Jurnal Teknologi Informatika dan Komputer
(e-ISSN 2622-8475 and p-ISSN 2656-9957) is a peer-reviewed journal which is published by Universitas MH Thamrin Jakarta. Since 2017, this journal publishes biannually in March and September focused to publish high-quality articles dedicated to all aspects of the latest outstanding research and developments in the fields of Information Technology. The scope of this journal encompasses to study of Computer Science, Information Technology, Digital Media, Information System, Knowledge Management, System Management, Data Mining, Internet Of Things (IOT), Artificial Intelligence (AI), Robotic, Computer Networks, Cyber Security etc. Submitted papers must be written in English or Bahasa Indonesia for an initial review stage by editors and further review process by a minimum of anymous reviewers. Reviewers will review any submitted paper. Review process employs a double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa. Journal Title Initials Frequency DOI ISSN (Print) ISSN (Online) Editor-in-Chief Publisher : Jurnal Teknologi Informatika dan Komputer : JTIK : Biannualy (March and September) : 10.37012/jtik : 2656-9957 : 2622-8475 : Ir. Yohanes Bowo Widodo, M.Kom : LPPM Universitas Mohammad Husni Thamrin
Jurnal Teknologi Informatika dan Komputer allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles and allow readers to use them for any other lawful purpose. The journal allows the author(s) to hold the copyright without restrictions. Finally, the journal allows the author(s) to retain publishing rights without restrictions Authors are allowed to archive their submitted article in an open access repository Authors are allowed to archive the final published article in an open access repository with an acknowledgment of its initial publication in this journal. Jurnal Teknlogi Informatika dan Komputer is licensed under a Creative Commons Attribution 4.0 International License.
Implementation of the TOPSIS Method in a Website-Based Hospital Employee Performance Evaluation System to Improve Assessment Objectivity
The rapid development of information technology has led to transformation in various fields, one of which is the health sector. Digitalization encourages public service institutions to adapt to new systems to increase efficiency, speed, and accuracy of services in hospitals. This study is based on the manual employee performance evaluation process at Bhina ​​Bhakti Husada Hospital, which causes the assessment to take longer, is prone to errors, and has the potential to cause bias between units or assessors. This study aims to develop a Website-based Decision Support System using the TOPSIS Method. The study was conducted using data collection methods through observation, interviews, and literature studies while implementing Rapid Application Development (RAD) for system development. The TOPSIS method is used to calculate preference values ​​based on ten assessment criteria. The results of the study show that the system is able to calculate employee ratings automatically, display a performance dashboard, and generate reports in PDF format. The system can improve the efficiency of assessment time, maintain objectivity through standardized criteria weights, while providing different access for admins, assessors, employees, and leaders. The results of the study are also in line with previous studies that state the TOPSIS Method is effective for performance assessment, this study also provides new contributions through the integration of the TOPSIS Method in a real-time website-based system. The system can be developed with several other features for the benefit of Hospital Management to make performance more flexible.
Copyright (c) 2025 Izzah Farikhah, A. Aviv Mahmudi, Fajar Sodiq https://creativecommons.org/licenses/by/4.0
Analysis of the Contribution of Open Source Software in Improving the Cost Efficiency of Information Technology Infrastructure at SMAN 1 Cicurug Using a Machine Learning Approach
The development of information technology (IT) globally has driven digital transformation in various sectors, including education. The rapid development of information technology (IT) in the education sector necessitates efficient and cost-effective infrastructure management. Budget limitations remain a major challenge for educational institutions in providing adequate hardware and software resources. Open Source Software (OSS) has emerged as a viable alternative due to its potential to reduce licensing costs while maintaining system functionality. This study aims to analyze the contribution of Open Source Software to improving the cost efficiency of information technology infrastructure at SMAN 1 Cicurug using a machine learning approach. This research employs IT inventory data consisting of Windows-based computers, Chromebooks, and smartphone devices utilized by the school community. Infrastructure cost data collected before and after OSS implementation were analyzed using machine learning algorithms, particularly regression and clustering techniques, to identify cost-saving patterns and efficiency levels. The application of machine learning enables an objective, data-driven evaluation of the financial impact of OSS adoption. The findings demonstrate that the implementation of Open Source Software significantly reduces software licensing costs and overall IT infrastructure maintenance expenses. Additionally, the use of machine learning models proves effective in supporting strategic decision-making related to IT infrastructure management. These results indicate that OSS adoption can serve as a sustainable and scalable solution for educational institutions seeking to enhance cost efficiency in information technology infrastructure.
