Scientific Publications & International Proceedings

Inclusive schools play a vital role in providing educational services to all learners, including students at risk of emotional and behavioral disorders (EBD). However, educators frequently face difficulties in identifying early signs due to a historical reliance on routine classroom observations lacking structured instruments. This community outreach initiative introduced the E-Motion EBD instrument to inclusive elementary school teachers under the auspices of the K3SD in Sungai Geringging District, Padang Pariaman, utilizing a framework of socialization, training, and direct mentorship. To evaluate shifts in teacher comprehension, five open-ended questions were administered pre- and post-intervention, followed by a pairwise analysis of the responses. A total of 20 paired responses were directly compared. The findings indicate significant improvements, most notably in the teachers' capacity to differentiate between EBD identification and assessment, alongside a broader conceptualization of inclusive education. Conversely, these advancements were not uniform across all domains; comprehension regarding the technical criteria of EBD and its subsequent impact on student development remained varied among educators. Overall, this downstream dissemination of the E-Motion EBD instrument effectively enhanced teachers' pedagogical insights regarding the early detection of EBD within inclusive educational settings.

This study aims to develop an interactive, web-based, multi-source sentiment analysis system for public opinion to facilitate the monitoring of societal perception regarding Universitas Negeri Padang. The methodology integrates a Transformer-based natural language processing model—specifically, a fine-tuned IndoBERT—executed through a client-server architecture (Next.js and FastAPI) and enhanced with a hybrid inference logic mechanism (lexicon-based override) for batch data processing. Evaluation results utilizing a confusion matrix indicate that the IndoBERT model achieved an accuracy of 95% in classifying 3,411 rows of test data, with a peak precision of 97% for positive sentiment and a recall of 96% for negative sentiment. The infrastructure implementation proved reliable in handling text-computational loads from diverse digital platforms (X, Instagram, and news portals) without latency issues. Consequently, this innovation establishes a scalable, accurate, and adaptive Decision Support System (DSS) solution, providing an objective foundation for institutional evaluation and decision-making.

The advancement of information technology has driven the use of educational websites as interactive learning media. However, most educational websites are still designed with static interfaces that do not consider user characteristics, particularly children with special needs (CSN), potentially causing confusion and learning barriers. This study aims to design a Confusion-Aware Adaptive User Interface based on a combination of facial expressions and user navigation patterns on educational websites for CSN. The system was developed using the Prototyping method with a rule-based approach to determine interface adaptation. Facial expression detection utilizes a combination of MediaPipe and Facial Emotion Recognition (FER), while navigation patterns are analyzed based on the number of clicks, misclicks, and page duration. Functional testing results using Black Box Testing indicate that all system features operate according to design. Testing of the Confusion-Aware Engine across 8 scenarios demonstrated a 100% classification accuracy of user states (normal, anxious, confused) with consistent implementation of the adaptive UI. The system successfully identified students through school-based unique code validation, displayed dynamic material according to animal categories (land, sea, air), and provided a comprehensive summary of learning outcomes equipped with PDF and Excel export features. This study contributes to the development of inclusive learning technology through adaptive interfaces that are responsive to user conditions in real-time.

Manual inventory recording and heuristic ordering practices remain common among Micro, Small, and Medium Enterprises (MSMEs), often leading to inaccurate demand estimation, excessive holding costs, and stockouts. This study develops and evaluates a web-based inventory information system that integrates Autoregressive Integrated Moving Average (ARIMA) forecasting with the Economic Order Quantity (EOQ) model to improve decision accuracy and cost efficiency. The system uses CodeIgniter 3 and MySQL and incorporates a Python-based time-series forecasting engine. Historical sales data were modeled using ARIMA, and the optimal specification was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The ARIMA(1,1,1) model achieved a Mean Absolute Percentage Error (MAPE) of 8.47%, indicating high forecasting accuracy for operational planning. The forecasted annual demand was integrated into the EOQ framework to determine the optimal order quantity, Reorder Point (ROP), and probabilistic Safety Stock. A one-year cost simulation demonstrated that the EOQ-based policy reduced total inventory costs by 22.73% compared with the existing approach. Functional validation through Black-Box testing confirmed full compliance with specified requirements. These findings demonstrate that integrating predictive analytics with classical inventory optimization enhances operational efficiency and reduces total inventory cost. The system provides a practical, data-driven inventory management framework for MSMEs undergoing digital transformation.

