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The rapid advancement of Internet of Things (IoT) technologies has transformed conventional residential environments into intelligent smart housing systems capable of enhancing energy efficiency, security, comfort, healthcare services, and environmental sustainability. However, existing studies remain fragmented, focusing on specific technologies or applications rather than providing a comprehensive understanding of the smart housing ecosystem. Therefore, this study aims to systematically review the major technologies employed in IoT-based smart housing systems, examine their contributions to residential benefits, and identify the challenges and future opportunities associated with their implementation. This research adopted a Systematic Literature Review (SLR) approach using the Scopus database as the primary source of literature. The review process followed a structured screening and selection procedure, resulting in the inclusion of 26 studies that met the predefined eligibility criteria. The findings indicate that Artificial Intelligence, Blockchain, Edge Computing, Fog Computing, Cloud Computing, Digital Twin, Smart Grid technologies, renewable energy systems, and advanced sensor infrastructures constitute the primary technological foundations of modern smart housing systems. The reviewed studies indicate that these technologies contribute to energy optimization, cybersecurity enhancement, intelligent automation, healthcare monitoring, safety improvement, and sustainable resource management. However, challenges related to security, privacy, interoperability, scalability, computational complexity, and implementation costs remain important barriers to widespread adoption. In conclusion, IoT-based smart housing systems have evolved into integrated and intelligent residential ecosystems, and future development is expected to be driven by the convergence of Artificial Intelligence, distributed computing, Digital Twin technologies, and sustainable smart infrastructure solutions.  

Problems that often occur in brooch pins are bent wire or detached from the hook, also the possibility of the brooch coming off the pin when worn as a problem encountered. The purpose of fabrication is to obtain a 60 mm long brooch pin, 0.5 mm diameter, AISI 1045 material. Fabrication methods include: brooch pin design; selection of AISI 1045 material for 0.5 mm diameter wire and for 0.3 mm plate; cutting wire 94 mm long and sharpening one end; cutting the brooch pin end hook plate; forming the wire into a circle with a 4 mm hole for one winding; cutting a 0.3 mm thick plate in the shape according to the design; forming a pointed end hook with a punch and die; pressing the base of the wire to one side of the shape plate; electroplating with Chromium solution material; ordering and installing the customer's taste brooch; unification between the brooch and the pin stem; and quality-dimensional inspection. The fabrication results are in the form of a 60 mm long brooch pin, 0.5 mm diameter, AISI 1045 material coated with Chromium with a total cost of IDR 290,-/piece and a production duration of 90 seconds/piece which means that the brooch pin can be used for various desired accessories.

Limited land availability is a major constraint in developing school infrastructure in active urban educational facilities. This study evaluates the classroom development plan at SDN 39 Lubuklinggau from the perspective of a planner, focusing on land limitation, functional needs, safety, constructability, and long-term building performance. A descriptive-evaluative approach was applied through technical observation, document review, alternative assessment, weighted scoring, and risk analysis. The results indicate that horizontal expansion is constrained by limited open space and may reduce internal circulation, student activity areas, and emergency space. Three alternatives were compared: horizontal expansion, building rearrangement, and a two-storey classroom scheme. The two-storey alternative obtained the highest score of 84, compared with 61 for rearrangement and 56 for horizontal expansion. However, its feasibility depends on further soil investigation, structural design, staircase and evacuation safety, utility coordination, construction phasing, and strict site safety control. The study recommends that the planner prioritize detailed site measurement, geotechnical investigation, detailed engineering design, construction safety planning, and post-construction functional evaluation. A vertical classroom strategy can be considered a rational solution for land-constrained schools when supported by accountable technical planning and integrated project control.

As a maritime nation with vast sea territories, Indonesia faces significant challenges in safeguarding its waters, particularly in strategic regions such as the Natuna Sea. This area is vulnerable to various threats, including territorial violations, marine resource theft, and illegal underwater activities. This study aims to formulate a concept for the utilization of Autonomous Underwater Vehicles (AUVs) as an alternative solution to enhance underwater security systems in the region. The method employed is Research and Development (R&D), adopting model development steps and complemented by a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to assess the feasibility and effectiveness of AUVs in the context of Indonesian maritime defense. The research findings indicate that the main strength of AUVs lies in their technological capability to effectively detect underwater threats. Although they have weaknesses, such as high operational costs, dependency on imported components, and limitations in battery endurance and sonar capabilities, these factors do not significantly hinder AUV effectiveness. Major opportunities arise from global technological advancements, yet challenges remain due to insufficient government policy support. The threats faced include the risk of sabotage and extreme weather conditions. Therefore, the recommended strategies are to maximize the use of technology, strengthen supporting regulations, and address logistical and financial barriers to realize effective and sustainable AUV operations in the Natuna Sea.

