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Governance IT Adoption and Technology Advance
Published by Universitas Telkom
ISSN : -     EISSN : 31247180     DOI : https://doi.org/10.25124/govita
Core Subject :
GovITA is a scientific journal that focuses on information technology adoption and technological advancement at both national and international levels. The journal publishes research articles, theoretical studies, and case studies that explore the application of digital technologies to improve efficiency, transparency, and innovation in public services. GovITA aims to serve as a publication platform for academics and practitioners to address the challenges of digital transformation and as a forum where governance meets innovation through research in model analysis, cybersecurity, and machine learning.
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Articles 10 Documents
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Search results for , issue "vol. 1 no. 2 (2026)" : 10 Documents clear
Data Governance In Traffic Management Based On Average Daily Traffic (ADT) Data Prediction Using Python Gohan Willy Christoper Sihite; Naila Syakirotul Rizkiyah
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11212

Abstract

Data governance is crucial to ensure that traffic data is collected, managed, and used accurately to support quick, precise, and evidence-based decision-making in traffic management. The main challenge faced by many transportation agencies is the lack of an established data governance framework, which means that the utilization of Average Daily Traffic (ADT) data remains descriptive and does not yet support predictive planning. This situation results in traffic management being reactive and less effective in handling vehicle surges during critical periods. This study aims to implement a data governance framework in traffic management by developing comprehensive data management practices, including data acquisition, data quality assurance, and data-driven decision-making through ADT data prediction using the Python programming language. The approach applied is simple linear regression, applied to four years of historical ADT data to create a systematic and accountable prediction model, in accordance with data governance principles: accuracy, affordability, and policy relevance. The data were coded as numerical variables and analyzed to estimate future vehicle volumes clearly and replicably. The study's findings indicate that a Python-based prediction model, when integrated into the data governance structure, can provide more accurate, measurable, and policy-relevant traffic volume projections. This contributes to improving the quality of Data Governance, particularly in providing reliable and relevant information for strategic decisions. By incorporating this prediction system into the data governance framework, the relevant agencies are expected to be able to plan more proactive traffic management strategies, including vehicle flow regulation, road capacity optimization, and effective resource allocation based on structured understanding and strong data governance.
Designing a Web-Based Company Profile Information System to Support Information Technology Governance in Event Organizers and Wedding Organizers Intan Azizah; Devian Annas Tawadzu
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11213

Abstract

Digital transformation requires business actors in the service sector, such as CV. Brother Indonesia, which is engaged in Event Organizer (EO) and Wedding Organizer (WO), to have a strong digital identity to increase credibility and market reach. So far, reliance on social media has caused the presentation of portfolio information and company profiles to be unstructured, making it difficult to access information for prospective clients of large institutions. This research aims to design and develop a professional web-based company profile information system as an integrated information media as well as a support tool for Information Technology (IT) governance. The development method used is the Waterfall model, which includes the stages of needs analysis, system design, implementation, and testing. The system is developed using the Laravel Framework and Tailwind CSS with interface design (UI/UX) designed through Figma. The results of the research resulted in an official digital platform that includes interactive portfolio features, communication contact integration, and an Admin Panel for content operational efficiency. Tests using the Black-Box method showed that the system ran stable and responsive on a variety of devices. The implementation of this website strategically supports the company's IT governance through the standardization of digital operational procedures that are more transparent, accountable, and aligned with long-term business goals.
Business Process Analysis and Information System Design to Support IT Governance Practices in Construction Services Novara Edyen Puspa Zahwa; Intan Azizah
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11228