Previous research has predominantly examined the effectiveness of learning applications through experimental designs, yet few have explored how these technologies shape students’ internal cognitive and psychological skills. This study makes a novel contribution by embedding a smart learning application within an integrated psychological-cognitive framework, i.e., prior knowledge, metacognition, cognitive engagement, and self-regulated learning, to explain the development of critical thinking disposition. The learning process was designed using the case method, involving 369 undergraduates in the Faculty of Engineering at Universitas Negeri Padang, Indonesia. Data were collected via an online survey and analyzed through Structural Equation Modeling (SEM) with SmartPLS 3 and Fuzzy C-Means Clustering (FCM) with JASP. Results of the direct effect analysis show that prior knowledge and metacognitive do not directly influence critical thinking, while cognitive engagement and self-regulated learning exert significant positive effects. Indirect effect analysis further indicates that prior knowledge and metacognitive significantly affect critical thinking when mediated by cognitive engagement and self-regulated learning. Clustering analysis reveals that most respondents engage with the application at a moderate level. Therefore, targeted interventions such as scaffolding strategies to guide learning processes, adaptive feedback to foster metacognitive monitoring, and gamification elements to sustain motivation are essential for enhancing engagement, reducing passive use, and maximizing the educational impact of smart learning technologies.

Pharmacotherapy is a form of treatment that utilizes pharmaceutical products as a medium for healing. The effectiveness of this therapy largely depends on patient adherence and consistency in taking medication as prescribed by physicians. Unfortunately, in practice, many cases of non-adherence occur, leading to medication errors and reduced treatment outcomes. This study proposes a solution by employing workflow automation technology combined with several API integrations, such as WhatsApp API and Google Calendar API, to provide a broader and more accessible user experience. The proposed system is a web-based platform connected to n8n, a workflow automation tool that supports integration with third-party applications like WhatsApp and Google Calendar. First-party users, such as doctors and hospital operators, can input patients’ medication schedules into the system. The system then sends reminders and confirmations to second-party users (patients) via WhatsApp, while also synchronizing schedules through Google Calendar. WhatsApp was selected due to its popularity and accessibility in Indonesia, while Google Calendar was chosen as one of the most widely used calendar applications worldwide. This research is expected to contribute to pharmacotherapy support by providing a simple yet practical technological approach to improve medication adherence.

This study aims to design and develop a Roblox-based educational game as an immersive learning medium to help prospective new students familiarize themselves with the school environment in an interactive and engaging manner. The application is developed as a three-dimensional virtual environment that enables users to explore the school area, interact with objects and non-player characters (NPCs), access school facility information, and participate in various educational minigames designed to enhance users' comprehension of the school environment. The system is developed using the Waterfall methodology, encompassing requirements analysis, design, implementation, testing, and maintenance phases to deliver an application that aligns with user requirements. System testing is conducted using Black-Box Testing to ensure that all application functions operate correctly in accordance with predefined specifications. Furthermore, user evaluation is performed using the System Usability Scale (SUS) method to measure the usability level and user-friendliness of the application. The evaluation involves 10 respondents comprising high school students and teachers as prospective users. The Black-Box testing results indicate that all application features—such as virtual environment navigation, object interaction, school information systems, NPCs, and minigames—function properly according to the test scenarios. The System Usability Scale evaluation yields an average score of 87.75, which falls into the "Excellent" category, demonstrating that the application possesses a high level of usability and is user-friendly. Based on the findings of this study, the developed Roblox-based educational game serves as an interactive, engaging, and effective medium for introducing the school environment while supporting the immersive learning process for prospective new students.