Flooding remains a recurrent hazard in the Special Capital Region of Jakarta (DKI Jakarta), causing substantial disruptions across multiple dimensions community life, including social well-being and economic activities. The availability of flood-related information through the Jakarta One Data Portal (Satu Data Jakarta) provides significant opportunities for broader data utilization; however, transforming such information into meaningful assessments regional vulnerability requires a systematic analytical approach to generate more comprehensive understanding of the conditions of individual urban villages (kelurahan). This study focuses on the classification areas based on the characteristics and impacts of flood events occurring throughout DKI Jakarta. The analysis utilizes flood event records from 2023 to 2025 and incorporates several indicators, including the number of flood occurrences, the number of affected neighborhood associations (RW), the number affected households, the number of affected residents, the number of evacuees, the number of evacuation sites, and floodwater depth. Data processing was conducted using the Knowledge Discovery in Databases (KDD) framework, encompassing data selection, cleaning and preprocessing, transformation, pattern exploration, result evaluation, and knowledge extraction. The findings demonstrate that three-cluster solution effectively captures variations in flood vulnerability levels, corresponding to low-, moderate-, and high-risk categories. A total of 96 urban villages were classified as low-risk, 27 as moderate-risk, and 46 as high-risk areas. The resulting clustering patterns provide a clearer spatial representation of flood-risk distribution across urban villages, thereby offering valuable insights for the development more targeted mitigation strategies, the prioritization of flood management interventions, and the enhancement of evidence-based decision-making processes in DKI Jakarta.

The Internet of Things (IoT) has attracted significant attention from the research community due to its wide range of applications. However, the limited energy, processing capability, storage, and communication capacity of IoT devices require routing solutions that are both lightweight and efficient. To address these constraints, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was introduced in 2012 as a routing protocol specifically designed for resource-constrained IoT environments. Although RPL performs reliably in static deployments, its performance degrades considerably in mobile environments because of frequent topology changes, slow Trickle timer convergence, and excessive parent churn. This paper proposes Anchor-Node RPL (AN-RPL), an infrastructure-assisted enhancement of RPL that strategically deploys distributed fixed anchor nodes as stable DODAG roots while requiring only minimal firmware modification on mobile sensor nodes, namely a single anchor-flag check during parent selection. Simulation experiments conducted in Cooja using both OF0 and MRHOF objective functions across four scenarios (static, mobile with one, two, and four anchor nodes) demonstrate that AN-RPL with four anchor nodes improves the Data Delivery Ratio (DDR) by up to 30.6 percentage points, reduces the average hop count by up to 51.2%, lowers parent churn by up to 89.5%, and decreases average energy consumption by up to 14.8% compared with conventional single-root mobile RPL. These results demonstrate that infrastructure-assisted anchor deployment provides an effective and practical approach for improving routing reliability and efficiency in mobile RPL-based IoT networks.

The primary challenge faced by the community in Dewasari Village, Ciamis Regency, is the ineffective management of household organic waste, along with limited knowledge and skills in converting organic waste into value-added products. This community service program aimed to strengthen community capacity in developing smart environmental management through community-based Black Soldier Fly (BSF) maggot cultivation. The program was implemented through educational sessions, practical training on BSF maggot cultivation, community mentoring, and learning evaluation using a participatory approach involving the village government, community groups, and local youth. The results showed significant improvements in participants' knowledge and practical skills in managing organic waste, cultivating BSF maggots independently, and utilizing the harvested maggots as alternative livestock feed and an additional source of household income. The program also increased community awareness of the importance of sustainable and environmentally friendly waste management. Therefore, community-based BSF maggot cultivation can serve as an innovative solution for promoting smart, productive, and sustainable environmental management while strengthening community economic empowerment and improving household livelihoods in Dewasari Village.

