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Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

This study aims to analyze the financial management practices of the Es Degan Bu Ulfa MSME and their compliance with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM). The study used a qualitative approach with a case study method. Data were obtained through interviews and observations, then analyzed descriptively and qualitatively.   This study aims to analyze the financial management practices of the Es Degan Bu Ulfa MSME and their compliance with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM). The study used a qualitative approach with a case study method. Data were obtained through interviews and observations, then analyzed descriptively and qualitatively. The results indicate that financial management is still carried out simply and does not yet implement written records. Business income and expenses still rely on memory, even though business owners perform daily profit and loss calculations and separate personal and business finances. The main obstacles faced include limited human resources and a low understanding of financial record keeping and the SAK EMKM. Based on the analysis, financial management practices are not fully compliant with SAK EMKM standards. Therefore, the implementation of simple financial record keeping is necessary to structure business financial information and support business decision-making. Proper financial management can help improve business performance, monitor cash flow, evaluate profitability, and enhance accountability. Furthermore, the application of SAK EMKM is expected to facilitate access to financing and strengthen the sustainability and competitiveness of MSMEs in an increasingly competitive business environment.

Medical students need to take the Competence test as Doctor Profession Program (UKMPPD) to be passed and earned their medical profession. Exams are one of the most common stressors experienced by medical students and the body will respond to these stressors in the form of feelings of depression or anxiety. Anxiety and depression itself can affect student performance during exam preparation. The purpose of this study was to determine the relationship between anxiety and depression on the preparation of the Professional Doctoral Program Competency Test (UKMPPD) for students of the Faculty of Medicine, University of Pattimura. The method used is quantitative analysis with cross sectional research design and total sampling technique. The sample is all the student population of the Faculty of Medicine, University of Pattimura who meet the inclusion and exclusion criteria, totaling 90 respondents. The results showed that the majority of respondents experienced mild anxiety symptoms, which is 37% during the CBT exam and 34% during the OSCE exam. The majority of respondents also did not experience depression, which is 68% during the CBT and 70% during the OSCE and the majority had an optimal level of preparation for UKMPPD, which is 63% during the CBT and 64% during the OSCE. Data analysis using the chi square test showed a significant relationship between anxiety symptom and the level of preparation for UKMPPD both CBT (p=0.030) and OSCE (p=0.012). There was no significant relationship between depression symptom and the level of preparation for UKMPPD for both CBT (p=0.123) and OSCE (p=0.07). It can be concluded that there is a significant relationship between anxiety and preparation for UKMPPD, but there is no significant relationship between depression and preparation for UKMPPD.

In the Education 4.0 era, schools face unprecedented challenges in managing teacher performance effectively. Traditional top-down performance management systems have proven inadequate for fostering continuous professional growth, intrinsic motivation, and adaptive teaching competencies required in increasingly technology-driven learning environments. This study aims to develop, validate, and assess the effectiveness of a School-Based Performance Management (SBPM) model designed to enhance teacher effectiveness across cognitive, affective, and pedagogical dimensions. Using a Research and Development (R&D) design following the ADDIE framework, the study was conducted in three phases: needs analysis, model development, and model validation. Participants included 12 school principals, 186 teachers, and 8 education experts from 24 public secondary schools in [Province, Country]. Validation by experts yielded a content validity index (CVI) of 0.91. Implementation resulted in statistically significant improvements in teacher effectiveness scores (t = 8.74, p < 0.001, Cohen's d = 1.23), digital pedagogy competency (mean increase = 22.4%), and student learning outcomes (mean improvement = 17.8%). The SBPM model provides a contextually responsive, evidence-based framework that empowers schools to manage teacher performance collaboratively, fostering professional accountability and sustainable instructional quality in Education 4.0.

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.

Construction project control requires careful planning to avoid delays and cost overruns. This study aims to evaluate the performance of the CBT Laboratory Building construction project at the Faculty of Medicine, Wahid Hasyim University, Semarang, in terms of cost and time using the Earned Value Analysis (EVA) method, and to analyze acceleration alternatives using the crashing method. The method used is a quantitative approach with a case study, based on project data such as the Budget Plan (RAB), S-curve, and work progress reports. The analysis was carried out using the Earned Value Analysis (EVA) method to assess project performance in terms of cost and time through indicators such as CV, SV, CPI, and SPI. Furthermore, an acceleration analysis was conducted using the crashing method to determine the optimal alternative for overcoming project delays by considering time and cost efficiency. The results of the Earned Value Analysis indicate that the project experienced delays and cost overruns, as shown by SPI < 1 and CPI < 1. The estimated project completion time increased to 227 days, longer than the initial plan of 217 days, while the estimated final cost reached RP 5,451,241,064.85, exceeding the initial budget. Acceleration efforts using the crashing method show that adding adding labor is more efficient than working hours (overtime), resulting in a project duration of 212 days with lower costs of RP 5,658,221,364.08. Thus, the Earned Value Analysis method is effective for evaluating project performance, while crashing can be a solution for acceleration by considering time and cost efficiency.