The high prevalence of feline and canine dermatological diseases in Indonesia, compounded by significant visual similarities across pathological conditions, frequently induces misdiagnoses. To address the limitations of conventional detectors regarding small lesions, this study develops a two-stage lesion-aware pipeline utilizing YOLOv12 for localization and EfficientNetV2-S for classification as an AI-integrated veterinary dermatology screening tool. The system is enhanced with temperature scaling for model confidence calibration and deployed via a Flutter/FastAPI-based mobile application. Evaluation results demonstrate a mean average precision (mAP@0.5) of 0.89 for felines and 0.64 for canines, with weighted classification F1-scores reaching 0.9976 and 0.9871, respectively. Notably, calibration (T=1.3174) successfully reduced the Expected Calibration Error (ECE) by 32.4% to 0.0209, satisfying the functional target (ECE < 0.15) with a mean system response time of 4.97 seconds. Usability evaluation yielded an "Excellent" score (85), whereas expert assessments validated the user interface quality (4.44/5.00). This calibrated pipeline proves viable as a first-line screening instrument, with recommendations for expanding disease coverage and conducting validation on independent test sets prior to clinical adoption.

The management of tourist destinations in the current digital era faces complex challenges related to operational efficiency and visitor management optimization. Often, tourist destinations experience serious obstacles in predicting accurate visitor volumes, which impacts the unreadiness of infrastructure as well as inefficiencies in the time-consuming manual ticket validation process. This study aims to design and implement a comprehensive website-based reservation system for Syaakirah The View by integrating the Neural Networks algorithm as a smart solution for forecasting future visitor numbers. In developing the system, the methodology used is the Waterfall model. This structured approach is chosen to ensure that each development stage ranging from requirements analysis, system design, implementation, to testing runs systematically and is well-documented. To ensure the reliability and validity of the prediction results, the Neural Networks model is trained using the 10-fold cross-validation technique on historical tourist visit datasets for the 2025–2026 period. This dataset has previously gone through a series of data preprocessing stages, namely data cleaning and normalization, to address noise or inconsistent data, allowing the model to learn visitor behavior patterns more accurately. The results of the study show a significant contribution to the operational activities of the tourist destination. First, the system successfully implements an online reservation feature integrated with QR-Code validation; this mechanism is proven to accelerate ticket verification time by up to 70% compared to previous conventional or manual methods. Second, the constructed Neural Networks model achieves a very satisfactory prediction accuracy level, with a Mean Absolute Percentage Error (MAPE) value of 3.41% or equivalent to an accuracy level of 96.59% on the test data. This achievement empirically proves that the model is capable of forecasting visitor numbers with a very low error rate without experiencing overfitting, thereby providing a more data-driven and precise decision-making foundation for the management of Syaakirah The View.

Digital service systems built on event-drivenarchitecture relied on infrastructure-level self-healing, leaving workflow-layer failures handled reactively and processes abandoned mid-execution even when infrastructure remained available. This study designed a failure-aware self-healingworkflowthat detected, classified, and recovered digital service failures automatically based on eventcontext, treating classification as mandatory before recovery. A prototype was built following the Prototyping Model, integrating n8n as workfloworchestratorand Apache Kafka as eventbrokerinside Docker, with recovery governed by a JavaScript finite statemachine, tested on five failure scenarios and two baselineconditions, each run for 30 iterations. The system classified all failures correctly across 150 iterations, reaching 100% recovery accuracyand a 100% success ratewhere fallbackwas available. Self-healinglowered Recovery Timeby 29.90% against a no-recovery baseline, though a static-recovery baselinereached a lower time with 0% success, showing recovery speed and correctness can move in opposite directions.

Modern waste management requires innovative digital solutions to increase community participation and material quality. This research develops a hybrid AI ecosystem for a mobile-based Smart Bank Sampah platform, integrating YOLOv8 Nano for real-time edge detection and Llama-3.1-8B-Instant for cloud-based cognitive reasoning. Unlike traditional systems, this framework converts visual data into personalized educational instructions, ensuring waste is properly prepared before transaction. Results show that the YOLOv8 model achieves a mAP50 of 95.4% with a rapid inference time of 15–25ms on mobile devices. The system successfully generates structured contextual guidance, validated by a 100% success rate in functional Black Box testing. Furthermore, usability testing via the System Usability Scale (SUS) yielded a score of 92.17, placing the application in the Best Imaginable (Grade A) category. This study demonstrates that combining Computer Vision and Large Language Models (LLMs) significantly improves administrative transparency and environmental literacy, providing a robust infrastructure for a sustainable circular economy