One of the most significant foods for the people of Indonesia, tempeh is made mostly from soybeans. Soybean quality selection is still mostly done by hand, relying on eye inspection and the expertise of business actors. This may cause subjective evaluations, lengthy decision-making processes, and uneven outcomes. With the TOPSIS technique as its foundation, this project intends to construct a Decision Support System (DSS) for selecting high-quality soybeans. Color, texture, price, scent, and flavor are some of the evaluation criteria. Observations and interviews were conducted in Rumah Tempe A-Zaki Padang to gather data for the research. Ranking the options according to how close they were to the positive ideal solution and how far away from the negative ideal solution was done using the TOPSIS approach. In terms of preference values, the Green Soybean option came out on top with 0.9692, followed by Black Soybeans with 0.2335, Yellow Soybeans with 0.1516, and Brown Soybeans with 0.1181. Based on these outcomes, it seems that TOPSIS can indeed provide objective suggestions for soybean selection according to a number of different standards. Business players in the tempeh industry may benefit from this decision support system by using it to choose high-quality soybean raw materials more efficiently and accurately.

Lead (Pb²⁺) contamination in wastewater is a major environmental concern due to its toxicity, persistence, and ability to accumulate in living organisms. Adsorption using biomass-based materials has been developed as an alternative treatment method because it is efficient, economical, and environmentally friendly. This study aimed to analyze the effect of bioadsorbent dosage and contact time on Pb²⁺ removal efficiency and evaluate Pb²⁺ mass distribution using mass balance analysis in a batch adsorption system. Ramie leaf (Boehmeria nivea) was utilized as a bioadsorbent due to its lignocellulosic content, which provides active sites for metal ion adsorption. The research was conducted experimentally through bioadsorbent preparation, chemical activation, and batch adsorption using artificial Pb²⁺ solutions with variations in bioadsorbent dosage and contact time. Pb²⁺ concentrations before and after adsorption were analyzed using Atomic Absorption Spectroscopy (AAS). The obtained data were used to determine removal efficiency and Pb²⁺ distribution between the liquid phase and bioadsorbent. The results showed that ramie leaf bioadsorbent effectively reduced Pb²⁺ concentration, with the optimum condition obtained at 4 g/L bioadsorbent dosage and 90 minutes contact time, achieving 98.46% removal efficiency. Mass balance analysis indicated that Pb²⁺ ions were successfully transferred from the solution phase onto the bioadsorbent surface. These findings demonstrate that ramie leaf bioadsorbent has potential as a sustainable alternative material for heavy metal removal in wastewater treatment.

The urgency of this research stems from the knowledge gap regarding contextual factors contributing to the underuse of learning media in rural elementary schools, which has not been adequately addressed in previous research, which has tended to focus on urban contexts. This study aims to examine teachers' perceptions at Sugayang Public Elementary School regarding the underuse of learning media in the learning process, as well as the factors that contribute to it. The method used was a descriptive qualitative method, utilizing in-depth interviews as a data collection technique. The data were then analyzed using data reduction, data display, and conclusion drawing techniques according to Miles and Huberman. The results showed that teachers experienced difficulties in using technology, difficulties in creating learning media, and the perception that using media was time-consuming. This reveals an important pattern that the underuse of learning media is a systematic problem rooted in the TPACK gap that is not supported by practical time management solutions. These findings imply that changes in learning practices will not occur simply through the provision of generic technology tools or training; rather, bottom-up and contextual solutions are needed. The novelty of this research lies in the in-depth qualitative approach applied to the under-researched context of rural elementary schools, which has not been studied before. This research contributes through recommendations for the implementation of a peer-mentoring program and the development of a ready-to-use digital learning media bank, thus offering valuable insights for future research with relevant stakeholders.

This study aims to develop a more systematic system for evaluating the performance of Community Health Centers (Puskesmas) based on the Health Center Management cluster. Performance evaluation of Puskesmas plays an important role in improving the quality of public healthcare services, particularly in Sragen Regency. However, the current evaluation process has not been integrated into a computerized system, making it less effective in supporting decision-making. Therefore, this study develops a decision support system by integrating the Analytic Hierarchy Process (AHP) and the *Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is used to determine the weight of each criterion based on its level of importance, while the TOPSIS method is applied to generate an objective ranking of Puskesmas performance. The results indicate that the system is capable of utilizing nine evaluation criteria to produce a ranking of Puskesmas within the Health Center Management cluster, from the highest to the lowest score. The developed system is expected to support a more effective evaluation process and improve decision-making. Future research may enhance the system by incorporating additional evaluation clusters, expanding the number of assessment criteria, and optimizing its features and security.

