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TPS 3R is a community-based waste management approach aimed at reducing waste generation through reduce, reuse, and recycle activities. However, the operational sustainability of TPS 3R still faces several challenges,including  waste processing effectiveness, and community participation. This study aims to analyze the community’s Willingness to Pay (WTP) for waste management fees and to formulate priority recommendations for improving the performance of TPS 3R Banjar Sugihan using the Quality Function Deployment (QFD) method. The research was conducted in Banjar Sugihan Village, Tandes District, Surabaya City, involving 563 household respondents. WTP analysis was carried out using the bidding game method, while QFD analysis was conducted through the preparation of the House of Quality (HoQ). The results showed that the community’s WTP ranged from IDR 16,000 to IDR 19,000 per month. If applied to all 3,758 households in Banjar Sugihan Village, the potential retribution revenue is estimated to reach IDR 60,128,000–IDR 71,402,000 per month. The QFD analysis indicated that the priority strategies for improving TPS 3R performance include enhancing infrastructure facilities, optimizing waste sorting and processing, increasing waste processing capacity, strengthening human resource capacity, and implementing a WTP-based retribution system. Therefore, the implementation of a WTP-based retribution system and priority strategies derived from QFD analysis are expected to support the sustainability of waste management at TPS 3R Banjar Sugihan.

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.

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

Diarrhea has the potential to cause an Extraordinary Event (KLB) and is currently a leading cause of mortality in Indonesia, especially in children. Antibiotics are necessary for diarrhea caused by bacteria, but irrational antibiotic prescribing can increase the risk of antibiotic resistance. This literature review was conducted to provide an overview of the level of rationality of antibiotic use in pediatric patients with diarrhea in several Indonesian health facilities. The literature review was conducted by reviewing articles obtained through a Google Scholar search using the keywords "rationality of antibiotic use," "diarrhea," "children," and "evaluation of antibiotic use." Articles were selected using inclusion and exclusion criteria, resulting in 10 suitable articles. The study results indicate that the evaluation methods for antibiotic use include the Gyssens criteria, World Health Organization criteria, Ministry of Health Decree Standards, the 2012 World Gastroenterology Organization Guidelines, and the Rational Drug Use Module. Commonly used evaluation parameters include appropriate indication, appropriate drug, appropriate dose, and appropriate patient. Most parameters have obtained rational results, but there are several studies that are not rational in the appropriate indication, appropriate drug, and appropriate dose categories. Variations in the results of rational antibiotic use at each health facility are influenced by patient characteristics, policies at the health care facility, and the level of compliance of health workers with therapy guidelines.

Diarrhea has the potential to cause an Extraordinary Event (KLB) and is currently a leading cause of mortality in Indonesia, especially in children. Antibiotics are necessary for diarrhea caused by bacteria, but irrational antibiotic prescribing can increase the risk of antibiotic resistance. This literature review was conducted to provide an overview of the level of rationality of antibiotic use in pediatric patients with diarrhea in several Indonesian health facilities. The literature review was conducted by reviewing articles obtained through a Google Scholar search using the keywords "rationality of antibiotic use," "diarrhea," "children," and "evaluation of antibiotic use." Articles were selected using inclusion and exclusion criteria, resulting in 10 suitable articles. The study results indicate that the evaluation methods for antibiotic use include the Gyssens criteria, World Health Organization criteria, Ministry of Health Decree Standards, the 2012 World Gastroenterology Organization Guidelines, and the Rational Drug Use Module. Commonly used evaluation parameters include appropriate indication, appropriate drug, appropriate dose, and appropriate patient. Most parameters have obtained rational results, but there are several studies that are not rational in the appropriate indication, appropriate drug, and appropriate dose categories. Variations in the results of rational antibiotic use at each health facility are influenced by patient characteristics, policies at the health care facility, and the level of compliance of health workers with therapy guidelines.

