Expert System for The Diagnosis of Depression in Students Using Certainty Factor Method: A Case Study of Ngudi Waluyo University

Depression is a growing mental health concern among university students, often fueled by academic pressure, social demands, and personal stress. This study presents the development of an expert system using the Certainty Factor (CF) method to diagnose depression specifically among students at Ngudi Waluyo University. The system categorizes depression into mild, moderate, and severe levels based on 12 validated symptom statements and expert-defined diagnostic rules. Implemented with PHP, JavaScript, and CSS, the system offers a user-friendly, accessible, and anonymous platform for self-assessment. Testing yielded an accuracy rate of up to 79% in diagnosing depression severity and a 71.7% user satisfaction rate based on a User Acceptance Test (UAT) involving 32 students. Results demonstrate that the system can effectively support early detection and mental health awareness within academic environments. Despite some limitations in UI and feedback depth, the expert system shows strong potential for broader application and further enhancement

Blockchain and IoT for Sustainable Agriculture: Innovations and Impacts

The integration of Blockchain and the Internet of Things (IoT) is emerging as a transformative force in sustainable agriculture. This review explores the synergistic potential of these technologies to enhance transparency, traceability, resource efficiency, and resilience in agricultural systems. We conducted a systematic review of peer-reviewed articles and conference proceedings published between 2015 and 2024, sourced from databases such as IEEE Xplore, Scopus, ScienceDirect, and SpringerLink. Studies were selected based on relevance to agricultural sustainability, the implementation of IoT and blockchain, and empirical or conceptual insights. The findings reveal that IoT devices enable real-time data collection and monitoring, while blockchain ensures secure, immutable records for supply chain transparency and smart contracts. Despite their promise, challenges persist, including high implementation costs, scalability issues, and limited digital infrastructure in rural areas. The review underscores the need for collaborative frameworks and policy support to foster adoption and recommends future research to focus on hybrid models and localized applications

Strategic IS/IT Planning for Enhanced Competitiveness and Operational Efficiency at PT. Songgo Jati Baru: Applying the Ward and Peppard Method

This study designs an integrated IS/IT strategy to enhance PT. Songgo Jati Baru's operational efficiency and global competitiveness in the trading and services sector. A qualitative approach was employed, utilizing data collection methods such as focus group discussions, document analysis, observation, and interviews. Analysis was conducted using the robust Ward and Peppard method, which incorporates SWOT analysis, Gap analysis, and the McFarlan Strategic Grid. The findings revealed that the company faced significant challenges, including a lack of system integration, limited data analytics capabilities, and suboptimal digital marketing strategies. To address these, the research recommends a cloud-based Enterprise Resource Planning (ERP) system for comprehensive business process integration, a Vendor Management System (VMS) for efficient collaboration, and a Customer Relationship Management (CRM) system for data-driven marketing, with a phased implementation planned for 2026-2028. This comprehensive strategy, underpinned by robust cloud infrastructure and continuous staff training, is poised to not only significantly enhance PT. Songgo Jati Baru's operational efficiency and global market reach but also to solidify its competitive position and ensure sustainable growth in the dynamic trade and services sector

Enhancing Hate Speech Detection: Leveraging Emoji Preprocessing with BI-LSTM Model

Microblogging platforms like Twitter enable users to rapidly share opinions, information, and viewpoints. However, the vast volume of daily user-generated content poses challenges in ensuring the platform remains safe and inclusive. One key concern is the prevalence of hate speech, which must be addressed to foster a respectful and open environment. This study explores the effectiveness of the Emoji Description Method (EMJ DESC), which enhances tweet classification by converting emojis into descriptive text or sentences. These descriptions are then encoded into numerical vector matrices that capture the meaning and emotional tone of each emoji. Integrated into a basic text classification model, these vectors help improve detection performance. The research examines how different emoji preprocessing strategies affect the performance of a BI-LSTM model for hate speech classification. Results show that removing emojis significantly reduces accuracy (68%) and weakens the model’s ability to distinguish between hate and non-hate speech, due to the loss of valuable semantic context. In contrast, retaining emoji semantics either through textual descriptions or embeddings boosts classification accuracy to 93% and 94%, respectively. The highest performance is achieved through emoji embedding, highlighting its ability to capture subtle non-verbal cues critically for accurate hate speech detection. Overall, the findings emphasize the importance of incorporating emoji-aware preprocessing techniques to enhance the effectiveness of social media content classification

Harnessing SVM for Sentiment Analysis: Insights from Gojek's Instagram Engagement

The development of digital technology has changed the transportation industry, including online services such as Gojek. Understanding customer sentiment is key in improving user experience and designing more effective business strategies. This research analyzes Gojek user sentiment on Instagram using Support Vector Machine (SVM). Data is obtained through web scraping, then processed through text cleaning, tokenization, common word removal, and stemming. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) before being classified with SVM. The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. Most positive comments highlight the convenience and efficiency of the service, while negative comments are more related to high tariffs, application constraints, and less responsive customer service. These findings provide insights for Gojek to improve marketing strategies, optimize customer service, and adjust fare policies based on user feedback. In addition, this analysis can help in predicting real-time customer satisfaction trends through sentiment monitoring on social media. As a development step, this research recommends further exploration with deep learning and Aspect-Based Sentiment Analysis (ABSA) to improve accuracy and understand the service aspects that have the most influence on customer satisfaction

