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 46 Documents
Comparative Analysis of Structural-Based Reduction and Brute Force Algorithms for Determining Metric Dimensions in Tree Graphs Afifah Farhanah Akadji; Abdul Gani F. S. H. Lihawa; Maharani Eka; Karina A. Sasmito; Hendy Prasetyo; Andi Sitti Dwi Auliyani
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.696

Abstract

Purpose – This study aims to overcome the computational inefficiency of the Brute Force method in determining the metric dimension of tree graphs by evaluating the performance of a Structural-Based Reduction Algorithm. The study addresses the high computational cost of exhaustive search approaches and proposes a more efficient structural alternative. Methods – This research applies a comparative computational experimental approach by implementing both the Brute Force method and the proposed reduction algorithm on non-isomorphic tree graphs obtained from the McKay dataset. The algorithm is based on Slater’s theorem regarding leaves and stem vertices in tree graphs. Instead of testing all possible vertex combinations, the algorithm utilizes structural relationships to determine the metric dimension more efficiently. The comparison focuses on result consistency and computational execution time. Findings – Experimental results show that the proposed reduction algorithm achieves 100% accuracy, producing metric dimension values identical to those generated by the Brute Force method for all tested graphs. In terms of efficiency, the proposed method performs significantly better. For a tree graph with 20 vertices, the Brute Force method requires approximately 79 seconds, while the reduction algorithm completes the computation in only 0.005 seconds. Research implications – The findings indicate that structural analysis can reduce computational complexity in determining metric dimensions of tree graphs. However, the current approach is limited to acyclic graph structures and may require modification for cyclic graphs. Originality – This study introduces a deterministic and scalable alternative for determining metric dimensions in tree graphs through structural reduction principles.
IoT-Based Automatic Ornamental Plant Watering System Using Mamdani Fuzzy Logic With Real-Time Web Monitoring Jaikarna; Niko Pahala Sihite; Vincent Angelo; Kristian Fredrico Aritonang; Jijon Raphita Sagala
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.772

Abstract

Purpose – Ornamental plants require consistent watering to maintain optimal growth; however, manual watering often causes uneven water distribution and inefficient water usage. This study aims to develop an Internet of Things (IoT)-based automatic watering system using the Mamdani fuzzy logic method to improve watering accuracy and water efficiency. Methods – The system utilizes an ESP32 microcontroller integrated with capacitive soil moisture, DHT22 temperature, and BH1750 light intensity sensors. The Mamdani fuzzy logic method with 27 rule bases was implemented to determine adaptive watering duration. A real-time monitoring website was developed using Node.js, WebSocket, and SQLite. Findings – The system generated watering durations of 48–51 seconds under dry soil conditions, 28–32 seconds under normal conditions, and 9–12 seconds under wet soil conditions. Sensor validation produced RMSE values of ±0.48°C for temperature, ±18 lux for light intensity, and ±4.7% for soil moisture measurements. In addition, the proposed system improved water usage efficiency by approximately 60.5% compared to manual watering. Research Implications – The developed system supports smart agriculture implementation through adaptive irrigation, reduced water waste, and real-time environmental monitoring. The results demonstrate that the proposed IoT-based Mamdani fuzzy watering system can function as an adaptive and water-efficient solution for ornamental plant maintenance, although its implementation remains limited to the tested prototype environment. Originality – This research integrates multi-sensor monitoring, Mamdani fuzzy logic with 27 rule bases, and real-time web-based monitoring into a single adaptive ornamental plant watering system.
Web-Based ERP Dashboard Integrated with Aerial Imagery for Oil Palm Harvest Monitoring and Plantation Decision Support M. Ilham Saputra; Raden Aris Sugianto; Andi Prayogi; Febriani Putri Wulandari
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.824

Abstract

Purpose – Oil palm plantation operations often rely on fragmented reporting systems that hinder information synchronization, operational visibility, and monitoring effectiveness. This study aims to develop a web-based Enterprise Resource Planning (ERP) dashboard integrated with Geographic Information System (GIS) mapping and aerial imagery visualization to support harvest scheduling and operational monitoring. Methods – This study employed a Research and Development (R&D) approach using the System Development Life Cycle (SDLC), including requirement analysis, design, implementation, testing, and evaluation. The system was developed using Laravel, MySQL, Bootstrap, and Leaflet GIS. Usability evaluation was conducted based on the ISO 9241-11 framework involving 11 participants consisting of plantation managers, field assistants, supervisors, and administrative staff at PT. Gerbang Benuaraya. Findings – The developed system successfully integrated operational reporting, plantation block mapping, aerial imagery visualization, and harvest monitoring within a centralized dashboard. Usability evaluation produced effectiveness, efficiency, satisfaction, learnability, and memorability scores of 72.12%, 69.70%, 72.12%, 65.45%, and 70.91%, respectively, resulting in an overall usability score of 70.06%, which indicates good usability based on the percentage-based evaluation criteria applied in this study. Research implications – The findings suggest that the system can support operational monitoring and information integration in plantation environments. However, the evaluation was conducted in a single plantation company with 11 participants, limiting broader generalization. Originality – This study contributes an integrated platform that combines ERP operational modules, GIS-based plantation mapping, aerial imagery visualization, and harvest monitoring within a single dashboard specifically designed for oil palm plantation management.
Decision Support System for Laptop Selection Using the TOPSIS Method on Web-Scraped iPrice Data Feby Charlos; Riska Septiani
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.666