Copyright (c) 2025 Azharudin, Uus Firdaus, Haikal Ali Hakim, Mutiara Nur Handayani https://creativecommons.org/licenses/by/4.0
Design and Development of Web-Based Cooperative Financial Applications at SMP Negeri 7 Prabumulih
The school cooperative is one of the supporting facilities for students' economic activities that plays an important role in providing school supplies and training students' independence and responsibility. The space is usually used by students to conduct transactions between students and cooperative officers. Currently, the cooperative of SMP Negeri 7 Prabumulih serves students in recording goods purchase transactions still using a conventional system. The method of inputting financial reports at this institution has used Microsoft Excel for monthly reports and manual recording in books, but the system used is still manual. This condition has the potential to cause various problems, such as delays in preparing reports, recording errors, and ineffective archiving of financial data. The purpose of this study is to create a Web-based Application to facilitate the Cooperative of SMP Negeri 7 Prabumulih in inputting financial reports, processing, and presenting financial data in a structured and systematic manner. The research method used in this study is a qualitative descriptive method with data collection in the form of observation, interviews and literature studies. The type of data used is qualitative data and data sources consist of primary and secondary data. System development is carried out using a prototype method to be able to adjust to user needs gradually. This web-based application uses the PHP programming language, a MySQL database, and PDF formats for reporting. Research shows that the application can improve the efficiency of cooperative financial management, minimize recording errors, and facilitate management in producing accurate and timely financial reports.
Copyright (c) 2025 Prederika Tri Pebiyolanda, Andi Christian, Nurmayanti https://creativecommons.org/licenses/by/4.0
Classification of Indonesian Batik Patterns Using Convolutional Neural Network with MobileNetV3 Architecture
Indonesia has a diversity of ethnic groups that give rise to cultural richness, one of which is batik as a national identity. According to the Great Dictionary of the Indonesian Language, batik is a patterned cloth made by applying wax onto the fabric and processed in a certain way. Batik is an Indonesian cultural heritage with a variety of motifs that reflect regional identity and philosophical values. However, public understanding of the range of motifs is still limited, so technological support is needed for automatic identification. Although batik has become a symbol of national culture, the public's knowledge about the types and meanings of its motifs remains limited. This study develops a system for classifying Indonesian batik motifs using a Convolutional Neural Network with MobileNetV3 Small, MobileNetV3 Large, and MobileNetV2 architectures based on transfer learning. The dataset consists of 3,000 batik images with 20 motif classes from public sources, processed through image resizing to 224×224 pixels, augmentation (rotation, flip, zoom, and random brightness), and splitting into training, validation, and test sets with proportions of 80%, 10%, and 10%. Evaluation was conducted using accuracy, precision, recall, and F1 score. The results showed that all architectures achieved accuracy above 88%, with the best values exceeding 99%. MobileNetV3 Large consistently maintained accuracy up to 99% in several configurations and proved to be the most stable architecture, whereas MobileNetV2 reached a maximum accuracy of 99.33% at a learning rate of 0.0001. Therefore, MobileNetV3 Large is recommended as the primary architecture for batik motif recognition applications.
Copyright (c) 2025 Aldi Ardiansyah Aldi, Tinaliah Tinaliah https://creativecommons.org/licenses/by/4.0
Analysis of Tableau Dashboard Visualization for Efficiency and Accuracy in Industrial Decision Making
As data complexity increases, Tableau-based data visualization dashboards play an important role in simplifying information and improving understanding for decision makers. This study uses a mixed methods approach by combining quantitative data from 154 questionnaire respondents and qualitative data through in-depth interviews with five informants from various industrial sectors. Statistical analysis using SmartPLS shows that ease of data comprehension has a significant effect on efficiency (coefficient = 0.703) and decision-making accuracy (coefficient = 0.566), while Tableau dashboard visualization has a positive but more moderate effect (coefficient = 0.207). Qualitative results reinforce these findings, where the application of interactive dashboards has been proven to accelerate the analysis process, reduce interpretation errors, and improve communication between departments within the organization. In addition, real-time data access helps decision makers obtain information more quickly and reliably. However, there are still challenges in implementing data visualization, particularly limitations in digital literacy and technological infrastructure. Therefore, effective data visualization integration needs to be accompanied by technical training and user competency improvement in order to optimize its benefits in the modern industrial era. The results of this study provide practical implications for industry in improving operational performance and the quality of data-based decision making.
Copyright (c) 2026 Dedy Susanto, Syaeful Anas Aklani ; Mangapul Siahaan https://creativecommons.org/licenses/by/4.0
Introduction to the Pasigala Website-Based Historical Site
History is one of the cultural heritages that holds significant value in shaping the identity of a nation. Historical sites serve as silent witnesses to the passage of time and store a wealth of valuable information about past civilizations, cultures, and events. This research aims to design and develop a website-based historical site information system for the Pasigala area (Palu, Sigi, Donggala). This system functions as an interactive digital medium that provides comprehensive information about historical sites, with the goal of increasing public awareness of the importance of preserving local culture and supporting the promotion of historical tourism. With the website-based information system, the public can easily access accurate and reliable information about the historical sites in Pasigala. The research method used is Research and Development, with stages including literature review, primary and secondary data collection, needs analysis, and design. With a website-based information system, the public can easily obtain accurate and reliable information about historical sites in Pasigala. The system includes development, testing, and implementation. The result is a responsive web-based application that contains complete data on historical sites along with a photo gallery, and allows user interaction through a suggestion feature. The system has also been tested for functionality and usability with satisfactory results. This research contributes to the preservation of cultural heritage in the digital era while supporting the development of information technology, particularly in the field of website-based information systems.