Background: Acne vulgaris is one of the most common skin problems and is closely associated with the activity of Propionibacterium acnes. Long-term use of antibiotics may lead to bacterial resistance; therefore, alternative antibacterial agents derived from natural products are needed. White rice (Oryza sativa L.) is known to contain bioactive compounds such as flavonoids, alkaloids, terpenoids, tannins, and saponins, which potentially exhibit antibacterial activity. Objective: This study aimed to evaluate the antibacterial activity of ethanol extract of white rice against the growth of Propionibacterium acnes. Methods: This research was conducted as a laboratory experimental study using a post-test only control group design. The ethanol extract of white rice was prepared by maceration using 70% ethanol. Antibacterial activity was evaluated using the disc diffusion method with extract concentrations of 100%, 50%, 25%, 12.5%, and 6.25%. Clindamycin was used as a positive control, while sterile distilled water served as a negative control. The parameter observed was the diameter of the inhibition zone. Result: The results of phytochemical screening showed that the ethanol extract of white rice (Oryza sativa L.) contains flavonoids, alkaloids, and terpenoids. Antibacterial activity test showed that the ethanol extract of white rice at concentrations of 100%, 50%, and 25% was able to inhibit the growth of Propionibacterium acnes by forming an inhibition zone, while at concentrations of 12.5% and 6.25% no inhibition zone was formed. The highest inhibition zone diameter was found at a concentration of 100%. The ethanol extract of white rice at concentrations of 100% (7.65 mm), 50% (6.77 mm), and 25% (6.15 mm) was able to inhibit the growth of Propionibacterium acnes, and was classified as having moderate inhibition. In contrast, at concentrations of 12.5% and 6.25%, the ethanol extract of white rice did not show any inhibitory activity detected in vitro using the disc diffusion method. The results of statistical analysis showed a p value <0.001, which means there was a significant difference in the diameter of the inhibition zone between the treatment groups. Conclusion: In conclusion, the ethanol extract of white rice exhibits antibacterial activity against Propionibacterium acnes in a concentration-dependent manner; however, its inhibitory effect remains lower than that of clindamycin.

Poor soil quality and inefficient nutrient utilization remain major constraints in tomato (Solanum lycopersicum L.) cultivation, often resulting in reduced plant growth, lower productivity, and suboptimal fruit quality. This study aims to evaluate the growth performance of rose tomatoes through the integration of open-field cultivation and hydroponic planting systems. The research methods included land preparation using the Pythagorean technique to ensure proper spacing and planting layout, the application of organic fertilizers to improve soil fertility, and the transition to a hydroponic drip irrigation system using rockwool as the growing medium during weeks 1 to 7 of plant development. Plant growth parameters, including plant height, leaf development, stem vigor, and overall plant health, were observed throughout the cultivation period. The results indicated that the combination of appropriate soil management practices and precise nutrient delivery in the hydroponic system significantly enhanced nutrient uptake and supported healthy plant growth. Furthermore, the drip irrigation system helped optimize water use efficiency while reducing nutrient losses. This integrated cultivation approach demonstrated its potential as an effective solution for maintaining crop quality and productivity, particularly in areas with limited land resources. Overall, the findings suggest that integrated management practices can provide a practical, efficient, and sustainable strategy for farmers seeking to improve horticultural production in modern agricultural systems.

The consistent decline in cucumber production in Indonesia indicates limitations in conventional cultivation systems, particularly due to land scarcity and inefficient resource management. This condition has encouraged the development of hydroponic systems as a more controlled and productive cultivation alternative. This study aims to critically analyze the key factors determining the success of hydroponic cucumber cultivation and to identify the most influential management aspects in improving yield. The method employed is a literature review, examining various recent studies related to hydroponic systems, nutrient management, growing media, and environmental factors. The results show that the advantages of hydroponics lie not only in land and water efficiency but also in the ability to precisely control growth variables. However, optimal productivity highly depends on the proper integration of nutrient management, particularly the regulation of pH, electrical conductivity (EC), and nutrient balance, as well as the control of environmental factors such as temperature, humidity, and light intensity. Inaccuracy in a single component can significantly reduce plant performance, even when other factors are optimal. Therefore, an integrated approach combining nutrient and environmental management simultaneously is essential to enhance hydroponic cucumber productivity. This study confirms that hydroponics has strong potential as a strategic solution to support sustainable agriculture amid land limitations in Indonesia.