Abstract

Purpose – This study develops and revises a web-based decision support system for laptop selection by integrating web-scraped iPrice Indonesia product data, reproducible preprocessing, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study responds to the difficulty consumers face when comparing many laptop alternatives with heterogeneous specifications, prices, and budget constraints. Methods – The prototype used a verified static CSV dataset derived from public product listings. Five complete laptop alternatives were evaluated with 12 criteria: brand, screen size, screen resolution, processor, storage type, storage capacity, graphics card, laptop weight, battery durability, new-price distance, preloved-price distance, and RAM. Categorical attributes were transformed into ordinal scores. TOPSIS was implemented in Python and Streamlit. Simple Additive Weighting (SAW), sensitivity analysis, and functional black-box testing were used as comparative and verification procedures. Findings – Under equal criterion weights of 1.5, a new-laptop budget of IDR 7,500,000, and a preloved-laptop budget of IDR 5,000,000, HP Envy x360 13-inch obtained the highest TOPSIS closeness coefficient of 0.746618238. SAW selected the same top alternative, although the complete ranking differed and produced a moderate Spearman correlation of 0.400. Research implications – The results show that transparent criterion transformation, budget-distance modeling, and interface-based preference adjustment can support practical laptop selection. The system does not replace consumer judgment because the ranking depends on the dataset, weights, scoring rules, and budget assumptions. Originality – This study contributes a reproducible DSS prototype that combines scraped price-comparison data, TOPSIS ranking, SAW benchmarking, sensitivity checking, and an Indonesian-language Streamlit interface for practical laptop recommendation.
Design and Development of a Web-Based Information System for Palm Oil Derivative Product Education Febriani Putri Wulandari; Ritna Wahyuni; Andi Prayogi; M. Ilham Saputra
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.839

Abstract

Purpose – Public literacy regarding palm oil derivative products remains limited, while structured and accessible educational resources are still scarce. This study aimed to design and develop a web-based information system that provides organized educational content on palm oil derivative products and supports public access to information on downstream palm oil industries. Methods – This study employed a library research approach using secondary data from scientific literature, government publications, and official references related to palm oil derivative products. System requirements were analyzed and modeled using Unified Modeling Language (UML). The system was developed using HTML, CSS, PHP, MySQL, the Laravel Framework, and Laragon. Functional evaluation was conducted using Black Box Testing on 14 core system features. Findings – The study produced a web-based educational information system containing categorized content on oleofood, oleochemical, and bioenergy products, supported by article, video, search, and administrative content-management features. Functional testing showed that all 14 test scenarios were successfully completed, resulting in a functional success rate of 100%. Research implications – The findings indicate that the system is functionally reliable for delivering structured educational information on palm oil derivative products. However, the evaluation was limited to functional testing and did not assess usability, user satisfaction, or educational effectiveness. Originality – This study contributes a specialized web-based educational platform that integrates categorized palm oil derivative product information, multimedia resources, and centralized content management within a single system.
Classification of Oil Palm Fresh Fruit Bunch Ripeness Levels Using the YOLOv11n Algorithm Ananda Apri Anata; Ratu Mutiara Siregar; Andi Prayogi
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.894

Abstract

Purpose – Manual assessment of oil palm fresh fruit bunch (FFB) ripeness remains subjective and may reduce harvest quality consistency. This study aims to evaluate YOLOv11n for six-class FFB ripeness detection using instance-level object detection metrics. Methods – A Roboflow dataset of 17,437 augmented images was split into training, validation, and held-out test subsets across six classes: Empty Bunch, Less Ripe, Abnormal FFB, Ripe FFB, Unripe FFB, and Overripe. A qualitative consistency check was conducted by one harvest foreman. Findings – Evaluation on 1,756 held-out test images containing 6,372 FFB instances achieved precision of 0.968, recall of 0.980, F1-score of 0.974, mAP50 of 0.988, and mAP50-95 of 0.901. Overripe was the weakest class, with mAP50-95 of 0.846. Research implications – The results indicate that YOLOv11n has strong potential to support automated FFB ripeness grading. However, broader field validation, multi-annotator agreement analysis, and testing under more diverse plantation conditions are still required. Originality – This study contributes a six-class YOLOv11n-based FFB ripeness detection evaluation using held-out test-set instance-level metrics and strict localization assessment through mAP50-95.
Design and Development of a Web-Based Attendance System Using QR Code ID Cards and Geofencing Validation Aslina Gulo; Ritna Wahyuni; Ratu Mutiara Siregar
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.901