Copyright (c) 2026 Rizka Fadliah Nur, Muliadi https://creativecommons.org/licenses/by/4.0
Evaluating the Impact of AI-Driven Decision Support Systems on Organizational Performance in the Digital Economy
This study aims to evaluate the impact of AI-driven Decision Support Systems (AI-DSS) on organizational performance within the context of the digital economy. Specifically, the research examines how the adoption of AI-based decision-support technologies influences decision quality, operational efficiency, strategic agility, and overall organizational effectiveness. Employing a quantitative research design, the study collected primary data through a structured questionnaire distributed to managers and decision-makers across various industries that have implemented AI-DSS. A total of valid responses were analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to assess both direct and indirect relationships among variables. The data analysis focused on measuring the reliability and validity of constructs, hypothesis testing, and the explanatory power of AI-DSS in predicting organizational performance outcomes. The findings indicate that AI-driven decision support systems have a significant and positive effect on organizational performance, primarily through improvements in decision accuracy, speed, and data-driven strategic alignment. Moreover, the results reveal that organizational digital capability acts as a partial mediator, strengthening the relationship between AI-DSS adoption and performance outcomes. This study contributes to the growing body of literature on digital transformation by providing empirical evidence on the strategic value of AI-enabled decision support systems and offers practical implications for organizations seeking to enhance competitiveness in the digital economy.
Copyright (c) 2026 Soleman https://creativecommons.org/licenses/by/4.0
YOLO in Suspicious Human Activity Recognition for Intelligent Environmental Security Systems: A Review
The rapid growth of intelligent environmental security systems has intensified the need for accurate and real-time suspicious human activity recognition. Computer vision techniques, particularly deep learning–based object detection models, have emerged as key enablers in addressing these challenges. Among them, You Only Look Once (YOLO) has gained significant attention due to its high detection speed, end-to-end architecture, and suitability for real-time surveillance applications. This review paper presents a comprehensive analysis of the application of YOLO-based models in suspicious human activity recognition for intelligent environmental security systems. It examines the evolution of YOLO architectures, their adaptations for activity and behavior analysis, and their integration with surveillance frameworks. The review further discusses commonly used datasets, performance evaluation metrics, and comparative results reported in existing studies. In addition, key challenges such as occlusion, varying illumination, complex backgrounds, privacy concerns, and computational constraints are highlighted. Finally, the paper outlines future research directions, including hybrid models, multi-modal data fusion, edge-based deployment, and explainable AI, to enhance the robustness and reliability of YOLO-driven security systems. This review aims to provide researchers and practitioners with a structured understanding of current advancements and open issues in YOLO-based suspicious human activity recognition.
Copyright (c) 2026 Yohanes Bowo Widodo, Sondang Sibuea, Rano Agustino https://creativecommons.org/licenses/by/4.0
IoT-Based Water Quality Monitoring System Using ESP32 with NSF-WQI Method on Various Water Sources
Water quality is an important indicator in maintaining environmental health and ecosystem sustainability. Direct water quality monitoring would be challenging for determining the quality of water sources. Therefore, it is necessary to design a real-time Water Quality Index (WQI) monitoring system based on the Internet of Things (IoT) using the ESP32 microcontroller with sensors for pH, temperature, turbidity (NTU), Total Dissolved Solids (TDS), and electrical conductivity (EC). The system is designed to be portable with a battery power source and is capable of sending data in real-time to a server for display via a web interface, as well as providing automatic notifications through Telegram.Water quality assessment is performed using the National Sanitation Foundation Water Quality Index (NSF-WQI) method with four main parameters: pH, temperature, NTU, and TDS. The system is also capable of classifying water quality into 5 categories: Excellent, Good, Medium, Bad, and Very Bad. Laboratory tests were conducted on various liquids (coffee, soap, and milk), followed by field tests at three locations: Tempur River, Paddy Field Irrigation Flow, and Mbelik Tirta Wening Minggirsari Water Source. The measurement results show WQI values of 65–69 (Medium category) for Tempur River, 75–83 (Good category) for Paddy Field Irrigation Flow, and 93–98 (Excellent category) for Mbelik Tirta Wening Minggirsari Water Source. The system successfully demonstrated accurate, responsive, and reliable performance, making it suitable for sustainable water quality monitoring.