The quality of boiler feedwater is a crucial factor in maintaining the operational reliability of ship machinery systems against the risks of corrosion and scale formation. This research focuses on the design and development of a real-time monitoring system based on the Internet of Things (IoT) to automatically determine water quality. The hardware development utilizes a NodeMCU ESP32 microcontroller that integrates four main sensors: a pH sensor, a DS18B20 temperature sensor, a Total Dissolved Solids (TDS) sensor, and a turbidity sensor. The methodology applied is an experimental systems engineering model, encompassing circuit design, software programming, and the implementation of simple artificial intelligence. The automatic decision-making process in this tool is designed using zero-order Sugeno fuzzy logic with input parameters of turbidity, salinity, and acidity level (pH). The obtained data is transmitted wirelessly to a Firebase database and visualized through a 0.96-inch OLED display. Functional tests on seven fluid characteristics including pure water, hard water, and muddy water show that this prototype can accurately classify water quality into Good, Fair, and Poor categories. The main contribution of this design is the provision of a preventive, efficient, and applicable boiler maintenance solution for the modern shipping industry.

The rapid development of information technology drives various business sectors, including the beauty and health industry, to adopt digital systems in their operational management. A Beauty Clinic, a medium-scale aesthetic clinic, faces significant operational challenges in managing human resources, finance, and digital marketing in an integrated manner. This study aims to analyze the existing manual operational system of A Beauty Clinic using the PIECES framework (Performance, Information, Economy, Control, Efficiency, Service), design and configure an Odoo ERP Community Edition implementation integrating the HR, Invoicing/Point of Sale, and Social Marketing modules, and evaluate the operational efficiency improvements achieved. The research employed a qualitative descriptive approach with a case study method and Rapid Application Development (RAD) as the system development methodology. Data was collected through direct observation and semi-structured interviews. The results show that the manual system experienced critical problems across all six PIECES dimensions. Post-implementation evaluation indicates an estimated 80% reduction in the secretary's administrative workload (from 20–25 hours to 3–5 hours per month), over 90% reduction in payroll error frequency, and an 87% reduction in transaction processing time per cashier transaction. The implementation of role-based access control and complete audit trails fundamentally addressed the security vulnerabilities identified in the as-is analysis. This study demonstrates that Odoo Community Edition is an effective, comprehensive, and cost-efficient ERP solution for small and medium-scale beauty clinics seeking to transform from fragmented manual systems to integrated, data-driven information systems.

The purpose of this study was to analyze the implementation of Al-Qur’an and Hadith teaching at MAN 2 Karawang, including the challenges faced and approaches to resolving them. The method used was qualitative with a descriptive approach. Information was collected through interviews and documentation with the principal, Al-Qur’an and Hadith teachers, and eleventh-grade students. The research findings indicate that teaching is conducted in accordance with the madrasah curriculum, using a thematic and contextual approach that focuses on moral development and the application of Islamic values in daily life. Teachers use various methods, such as lectures, discussions, questions and answers, and hadith memorization. However, the teaching and learning process still faces several obstacles, such as a lack of infrastructure, including projectors, limited availability of textbooks, and unsupportive classroom conditions, which affect students’ concentration. Several solutions implemented include the gradual provision of facilities, the use of digital technology through students’ mobile phones and e-books, and the implementation of more interactive teaching methods to increase student motivation.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

This study investigates the development of Indonesian language literacy among third-grade elementary school students. The primary problems identified were acute classroom passivity and low reading comprehension, evidenced by an initial learning mastery of only 45.16%, which were largely driven by conventional teacher-centered pedagogy. The objective of this research is to enhance early-grade reading literacy and active participation through an innovative instructional intervention. The proposed method employed a two-cycle Classroom Action Research (CAR) design at SDN Jrebeng Kulon 1, integrating the Problem-Based Learning (PBL) model assisted by serial picture media. This approach utilizes chronological visual scaffolding to facilitate narrative comprehension for students in the concrete operational stage. The results demonstrated a highly significant academic progression: classical learning mastery increased to 70.97% (mean score: 80.80) in Cycle I and culminated in an absolute 100% mastery rate (mean score: 94.51) by the end of Cycle II. The synthesis of these findings reveals that transitioning from static visual aids to serial visual stimuli within a problem-oriented framework effectively mitigates cognitive dissonance and eradicates classroom passivity. In conclusion, the integration of the PBL model with serial picture media serves as a comprehensive pedagogical solution that not only maximizes cognitive reading comprehension but also reconstructs students' verbal articulation and social-collaborative skills, offering a highly scalable strategy for early primary education.