The increase in activity and security threats on Ubuntu server causes the volume of system logs to become very large and difficult to analyze manually. This condition potentially leads administrators to experience delays in detecting abnormal activities, such as repeated login attempts and web access patterns related to online gambling promotions. Therefore, this research aims to develop a machine learning-based Early Warning System capable of automatically detecting anomalous activities. The system is developed using the Python programming language and runs on an Ubuntu server by utilizing authentication logs and web access logs as the main data sources. The anomaly detection model is trained using normal activity data collected directly from the Ubuntu server logs to learn standard system behavior patterns. During the operational phase, the system reads server logs in real-time, extracts activity features, and analyzes them using the Isolation Forest algorithm. Activities detected as anomalies trigger alert notifications via Telegram to the administrator without performing automatic blocking. The results show that the system is able to provide early warnings for suspicious activities, thereby helping to improve server security more effectively.

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)

Gorontalo Province possesses significant mineral resource potential, particularly gold, silver, and copper, positioning the mining sector as a key driver of regional economic growth. However, a shortage of skilled local labor and the scarcity of vocational educational institutions in the mining field severely hamper human resource development in this sector. This study aims to design a Mining Polytechnic Campus in Gorontalo by applying sustainable architecture principles, encompassing energy efficiency, environmentally friendly materials, sound wastewater management, and user comfort. The research approach involves literature studies, field observations, interviews with relevant stakeholders, and quantitative data analysis regarding resource potential, the number of senior high school students, and educational space requirements. The design results emphasize site arrangement, building mass configuration, utility systems, and interior and exterior spaces that support academic, social, and community activities. The application of sustainable architecture principles is expected to create a campus that not only meets the needs of mining vocational education but also contributes to environmental conservation and sustainable regional development.

The presence of Generation Z, who grew up entirely in the digital era, has triggered a fundamental transformation in Muslim religious practices, where social media has replaced conventional religious institutions as the primary source of religious information. This condition creates a paradox: Gen Z has become a generation that is highly religious online, yet is often disconnected from physical communities and traditional religious authorities. This study aims to analyze the character of Muslim Gen Z religiosity, identify its forming factors, and examine the impact of the digital era on their religiosity. This study employed a descriptive qualitative approach using a library research method. The findings indicate that Muslim Gen Z religiosity is characterized by personalization, flexibility, and digital spirituality, strongly influenced by social media. These characteristics are shaped by massive digital technology accessibility, the effectiveness of contextual Islamic preaching on platforms such as TikTok, as well as spiritual needs and social pressure from the digital environment. The digital era brings positive impacts in the form of increased accessibility and religious literacy, but also negative impacts including shallow religious understanding, vulnerability to information bias, and potential exposure to extreme ideologies. This study implies the importance of an integrated digital religious literacy strategy through critical thinking-based Islamic Religious Education curriculum reform, enhancement of educators' digital capacity, and cross-sector collaboration to strengthen Gen Z's moderate and reflective religious understanding.

The Padang State University Community Service Program (KKN) in Jorong Koto Tingga, Nagari Sirukam, Payung Sekaki District, Solok Regency, West Sumatra, implemented a public space revitalization program through the construction of a creative garden based on rice field views. This program was created in response to the lack of representative public spaces and the decline in social interaction between residents due to modernization. Using a participatory approach, the community and youth of the jorong were involved in every stage, from planning, land clearing, to the construction of seating facilities overlooking the expanse of rice fields. As a result, the empty land was successfully transformed into a functional, aesthetic, and locally valuable public space. The high level of community participation demonstrated the success of the approach implemented and fostered a sense of ownership of the park being built. This program is expected to become a model for revitalizing public spaces based on local potential for other rural areas. Although the construction was not fully completed when the KKN program ended, the community and youth of Jorong Koto Tingga are committed to continuing it independently. This program is expected to become a model for revitalizing public spaces based on local potential for other rural areas with similar characteristics.