Copyright (c) 2026 Ulfa Niswatul Khasanah, Moh. Mimbar Fauzi, Fatra Nonggala Putra, Imroatul Fatikha Kurniati, Januar Gilang Ramadhan https://creativecommons.org/licenses/by/4.0
Zero-Day Attack Detection Using Autoencoder and XGBoost
Advances in information and communication technology have significantly impacted progress in various sectors, but they have also given rise to increasingly complex network security threats. Cyberattacks such as Distributed Denial of Service (DDoS), ransomware, and software vulnerability exploits continue to increase year after year. Signature-based Intrusion Detection Systems are often ineffective in identifying novel cyberattacks since they rely solely on previously known attack patterns. To address this limitation, this study proposes a hybrid approach that integrates Autoencoders, including Dense and Memory-Augmented variants, with Extreme Gradient Boosting (XGBoost) to enhance zero-day attack detection using the UNSW-NB15 dataset. The research methodology encompasses data exploration, preprocessing with a split-before-transform strategy to prevent information leakage, Autoencoder training to model normal network behavior, reconstruction error computation for anomaly detection under both fixed and adaptive thresholding, and the utilization of these errors as input features for XGBoost classification. Experimental results demonstrate that adaptive thresholding improves F1 performance compared to fixed thresholds, while the hybrid Autoencoder–XGBoost integration achieves a significant performance boost. The proposed model consistently obtained F1 scores above 0.80 and PR-AUC values exceeding 0.81 with a balanced trade-off between precision and recall. These findings confirm that the hybrid approach is more effective, consistent, and adaptive in detecting intrusions, particularly zero-day attacks, and highlight its potential as a robust framework for advancing network security in dynamic threat environments.
Copyright (c) 2026 Mujibbur Rohman, Dharmayanti https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Indihome and Starlink Network Performance at the Banda Aceh City Office Ookla
The availability of a fast, stable, and reliable internet network is a crucial factor in supporting office operational activities in the digital era. This study aims to analyze and compare the performance of the IndiHome and Starlink networks in the Banda Aceh City Office environment using the Ookla Speedtest application as the main measuring tool. The research method used is a quantitative approach with a comparative descriptive design. Data collection was carried out through direct network speed testing and the distribution of Likert-based questionnaires to 10 IndiHome office staff. The evaluation approach in this study focused on measuring internet network service quality parameters, such as access speed and delay to objectively assess the performance of IndiHome and Starlink services. Parameters analyzed include download speed, upload speed, latency, network stability, weather effects, ease of installation, and service costs. The results showed that the IndiHome network achieved a success rate of 96%, while the Starlink network achieved 95%, both of which are included in the very feasible category. However, based on Ookla testing results and user perceptions, the IndiHome network demonstrated more stable and consistent performance, even though both services have the same speed capacity of 300 Mbps. These findings indicate that the IndiHome network is more recommended as the primary internet connectivity solution to support office operational needs in Banda Aceh.
Copyright (c) 2026 M Ulul Azmy, Firmansyah https://creativecommons.org/licenses/by/4.0
A Comparative Study of DenseNet-201 and Swin Transformer for Malignant and Benign Skin Lesion Classification
Skin cancer has a high global prevalence, underscoring the need for accurate and efficient early detection systems to support screening. This study presents a comparative analysis of DenseNet-201 and Swin Transformer for binary classification of malignant and benign skin lesions using the BCN20000 dataset, which contains 12,413 dermoscopic images. The proposed workflow includes image preprocessing and augmentation, transfer learning-based model training, and evaluation under a 5-fold stratified cross-validation protocol. Performance is assessed using Accuracy, Precision, Sensitivity (Recall), F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). In addition, computational efficiency is examined in terms of parameter count, model size, and training time. Across five folds, DenseNet-201 achieved 88.05% Accuracy, 88.90% Precision, 89.48% Sensitivity, 89.17% F1-score, and 94.73% AUC, whereas Swin Transformer achieved 87.42% Accuracy, 89.77% Precision, 87.06% Sensitivity, 88.39% F1-score, and 94.33% AUC. A paired t-test at α = 0.05 indicated no statistically significant performance difference between the two models. Model interpretability was investigated using Grad-CAM for DenseNet-201 and EigenCAM for Swin Transformer to verify that predictions were driven by lesion-relevant regions. Overall, the results suggest that both architectures are suitable candidates for dermoscopic image-based skin lesion screening support systems, including teledermatology applications.
Copyright (c) 2026 Dahlan Hidayat, Ahmad Musyafa, Murni Handayani https://creativecommons.org/licenses/by/4.0
User Interface and User Experience Design for Character Development System Web Application at State Vocational School 56 Jakarta Based on Prototypes with The User Centered Design Method
In the digital era, information technology plays a crucial role in supporting various activities, including education. One increasingly developed technology implementation is a web-based student violation recording system. This system not only simplifies the process of recording and monitoring violation data but can also be integrated with student character development programs. At SMK Negeri 56 Jakarta, character development is a crucial part of the educational process, but its implementation still faces various challenges, particularly regarding the effectiveness and efficiency of digital data recording. This research aims to design the User Interface (UI) and User Experience (UX) for a prototype-based character development system web application using the User-Centered Design (UCD) method. This method is used to ensure that the application design truly centers on the needs of the user, in this case the Student Affairs Team. Through this approach, the application is expected to present an easy-to-use, engaging interface and provide a positive user experience. The result of this research is a web application prototype design that can display historical student violation data, facilitate the development process more efficiently, and present interactive data visualizations to assist the school in analyzing student character development. Therefore, this application is expected to support the increased effectiveness of character development at SMK Negeri 56 Jakarta and become a digital solution that adapts to the needs of modern education.
