cover
Contact Name
M. Miftach Fakhri
Contact Email
fakhri.abcollab@gmail.com
Phone
+6281343505565
Journal Mail Official
dtcs@abcollab.id
Editorial Address
Jalan Cempaka Mekar Raya No. 10 Bandung, Jawa Barat, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
Journal of Digital Technology and Computer Science
ISSN : 30310318     EISSN : 30308127     DOI : https://doi.org/10.66053/dtcs
Digital Technology and Socio-Technical Innovation, including the design, development, implementation, and evaluation of digital solutions, platforms, applications, and infrastructures that support modern socio-technical systems, digital transformation, and technology-enabled services. Computer Systems, Software, and Networking, encompassing distributed systems, computer networks, network architectures, communication protocols, network performance, next-generation connectivity, software systems, and integrated computing environments. Artificial Intelligence, Machine Learning, and Intelligent Systems, covering intelligent systems, machine learning algorithms, deep learning, natural language processing, expert systems, knowledge-based systems, computational intelligence, and applied AI across scientific, industrial, and societal domains. Decision Support, Fuzzy, and Evolutionary Systems, including decision support systems, fuzzy logic, fuzzy control, evolutionary computing, optimization algorithms, swarm intelligence, hybrid intelligent methods, and data-driven decision models. Image, Audio, and Multimedia Processing, including computer vision, image processing, sound and speech processing, multimedia analysis, signal processing, pattern recognition, and audio-visual computing applications. Information Security and Cybersecurity, focusing on information security, system security, network security, cybersecurity governance, cryptography, privacy protection, secure software engineering, threat detection, intrusion prevention, digital forensics, and cyber risk management. Cyber Crime and Digital Investigation, including cybercrime detection and analysis, cyber law and policy in digital environments, forensic investigation, online fraud, identity theft, malicious activity analysis, and digital evidence management. Social Network and Digital Security, covering security and trust in social media and online platforms, digital identity, misinformation and disinformation detection, privacy in social networks, human factors in cybersecurity, and safe digital interaction ecosystems. Operating Systems, Computer Architecture, and Embedded Computing, including operating systems, processor and memory architecture, virtualization, system-level optimization, embedded systems, real-time computing, firmware, and performance engineering. Cloud, Edge, and Ubiquitous Computing, covering cloud platforms, fog and edge computing, distributed intelligence, service orchestration, scalable infrastructures, reliability, resource management, and pervasive computing environments. Internet of Things (IoT), Sensor Networks, and Cyber-Physical Systems, including smart devices, wireless sensor networks, industrial IoT, IoT platforms, connected environments, cyber-physical systems, and real-world deployment challenges in intelligent sensing and control. Big Data, Analytics, and Data-Driven Computing, encompassing data engineering, data mining, large-scale data processing, predictive analytics, visual analytics, business intelligence, and advanced computational methods for complex datasets. Wearable Devices and Smart Sensing Technologies, including wearable computing, body-area networks, health and activity monitoring systems, smart textiles, mobile sensing, and human-centered intelligent devices. Embedded Robotics and Microcontroller Systems, including robotic systems, embedded robotics, autonomous control, low-level hardware-software integration, microcontroller-based applications, robotic sensing, and intelligent actuation systems. Micro and Nano Technology, including microelectronics, nanoelectronics, microsystems, nanosystems, MEMS/NEMS-related applications, miniaturized intelligent devices, and sensor-oriented micro/nano technological innovations. Renewable Energy and Intelligent Energy Systems, including digital technologies for renewable energy, smart energy monitoring, intelligent control systems for energy efficiency, IoT-enabled energy systems, sustainable computing, and computational methods for energy optimization. Software Engineering and Information Systems, including software design, software quality assurance, software testing, requirements engineering, enterprise systems, information systems development, human-centered software solutions, and digital service integration. Robotics, Automation, and Autonomous Systems, covering intelligent robotics, automation systems, control engineering, autonomous agents, robotic perception, human-robot interaction, and smart manufacturing applications. Human-Computer Interaction and Digital Experience, including user interface design, user experience, usability evaluation, interactive systems, accessibility, persuasive technologies, and digital behavior in technology-mediated environments. Green Computing and Sustainable Digital Systems, including energy-efficient computing, sustainable software and hardware design, green AI, carbon-aware digital infrastructures, smart resource management, and digital technologies for environmental sustainability.
Articles 53 Documents
KLASIFIKASI KERENTANAN PHISHING DI KALANGAN MAHASISWA MENGGUNAKAN DECISION TREE Mhd. Murini Ramadhani; Muhammad Ikhsan
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i3.1165

