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Methanol plant from glycerol is a good prospect in Indonesia. Until 2021, methanol is only supplied by one methanol producer located in East Kalimantan, namely PT. Kalimantan Timur with a production capacity of 660,000 tons/year. Therefore, the methanol plant from glycerol has a very important meaning for the acquisition of foreign exchange as well as employment. This article presents an economic evaluation of a methanol plant from glycerol with a capacity of 40,000 tons/year. Profitability analysis calculation is done by two methods, the method that do not consider the time value of money and the method that consider the time value of money. The results of the method that do not consider the time value of money obtained ROI value (before tax) 34.62%, ROI value (after tax) 24.24%, and the value of PBP 2.98 years. Using the method that consider the time value of money, the NPW value was Rp. 1,458 billion and the DCFRR / IRR was 39.25%. The sensitivity analysis also deserves to see the effect of the factor that influence the economic viability, and provide more information for the optimization of Methanol production.

This research investigates the influence of fuel atomizer performance on maintaining ship diesel engine performance, focusing on ANT (Nautika - Deck Officers) and ATT (Teknika - Engine Officers) students at STIP Jakarta. The study aims to analyse the practices related to the Planned Maintenance System (PMS) and their impact on fuel atomizer performance among these student officers. Qualitative research methods, including questionnaires, were employed to collect data from ANT and ATT students. The Likert scale was used to measure attitudes, opinions, and perceptions regarding fuel atomizer performance and PMS practices. The research highlights the critical role of fuel atomizers in diesel engine optimization and underscores the importance of effective PMS practices. The findings provide valuable insights for enhancing ship diesel engine performance through improved maintenance strategies.

This research investigates the effectiveness of new methods in improving computer system performance through a rigorous scientific approach. The research method applied includes the steps of data collection, implementation of new methods, data analysis, and evaluation of results. The research results show that the proposed innovative approach successfully improves the performance of the computer system in various test scenarios. Key findings include significant improvements in data processing speed, with increased system responsiveness and more optimal resource use efficiency. Statistical analysis shows a 25% increase in data processing speed and up to 20% improved resource usage. These results provide a strong empirical basis for the recognition of a scientific approach in improving computer system performance, as well as demonstrating the potential for further application in the context of computer system performance optimization. Thus, this research provides a valuable contribution to the understanding and practice of computer system performance optimization.

Web Usage Mining (WUM) is the use of data mining methods to extract knowledge from web usage data. One function of WUM is to support Business Intelligence (BI) purpose in which one of the important information needed is the classification of web users that can be used for acquisition, penetration, and user retention activity. There are two main problems encountered in conducting the classification of web users. The first is the determination of antecedent attributes as a term of classification rules, which is a major problem in data mining classification function in general. The second problem is the preprocessing activity which involves preparing the supporting data for the web users’ classification need which is the most difficult stage in WUM. For the web user classification method, we propose a classification method based on ant colony optimization method (ACO) as a distributed intelligent system using heuristic function which is in line with the problem areas. We proposed a heuristic functions for web user classification based on web usage data that uses entropy of antecedent candidate, information gain from attribute of total number of web user access  and average of access duration of web user.  For preprocessing purpose, a method of data preparation that can support the needs of web users’ classification is proposed. The data used consists of web access log data, web user profile data and web transaction data. The preprocessing activity consists of parsing, data cleansing, and extraction of the web user sessions using heuristic method concerning web page access timeout and differences in web browser agent. Testing is done by comparing the performance of the proposed algorithm with Ant-Miner algorithm, cAnt-Miner algorithm, and the Continuous Ant-Miner algorithm. The results of testing of four web data shows that the performance of the proposed algorithm is better in terms of accuracy of rules and simplification of rules.

This research purpose to determine the influence of Search Engine Optimization, Online Advertising and Time Pressure on Purchase Intentiom at the Uniqlo Flash Sale. This research uses quantitative methods with a descriptive approach. The sampling technique used was purposive sampling with a total of 96 respondents. Data collection techniques by distributing questionnaires. The data analysis technique uses Partial Least Square (PLS) using the Smart-PLS Version 3.0 program. The results of this research show that there is an effect of Search Engine Optimization on Purchase Intention. Online Advertising also has an effect Purchase Intention. Meanwhile, Time Pressure has no effect on Purchase Intention.

This research aims to analyze optimal strategies for improving the performance of Village-Owned Enterprises (BUMDes) through human resource management (HR). A qualitative approach was used by collecting data through interviews and documentation studies. The research results show that effective HR management can make a significant contribution to improving the performance of BUMDes. Factors such as selective recruitment, training and development, and good performance management are the keys to achieving optimization of BUMDes performance. The implication of this research is the importance of greater attention to HR management in the context of BUMDes development.