The management of State-Owned Assets (Barang Milik Negara/BMN) at the ICT Division of the West Sumatra Regional Police faces three fundamental challenges: reliance on error-prone manual recording, data inconsistencies between official government systems (SAKTI and SIMAN) and actual field conditions, and the absence of an objective mechanism for determining asset maintenance priorities. This study designs and develops a web-based asset management system integrating Radio Frequency Identification (RFID) Portable Handheld Reader technology with the Fuzzy-MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) method as a Decision Support System (DSS) for multi-criteria maintenance prioritization, developed using the Prototype methodology with Laravel and React frameworks. The results demonstrate that the system successfully automates real-time asset location tracking through RFID integration, improves the efficiency of asset data management, generates accurate maintenance priority scores via the Fuzzy-MARCOS algorithm with zero deviation from manual calculations, and achieves a 100% functional success rate across all system modules. The integration of RFID Handheld technology and Fuzzy-MARCOS within a single unified platform for government BMN asset management constitutes the novelty of this study, with strong potential as a digital asset governance model supporting e-government implementation in law enforcement institutions.

This study aims to develop an interactive, web-browser-based Sistem Isyarat Bahasa Indonesia (SIBI) gesture classification system to facilitate inclusive learning for the Deaf community. The methodology integrates MediaPipe landmark extraction and a Support Vector Machine (SVM) algorithm executed entirely via client-side inference using the ONNX format, complemented by an Adaptive Gesture Difficulty Leveling (AGDL) mechanism. Evaluation results demonstrate that the SVM model achieves an accuracy of 98.92% across 36 gesture classes, while yielding a System Usability Scale (SUS) score of 86.0 (Best Imaginable). Furthermore, the implementation of a client-side architecture successfully eliminates server latency and ensures user data privacy. Concurrently, the deployment of adaptive gamification elements dynamically maintains user engagement within the flow zone. The broader impact of this innovation lies in providing a scalable, secure, and widely adoptable educational technology (EdTech) solution that bridges communication gaps without requiring high-cost cloud infrastructure.

UAVs with RTK technology offer high-accuracy aerial mapping, with RTK Radio and RTK NTRIP as two correction methods affecting orthomosaic precision. While previous studies compared these methods in terrestrial surveys, their impact on UAV-based orthomosaics remains underexplored. This research evaluates and compares the accuracy of UAV-derived orthomosaics using RTK Radio and RTK NTRIP to provide practical guidance for optimal mapping applications. This research combined UAV RTK photogrammetry and GIS to produce high-precision aerial maps in two areas of Padang City with different topographies. Data were collected using DJI Mavic 3 Enterprise RTK with RTK Radio and RTK NTRIP techniques, supported by GCPs, BMs, and satellite imagery, then processed into orthomosaics for analysis. Accuracy was assessed using ICPs and statistical measures like RMSE to compare the spatial precision of both correction methods under varying terrain conditions. The results show a difference in mapping accuracy between RTK Radio and RTK NTRIP aerial photographs, with RTK Radio achieving 0.683 accuracy and 0.450 RMSE, while RTK NTRIP reached 0.563 accuracy and 0.371 RMSE. RTK Radio is more suitable for areas lacking CORS access but still requires GCP verification. RTK NTRIP performs better in urban areas with strong CORS networks. Combining both methods with adequate GCPs provides an effective solution for mapping in varied or densely vegetated terrains.

Nutritional deficiencies during early childhood, particularly within the first 1,000 days of life, trigger physical and cognitive growth impairments known as stunting. This study aims to develop "Tumbuh Cerdas," an interactive mobile-based monitoring system designed to facilitate early detection and prevention of stunting. The proposed approach integrates a Random Forest classification model, decision transparency via Explainable AI (XAI) using the SHAP method, and an adaptive rule-based gamification feedback mechanism. Evaluation results demonstrate that the Random Forest model achieves an accuracy rate of 99.18% in classifying child nutritional status based on anthropometric parameters. Functional evaluation through Black Box Testing confirms that the entire system operates according to the designated scenarios, with data synchronized in real-time. The impact of this innovation lies in the creation of a digital health decision support system that is transparent, secure, and educational for both parents and community health workers (kader posyandu) to sustainably reduce stunting prevalence.