Online maps applications have become an essential tool for modern society in finding the fastest and most efficient routes. However, these applications often fail to detect current road conditions such as flooding, demonstrations, accidents, or public events, causing users to get stuck in problematic routes. This study aims to develop a prototype of a community-based road condition reporting system, visualized through a web-based digital map. The system allows users to directly report road conditions by providing photo evidence, descriptions, and event categories. It is also equipped with features for designing event routes such as carnivals and suggesting alternative paths based on community reports. The development process was carried out using a simulation-based approach with scenario testing that reflects real field conditions, without involving direct user data. The implementation results show that all core features work properly. The technologies used include Leaflet.js, OpenStreetMap, and the Nominatim geolocation API. This research produces an adaptive community-based GIS model that can be further developed as an intelligent navigation solution at the city scale

This study was conducted to analyze the relationship between students’ critical thinking skills and mathematical problem-solving abilities through a literature review approach. The study is based on the importance of mastering higher-order thinking skills in the mathematics learning process, particularly when students are faced with complex problems related to real-life situations. The method used in this research was a literature review by examining various relevant scientific journals and academic books published between 2021 and 2026. The data analysis process was carried out through stages of identification, classification, evaluation, and synthesis of the collected sources. The findings revealed a significant and positive relationship between critical thinking skills and students’ mathematical problem-solving abilities. Critical thinking skills play an important role in helping students understand problems, process and analyze information, select appropriate solution strategies, and systematically review the results obtained. Students with strong critical thinking skills generally demonstrate more optimal mathematical problem-solving abilities. In addition, the implementation of learning models such as Problem-Based Learning and contextual approaches has been considered effective in improving both abilities. Therefore, critical thinking skills are regarded as an essential aspect that needs to be developed in mathematics learning in order to enhance students’ mathematical problem-solving abilities.

Indonesia’s Free Nutritious Meal Program (MBG) requires an efficient and adaptive supply chain system to ensure timely distribution, cost efficiency, and adequate nutritional delivery for a large number of beneficiaries. However, conventional supply chain approaches are generally static and unable to respond effectively to dynamic demand, supply uncertainty, and logistical constraints. This study proposes a Multi-Objective Reinforcement Learning (MORL) model to optimize the MBG supply chain by simultaneously considering distribution cost, delivery timeliness, service level, nutritional adequacy, and food waste reduction. The model is developed using a simulation-based environment representing real-world supply chain conditions, including demand variability, transportation limitations, and kitchen capacity constraints. The results show that the proposed approach achieves cost reductions of 15–22%, improves delivery timeliness by 18–25%, maintains a service level above 90%, increases nutritional fulfillment by 12–18%, and reduces food waste by 10–15% compared to baseline methods. Sensitivity analysis further demonstrates the robustness of the model, with minimal performance degradation under disruption scenarios. These findings indicate that Reinforcement Learning provides a scalable and adaptive solution for optimizing large-scale public food distribution systems. The proposed model contributes both theoretically by integrating multi-objective optimization within an RL framework and practically by supporting data-driven decision-making for improving the effectiveness of the MBG program in Indonesia.

Motor Vehicle Tax (PKB) is a key pillar of Regional Original Revenue (PAD) that supports development funding. However, seasonal fluctuations in payment realization create uncertainties in local budget planning. This study aims to address the limitations of the standard Random Forestalgorithm, which suffers from extreme prediction failures on time-series data due to its inability to capture temporal transitions between months. The proposed solution implements feature engineering using a Cyclical Encoding approach (Sine and Cosine transformations) and Lagged Variables. The dataset comprises historical records of motor vehicle tax potential and realization from January 2021 to November 2025. The baseline model evaluation without feature engineering yields highly inaccurate predictions with a Mean Absolute Percentage Error (MAPE) of 203.47% (accuracy of -103.47%). Conversely, after integrating Cyclical Encoding and Lagged Variables, the proposed model's performance improves drastically, achieving a MAPE of 14.40% (an accuracy rate of 85.60%), an MAE of 9,317 units, and an RMSE of 12,638 units. Feature Importance analysis confirms that the cyclically encoded month feature contributes the highest weight to the model's decisions with a score of 0.5031, followed by the potential feature at 0.1798. This study demonstrates that time-based feature engineering effectively optimizes Random Forestfor precise tax revenue forecasting.