Abstract

This study aims to analyze system requirements and design a web-based company profile information system to support the effectiveness of IT governance in a construction services organization. This study occurred due to the lack of a structured and integrated information system, which led to inefficient information delivery and limited accessibility for stakeholders. A qualitative approach was used through observation and interviews to identify system requirements and analyze existing business processes. Business process modeling was performed using Business Process Model and Notation (BPMN) to describe the current (as-is) and proposed (to-be) processes. Next, the system design was developed using Unified Modeling Language (UML), including Use Case Diagrams, Activity Diagrams, Sequence Diagrams, and Class Diagrams to provide a structured overview of the system. The database was designed using Entity Relationship Diagrams (ERD) to ensure structured and integrated data management. The system design provides a structured overview to manage and convey company information more efficiently. Data in the system is managed by the administrator and accessed dynamically by users. In addition, this design supports the principles of IT governance by ensuring alignment between business processes and information technology, improving data management, and increasing transparency and control. This study concludes that the designed system can improve the effectiveness of information management and contribute to better IT governance. Further research is recommended to implement and evaluate this system in a real-world environment
Integration of Data Governance and Development of Business Intelligence Dashboard for Decision-Making in Regional Water Utility Cindy Miswaty Marhardika Sinaga; Gohan Willy Christoper Sihite
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11229

Abstract

Public service organizations need effective data management in order to deliver better services and improve decision making. This study analyzes the implementation of Data Governance and Business Intelligence at Perumda Air Minum. Prior to the conduct of this study, customer complaint data was captured manually, and the formats of those inputs were disparate. This resulted in unreliable information. The study took a qualitative descriptive approach using observation, interviews and document analysis. Data Governance was formalised by cleansing, validating, and managing data quality. Google Spreadsheet was used as a staging layer where customer complaint information was ingested through an Application Programming Interface (API) connection. The data was then This data was then visualised with a Business Intelligence dashboard using Looker Studio. Insights were offered for complaint trends, category distributions, branch-level complaint analysis, and operational monitoring tables on the dashboard. The study finds that Data Governance enhanced data consistency, completeness and reliability of reports. The Business Intelligence dashboard helped with operational monitoring and decision making as well. Further, the combination of Data Governance and Business Intelligence enhanced reporting efficiency and organizational transparency.
Analisis Pengaruh E-Costumer Relationship Management Terhadap E-Satisfaction dan E-Loyalty Pengguna Access by KAI Indah Indri Arti; Muhamad Awiet Wiedanto Prasetyo
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11435

Abstract

The rapid development of information and communication technology has significantly transformed consumer behavior, particularly in the way people access and purchase products and services. The increasing adoption of digital platforms has encouraged consumers to shift from conventional offline transactions to online-based services, including the purchase of railway transportation tickets. In Indonesia, PT Kereta Api Indonesia (KAI) has responded to this trend by developing the Access by KAI mobile application, which provides various services such as ticket reservations, payment facilities, travel information, and customer support. However, the relatively low number of user reviews on the Google Play Store and Apple App Store compared to the total number of application downloads indicates the existence of several issues related to customer engagement, satisfaction, and loyalty. These conditions highlight the importance of evaluating the effectiveness of Electronic Customer Relationship Management (E-CRM) in maintaining positive relationships with customers. This study aims to analyze the influence of E-CRM on E-Satisfaction and E-Loyalty among users of the Access by KAI mobile application on railway routes within the Java region. The research employed a quantitative approach using a non-probability sampling method with a purposive sampling technique. A total of 400 respondents who had experience using the Access by KAI application participated in the study. Data were collected through questionnaires and analyzed to examine the relationships among the research variables. The findings reveal that E-CRM has a positive and significant effect on E-Satisfaction. In addition, E-CRM also positively influences E-Loyalty. Further analysis demonstrates that E-Satisfaction plays an important mediating role in the relationship between E-CRM and E-Loyalty. Although the direct effect of E-CRM on E-Loyalty is relatively limited, customer satisfaction significantly strengthens customer loyalty toward the application and its services. These results indicate that effective implementation of E-CRM strategies can enhance customer satisfaction, which in turn contributes to stronger customer loyalty. Therefore, organizations should focus not only on improving customer relationship management systems but also on continuously enhancing customer satisfaction to ensure long-term customer retention and loyalty in the digital service environment.
Evaluasi Service dan Support Automation dalam Mendukung Customer Relationship Management pada Layanan Pasien di RSUP Dr. Sardjito Bayu Arya Putra Pradana; Agung Wicaksono; Syahzada Arsa Sabian; Sukmadiningtyas
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11456