The primary issue in technology-based learning assessment is the limited ability of teachers to utilize and develop gamification methods. Most teachers still rely on manual, paper-based evaluation techniques, resulting in suboptimal integration of technology in the assessment process. This challenge necessitates further exploration by education practitioners regarding the implementation of information technology (IT) in learning, particularly for MGMP Informatics teachers. This community engagement program, titled "Development of Internet-Based Testing (IBT) Using Gamification Learning Methods", was conducted at SMPN 1 Tanah Datar. The objectives of this program were to: (1) enhance technological literacy in the education sector; (2) strengthen interest and skills in technology literacy among MGMP Informatics teachers at the junior high school level in Tanah Datar Regency; and (3) improve teachers' ability to design internet-based assessment instruments using a gamification approach. The program was implemented using a participatory approach, comprising training sessions, interactive workshops, and hands-on practice in developing internet-based assessment tools. Participants were introduced to various digital evaluation platforms, such as Google Forms, Quizizz, and Kahoot, and were trained on how to integrate gamification elements into learning assessments. The results indicated that 85% of participants showed an improvement in their understanding and skills in utilizing internet-based assessment technology. Additionally, teachers began adopting gamification methods in assessments, leading to increased student engagement in the learning process. This initiative is expected to encourage teachers to continuously develop innovative and interactive assessment methods aligned with technological advancements in education.

In today’s educational landscape, integrating algorithmic approaches into school admission systems is crucial to ensure fairness, transparency, and efficiency. This study investigates the application of the Gale-Shapley algorithm to address the challenges of student-school matching, which often result in mismatches and inequities. This study aims to explain how the Gale-Shapley algorithm can ensure stable student placement, where no pair of students prefers each other over the post-assignment. Employing a mixed-methods approach, we combined a literature review with a simulation-based implementation using Python. A test case involving four students and four schools was used to validate the algorithm’s performance. The preferences of both students and schools were modeled, and the Gale-Shapley algorithm was applied to generate stable matchings. Authors analysis focused on evaluating the stability, fairness, and efficiency of the outcomes. The results demonstrate that the algorithm consistently produces optimal and conflict-free placements aligned with participant preferences. These findings highlight the algorithm’s potential to enhance the equity and effectiveness of school admission processes, particularly when applied to real-world educational settings. The implications of the discussion show that it supports trust in the admission system, because the stability and transparency of the process increase legitimacy and acceptance by all parties, including students, schools, and educational authorities.

A scholarly article by authors Ciptro Handrianto, Elise Muryanti, and Randi Proska Sandra, Ary Kiswanto Kenedi, Shahid Rasool published in The International Journal of Interdisciplinary Educational Studies

Abstract Virtual reality (VR) has emerged as a transformative technology in education, enabling immersive simulations that foster student engagement and improve knowledge retention. This study presents a bibliometric analysis of global trends, research patterns, and challenges related to the integration of VR in educational contexts. Data were collected from the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Emerging Sources Citation Index (ESCI) within the Web of Science Core Collection, including only articles also indexed in Scopus to ensure high-quality and comprehensive coverage. Of the 1220 articles published between 2010 and 2024, 97 met the inclusion criteria based on relevance, accessibility, and methodological rigor. We performed keyword analysis, citation analysis, and thematic mapping using RStudio and the Bibliometrix package. The findings indicate that the United States, China, and the United Kingdom are the most active contributors to VR research in education. VR has been applied across diverse learning modalities, enriching both formal and informal educational environments. However, its widespread adoption faces several barriers, including high implementation costs, health and safety concerns, limited curricular integration, and insufficient educator training. Proposed solutions include strategic partnerships with technology providers and the use of cloud-based infrastructures to reduce costs and complexity. This study offers actionable insights for educators, policymakers, and developers, providing a robust, data-driven foundation for the effective and equitable integration of VR in education.

This study explores the application of the Naïve Bayes algorithm within the framework of Project-Based Learning (PjBL) to predict student graduation timing and likelihood. The evaluation of student competencies across several performance dimensions, such as problem analysis, project planning, data preparation, feature extraction, and algorithm implementation, demonstrates the effectiveness of the approach. Predictive analysis and result interpretation were successful, indicating a strong correlation between project-based learning outcomes and graduation success. Additionally, the research uncovers insights into the role of creativity and innovation in predicting student graduation. This study highlights the potential benefits of integrating PjBL into educational curricula and underscores the utility of the Naïve Bayes method in forecasting graduation outcomes in higher education.