Increased caffeine intake is commonly used by students to cope with academic demands, especially during examinations and the completion of final projects. Excessive caffeine consumption may affected sleep quality and potentially influence nutritional status. This study aimed to determine the correlation between caffeine intake, sleep disturbances, and nutritional status among final – year unsergarduated students at Dr. Soekardjo University. This study used quantitative approach with an observational analytic and cross-sectinal design. A total of 108 students participated in the study. Data were collected using questionnaires and analyzed using the Spearman Rank correlations test. The findings indicated that the most of respondents with low caffeine intake experienced mild sleep disturbances, totaling 56 respondents (80%). Meanwhile, respondents with moderate and high caffeine intake mostly experienced moderated sleep disturbances, with 13 respondents (61.9%) and 9 respondents (52.9%), respectively. The results showed a significant correlation between caffeine intake and sleep disturbances (p-value=0.000). In all categories of caffeine intake, most respondents had normal nutritional status. However, no significant relationship was found between caffeine intake and nutritional status (p-value=0.351). In conclusion, caffeine intake was associated with sleep disturbances but not with nutritional status among final-year undergraduate students.

This study is motivated by the increasing complexity of global culture, which demands a value system capable of providing direction and meaning in human life. In this context, Islam is viewed as having the potential to serve as a global value system through a cultural concept grounded in revelatory values. This study aims to analyze the concept of culture from an Islamic perspective as a global value system by examining the thought of Ali Ahmad Madkur. The method used is a qualitative approach based on a Systematic Literature Review (SLR), systematically and critically examining various relevant scientific literature. The results of the study indicate that Islamic culture in Madkur’s thought is built upon two main dimensions: the normative dimension, which is derived from the Qur’an and Sunnah, and the practical dimension as its actualization in social life. Although this approach has strengths in maintaining the integrity of values, it still faces challenges in responding to the complex, dynamic, and hybrid nature of global cultural dynamics. Therefore, a more integrative approach is needed to ensure that Islamic cultural values remain relevant and operational in a global context. This study has implications for strengthening more contextual studies of Islamic culture and developing Islamic education that is adaptive to the changing times.

The study aims to analyze the role of digital media as a distracting factor affecting the faith loyalty of Catholic adolescents in their relationship with God amid religious pluralism in Indonesia. The development of digital media has transformed how adolescents understand, express, and live out their faith, while simultaneously presenting both opportunities and ambivalent challenges. This research employs a qualitative approach using a library research method, analyzing various theological, sociological sources, and Church documents. The findings indicate that digital media can serve as a broad and participatory space for expressing faith, but it also has the potential to create distractions in three main dimensions: cognitive, affective, and practical. Cognitively, adolescents experience confusion in understanding faith teachings due to the diversity of information available. Affectively, there is a shift in orientation from a relationship with God toward the pursuit of emotional gratification in digital spaces. Practically, there is a decline in engagement in concrete religious practices. Within the context of religious pluralism, this complexity intensifies and influences the formation of adolescents’ faith identity. This study also finds that digital media does not only have negative impacts but also holds positive potential as a means of deepening faith when used critically and reflectively. Therefore, the role of the Church is essential through contextual, dialogical, and integrative pastoral accompaniment combined with critical digital literacy. Through this approach, adolescents are expected to integrate their digital experiences into a more mature, authentic faith life rooted in a deeper relationship with God.

The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 byToni Firmansyah, S. Farm., Apt. and Asrianty Salam, Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.