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The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.

Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method and divided into two parts: training data (80%) and test data (20%). The analysis results show that the regression model is able to explain 85% of the variation in salary, with an R² value of 0.85 and a Root Mean Square Error (RMSE) of USD 23,221. The three main factors identified as having a significant influence on salaries in the AI field are work experience, company location, and the industry in which the company operates. The experience factor reflects the skills and knowledge developed over many years, which can increase productivity (Rony et al., 2023). Company location also plays an important role, as the cost of living and demand for skilled labor varies by region (Badran, 2019). Additionally, the specific industry in which an employee works influences salary, given that more developed industries can often offer higher compensation (Huang, 2025). This research makes a significant empirical contribution to the understanding of compensation structures in the AI labor market.

PLN (Persero) is the state-owned company responsible for supplying electrical energy in Indonesia and plays a crucial role in ensuring the reliability and continuity of power distribution. Therefore, all electrical equipment, particularly power transformers, must be maintained in safe and reliable operating conditions. Transformers function to change voltage levels from high to low or vice versa and are essential components in the electric power distribution system. One important transformer component that requires periodic maintenance is the On Load Tap Changer (OLTC). To maintain transformer performance and extend its service life, routine maintenance activities such as OLTC oil replacement are necessary. PT. PLN (Persero) ULTG Rangkasbitung conducts regular OLTC oil replacement twice a year as part of its preventive maintenance program. Transformer oil serves as both an insulating medium and a cooling agent; therefore, its condition greatly affects transformer reliability and operational safety. Degraded oil quality can cause insulation failure and reduce transformer efficiency. This study aims to describe the procedure and implementation of On Load Tap Changer oil replacement at the transformer substation of PT. PLN (Persero) ULTG Rangkasbitung. The research method used is field research, carried out through direct observation and interviews with maintenance personnel. The results of this study are expected to provide a clear understanding of the OLTC oil replacement process, support proper maintenance practices, and emphasize the importance of transformer maintenance in ensuring the reliability and sustainability of the electric power system.

This research aims to design and develop a decision support system to determine the recipients of assistance in the Family Hope Program (PKH) in Pogo Tena Village. This system uses the (Technique for Order Preference by Similarity to Ideal Solution) method which aims to assist decision-makers in selecting families who meet the criteria to receive social assistance based on several predetermined factors, such as income level, number of family dependents, health conditions, and education. The method used in this study is a qualitative and quantitative method with a case study approach on PKH Pogo Tena Village. Data was obtained through interviews with related parties, field observations, and data collection from existing documents. In this system, the assessment is carried out by comparing alternative performance values based on pre-established criteria, and then using the TOPSIS method to determine the families who are eligible for assistance. The results of this study show that the designed decision support system can provide more objective and transparent recommendations for aid recipients. Using the TOPSIS method, the system can prioritize beneficiaries based on their proximity to the ideal solution, which helps minimize subjective errors in the beneficiary selection process. This research is expected to contribute to increasing the effectiveness and efficiency in the implementation of the Family Hope Program in Pogo Tena Village, as well as as a reference for other agencies that want to apply similar methods in social assistance programs.

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.

This article aims to describe the forms of danger, identify the causal factors, formulate strategies, and explain the role of parents in creating safe and inclusive parenting for children with disabilities. Children with disabilities are highly vulnerable to various forms of danger, including physical and emotional neglect, due to a lack of parental understanding and strong social stigma. The method used is descriptive qualitative with a literature review approach, analyzing data from books, scientific journals, and official reports. The results indicate that risky parenting is caused by a lack of knowledge, economic pressure, and insufficient support from the environment and Non-Formal Education (NFE) institutions. The key strategy to overcome this is through strengthening parental education (inclusive parenting classes), collaboration between NFE institutions and the community, and creating accessible and secure learning environments. In conclusion, NFE has a strategic role as a bridge for parents to gain knowledge and support, thus strengthening a safe, loving, and difference-appreciating family environment. This research emphasizes the novelty of NFE’s role in integrated family-community support to ensure optimal development for children with disabilities.

