to create a Face ID-based attendance system that incorporates liveness detection, facial landmark detection, and face recognition into an online application. YuNet was used to recognize faces, MediaPipe Face Mesh was used to extract 478 facial landmark points, and SFace was used to create face embeddings and carry out cosine similarity matching. The Eye Aspect Ratio (EAR) method was used to detect eye blinks in order to accomplish liveness detection. The registration procedure successfully processed all 15 face frames and produced a 128-dimensional embedding, according to implementation results. While other tests produced scores as high as 0.945, face recognition testing effectively identified registered users with a similarity score of 0.921. Additionally, the system recorded attendance as "PRESENT," identified liveness as "REAL," and stopped duplicate entries by designating a "DUPLICATE" status. Testing with unidentified faces produced a "UNKNOWN" classification, which meant that attendance was not noted. In addition, the system offers a dashboard and reporting tools with data filtering and export features, sends email notifications, and stores data in a database. These findings show how the created system successfully combines facial recognition, liveness verification, attendance confirmation, notifications, and reporting into a single web-based platform
Analisis Pengaruh Kualitas Layanan Website Penerimaan Mahasiswa Baru (PMB) UNAI terhadap Loyalitas Pengguna Lestari Sri Ulina Sinaga; Raymond Maulany
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/e05k9x12

Abstract

Higher education institutions are increasingly using online systems for New Student Admission (PMB), including Universitas Advent Indonesia (UNAI) through its PMB website. Although the website facilitates access to information and registration, some users still experience issues such as delayed responses and unclear information. This study aims to analyze the effect of PMB website service quality on user loyalty using service quality adapted from SERVQUAL and the Net Promoter Score (NPS). The study involved 87 student respondents who used the UNAI PMB website, with 86 data points included in the analysis after one data point was excluded through listwise deletion. Service quality was measured using 10 items covering ease of access, information clarity, system speed, interface and navigation, and user support, while user loyalty was measured using NPS. The service quality instrument was tested using Pearson correlation and Cronbach’s Alpha. The results showed that all 10 service quality items (P1-P10) met the validity criteria, and the instrument demonstrated good internal consistency, with a Cronbach’s Alpha value of 0.933. The results of the linear regression analysis showed that service quality had a positive and significant effect on user loyalty, with an R value of 0.405, an R Square value of 0.164, and a significance level of <0.001. The NPS measurement resulted in a score of -3.49, with 29.07% of respondents classified as Promoters, 38.37% as Passives, and 32.56% as Detractors. These findings indicate that improvements in service quality, particularly in aspects receiving relatively lower evaluations, are needed to support increased user loyalty toward the UNAI PMB website.
Classification of User Satisfaction with Spotify Playlist Recommendations Using Random Forest and Hyperparameter Tuning Hervilla Amanda; Sriani
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/cfq3dq18

Abstract

Music playlist recommendations require an assessment of user satisfaction to evaluate their suitability for listeners’ preferences. This study applies Random Forest to classify user satisfaction with Spotify-recommended playlists and examines the effect of hyperparameter tuning on classification performance. A quantitative experimental approach was used with questionnaire data from 205 respondents, comprising 14 predictors and one satisfaction target with five categories. The dataset was divided into 164 training and 41 test observations. The baseline model used 100 trees, and hyperparameter optimization was conducted using GridSearchCV. The selected configuration comprised 200 trees, the gini criterion, unrestricted maximum depth, a minimum of two samples for splitting, and one sample per leaf. Its mean cross-validation accuracy was 77.47%. Both models achieved 85.37% accuracy, 89.16% weighted precision, 85.37% weighted recall, and 85.16% weighted F1-score, with 35 correct and six incorrect test predictions. The frequency of liking recommended songs was the most important feature, contributing 17.24%. Tuning selected the best validation configuration within the tested search space but did not improve test performance. These findings support satisfaction classification as an input for evaluating playlist recommendations. However, performance for the very dissatisfied category could not be assessed because it was absent from the test set.  
Analysis of Garment Order Clustering Using the K-Means Clustering Algorithm Rika Khairani; Sriani Sriani
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/dccg0b20

Abstract

The garment industry generates diverse order data, requiring proper data management and analysis to support production planning and improve production efficiency. The main problem faced by Sumatera Konveksi is the suboptimal utilization of order data to identify order characteristics based on processing time, product type, order type, and total order quantity. This study aims to apply the K-Means Clustering algorithm to group garment order data based on similarities in their characteristics to support production decision-making. The method employed is K-Means Clustering, with the optimal number of clusters determined using the Elbow method and the clustering results evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results indicate that the optimal number of clusters is K=4. The clustering process converged at the eighth iteration, producing four clusters: C0 with 233 data points, C1 with 322 data points, C2 with 465 data points, and C3 with 200 data points. The evaluation results yielded a DBI value of 1.0888 and a Silhouette Score of 0.4949. These two metrics provide insights into the compactness of data within each cluster and the degree of separation between clusters. The resulting clusters are expected to serve as a basis for production planning, scheduling, and more effective resource management at Sumatera Konveksi.  
Automated Real-Time Smart Trash Bin Monitoring System Design using IoT Technology with Ultrasonic Sensing and Web-Based Dashboard Integration Marchel Thimoty Tombeng
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/qx6b1q31

Abstract

Inefficient solid waste management frequently leads to environmental and public health concerns, particularly when collection delays result in overflowing bins. This study presents the design and implementation of an Internet of Things (IoT) Automated Smart Waste Bin system, integrating HC-SR04 ultrasonic sensors and a web based monitoring dashboard. The system autonomously operates bin lids through servo motor actuation upon user proximity detection, while concurrently monitoring fill levels in real time. The Prototyping model was adopted as the development methodology. Core hardware includes an ESP32 microcontroller as the central computation and data transmission unit, dual ultrasonic sensors for organic and non organic waste level measurement, and RGB LED status indicators. The software stack comprises a React.js frontend, a Flask (Python) backend providing REST API services, and Supabase as the cloud database management platform. Telegram Bot integration delivers automated threshold triggered alerts to sanitation personnel when bin capacity reaches critical levels. Experimental validation confirms that all core functionalities performed reliably, offering a scalable, cost effective solution for automated waste management in residential and institutional environments.

Page 23 of 23 | Total Record : 225

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