Rapid population growth in Sidoarjo Regency has triggered massive land-use changes, resulting in increased surface runoff and reduced performance of the drainage system. This study aims to evaluate the hydraulic capacity of drainage channels in the Pondok Sidokare Indah Housing area against design flood discharges with return periods of 2, 5, and 10 years. The method used is a descriptive quantitative approach, involving hydrological analysis using maximum daily rainfall data from 2015–2025 and hydraulic modeling of the existing channel along 350 meters. The frequency analysis results indicate that the Log Pearson Type III distribution is the most suitable method based on statistical parameters and the Smirnov-Kolmogorov goodness-of-fit test. The calculation of design flood discharge using the rational method yields values of 0.749 m³/s (2-year), 1.003 m³/s (5-year), and 1.164 m³/s (10-year). Meanwhile, the maximum capacity of the existing channel ranges only between 0.534 m³/s and 0.733 m³/s. The comparison between hydrological load and channel capacity shows that all observation points (Sta 0+000 to Sta 0+350) are in overflow condition, even for the lowest return period flood discharge. This condition confirms that the current channel dimensions are no longer adequate and require normalization to mitigate annual flooding in the area.

Education is the main driver of a nation's progress, which is not only related to intellectual intelligence, but also the formation of student character. Unfortunately, currently the world of education is facing serious problems, such as declining interest in learning and increasingly complex external factors in the digital era. This study aims to explore in depth the impact of learning motivation on student academic achievement, identifying obstacles that hinder this achievement. The method used in the current study is a literature study with a qualitative approach. Data were collected through theoretical studies and analysis of various relevant scientific sources, including research journals and textbooks, which were then combined to develop a comprehensive argument. The findings of this study indicate that learning motivation is the main factor that encourages student persistence and consistency in achieving the best learning. Academic success is defined as the result of a complex interaction between internal and external factors, where the existence of a supporting ecosystem such as good learning facilities, a supportive environment, and effective communication between lecturers and students plays a very important role. On the other hand, phenomena such as digital fatigue, low independence, and excessive workload are identified as significant barriers that can reduce academic performance. This analysis concludes that there is synergy in strengthening psychological aspects and creating an adaptable learning environment to maintain the stability of academic achievement amidst global demands.

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.

This study examines the performance of student administrative services in the Society 5.0 era at the Bureau of Academic Affairs, Student Affairs, and Planning of Gorontalo State University, focusing on scholarship services. It addresses the need for accessible, responsive, transparent, inclusive, and student-centered administrative services in higher education. A descriptive qualitative approach was employed through in-depth interviews, participatory observation, and document analysis. Informants included bureau leaders, scholarship and financial aid administrators, operational officers, and students receiving various scholarship schemes. Thematic analysis was conducted through data reduction, data display, and conclusion drawing, while validity was ensured through source triangulation, method triangulation, and member checking. The findings show that accessibility has improved through service counters, websites, social media, WhatsApp, online forms, SIMPEL BAKP, and coordination with faculties and study programs. However, information remains fragmented, digital standard operating procedures are not uniform, and disability-inclusive access is limited. Responsiveness is supported by direct and digital communication, but it lacks a dedicated complaint system, automatic notifications, selection-status dashboards, and real-time disbursement tracking. Service quality is perceived as fairly good because students experience professional, friendly, and fair treatment. Nevertheless, service documentation, procedural standardization, staff training, and humanistic technology integration need strengthening. The study concludes that scholarship administration should be transformed into a centralized, inclusive, responsive, transparent, and student-centered digital-humanistic service model.

Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.

 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)

This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

Accountability in preparing financial reports is a crucial instrument for the sustainability of business entities, because inaccurate financial data management can hinder decision-making and harm business performance in the future. For MSME actors in the PKK Tanggamus community, strengthening financial reporting competence is an urgent need so they can map expenditure structures, record income, calculate profit, and evaluate business development periodically. This community service activity aims to improve participants’ financial discipline, particularly in separating personal assets, business capital, and gross profit, while introducing accessible office technology. The training focused on optimizing LibreOffice Calc as an alternative to Microsoft Excel with similar functions for creating transaction tables, cost recapitulations, and simple financial reports. The activity method included material presentation, software demonstrations, report preparation practice, and interactive discussions based on participants’ business needs. Training results showed high enthusiasm, improved understanding, and readiness to use LibreOffice Calc as a more organized, transparent, and sustainable financial recording tool. Thus, this activity provides practical contributions to building an accountable financial administration culture for community-based MSMEs.

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 development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

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.