Abstract

Abstract—Penelitian ini bertujuan untuk mengevaluasi implementasi service automation dalam mendukung Customer Relationship Management (CRM) pada layanan pasien di RSUP Dr. Sardjito. Penelitian menggunakan pendekatan analisis deskriptif dengan memanfaatkan data sekunder yang diperoleh melalui studi dokumentasi dari laporan kinerja rumah sakit, laporan PPID, dan dokumenpendukung lainnya. Analisis data dilakukan menggunakan analisis deskriptif, Porter’s Five Forces, Value Chain Analysis, pemodelan proses bisnis menggunakan Business Process Model and Notation (BPMN), serta Gap Analysis untuk membandingkan kondisi aktual dengan kondisi ideal. Hasil penelitian menunjukkan bahwa RSUP Dr. Sardjito telah menerapkan berbagai layanan berbasis teknologi,seperti pendaftaran online, rekam medis elektronik, dan sistem informasi pendukung pelayanan pasien yang menjadi fondasi dalam penerapan service automation. Analisis lingkungan eksternal dan internal menunjukkan adanya peluang sekaligus tantangan dalam pengembangan layanan berbasis teknologi. Selain itu, hasil Gap Analysis menunjukkan masih terdapat kesenjangan pada aspek integrasi layanan, komunikasi dengan pasien, dan layanan pasca kunjungan. Berdasarkan temuan tersebut, penelitian ini menghasilkan rancangan kondisi ideal (to-be) serta rekomendasi pengembangan service automation yang diharapkan dapat meningkatkan kualitas layanan, efisiensi operasional, dan hubungan jangka panjang antara rumah sakit dan pasien.
Sentiment Analysis of Pertamax on Social Media and MyPertaminta Data Using the IndoBert Algorithm Dzulfan Yumna Azis; Anthony Dewantoro; Kumara Galan Pramana; Mahazam Afrad; Hari Widi Utomo
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11679

Abstract

The rapid growth of digital services in Indonesia has accelerated the adoption of online platforms for fuel distribution through the MyPertamina application developed by PT Pertamina. Public responses toward the application and related fuel distribution policies are widely expressed through social media and application reviews. This study aims to analyze public sentiment toward MyPertamina using multi-platform data collected from Google Play Store, Instagram, and Twitter. The research employed a Natural Language Processing approach using the Transformer-based IndoBERT model. The methodology included data collection, data integration, text preprocessing, sentiment labeling, model fine-tuning, performance evaluation, and result visualization. The collected textual data were classified into positive and negative sentiment categories to represent public opinion. Experimental results showed that IndoBERT achieved an accuracy of 92.04%, with balanced precision, recall, and F1-score values. These findings demonstrate that IndoBERT effectively handles unstructured and informal Indonesian text from multiple digital platforms. Overall, integrating multi-platform data with IndoBERT-based sentiment analysis provides comprehensive insights into public perceptions of MyPertamina and supports strategic decisions. Future studies should expand data sources, increase dataset size, compare additional Transformer models, and evaluate broader sentiment patterns across diverse digital environments effectively.
Implementation of Customer Relationship Management Based on Visitor Segmentation at the General Sudirman Museum to Support Digital Promotion: Penelitian ini mengimplementasikan sistem Customer Relationship Management berbasis segmentasi pengunjung pada Museum Panglima Besar TNI Jenderal Sudirman menggunakan metode K-Means Clustering dan platform Odoo CRM. Dari 50 data kuesioner dan 270 data Kaggle, diperoleh dua segmen pengunjung dengan karakteristik perilaku digital berbeda sebagai dasar strate Shahifa Sajadiyah; Marsya Valeria valemar; Bunga Ramadhani; Rona Nisa Sofia Amriza
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11237