Objective – This research clarifies the factors that encourage creative economic inclusiveness for MSME actors by examining the effect of optimizing MSME empowerment, the role of digital technology literacy and ease of access for MSME actors. This research also examines analysis direct influence optimization empowerment on digital technology literacy and ease of access, the influence of digital technology literacy on ease of access and the mediating role of digital technology literacy and ease of access. Design/methodology/pe approach – Data was taken from an online questionnaire of respondents, namely MSME actors in Purwakarta Regency. Partial least squares structural equation modeling and mediation analysis using the bootstrap method. Findings – Optimizing empowerment and ease of access has a significant direct impact on the inclusiveness of the creative economy. Both optimizing empowerment and digital technology literacy have a direct, positive and significant influence on ease of access. Optimizing empowerment has a positive effect on digital technology literacy. Mediation analysis revealed that ease of access was unable to mediate the influence between optimizing empowerment and inclusiveness of the creative economy and between digital technology literacy and inclusiveness of the creative economy. Research limitations/implications – This research focuses on three factors that encourage creative economic inclusiveness of MSME actors in Purwakarta Regency. Originality/value – This study offers new insights into the factors that encourage the inclusiveness of the creative economy of MSME players. These findings reveal the importance of optimizing MSME empowerment and encouraging digital technology literacy for creative economic inclusivity among MSME players.

This research explores how leadership practices contribute to the resilience of small businesses during crises. Through case studies of SMEs (small and medium enterprises) affected by economic downturns and disruptions, the study highlights strategies such as resource optimization, strategic partnerships, and effective communication. Findings reveal that proactive leadership ensures business continuity and recovery.

Teaching the Al-Qur’an is the core of Islamic education, with the main aim of guiding children in reading, studying and understanding the Koran from an early age. The Al-Qur'an Education Park (TPQ) serves as an important platform to instill a love of the Al-Qur'an in children. Even though many TPQs have been successful with good administration and management and a large number of students, there are still TPQs that have not developed, such as the TPQ in Glidah Kedamean Village, Gresik. Observations show that learning management at TPQ does not meet recognized management and curriculum standards in education, resulting in suboptimal educational attainment. Responding to this problem, the Glidah Village TPQ Teacher Management Optimization team, together with KSPPS BMT Khoiru Ummah, initiated an intensive community service-based mentoring program using the Participatory Action Research (PAR) method. The mentoring process involves several stages, starting from identifying problems through field observations, organizing and planning programs, to implementing program actions. The results of discussions and implementation of mentoring revealed a number of problems faced by teachers regarding TPQ management. The implementation of this mentoring activity has been empirically proven to have a positive impact, increasing the effectiveness of management and the quality of education at the TPQ.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning techniques like RL, deep Q-networks (DQN), and deep deterministic policy gradient (DDPG), enhance decision-making capabilities in dynamic production environments. The study develops a hybrid RL robust control framework and tests it in both simulation and real-world scenarios. Performance metrics, including production efficiency, system stability, and adaptability, are assessed under various disturbance conditions, such as machine failures and fluctuating demands. The hybrid model significantly outperforms traditional PID control in terms of efficiency and stability, demonstrating faster convergence and better adaptability in dynamic environments. Statistical analysis confirms the superiority of the hybrid system over standalone RL models and traditional PID control. This model’s scalability and adaptability make it a promising solution for Industry 4.0 applications, addressing key challenges in real-world manufacturing systems by ensuring computational efficiency and the ability to manage large-scale data. The findings contribute to the development of more robust and efficient control strategies for autonomous manufacturing systems in uncertain environments.

Community health centers (Puskesmas) play a strategic role as primary health care facilities in improving public health status. However, Puskesmas Bumi Ayu still faces several challenges, including limited human resources, inadequate facilities, and suboptimal community participation in supporting health services. This Community Service Program (PKM) aims to enhance the quality of community health services through a community organizing approach that actively involves health workers and service users. The methods employed include health education, interactive discussions, and participatory monitoring and evaluation conducted at Puskesmas Bumi Ayu, Dumai Selatan District. The results indicate an improvement in the understanding of health workers and the community regarding patient-oriented service quality, optimization of available resources, and strengthened collaboration among the health center, local government, and the community. Furthermore, the program fostered collective awareness, positive behavioral changes, and the emergence of local change agents supporting community-based health services. Overall, this PKM contributes to the development of more responsive, inclusive, and sustainable primary health care services in the working area of Puskesmas Bumi Ayu

This research explain how to implement civil cases settlement before and after the implementation from of E-Court system in electronic and conventional court. on this case it explains that civil cases settlement in a modern manner is described according to Supreme Court Regulation Number 7 of 2022 while conventionally described according to HIR / RBG. Before the advent of  system E-Court, the testing method was conducted conventionally. The optimization of these two forms of civil case settlement is then examined according to Law Number 48 of 2009 concerning Judicial Power where the judiciary is administered out with the principles of simple, fast and low cost. This research is a normative legal research with Statute Approach and Conceptual Approach methods. The research concluded: first, the existence of court dualism is still needed for justice seekers who still need conventional lawyers. Second. The implementation of Civil Case Settlement with the presence of E-Court as an effort to realize the principles of simple, fast and low cost in reality has not been optimal, however, E-Court has not fully resolved the cases received by the Supreme Court and the lower courts. That is, most cases are still conducted conventionally.

