Vilianty Rafida
STMIK Widya Cipta Dharma, Samarinda

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Klasifikasi Tingkat Pemahaman Siswa Kelas VI Sekolah Dasar terhadap Perangkat Keras Komputer Menggunakan Metode Decision Tree Suchi Azzahro Syarif; Vilianty Rafida; Rizky Zakariyya Rasyad
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1095

Abstract

The development of information technology in education requires students to have a basic understanding of computer hardware from an early age. However, the level of students’ understanding of computer hardware still varies, especially at the elementary school level. This condition can affect students’ ability to understand the use of technology more effectively in computer-based learning processes. This study aims to classify the level of understanding of sixth grade elementary school students regarding computer hardware using the Decision Tree method. The research data were obtained through a questionnaire consisting of 25 questions related to computer hardware. Each student’s answer was assigned points based on its correctness level, then the total score was calculated and converted into a 0–100 scale before being categorized into three classes, namely High Understanding, Moderate Understanding, and Low Understanding based on score ranges adjusted to the distribution of the research data. The data show that there are 30 students in the High Understanding category, 18 students in the Moderate Understanding category, and 6 students in the Low Understanding category. The classification process was carried out using the Decision Tree method with 80% training data and 20% testing data. The model achieved an accuracy of 45% on the test data. The result indicates that the model is not yet optimal in performing balanced classification across all categories of student understanding. The findings of this study contribute to the application of the Decision Tree classification method in elementary education, particularly in identifying students’ understanding of computer hardware based on questionnaire data.
Implementasi Algoritma C4.5 Untuk Klasifikasi Pengenalan Warna Dasar di Taman Kanak-Kanak Anandaya Difi Dzulardi Kalimanti; Vilianty Rafida; Aisyah Fajriantini
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1102

Abstract

Differences in early childhood ability to recognize basic colors at TK Negeri 01 Barong Tongkok indicate the need for a structured evaluation system to ensure objective assessment. The classification of these abilities is carried out by applying the C4.5 algorithm within a quantitative experimental framework. Data are collected through observations and color recognition tests involving 35 children as respondents, then processed using predefined attributes to construct a classification model. The analysis results group children’s abilities into three categories: Sangat Mengenal (High), Mengenal (Moderate), and Cukup Mengenal (Low). The experimental results indicate that the C4.5 algorithm is highly effective and stable, achieving an average classification accuracy of 85.71% through 5-Fold Cross-Validation. Furthermore, the resulting decision tree provides an intuitive and transparent structure that assists educators in interpreting evaluation outcomes and understanding the dominant variables that determine student learning success more clearly than black-box models. The primary contribution of this study lies in the provision of a data-driven evaluation model that generates empirically measurable decision rules (if-then rules), while simultaneously serving as a methodological bridge to create differentiated learning strategies at the early childhood education (PAUD) level. Consequently, the implementation of the C4.5 algorithm represents a strategic, efficient, and scientifically accountable alternative for enhancing pedagogical effectiveness and cognitive monitoring in early childhood education.
Rancang Bangun Sistem Keamanan Laser Berbasis Internet of Things Menggunakan Metode Prototyping dengan Peringatan Dini dan Bukti Visual Real-Time Untuk Mencegah Pencurian Randikal Hikrenc Menono; Vilianty Rafida; Ahmad Fajri
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1098

Abstract

The rapid development of digital technology, particularly in the field of the Internet of Things (IoT), has brought significant changes to various aspects of human life, including security systems. Security is a crucial aspect for business owners, especially those operating in environments with a high risk of theft. This study aims to design and develop an IoT-based laser security prototype capable of providing real-time early warning notifications to users. The research employed the Prototyping method, which consists of communication, quick plan, modeling quick design, construction of prototype, and deployment and feedback stages. The system utilizes an ESP32-S3 Cam microcontroller as the main controller integrated with an LDR sensor and a SIM800L GSM module. The system operates by detecting interruptions in a laser beam caused by objects passing through the monitored area, which serve as intrusion triggers. When the laser beam is interrupted, the light intensity received by the LDR decreases significantly, prompting the microcontroller to automatically instruct the SIM800L module to place a phone call to the user. In addition, the ESP32-S3 Cam captures images of the monitored area and sends them through a Telegram bot as visual evidence. The testing results indicate that the system performs effectively, with an average response time of 8 seconds from intrusion detection to phone call notification. The success rate of phone call notifications reached 90%, while the visual evidence transmission through Telegram achieved a success rate of 100%. These results demonstrate that the system is capable of providing real-time alerts and visual information to users. The contribution of this research lies in the successful integration of intruder detection based on laser beam interruption, alarm notifications through telephone calls using the SIM800L module, and visual evidence transmission using the ESP32-S3 Cam and Telegram application into a single Internet of Things (IoT)-based security system capable of providing early warning notifications. When a laser beam interruption is detected, the system can automatically notify users through a phone call and send visual evidence in real time, thereby enhancing the effectiveness of remote security monitoring and response. However, the system performance is still affected by the stability of the GSM network used by the SIM800L module and is limited to detecting objects that interrupt the laser beam path. Therefore, the proposed system offers a smart, responsive, and cost-effective security solution that enables remote monitoring and supports theft prevention.