This study analyzes Muhammad Asad's Islamic political thought, which seeks to unite Islamic spiritual values with modern political thought. Asad rejects the limited view that considers Islam merely a ritualistic religion, and asserts that Islam is a value system that governs various aspects of life, including politics and government. Using a qualitative approach and library research methods, this study explores Asad's ideas on the Islamic state, democracy and shura, social justice, and the interaction between religion and the state. The findings of this study show that Asad views the Islamic state as an ethical entity that upholds justice, the rule of law, and moral responsibility. The concept of shura is understood as a type of moral democracy based on the values of justice and public participation. Asad also emphasizes that social justice is an important aspect of Islamic government that supports the welfare of society. Asad's thinking rejects both extreme secularism and absolute theocracy, and offers a model of Islamic politics that is rational, participatory, and based on morality. The relevance of this thinking lies in its ability to provide solutions to the crisis of ethics and justice in modern politics through a combination of spirituality and rationality in government.

Tax Management refers to a company’s efforts to manage its tax obligations efficiently and legally in order to optimize net income. This study aims to examine the effect of Fixed Asset Intensity and Leverage on Tax Management, with Profitability as a moderating variable, in mining companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2024 period. This research is conducted because tax management practices are considered to potentially influence corporate profitability and financial performance. The study is grounded in Agency Theory and employs a quantitative approach. The sample was selected using purposive sampling, resulting in 28 companies observed over four years, with a total of 112 secondary data observations obtained from annual reports or financial statements. Data analysis was performed using EViews 13 with a Moderated Regression Analysis (MRA) approach. The findings indicate that: (1) Fixed Asset Intensity has no significant effect on Tax Management; (2) Leverage has a significant negative effect on Tax Management; (3) Profitability does not moderate the relationship between Fixed Asset Intensity and Tax Management; and (4) Profitability strengthens the effect of Leverage on Tax Management.

The consumption of ultra-processed foods (UPF) has been increasing among various age groups, including children and adults. UPF is generally low in iron content and can interfere with iron bioavailability, thereby increasing the risk of anemia. Additionally, the high calorie content of UPF contributes to the development of degenerative diseases. This study aims to analyze the relationship between UPF consumption, iron intake, and the risk of anemia. The study employs a narrative review method. Literature search was conducted using PubMed, Google Scholar, and Scopus databases, using controlled terms. The results showed that high UPF consumption had a negative impact on iron intake and increased the risk of anemia. Similar effects were found in high fat and sodium consumption. Some studies showed varying results regarding protein intake, fiber, and micronutrients such as sodium and calcium. In general, it can be concluded that excessive UPF consumption negatively impacts the quality of nutrient intake, both macro and micro, and contributes to the occurrence of anemia. These findings highlight the need for public health strategies to limit UPF consumption, particularly among vulnerable populations, as a preventive effort against anemia.

Postoperative pain is a prevalent condition encountered by patients after surgical interventions and may negatively influence physical recovery as well as psychological well-being when inadequately managed. Conventional pain management strategies that primarily depend on pharmacological agents may lead to undesirable side effects, thereby emphasizing the importance of complementary non-pharmacological approaches. One intervention increasingly applied in nursing care is guided imagery. This review aimed to systematically evaluate the effectiveness of guided imagery in alleviating pain intensity among postoperative patients. A scoping review methodology was employed, with article identification guided by the PICO framework across national and international academic databases. Eligible studies included full-text research articles published within the past five years, written in either Indonesian or English, and specifically examining the use of guided imagery in postoperative settings. A total of ten studies met the inclusion criteria and were subjected to in-depth analysis. The results consistently demonstrated a significant reduction in pain intensity following the implementation of guided imagery across various surgical procedures, including cesarean delivery, appendectomy, fracture surgery, cholelithiasis, and abdominal operations. The intervention, delivered for 10–20 minutes per session, effectively reduced pain levels from moderate to severe to mild, with statistically significant outcomes (p < 0.05). Moreover, guided imagery was found to enhance patient relaxation, comfort, and psychological stability. These findings indicate that guided imagery represents a safe and effective non-pharmacological nursing intervention that can be integrated into evidence-based postoperative pain management.  