Copyright (c) 2026 Miftahudin Rifki, Verdi Yasin, Anton Zulkarnain Sianipar https://creativecommons.org/licenses/by/4.0
Application of Artificial Neural Network in Estimating Harvest Time of Lettuce and Spinach Plants in Nutrient Film Technique Hydroponic System
Hydroponic farming using the Nutrient Film Technique (NFT) system is widely implemented due to its efficiency in nutrient management and water use. Spinach and lettuce are leafy commodities widely cultivated using this system because they have a relatively short growth cycle and high economic value. However, determining harvest time is still often done manually based on experience, potentially leading to inaccurate decisions that impact the quality and quantity of production. This study aims to develop a prediction model for harvest time for hydroponic spinach and lettuce plants based on Artificial Neural Network (ANN) by utilizing environmental and physiological parameters of the plant. The parameters used include water temperature, air humidity, light intensity, pH, Electrical Conductivity (EC), and plant age. The dataset used consists of 1,200 observation data of NFT hydroponic cultivation results from January to July 2025. The data went through a preprocessing stage in the form of cleaning, normalization, and dividing training data and test data with a ratio of 80:20. The ANN model was built using the backpropagation method with training parameter optimization. Data was obtained from plant growth monitoring, then normalized and divided into training and test data. Test results showed a prediction accuracy of 92.8% based on MAPE, MAE, and R-squared. This model was implemented in a Streamlit-based web application to facilitate farmer use, making harvest timing more objective, measurable, and data-driven.
Copyright (c) 2026 Zilfa Agustina Munawar, Gina Purnama Insany, Ivana Lucia Kharisma https://creativecommons.org/licenses/by/4.0
Analysis of IT Service Management at PT Bukit Asam Tbk using the ITIL V4 Framework
PT. Bukit Asam Tbk (PTBA) is a mining company that utilizes information technology to support its business. In practice, IT services play an important role in supporting business operations from production, security, to supply chains. These services are also subject to Incident s and problems such as network disruptions, application troubleshooting, password resets, work device repairs, and access rights requests. Therefore, PTBA needs to implement Incident and Problem Management practices based on the ITIL V4 framework to handle Incident s as quickly as possible, so that their impact can be minimized and repeated Incident s prevented. This study aims to determine the extent of the implementation of Incident and Problem Management practices at PTBA using the ITIL V4 Maturity Level Self Assessment questionnaire. The questionnaire was distributed to eight stakeholders selected through purposive sampling. The results of the study show that the implementation of Incident Management practices at PTBA is at Level 3 (Product) with a gap of 1 from the target level, and Problem Management practices are at Level 3.5 (Quality Control) with a gap of 0.5 from the target level. Core processes such as recording, classification, prioritization, escalation, handling, and closure have mostly been implemented. However, there are weaknesses such as prioritization not being based on impact, and cross-party coordination not being fully structured. Therefore, recommendations were compiled based on ITIL V4 Incident and Problem Management practices, with the hope of helping PTBA improve the quality of its IT services.
Copyright (c) 2026 Alda Putri Zahara, Yulhendri https://creativecommons.org/licenses/by/4.0
Performance Evaluation of ARIMA, LSTM, and Hybrid ARIMA–LSTM Models for Daily Solar Energy Prediction in Bali
Solar energy is one of the most promising renewable energy sources in Indonesia, particularly in Bali, which has relatively high solar irradiance throughout the year. However, daily variability in solar radiation caused by weather conditions and atmospheric factors leads to fluctuations in solar energy production, making accurate forecasting essential for effective energy planning. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and hybrid ARIMA–LSTM models in forecasting daily solar energy at the Jembrana Climatological Station, Bali. The dataset consists of 10-minute solar radiation observations obtained from an Automatic Weather Station (AWS) for the period January 2023 to September 2025, which were aggregated into daily solar energy values expressed in kWh/m². Data preprocessing included missing value handling, outlier correction, normalization, and an 80:20 split between training and testing datasets. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–LSTM model achieved the best performance, with an RMSE of 0.960 kWh/m², MAE of 0.771 kWh/m², and MAPE of 22.245%, outperforming both the ARIMA and LSTM models. These findings indicate that the hybrid approach is more effective in capturing both linear and nonlinear characteristics of daily solar energy time series.