Abstract

Purpose – Manual attendance in plantation operations suffers from high administrative vulnerability and data fraud, creating an efficiency gap for remote work units. This study aims to develop a low-cost, web-based employee attendance information system utilizing static QR Code ID cards for Palm Oil Plant. Methods – The system was engineered using a plan-driven Waterfall lifecycle model, encompassing UML design, Laravel 10 framework implementation, a MySQL database layer, and rigorous verification via 22 granular Black Box Testing operational scenarios. Findings – The prototype successfully deployed multi-layered authentication features, returning a 100% "Compliant" status during testing. Single-user performance benchmarks recorded highly responsive empirical latencies: 320 ms for QR matrix string extraction, 145 ms for geofence processing, and 42 ms for MySQL database commits. Research Implications – The study's scope was limited by single-user benchmark testing and a lack of live database API integration into corporate SAP payroll systems. Practically, this architecture supports the technical feasibility of open-source web applications in shifting infrastructure weights away from enterprises operating in severe industrial dust environments. Originality – The value lies in the operational synthesis of an open-source framework, Haversine-driven geofencing, and visual capture customized for low-connectivity plantation workers. Future steps must focus on transitioning toward offline-first Progressive Web Apps (PWAs) and integrating lightweight machine learning models (such as FaceNet or MediaPipe) to transition from passive visual documentation to automated biometric verification.
Design of an Android-Based Gerdcare Dietary Management System for Gerd Patients Bunga Nila Kinanti; Matahari; Dian Nitari Ribanor Sabarudin
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.913

Abstract

Purpose - This study aimed to design and develop GERDCARE, an Android-based dietary management and health education application to support self-management among individuals with or at risk of Gastroesophageal Reflux Disease (GERD). Methods - A Research and Development approach using the ADDIE model was employed. A needs assessment involving 122 university students was conducted to identify GERD symptoms and dietary patterns as the basis for system development. The application was evaluated through content validation by a general practitioner and user testing involving 20 participants using the System Usability Scale (SUS). Finding - A total of 13.1% of respondents were identified as being at risk for GERD, while the average dietary pattern score was categorized as moderate (67.64%). The GERDCARE application integrates early GERD risk detection, health education, food and beverage recommendations, meal and medication reminders, and a doctor consultation module. Expert validation yielded a feasibility level of 95.56% (highly valid), while user testing demonstrated a SUS score of 78.00, classified as Good and Acceptable. Research Implications - GERDCARE supports dietary management and health education for individuals with GERD by integrating multiple self-management features within a single Android-based platform. Users identified as being at risk are advised to seek further consultation with qualified healthcare professionals. Originality - This study presents GERDCARE as an Android-based platform that distinctively integrates preliminary GERD risk screening, structured health education, dietary recommendations, behavior reminders, and a doctor consultation module within a single system an integration not found in existing fragmented mHealth solutions for GERD.
AI-Assisted Barcode Inventory Management for Plantation Warehouse Operations in a Desktop Environment Bintang Florentina Aruan; Ritna Wahyuni; Ratu Mutiara Siregar
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.933

Abstract

Purpose – This study aims to Development of a Desktop-Based Warehouse Inventory Management Application with Barcode Technology Integration Methods – The study used a Research and Development approach with the Waterfall development model. Data were collected through literature study, observation, and interviews, while system functions were validated using Black Box Testing. Findings – The developed application supports item data management, category, unit, warehouse location, incoming goods, outgoing goods, stock adjustment, inventory reporting, minimum stock alerts, and an Inventory AI Assistant as a supporting database-search feature. The testing results indicate that the main functions run according to the planned scenarios. Research implications – The system can serve as an initial solution to support more orderly and traceable inventory administration in a plantation warehouse environment. Originality – This study positions barcode as an item-identification mechanism in a local desktop application and includes an Inventory AI Assistant that is limited to database-based information retrieval.  
Cloud-Based Mobile Monitoring of Mechanic Work Performance and Reporting in Oil Palm Plantation Workshops Mutiara Cahyani; Ritna Wahyuni; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.935

Abstract

Purpose – Work reporting and mechanic activity monitoring in oil palm plantation workshops are still largely performed manually, resulting in delays in information delivery, recording errors, and difficulties in tracking work histories. This study aimed to develop a mobile application to support mechanical performance monitoring in a more structured and documented manner. Methods – This study employed the Extreme Programming (XP) methodology, which consists of the Planning, Design, Coding, and Testing phases. Data were collected through observations, interviews with the Workshop Assistant, and a literature review. The application was developed using C#, .NET MAUI, and a cloud database for centralized data management. Findings – The developed application integrates daily work reporting, activity documentation, work history management, user management, and report downloading into a single platform accessible to administrators and mechanics. Black Box Testing showed that all tested functions operated according to predefined requirements. Research Implications – The findings demonstrate the potential of mobile and cloud-based information systems to support the digitalization of reporting processes and operational information management in oil palm plantation workshops. However, the evaluation was limited to functional testing within a single workshop environment and did not include usability, user acceptance, or long-term operational impact assessments. Originality – This study presents a mobile application specifically designed for oil palm plantation workshops by integrating mechanic performance monitoring, work reporting, activity documentation, work history management, and cloud-based data management within a single platform.