Breast cancer is one of the leading causes of death among women, largely due to delayed diagnosis. Early detection through Clinical Breast Examination (CBE/SADANIS) is an important strategy to identify breast cancer at an early stage. However, the level of knowledge and awareness of Women of Reproductive Age regarding SADANIS remains low. This Community Service activity aimed to improve knowledge and awareness of SADANIS among Women of Reproductive Age through health education at PMB Rama Agustina. The activity was conducted through preparation, implementation, and evaluation stages. Health education was delivered using interactive lectures and discussions, supported by educational media such as leaflets and posters. Evaluation was carried out using pre-test and post-test questionnaires to assess changes in participants’ knowledge. The results showed an improvement in participants’ knowledge and a positive change in attitudes toward the importance of early breast cancer detection. In addition, participants demonstrated increased interest and willingness to undergo SADANIS examinations, either at the midwifery practice or at nearby primary healthcare centers. In conclusion, this activity was effective as a promotive and preventive effort to support early detection of breast cancer among women of reproductive age.

Creative technology is the result of the convergence between human creativity and digital innovation that significantly changes the way industries operate. This journal discusses the definition of creative technology, its important role in supporting the development of the digital economy, and the challenges that arise in the implementation process. The research methodology uses a literature study of the latest technological trends, such as artificial intelligence (AI), augmented reality/virtual reality (AR/VR), and digital design. The results of the study show that the integration between technology and creativity is a key factor in increasing competitiveness in the future. Creative technology not only functions as a means of production, but also as a medium of innovation that is able to create added value, expand business opportunities, and encourage digital transformation in various sectors. However, the challenges faced include limited human resources, digital infrastructure readiness, and ethical and regulatory issues. Therefore, creative technology development strategies need to be directed at increasing digital literacy, cross-sector collaboration, and policies that support sustainable innovation.

The policy to implement the deep learning approach in Indonesian schools starting from the 2025/2026 academic year requires teachers to be well prepared to design and conduct meaningful and in-depth learning. This community service program aimed to enhance the understanding and pedagogical competence of senior high school and madrasah economics teachers in Tasikmalaya City in implementing the deep learning approach comprehensively. The program employed a community-based science and technology application scheme through training and mentoring activities conducted in face-to-face, online, and hybrid modes, consisting of planning, implementation, and evaluation stages. The training materials covered the concept and implementation of deep learning, lesson planning, instructional design and models, learning evaluation, development of innovative technology-based learning media, and reinforcement of microeconomic and macroeconomic content. A total of 78 economics teachers participating in the MGMP of Tasikmalaya City were involved in the program. The results indicate that the training improved teachers’ understanding of deep learning, lesson planning, and assessment strategies that support meaningful learning. Participants’ reflections revealed high motivation and strong relevance of the training materials to their professional duties. This program contributes to strengthening teachers’ readiness to implement deep learning sustainably and supports the improvement of economics learning quality in schools.

Low levels of political literacy and political participation awareness among female university students remain a challenge for strengthening democracy at the local level. This condition is also found among female students in East Lombok, who tend to perceive political participation narrowly and lack the skills and confidence to engage actively as citizens. This community service program aims to enhance political literacy, participatory awareness, and the capacity of female students to understand and practice political participation in a critical and responsible manner. The program was implemented at the Institut Teknologi Sosial dan Kesehatan Muhammadiyah Selong using a community organizing approach integrated into the academic learning process. The methods included participatory discussions, mentoring, simulations, and critical reflection, positioning female students as active subjects throughout the activities. The results indicate an improvement in participants’ understanding of the meaning and forms of political participation, increased confidence in expressing opinions, and the emergence of new awareness regarding their roles as democratic citizens. In addition, a female student discussion group was formed as a space for collective learning and an early platform for local leadership. This program highlights the role of higher education institutions in promoting social transformation and strengthening women’s political participation at the local level.