Copyright (c) 2026 Aslimah, Sajarwo Anggai, Tukiyat https://creativecommons.org/licenses/by/4.0
Applying SEO Methods (On-Page and Off-Page) to Optimize the Ranking of the Top Herbal Atsiri Website
Search Engine Optimization (SEO) is an important strategy for improving website rankings in search engines. The website of PT. Unggul Herbal Atsiri, which operates in the herbal health product sector, still does not appear on the first page of Google search results even though it has used relevant keywords such as "telon oil" and "essential oil". This condition indicates a problem in the implementation of SEO strategies that are not optimal, so a more effective approach is needed to improve the website's search performance. This study used On-Page and Off-Page SEO methods, both individually and in combination. The implementation was carried out based on 3 (three) different scenarios and analyzed by considering the results on 2 (two) types of devices, namely mobile and desktop. The results showed that the application of the combination of On-Page and Off-Page SEO methods provided the best results in 2 (two) main parameters, namely increased visitor traffic and website page loading speed. However, in the keyword search ranking parameter, the results were not optimal. Of the 7 (seven) keywords tested, only 1 (one) managed to enter the first page of Google. Thus, the combination SEO strategy has proven effective in certain aspects, but still needs strengthening to improve performance in keyword-based searches. This research is expected to provide more specific and relevant solutions for improving website rankings, both in mobile and desktop search engines, thereby supporting the achievement of effective digital marketing goals.
Copyright (c) 2026 Chelsy Jelita Chinta Putri Herwindo, Endang Sulistiyani https://creativecommons.org/licenses/by/4.0
Comparison of Faster R-CNN and YOLO v12 on Passport Text Extraction Based on Optical Character Recognition
Current developments in information technology are driving the need for digitalization of official identity documents, including passports, to improve service efficiency and reduce reliance on manual processes. The digitalization of official identity documents such as passports still faces efficiency and accuracy challenges due to manual data entry processes. This study aims to compare the performance of Faster R-CNN and YOLO v12 in an automatic text extraction system based on Optical Character Recognition (OCR). The research employed an experimental method with a comparative approach using 31 preprocessed passport images. YOLO v12 was integrated with EasyOCR, while Faster R-CNN was combined with a PyTorch-based OCR module. The evaluation metrics included mAP, Character Accuracy Rate (CAR), Word Error Rate (WER), F1-score, and inference time. The results indicate that YOLO v12 outperforms Faster R-CNN in object detection, achieving an mAP@50 of 95.0% and mAP@50–95 of 90.0%, compared to 93.0% and 89.0%, respectively. In terms of text extraction accuracy, Faster R-CNN achieved a CAR of 50.01% and an F1-score of 55.75%, slightly higher than YOLO v12 with a CAR of 47.72% and an F1-score of 53.84%. However, YOLO v12 produced a lower WER and faster inference time of 2.4202 seconds (0.45 FPS). The findings suggest that YOLO v12 excels in efficiency and detection performance, while Faster R-CNN performs better in specific text extraction accuracy.
Copyright (c) 2026 Masniari Samosir, Sajarwo Anggai, Taswanda https://creativecommons.org/licenses/by/4.0
Digital Business Model Development through the Implementation of a Smart Tuition Payment System
The tuition payment system is an essential component of school financial administration that supports educational operations. However, many schools still rely on manual or semi-digital payment processes, which often result in delayed transaction recording, data entry errors, and limited transparency in financial reporting. This study aims to develop a web-based online tuition payment application to improve the efficiency, accuracy, and transparency of school financial management. The research employed a qualitative descriptive approach with data collected through observation, interviews, and literature review. System development was conducted using the Agile method, allowing the application to be refined iteratively according to user needs. The system was implemented using PHP and MySQL and includes features such as student data management, tuition billing generation, payment recording, digital receipt generation, and real-time financial reporting. The results indicate that the developed system enhances administrative efficiency, reduces recording errors, and improves the timeliness and transparency of financial reports. Furthermore, the implementation of this system supports the achievement of Sustainable Development Goal (SDG) 4: Quality Education by strengthening governance and sustainability in educational services.
Copyright (c) 2026 Laqma Fitrani, Angga Lisdiyanto, Masti Fatchiyah Maharani, Yerezqy Bagus, Dina Zatusiva Haq https://creativecommons.org/licenses/by/4.0
Zero Trust Network Architecture Design For Mid-Scale Organizations
The development of information technology, the adoption of cloud-based services, and the implementation of remote work patterns increase network security risks in medium-sized organizations. Traditional network security approaches that focus on the perimeter are considered no longer adequate in facing increasingly complex cyber threats. This study presents the design of a Zero Trust Network (ZTN) architectural blueprint specifically designed for medium-sized organizations. The research method used is a conceptual approach through literature review, network security requirements analysis, and the design of the ZTN logical architecture. The research results are a Zero Trust Network architectural blueprint that emphasizes continuous verification, identity-based access control, and the application of the principles of least privilege and micro-segmentation. The architectural design is arranged in a modular and phased manner and is aligned with the NIST SP 800-207 framework, so it remains realistic for adoption without requiring drastic infrastructure changes. The resulting ZTN architectural blueprint can be used as an initial reference for medium-sized organizations in designing a Zero Trust-based network security strategy. This research is conceptual in nature and does not include the implementation stage or empirical testing in a real operational environment. It is hoped that the results of this study can serve as an initial reference for medium-sized organizations in designing a more adaptive and sustainable network security strategy.