Abstract

Purpose – This study aims to classify phishing vulnerability among college students and identify the most influential factors using the C4.5 Decision Tree algorithm. Methods – A quantitative survey was conducted among 171 active Computer Science students at the State Islamic University of North Sumatra, Medan, Indonesia, from the 2023–2025 cohorts. Behavior, Knowledge, and cybersecurity training experience were used as predictor variables, while the vulnerability labels Vulnerable, Alert, and Aware were derived from responses to four hypothetical phishing scenarios. The model was developed using RapidMiner and evaluated through 10-fold cross-validation, a confusion matrix, and multiclass Receiver Operating Characteristic Area Under the Curve (ROC-AUC) using the One-vs-Rest approach. Findings – The dataset consisted of 131 Aware students (76.61%), 28 Alert students (16.37%), and 12 Vulnerable students (7.02%). The primary C4.5 model achieved 85.96% aggregate accuracy, but performance was uneven across classes: the macro-average F1-score was 69.84%, the Alert-class F1-score was 46.51% with 35.71% recall, and the Aware-class F1-score was 93.77%. The macro-average AUC was 0.8263. Phishing knowledge was the dominant predictor and formed the root node of the decision tree. Research implications – The model performed strongly for the Aware class but inconsistently across classes. The findings may inform exploratory cybersecurity education planning, but interpretation should remain cautious because of severe class imbalance, sensitivity to the operational vulnerability cutoffs, and recruitment from a single study program. Originality – The contribution is primarily contextual and application-oriented: phishing vulnerability is operationalized from scenario responses and examined together with behavioral, knowledge, and training factors in an interpretable multiclass model for an educational cybersecurity context, rather than as a methodological innovation in the C4.5 algorithm.
Comparing Decision Tree and SVM with Hyperparameter Tuning for Classifying Perceived Social Media Impact on Academic Performance Nur Sitorus; Yusuf Ramadhan Nasution
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i3.1173

Abstract

Purpose – This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying students’ perceived academic impact of social media use and examines the contribution of Grid Search-based hyperparameter tuning to model performance. Methods – A quantitative experimental approach was applied to a subset of records obtained from a publicly available Kaggle dataset. The records were retained in their original sequence. Data preprocessing included data quality inspection, binary target encoding, One-Hot Encoding for categorical variables, and feature scaling for numerical variables used in the SVM model. The dataset was sequentially divided into training, validation, and testing subsets without shuffling or stratification. Grid Search was performed using a predefined validation split, while model performance was assessed using accuracy, weighted precision, weighted recall, and weighted F1-score. Findings – Hyperparameter tuning improved the overall performance of the Decision Tree model compared with its default configuration. In contrast, the default and optimized SVM models produced identical results because Grid Search selected the same parameter configuration as the default settings. Among the evaluated models, SVM achieved the highest observed testing performance. Research implications – The findings indicate that the effectiveness of hyperparameter tuning may vary across machine learning algorithms. Grid Search improved the Decision Tree configuration while confirming the suitability of the default SVM settings within the evaluated search space. Since the target variable reflects students’ perceived academic impact rather than objectively measured academic achievement, the results should be interpreted within this specific classification context and the limitations of a single public dataset. Originality – This study provides a controlled comparison of default and optimized Decision Tree and SVM models under identical experimental conditions, highlighting algorithm-specific responses to Grid Search-based hyperparameter tuning.
Clustering of Student Personality Types Based on Extracurricular Activities using The K-Means Algorithm Approach Rifdah Syahputri; Ilka Zufria
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i3.1267

Abstract

Purpose – This study aims to identify patterns of student personality tendencies at Universitas Islam Negeri Sumatera Utara (UINSU) using a data-driven clustering approach based on extracurricular participation characteristics and personality-related questionnaire responses. The study was motivated by the need to systematically analyze the relationships and patterns among students' organizational activity, activity frequency, organizational roles, social interaction, and personality tendency characteristics. Methods – This study employed a quantitative approach using data mining. Data were collected through a questionnaire distributed to UINSU students, resulting in 604 respondents. The data were processed through cleaning, missing-value checking, transformation, and Min-Max normalization. The K-Means Clustering algorithm was applied to group students according to their extracurricular activity patterns. The clustering quality was evaluated using the Silhouette Score, while PCA was used to visualize the clustering results. Findings – The clustering process identified three personality tendency groups: Extrovert, Introvert, and Ambivert. The resulting clustering obtained a Silhouette Score of 0.3823, indicating a moderate level of cohesion and separation among the clusters, although some observations remained relatively close to other clusters. Research implications – The findings provide an overview of student personality tendencies based on extracurricular activity patterns. However, the results represent personality tendencies derived from clustering characteristics and should not be interpreted as psychological diagnoses. Originality – This study applies K-Means Clustering to extracurricular activity data as a basis for identifying student personality tendencies into Introvert, Extrovert, and Ambivert groups, providing a data-driven perspective for understanding student characteristics.