This community service program aims to improve the entrepreneurial skills of high school students through the understanding and application of SMART Goal Setting. The activities were carried out in the form of workshops, group discussions, and intensive mentoring for eleventh-grade students. Data were collected through observations, short interviews, participant reflection notes, and documentation of business plans. Analysis was conducted using descriptive qualitative methods, emphasizing the participants' experience patterns and behavioral changes after participating in the program. The results showed that students were better able to formulate specific, measurable, realistic, relevant, and time-bound business goals. In addition, participants reported increased self-confidence, regularity in developing business steps, and awareness of the importance of structured planning. These findings confirm the relevance of implementing SMART Goal Setting in high school entrepreneurship education as a practical strategy that can support the emergence of aspiring young entrepreneurs.

Converting used cooking oil into aromatherapy candles represents an environmentally sustainable solution for household waste management while supporting community-based economic development. This research examines the process and results of a participatory empowerment initiative conducted in Buddagan Village, aimed at producing value-added aromatherapy candles from used cooking oil. Employing a qualitative descriptive methodology with a participatory approach, the study involved 60 housewives as active contributors. Program activities were implemented through direct, in-person sessions encompassing education on health and environmental hazards of improper oil disposal, hands-on candle-making training, and basic instruction on product marketing. The production process consisted of oil purification using activated charcoal, mixing with stearic acid, heating, adding colorants and fragrances, and molding. Data were collected through direct observation of participant involvement, skill development, and product quality, and analyzed inductively. The results demonstrate increased environmental awareness, enhanced technical competence, and the successful production of aromatherapy candles with satisfactory shape, fragrance, and burning consistency.

The low level of awareness among vocational high school (SMK) students to continue their education to higher education remains a major issue, particularly at SMK Mambail Futuh, Jenu District, Tuban Regency. Economic constraints, limited access to information, and an orientation toward immediate employment are dominant factors influencing students’ decisions. This community service activity aims to build students’ awareness and motivation regarding the importance of higher education as a long-term investment. The implementation method includes educational socialization, interactive discussions, the presentation of inspirational experiences, and the introduction of higher education admission pathways and financing schemes. The results show an improvement in students’ understanding of the benefits of higher education and a growing interest in pursuing further studies after graduation. This activity emphasizes that communicative and contextual educational approaches play a crucial role in encouraging positive mindset changes among SMK students toward higher education.

The waste problem is still an environmental issue that requires serious attention, especially regarding the low level of public awareness in sorting organic and inorganic waste at its source. Environmental education needs to be given from an early age to form attitudes and behavior that care for the environment in a sustainable manner. This community service activity aims to increase the understanding and awareness of grade III–IV elementary school children towards sorting organic and inorganic waste through a game-based learning approach. The activity was carried out in the Bojongsari area, Depok, involving 10 children as participants. The methods used include delivering interactive material, waste sorting competitions through the Eco-Race Little game, and making mini posters with environmental themes. The results of the activity showed that around 90% of participants were able to differentiate and sort organic and inorganic waste correctly. Apart from increasing cognitive understanding, this activity also fosters an attitude of cooperation, creativity and concern for the environment. Thus, the Little Eco-Race activity is considered effective as a fun and applicable environmental education model for elementary school age children.

Appropriate Technology Training (TTG) in Tambah Dadi Village, Purbolinggo Subdistrict aims to improve the community's understanding and ability to manage livestock waste more efficiently.  Often, livestock waste produced in this village is not utilized properly.  Training participants will be introduced to various waste management methods that can be used as sources of organic fertilizer and organic compost. The training includes awareness-raising, demonstrations, and hands-on practice in processing waste into useful products such as organic fertilizer. The results of the training show that participants are able to make organic fertilizer from livestock waste.  It is hoped that this activity will serve as an example for other villages to manage livestock waste in a more sustainable manner, which will improve agricultural and livestock yields in the area and increase the welfare of farmers in Tambah Dadi Village. The results of this activity show that the application of appropriate technology not only focuses on waste management, but also aims to improve the economic conditions of the community.