Copyright (c) 2026 Nurul Kamila, Makhsun, Sudarno https://creativecommons.org/licenses/by/4.0
Implementation of Information System and Software Quality Testing in Company Operational Applications Based on ISO/IEC 25010 (Case Study: PT Snapdev Digital Indonesia)
Implementation of information system quality testing is an important factor in supporting the effectiveness and efficiency of company operations. This study aims to implement an information system and conduct software quality testing based on the ISO/IEC 25010 standard at PT Snapdev Digital Indonesia. Quality testing is carried out by measuring seven main characteristics, namely Performance Efficiency, Compatibility, Usability, Reliability, Security, Maintainability, and Portability. The research methods used include observation, data collection, and analysis of test results based on indicators in each ISO/IEC 25010 characteristic. The measurement results show that the percentage value for each aspect of system quality is in the range of 79%–83%, which indicates that the system has met most of the established quality criteria. The overall average value of 80.97% places the system quality in the very good category. This indicates that the system has efficient performance, adequate levels of security and reliability, good ease of use, and can be maintained and run in various environments. However, there are still opportunities for improvement in several aspects to improve system quality to be more optimal. Overall, the implemented information system is considered suitable for use and complies with international software quality standards.
Copyright (c) 2026 Chairul Anwar https://creativecommons.org/licenses/by/4.0
Application of Transfer Learning Method on Convolutional Neural Network (CNN) to Identify Genuine and Fake Diplomas
The authenticity of diplomas plays a crucial role in maintaining the integrity of the education system and ensuring that recognized academic competencies align with an individual's actual achievements. Diplomas are not merely administrative documents, but strategic instruments in job recruitment and professional qualification assessment. However, with increasing educational mobility, document misuse through diploma forgery is becoming increasingly prevalent, potentially undermining public trust in educational institutions. Currently, the verification process is still largely carried out manually through visual inspection of document elements such as layout and stamps. The reliance on the examiner's experience makes this method vulnerable to inconsistencies and human error, especially when dealing with fake diplomas with visual qualities that increasingly resemble genuine documents. Diploma forgery is a problem that impacts the credibility of educational institutions and the validity of academic data. Manual inspection is often inconsistent and time-consuming. This study develops a model for classifying genuine and fake diplomas using a Convolutional Neural Network (CNN) with a transfer learning scheme. The performance of the ResNet50, VGG16, and MobileNetV2 architectures is comparatively analyzed. Data preprocessing included resizing, normalization, and augmentation. Test results showed the ResNet50 architecture achieved optimal performance with 92.63% accuracy, 92.16% precision, 94.00% recall, and 93.07% F1-score. The system was implemented in a Streamlit-based web application to facilitate the verification process.
Copyright (c) 2026 Rifa Awaludin, Anggun Fergina, Gina Purnama Insany https://creativecommons.org/licenses/by/4.0
Application of Data Mining to Analyze Sales Patterns of Merchandise in MSMEs in Sait Buttu Saribu Tourism Village Using the Apriori Algorithm
The development of information technology in the era of globalization has brought significant changes to various aspects of life, including business and trade activities. Information is a crucial component in supporting fast, precise, and accurate decision-making, both at the operational and strategic levels. This study aims to apply the Apriori algorithm to analyze the sales patterns of MSMEs in the Sait Buttu Saribu Tourism Village and to develop a web-based system that can support business decision-making. MSMEs play a vital role in the local economy, but sales data utilization is still suboptimal. Sales transaction data from 2023–2024 from 57 active MSMEs were used as the research object. The research method used is Research and Development (R&D) with the system development using the Waterfall method. The Apriori algorithm was applied to find association patterns between products based on support and confidence values. The results show that the Apriori algorithm is able to identify product combinations that are frequently purchased together so that they can be utilized for sales strategies such as product bundling, product arrangement, and inventory control. The developed system is expected to help MSMEs manage sales data more effectively and support data-driven decision-making. Therefore, this study applies the Apriori algorithm to analyze the sales patterns of MSME merchandise in Sait Buttu Saribu Tourism Village and builds a web-based system as a decision- making tool.