Abstract

Museum Panglima Besar TNI Jenderal Sudirman is a historical tourism destination in Purwokerto, Central Java, that does not yet have a structured visitor data management system, resulting in limited visitor information being recorded. Based on museum reports from 2024 to 2025, recorded data only includes daily visitor counts, visit dates, adult and child categories, and ticket and parking revenue, with no demographic information available. This study implements a Customer Relationship Management system based on visitor segmentation to support digital promotion strategies. Visitor data was collected through questionnaires distributed to 50 museum visitors using purposive sampling. A public Kaggle dataset of 270 rows served as training data, while the 50 questionnaire responses served as testing data. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, yielding K=2 as optimal with a Silhouette Score of 0.2611. Clustering results identified two segments: Cluster 0 with 37 respondents dominated by visitors aged 15 to 30 years actively using TikTok and Instagram, and Cluster 1 with 13 respondents dominated by adults over 30 years primarily using Facebook and WhatsApp. Results were implemented into Odoo CRM as a structured visitor data management foundation. This study contributes by combining Odoo CRM implementation, K-Means Clustering, and digital promotion strategy recommendations for a historical museum in Indonesia.
A Self-Supervised Graph Transformer Framework for Explainable Sequential Recommendation on Amazon Beauty Reviews Ilham Nur Fajri; Anang Ma'ruf; Fajar Alifianto Sinaga; Putra Bhanu Anggoro
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11760

Abstract

Sequential recommenders often obtain accurate next-item predictions by exploiting short-range transitions, yet these shortcuts can obscure longer-term preferences and weaken explanation fidelity. This risk is pronounced in catalogs containing multiple product identifiers with identical titles, where repeat-aware evaluation can overstate generalization. This study proposes a Calibrated Candidate-Transition Self-Supervised Graph Transformer, termed SSGT-CTF, which integrates a Transformer sequence encoder, review semantics, user-item graph propagation, item-transition priors, self-supervised objectives, validation-based calibration, and attention-transition rationales. Experiments used 252,056 raw Amazon Beauty reviews; after exact deduplication and iterative 5-core filtering, 26,606 interactions from 2,256 users and 1,220 items were divided by a chronological leave-one-out protocol. Across three random seeds, SSGT-CTF produced mean NDCG@10 values of 0.6300, 0.6921, and 0.5759 under repeat-aware, no-repeat, and no-repeat-title evaluation, respectively, and it exceeded the neural baselines in every protocol. The Markov baseline remained stronger in the first two protocols, whereas SSGT-CTF surpassed it in the no-repeat-title protocol; the paired seed-42 gain was 0.0129 with a Wilcoxon p-value of 0.0022, and deletion analysis across 120 cases showed larger score reductions after removing top rationales than after random deletion for one to three removals. The findings indicate that candidate transitions are indispensable but must be evaluated under title-aware controls, and future work should validate the model on newer catalogs, correct implementation-sensitive padding conventions, and test cross-domain robustness.
KLASIFIKASI GANGGUAN RANTAI PASOK MENGGUNAKAN METODE RANDOM FOREST PADA INDUSTRI LOGISTIK UD SUWARA JAYA Anggita Putri Cahyani; M Yoka Fathoni; Nisrina Hanifa Setiono
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11843

Abstract

Supply chain disruptions are one of the factors that can hinder the smoothness of logistics activities, especially in companies that need to maintain distribution accuracy and daily operational stability. Usaha Dagang Suwara Jaya, as a business engaged in chicken distribution, faces operational conditions that may potentially experience disruptions, such as discrepancies between pickup quantities and order quantities, remaining stock, shrinkage, and other operational adjustments. This study aims to develop a classification model capable of categorizing daily operational status into two classes: Disruption and Non-Disruption using the Random Forest algorithm. The disruption labels in this study were obtained based on validation from Usaha Dagang Suwara Jaya as the research partner and domain expert. The research stages include operational data collection, data preprocessing, operational variable formation, training and testing data splitting using 80:20 and 70:30 scenarios, Random Forest model training, model performance evaluation, Feature Importance analysis, and Streamlit dashboard implementation. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest model with the 80:20 data split scenario achieved the best performance, with an accuracy of 0.9070, precision of 0.8947, recall of 0.8947, and F1-score of 0.8947. Meanwhile, the 70:30 scenario obtained an accuracy of 0.8462, precision of 0.8800, recall of 0.7586, and F1-score of 0.8148. Based on the Feature Importance results, Remaining Stock, Shrinkage, and average_temperature were the most contributing features to the classification results. This study also produced a Streamlit-based dashboard that can be used to display classification results, prediction probabilities, operational data calculations, new dataset uploads, and prediction history. The developed model and dashboard can be used as a supporting medium for monitoring daily supply chain disruptions at UD Suwara Jaya.

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