Publication Search

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

This study aims to analyze the impact of business combinations through acquisition on the financial performance of PT Sarana Menara Nusantara Tbk. The research employs a descriptive quantitative approach, focusing on the acquiring firm in the Indonesian telecommunications infrastructure sector. The data used are secondary data obtained from the company’s annual financial statements for the period 2019–2023, sourced from the Indonesia Stock Exchange and the company’s official website. Financial performance is analyzed using Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Debt to Equity Ratio (DER) by comparing the periods before, during, and after the acquisition conducted in 2021. The results indicate that the acquisition exerted short-term pressure on asset efficiency and profitability, as reflected by the decline in ROA and NPM in the year of acquisition. However, in the post-acquisition period, the company demonstrated an improvement in operational performance, particularly in Net Profit Margin, suggesting that the economic benefits of the business combination gradually materialized. Meanwhile, fluctuations in ROE and DER reflect adjustments in the capital structure following the acquisition. These findings suggest that the success of an acquisition cannot be evaluated solely based on short-term financial performance but requires continuous assessment to capture its medium- and long-term effects. This study provides practical implications for management in formulating post-acquisition integration strategies and contributes empirically to the accounting and finance literature on business combinations in Indonesia.

This study aims to analyze the strengthening of higher education governance in realizing transparent and accountable academic services. In the context of the globalization of higher education and increasing public accountability demands, universities are required not only to excel academically but also to provide services that are open, responsive, and accountable. This research employs a qualitative approach with a descriptive-analytical design. Data were collected through in-depth interviews, observations, and documentation studies involving university leaders, academic administrators, lecturers, and students. Data analysis was conducted interactively through data reduction, data display, and conclusion drawing. The results indicate that the principle of transparency has been implemented through digital-based academic information systems; however, consistency in updating information still needs improvement. Accountability has been supported by standard operating procedures and service evaluations, although performance measurement based on indicators has not been fully integrated. Stakeholder participation has been facilitated through evaluation forums, but involvement in strategic decision-making remains limited. Overall, the dimensions of transparency, accountability, participation, effectiveness, and responsibility are interrelated in shaping the quality of academic services. This study emphasizes that strengthening governance must be systemically internalized within organizational culture and institutional operational systems to enhance trust and stakeholder satisfaction.

The growing demand for transparency, accountability, and measurable performance has transformed higher education institutions into complex public organizations required to deliver reliable and stakeholder-oriented services. Within this governance-driven environment, institutional governance plays a fundamental role in shaping service excellence and institutional legitimacy. Drawing on a public administration perspective, this study examines how governance dimensions influence academic service performance in higher education. This research employs a qualitative descriptive-analytical design. Data were collected through in-depth interviews, document analysis, and institutional observations involving university leaders, academic administrators, faculty members, and students. The analysis focuses on governance dimensions—transparency, accountability, participation, effectiveness, and responsibility—and their integration into institutional systems such as performance management, quality assurance, and digital infrastructure. The findings reveal that governance frameworks are formally established through regulations and digital systems; however, their operational integration remains uneven. Transparency improves service reliability when supported by consistent information management, while accountability mechanisms tend to emphasize procedural compliance rather than performance-based evaluation. Stakeholder participation is institutionalized but largely consultative. The study concludes that service excellence in higher education is a governance-driven outcome that requires systemic alignment between governance principles, institutional capacity, and performance management processes. Strengthened governance integration enhances service reliability and institutional legitimacy.

This research was conducted at PT PLN Nusantara Power UP Paiton, one of the largest coal-fired power plants (PLTU) in Indonesia, which focuses on reducing the generation of hazardous and toxic oil waste through the implementation of an oil purification system. The use of large amounts of lubricating oil in the Electro-Hydraulic System (EHS) has the potential to produce high amounts of used oil waste. To address this, the company implemented two main technologies, namely Water Content and Varnish Removal, which function to reduce water content and varnish layers so that the oil can be reused without reducing engine performance. This research used a quantitative descriptive method with data collection techniques through field observations, interviews, and operational documentation from 2021 to 2024. The results showed that the oil purification system was able to reduce hazardous and toxic waste generation by 11.46 tons over four years. In addition to providing environmental benefits, the implementation of this system also resulted in savings in oil waste costs of approximately Rp6,200,560,000. Technically, purification maintains engine performance by reducing water and varnish content, while from an environmental perspective, this activity supports the principle of reduce in hazardous and toxic waste management. Overall, the oil purification system has proven effective in improving operational efficiency, extending oil life, and supporting sustainable waste management for industrial operations.