Taxicab Entrepreneurs’ Attitude to Continue Using e-Hailing Platforms in South Africa

Taxicab entrepreneurs who operate on e-hailing platforms in South Africa face challenges such as earning below minimum wage, lacking employment benefits, working long hours, and experiencing victimisation by traditional taxicab operators. The key question is why these entrepreneurs continue using e-hailing platforms despite unfavourable working conditions. This study proposed that technology adoption factors enable entrepreneurs to overcome challenges and encourage them to keep using e-hailing platforms. Based on this assumption, this study investigated the determinants of technology adoption that influence the attitude of taxicab entrepreneurs to continue using e-hailing platforms in South Africa. The researchers gathered quantitative data from 253 entrepreneurs in Johannesburg, South Africa and tested the hypotheses with multiple regression analysis. The results demonstrated that perceived usefulness, benefits, and security strongly influenced entrepreneurs' willingness to continue operating on e-hailing platforms. However, perceptions of convenience, trust, and perceived ease of use did not affect their decision to use e-hailing services. Theoretically, this study pinpointed the factors that drive and hinder the continued use of e-hailing applications. Practically, the results provide insights into understanding long-term usage, user satisfaction, and the success of e-hailing in developing countries undergoing digital transformation, such as South Africa

The Trajectory of Scaled Agile Research: A Bibliometric Analysis and Visualization Approach

Modern project management in organisations is moving towards Scaled Agile to achieve success. Scaled Agile refers to a set of organisational structures and processes for implementing agile practices that are applied on an enterprise scale.  This study explores Scaled Agile growth and impact by analysing 238 publications obtained from the Scopus databases using bibliometric analysis. The results show that publications on Scaled Agile have steadily increased, with more contributions from developed nations than developing countries. In terms of the geographic distribution of publications, Germany is the leading followed by Sweden and the United States. The results also show that Scaled Agile is being applied across different fields, but is dominated by computer science, engineering, and business. We visualized the high-frequency terms using a word cloud and the keyword co-occurrence map, and a density map using VOSViewer. The h-index of 21 for the analysed articles indicates the significant scholarly impact of the publications. The study identified the following key themes: team dynamics, organisational structures, and practical applications of Scaled Agile. The study also identifies the major challenges associated with Scaled Agile, namely cultural issues and scalability issues, effective organisational design, and change management strategies. The findings of this study offer valuable insights into the current state of Scaled Agile that appeal to industry practitioners and academics interested in Scaled Agile research and implementation

Deploying a GIS for Enhancing Clinic Accessibility in Indonesia: An Agile QGIS Approach

Health is a fundamental necessity for all living beings, and clinics represent one of the most accessible healthcare facilities for communities. The spatial distribution of clinics can be effectively analysed and visualized through a Geographic Information System (GIS). This study proposes the development of a web-based clinic GIS for Indonesia using Quantum GIS (QGIS) software, implemented through the Agile methodology. The integration of Agile practices ensures that the system is accurate, adaptable, and responsive to evolving user needs. The resulting GIS website was successfully developed and tested, achieving a usability score of 88.76%, with effectiveness, efficiency, and satisfaction ratings of 91%, 90%, and 85%, respectively. The platform aims to support policymakers and healthcare providers in gaining a deeper understanding of health service distribution, ultimately promoting more equitable, data-driven decision-making in healthcare planning and resource allocation

Enterprise Architecture Model for Smart Government Implementation

This research aims to develop an enterprise architecture (EA) model to support the implementation of Smart Government that utilizes information and communication technology (ICT) to enhance efficiency, transparency, and public participation in governmental processes. The development of this EA model adopts a holistic approach, integrating various components of technology, organization, and business processes within the context of government to provide guidance for government agencies in planning, implementing, and managing the digital transformation required to achieve Smart Government. The findings of this study indicate that the proposed enterprise architecture model offers a clear and flexible structure to support the integration of government agencies, public service providers, and citizens. It is expected that by utilizing this EA model, governments can improve public services, operational efficiency, and create a more transparent and accountable environment. By leveraging this EA model, government agencies can streamline processes, enhance decision-making through data-driven insights, and foster greater inter-agency collaboration. It is expected that this model will improve public sector services by increasing accessibility, reducing administrative burdens, and optimizing resource utilization. Furthermore, the adoption of this model is anticipated to accelerate the digital transformation of the public sector, driving significant improvements in government service delivery, responsiveness, and citizen engagement

Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation

This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. This investigation lends itself to the advancement of a proactive identification framework, which hold potential for incorporation within collegiate mental well-being surveillance infrastructures. Future implementations may consider real-time models and expand data sources through digital counseling systems and behavioral analytic