Copyright (c) 2026 Khofifah Fauzani, Ali Ikhwan https://creativecommons.org/licenses/by/4.0
East Java Inflation Prediction Based on Exchange Rates and International Trade Using the ARIMAX Approach
East Java's macroeconomic stability is vulnerable to external shocks, particularly imported inflation, which impacts domestic price formation. This study aims to develop an inflation forecasting model using the AutoRegressive Integrated Moving Average with Exogenous Variables (ARIMAX) approach as an Early Warning System instrument for regional policymakers. This study uses monthly time series data for the 2015–2024 period. Exogenous variable selection was conducted through the Granger Causality Test, which showed that only import volume had a significant effect on inflation, while exchange rates and exports were insignificant. The estimation results show that the ARIMAX(1,0,1) model is the best model with a forecasting accuracy level based on the Mean Absolute Percentage Error (MAPE) value of 10.40%, which is categorized as good. East Java's inflation projection for the 2025–2029 period shows an increasing trend from 1.99% to 3.28%, indicating the need for vigilance for policymakers in strengthening import supply chain management to mitigate the risk of future price pressures. Based on the theoretical and empirical review, this study hypothesizes that the exchange rate and international trade indicators have a significant predictive relationship with the inflation rate in East Java Province. This study aims to develop an inflation forecasting model for East Java using the ARIMAX approach, considering the exchange rate and international trade variables as candidate exogenous variables.
Copyright (c) 2026 Nafisah Zahrotun, Rizky Aditya Nugroho, Awang Andhyka https://creativecommons.org/licenses/by/4.0
Design of Web-Based Warehouse: A Case Study of Web-Based Project Equipment Inventory System Using Waterfall Method
Inventory is crucial for a company. The warehouse still uses books to record receipts, expenditures, and stock. Manual inventory management often leads to various problems, such as data accumulation, inconsistencies in stock quantities, late reporting, and the risk of data loss. This study aims to design and build a web-based inventory information system to replace manual recording and improve warehouse management efficiency in a contractor company to address these issues. The research method used is the waterfall method, which includes requirements analysis, design, coding, and testing. The system was developed using the PHP programming language with a MySQL database and an easy-to-use web-based interface. The results show that the system was successfully built with features for managing incoming and outgoing goods, suppliers, customers, and monthly reports. Black Box Testing on the login form produced all valid scenarios as expected. This system has been proven to accelerate the process of recording and generating inventory reports accurately and efficiently.
Copyright (c) 2026 Handa Gustiawan, Hesti Rian, Akbar Muhamad Irfan https://creativecommons.org/licenses/by/4.0
Comparison of ResNet50, ResNet101, and ResNet152 Architectures in Image-Based Rice Leaf Disease Classification
Rice leaf diseases are one of the main threat that can reduce rice crop productivity especially if they are not detected at an early stage. Conventional disease identification still has limitations because it relies on visual observation and the experience of farmers. Therefore, this study proposes a rice leaf disease classification approach based on digital images using deep learning methods. This study aims to compare the performance of three Residual Network architectures, namely ResNet50, ResNet101, and ResNet152. The dataset used was collected from three public Kaggle datasets, consisting 7.322 images divided into four classes (healthy, hispa, sheath blight, and brown spot). The dataset was split into training, validation, and testing sets with a ratio of 70:20:10 and processed through image preprocessing and data augmentation. All models were trained using a transfer learning approach with the same training configuration to ensure a fair comparison. Model performance was evaluated with the test sets using loss, accuracy, and confusion matrix analysis. The experimental results show that ResNet101 achieved the best performance with a loss value of 0,0146 and an accuracy of 0,9973. Followed by ResNet50 with an accuracy of 0,9918, and ResNet152 with an accuracy of 0,9837. These results indicate that ResNet101 provides the best balance between network depth and classification performance.
Copyright (c) 2026 Ardi Setyiawan, Anindita Septiarini, Andi Tejawati https://creativecommons.org/licenses/by/4.0
Implementation of Temporal Fusion Transformer (TFT) for Short-Term Sales Prediction of Telkomsel Data Packages in East Java
The development of the cellular telecommunications industry has driven an increasing demand for fast, stable, and affordable data services. Accurate forecasting of data package sales is a significant challenge for telecommunications operators due to high demand fluctuations and the complexity of time series patterns. This study aims to implement a Temporal Fusion Transformer (TFT) model based on Seasonal-Trend Decomposition using Loess (STL) to predict short-term sales of Telkomsel data packages in East Java. The data used are sales transactions with hourly time resolution from January to June 2024, focusing on the five data packages with the highest transaction volume. The STL method is applied in the pre-processing stage to separate the trend, seasonal, and residual components, which are then used as additional features in the TFT modeling. Model performance is evaluated using Mean Absolute Error (MAE) and Quantile Risk (q-Risk). The results show that the TFT model is able to produce accurate predictions with an MAE value of 3.6941 and an average q-Risk of 0.0808. Furthermore, interpretability analysis revealed that historical sales variables, seasonal components, and calendar variables significantly contributed to the prediction results. These findings indicate that the STL-based TFT approach is effective for short-term sales forecasting and has the potential to support data-driven operational decision-making in the telecommunications sector.
Copyright (c) 2026 Muhammad Azkiya Akmal, Trimono, Alfan Rizaldy Pratama https://creativecommons.org/licenses/by/4.0