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 63 Documents
Mobile-Based Live Marathon Tracking using Route-Corridor Geofencing for Automatic Checkpoint Detection and Participant Out-of-Route Alerts Hamdoni Zaejuli; Sawali Wahyu
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: 10.66053/dtcs.v3i3.1151

Abstract

Purpose – Smartphone-based marathon-monitoring systems may face limitations in continuous monitoring, route-progress reporting, and automatic route-deviation detection. This study proposes RunTrack+, a mobile application combining distance-based checkpoints with route-corridor geofencing for independent and event-based activities. Methods – RunTrack+ was developed using Mobile-D, based on requirements identified through a literature review, observations, and semi-structured interviews with 15 respondents. The Android application uses Kotlin, Jetpack Compose, the Fused Location Provider, Google Maps API, Firebase Authentication, and Firebase Realtime Database. Checkpoints are generated from validated accumulated distance at one-kilometer intervals, while event-mode route status uses a 50 m route corridor. End-to-end update latency was measured from sequence-identified location updates using server-time-aligned transmission and reception timestamps. Findings – Four independent-tracking sessions produced all 80 expected checkpoints, yielding a functional checkpoint-recording success rate of 100% under the documented conditions. Event-based testing confirmed on-route and out-of-route warning states. Analysis of 93 unique location updates produced a mean end-to-end latency of 146.74 ms, median 127 ms, 95th percentile 336 ms, and range 25–471 ms. The System Usability Scale involving 15 respondents produced an average score of 83.67, while User Acceptance Testing produced an acceptance rate of 95.3%. Research implications – Evaluation was limited to route simulations, a small respondent group, two documented geofencing conditions, and one latency test condition. Generalized GPS accuracy, route-deviation accuracy, and performance across heterogeneous devices and networks require further controlled testing. Originality – RunTrack+ integrates validated GPS processing, distance-based checkpoints, route-corridor geofencing, visual and voice warnings, route visualization, and cloud synchronization in one Android application.
IMPLEMENTASI AGGLOMERATIVE HIERARCHICAL CLUSTERING DENGAN COMPLETE LINKAGE UNTUK ANALISIS MASA PERTUMBUHAN BALITA Zulaiha; Sriani
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: 10.66053/dtcs.v3i3.1219

Abstract

Purpose – This study explores anthropometric patterns among children under five using Agglomerative Hierarchical Clustering (AHC) with Complete Linkage based on age, body weight, and height. Methods – A quantitative observational design with a data mining approach was employed using 1,000 individual child records from a primary healthcare setting. Age, body weight, and height were statistically standardized before clustering. AHC with Complete Linkage and Euclidean distance was applied, while cluster quality was evaluated using the Silhouette Coefficient for solutions ranging from two to seven clusters. Findings – The two-cluster solution achieved the highest Silhouette Coefficient (0.6325), indicating the strongest internal validation, while the three-cluster solution produced a slightly lower value (0.6254) and was retained for secondary exploratory analysis. The three-cluster configuration comprised a dominant group of 937 observations, a smaller group of 62 observations, and one isolated observation. The two main groups had similar mean ages but differed in body weight and height, indicating heterogeneity in combined anthropometric characteristics. Research implications – Complete Linkage clustering provides an exploratory approach for identifying multidimensional patterns in individual-level anthropometric records. However, the clusters should not be interpreted as clinical or nutritional-status classifications because age- and sex-standardized anthropometric indicators were not used. Originality – This study demonstrates the value of Complete-Linkage AHC for exploring heterogeneity in routine child anthropometric data while clearly distinguishing exploratory clustering from established nutritional assessment.
K-Means Clustering of Salty Snacks Based on Nutritional Information for Calorie-Deficit Diet Support Athiyyah Anandira; Mustari S Lamada; Muhammad Ayat Hidayat; Dewi Fatmarani Surianto; Abdul Rahman Patta
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: 10.66053/dtcs.v3i3.1389

Abstract

Purpose – This study aimed to cluster salty snack products based on their nutritional information using the K-Means algorithm and to identify the most suitable cluster for supporting a calorie-deficit diet, addressing high obesity rates in Indonesia and consumer difficulties with nutrition labels. Methods – This study used a quantitative data mining approach, 620 products were collected from FatSecret Indonesia via purposive sampling across variables of calories, fat, protein, fiber, sugar, and sodium. The data were processed through cleaning, missing-value checking, transformation, and Z-Score normalization, followed by K-Means clustering, Elbow method optimization, and evaluation via Silhouette Coefficient and Davies-Bouldin Index (DBI). Findings – The analysis identified three optimal clusters (k=3). The overall Silhouette Coefficient was 0.431 and DBI was 1.1534. C1 (461 products) featured lower calories, fat, sugar, and sodium, making it the most suitable cluster among the identified clusters. However, only 185 of the 461 products in C1 met the defined product-level safety criteria. C2 (96 products) was high in calories and fat, and C3 (63 products) had high protein but extreme sodium levels. Research implications – The study's validity is constrained by its reliance on secondary online data without direct laboratory verification. Furthermore, the use of mean imputation for the missing fiber data may have attenuated inter-cluster variance and reduced the discriminatory role of fiber. Originality – This research provides a novel contribution in grouping salty snack products by combining six main nutritional variables along with comprehensive model performance evaluation. C1 was identified as the most suitable cluster for supporting a calorie-deficit diet.