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group ( N = 30) and the control group ( N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills.

Creativity is critical in education, especially for teachers in crafting instructional media that captivates and engages students' interest in comprehending the subject matter. Each student possesses distinct interests and enthusiasms towards the instructional press, allowing educators to innovate in devising captivating learning resources. One form of instructional media that offers a unique experience is augmented reality (AR) based media, which delivers individualized audio-visual interactions and experiences. In this research, we harness the Assemblr Edu platform, an AR and VR-based instructional media tool, within a community engagement workshop held at SMPN 3 Batusangkar, involving participation from educators of the Natural Sciences Teachers' Subject Study Group (MGMP IPA) in Tanah Datar Regency. The research aims to enhance the creative aptitude of educators in designing instructional media using the augmented reality technology of Assemblr Edu. Employing a quantitative approach, we gauge the extent of success among educators in crafting interactive instructional media using the Assemblr Edu platform, yielding augmented reality instructional media accessible through Android smartphones. The research findings underscore that Assemblr Edu, integrated with augmented reality technology, remarkably stimulates students' interest, amplifies their learning motivation, and assists them in comprehending subject matter while efficiently tackling assignments

Augmented Reality (AR) is a technology that combines objects from the real world and virtual or virtual objects with real-time or direct conditions. The merging of real and virtual objects occurs because of the right technological support while the interactions that are carried out can occur using certain devices. AR is a variation of Virtual Environments (VE), better known as Virtual Reality (VR). This VR technology allows users to fully immerse themselves in a virtual environment. While Augmented Reality technology is developing very fast, many applications in Indonesia use AR technology. This study uses the waterfall system development method, where the research flow will follow the phases of the waterfall. This application is built using Unity and Vuforia. The purpose of this research is an interactive application to make it easier for students and museum visitors to understand augmented reality through mobile devices. So that museum visitors and students can see objects containing information interactively

Abstract Students’ active learning activities are one indicator of success in a learning process. In fact, students tend to listen to teacher explanations which hamper students learning activities. This point make students not free to develop their potential, so the most students are not interested in mathematics learning. Therefore, there needs to be an effort in this problem, namely by using the steps in the learning model. This study aims to determine the application of MASTER based on mind mapping in increasing the learning activities of students at SMA Pembangunan Laboratorium UNP. This research used non-test techniques in the form of observation sheets and field notes. Based on the results of the study, it was concluded that the learning activities of implementation of the MASTER based on mind mapping experienced an increase in the A4 indicator and experienced fluctuations in indicators A1, A2, A3, A5, and A6. Even so, the overall average of this research succeeded in reaching 87.5% in the very good category. It means that the implementation of a mind mapping-based learning model shows an active learning activity.

Abstract indexed from scholarly registry.

The Education Series aligns with Sustainable Development Goal (SDG) 4 by ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all. This edited volume is divided into four sub-themes: primary and secondary education, literacy and numeracy education, vocational education and training, and early childhood education (ECE) and inclusive education. These four sub-themes represent critical areas of education that require careful consideration in the formulation of future policy directions to achieve the goals of Golden Indonesia 2045. This book is expected to serve as a valuable resource for the Indonesian public, particularly educational stakeholders and educators across all academic levels. Explore diverse perspectives on efforts to improve the quality of education in Indonesia, contributed by authors who are Indonesian education actors studying abroad.

Rent-house’s Searching and Promotion in Padang city using brochure or leaflet which sticked in public place. It could be ‘wild-advertising’ in the public spaces. The other problems of Rent-house in Padang city is lack on resident’s security and irrelevant information between promotion material and reality. Clickost web-based application is the means to force the Rent-house business in Padang city move to digital technology to expand the market and ease the potential resident. Clickost web-based application using HMVC (Hierarchical Model View Controller) architecture with OOP (object oriented programming) approach and UML’s model. Clickost using several language programming including PHP, CSS, HTML5, and Javascript, CodeIgniter as framework and MySQL as database. This Application will spend the time, cost and energy efficiently in handling Rent-house’s searching and promotion. Keywords : Clickost, Website, Rent-house, HMVC, Searching and promotion