Analysis of the use of the Boruvka algorithm method in the context of electricity supply is an important topic in the development of electricity systems. In this study, we evaluate the effectiveness and application of the Boruvka algorithm in distribution optimization and power grid management. We study ways in which the Boruvka algorithm can be used to identify optimal electricity distribution paths, improve system efficiency, and minimize the potential for grid damage or failure. This research provides a deeper understanding of the potential and limitations of the Boruvka algorithm in the context of modern electricity infrastructure.

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.

Nutmeg seeds are one of the herbs and spices that are rich in benefits and the content of alkaloids, flavonoids and saponins other very important roles in its utilization. Nutmeg seeds contain trimyristin which its content can be used in the manufacture of liquid bath soap. One of the application of the compound contained is made in the form pHarmaceutical preparations. Liquid soap is made through a saponification reaction from oil and fat reacted with KOH. This research. aim to do optimization of liquid soap formula between concentration variations of 8%, 9%, 10% so can find out the optimum liquid soap formula and know the evaluation of properties pHysics and chemistry of preparations. Nutmeg seed extract is obtained by extraction using the maceration method using 70% ethanol solvent, then carried out evaporation using a rotary evaporator instrument until the extract is obtained thick. Formulation of liquid soap by heating method for 45 minutes. Evaluation preparations include organoleptic observations, homogeneity. pH, foam height. Results showed that the liquid soap formulated from elstract: colored nutmeg brownish, has a characteristic odor of nutmeg seed extract, is in the form of a homogeneous liquid, has a pH pH 8-10, foam height 93mm-139mm. Optimum formula based on results showed no significant difference from all formulas. Variation concentration 8%, 9%, 10% pH test and high foam test. the formula has complied requirements in accordance with the standards set by SNI 06-4085-1996. Where the normal pH of liquid soap is ph 8-11 and the high foam of liquid soap is 13mm-220mm  

This research focuses on optimizing the profits of  UMKM production of banana chips using the simplex linear programming method and the help of POM-QM software. This research aims to find out ways to use profit optimization methods in banana chips MSMEs so that they can find out maximum profits, production costs, income and raw materials used. The method used is a qualitative descriptive method, where the data obtained is more descriptive in nature from the results of interviews and observations that have been carried out and then the data will be analyzed qualitatively. Data collection techniques in this research include the use of primary data and secondary data. Primary data is obtained through observation, interviews, or distribution of questionnaires to relevant sources. Meanwhile, secondary data was obtained through literature searches from books and magazines that were relevant to the research topic. The analysis technique used is linear programming using the simplex method. The research results and discussion show that this function has reached optimal conditions. Therefore, the calculation stops once all the values in the objective function become positive. The result of this solution is the variable value x1 = 6 ; x2 = 2 ; x3 = 0 dan x4 = 0. Increase in profit from the initial value of Rp. 675,000 to reach a maximum profit (Zmaks) of Rp. 900,000, then the difference between profits before and after optimization is IDR. 225,000, and with the help of POM-QM for Windows software calculations become more relevant because this tool helps in completing calculations to achieve maximum profit levels quickly, accurately and efficiently.

household industry or usually known quantity when UMKM  is an arising chance. Rephrase. This happens because there are very few job break. However, some UMKMs still have adversity determining the optimal  production abundance broad- based on accessible expedient, so  these troop cannot amplify. One of them is Bu Ida fruit juice, which consistently produces fruit juice according to crowd-pleasing orders of average consumer credit. admittedly utilize capital and basic materials enough, Ms. Ida's business is growing day by day. From this research, problems were distinguish and answer live needed, comprehend the employment of linear programming to determine the  sales abundance that absence to be optimized to attain maximum earnings from juice sales Ida's fruit. There are 6 (six) juices and 6 (six) ingredients. In this study, a simple method linear programming algorithm is applied to obtain an optimization model to maximize the  juice production quantity and obtain optimal profits. The POM-QM Windows application provides fast, accurate and precise  calculation results.

Perkembangan Teknologi Informasi yang pesat menyebabkan munculnya berbagai model digital marketing, yang diharapkan memiliki dampak signifikan bagi perkembangan sebuah usaha. Penelitian ini bertujuan untuk mengamati sejauh mana pengaruh penerapan model digital marketing terhadap perkembangan peningkatan sebuah usaha. Studi kasus dilakukan pada bisnis kuliner Nyamm Ayam di Salatiga, yaitu sebuah bisnis yang melayani penjualan masakan ayam dengan berbagai kreasi dan menu pilihan dengan cita rasa nusantara. Penerapan digital marketing dalam penelitian ini dimulai dari tahap penentuan tujuan dan target audiens, pemilihan saluran digital dan perencanaan konten untuk meningkatkan brand awareness, pengembangan web dan Search Engine Optimization, penggunaan media sosial serta analisis dan pengukuran kinerja. Dari studi kasus yang dilakukan dapat disimpulkan bahwa penerapan digital marketing memiliki dampak yang positif bagi peningkatan usaha, khususnya pada bisnis kuliner Nyamm Ayam di Salatiga.