This study aims to evaluate the effectiveness of various doses of 6.25% Poly Aluminium Chloride (PAC) and 0.1% polymer flocculant in reducing Total Suspended Solids (TSS) and assessing pH changes in coal stockpile wastewater at PT PLN Nusantara Power UP Paiton Unit 9. Stockpile wastewater typically contains high levels of suspended solids originating from water spray activities that carry fine coal particles. The coagulation–flocculation process was performed using the jar test method with PAC dosages of 35 ppm, 50 ppm, and 65 ppm, along with flocculant dosages of 6 ppm and 7 ppm. pH and TSS were analyzed before and after treatment to assess process effectiveness. The results indicate that a PAC dosage of 35 ppm combined with a 6 ppm flocculant achieved the highest TSS removal efficiency of 98.15%. Increasing PAC dosage resulted in reduced performance due to overdosing effects, leading to charge destabilization and impaired floc formation. These findings highlight the importance of optimizing coagulant dosage to improve stockpile wastewater quality for safe reuse in operational activities.

This study aims to examine the effect of green innovation on company value, with financial performance as a mediating variable, in the mining industry. This study uses a systematic literature review approach by examining various relevant previous studies. The results of the study indicate that green innovation plays a significant role in improving environmental performance and operational efficiency of companies, which in turn positively impacts financial performance. Good financial performance is a key factor in strengthening company value and stakeholder trust. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies. This study also found that companies that successfully implement green innovation tend to have a better image in the eyes of investors and the public, which contributes to increasing the company's market value. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies, strengthening their position in an industry that increasingly prioritizes sustainability and social responsibility.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

The objective is to analyze the competitiveness of modern retail through the integration of SWOT analysis as a basis for formulating more targeted strategies using qualitative descriptive methods. This paper identifies the strengths, weaknesses, opportunities, and threats that drive the performance of modern retail based on literature and empirical findings from various previous studies. The results of the analysis show that the main strengths of modern retail lie in product completeness, competitive prices, strategic locations, and operational efficiency. Meanwhile, weaknesses arise from suboptimal digitization, minimal online promotion, limited service innovation, and outdated inventory management. Meanwhile, opportunities arise from changes in digital-based shopping behavior, while threats emerge from the dominance of large retailers, e-commerce competition, and economic fluctuations. Based on the SWOT integration, a strategy is formulated that includes strengthening digitalization, optimizing online marketing, improving service quality, and modernizing operational systems to support long-term competitiveness.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

PT. Tirta Fresindo Jaya, specifically its Pasuruan plant as the producer of Pucuk Harum Tea beverage, requires a large supply of steam to support various production processes. This steam is used in the process of heating tea leaves, heating water through a heat exchanger, and heating chemicals and water in the cleaning in place (CIP) process. Steam pressure of 5 bar that is flowed to the process equipment will undergo condensation to produce condensate. To separate steam and condensate, steam traps are used, consisting of several types, namely mechanical, thermostatic, and thermodynamic. However, in operational practice, steam trap malfunctions often occur, either in the form of steam leaks that are wasted with condensate or failure to drain condensate from the system. This condition causes a decrease in the efficiency of the steam piping system and increases the workload of the boiler. As a result, fuel consumption and boiler feed water requirements become greater than ideal conditions. Therefore, this study aims to analyze the energy and operational losses caused by steam trap malfunctions, as well as evaluate their impact on boiler system performance and steam utilization efficiency at PT. Tirta Fresindo Jaya Pasuruan plant.      

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The increasing intensity of business competition requires companies to maintain strong financial conditions to avoid financial distress that may disrupt business continuity. This study aims to assess the financial stability and predict the potential bankruptcy of PT Sido Muncul Tbk for the 2022–2024 period using the Altman Z-Score model. A descriptive quantitative approach was applied, utilizing secondary data obtained from annual reports published by the Indonesia Stock Exchange and the company’s official website. Five key ratios in the Altman model were used as indicators to evaluate the company’s financial position and resilience. The results show Z-Score values of 4.74 in 2022, decreasing slightly to 4.66 in 2023, and rising again to 4.79 in 2024. These scores are significantly above the safe threshold of 2.675, indicating that the company is in a healthy financial state with a very low risk of bankruptcy. Overall, PT Sido Muncul Tbk demonstrates stable financial performance, supported by a strong capital structure and consistent operational results. The Altman Z-Score model also proves to be an effective early-warning tool for identifying potential financial problems.

This study analyzes the synergy between human resource development (HRD), work culture, and decision-making systems in enhancing productivity and organizational performance. The approach used is a descriptive qualitative literature review, with sources from peer-reviewed articles from 2021-2025 obtained through databases such as Google Scholar and ScienceDirect. The research findings indicate that HRD can improve employee competence by up to 28.7%, while work culture contributes 41.4-55% to motivation and collaboration within teams. Decision-making systems, such as AHP (Analytical Hierarchy Process) and BI (Business Intelligence), play a role in optimizing operational efficiency by up to 65%. The synergy of these three elements forms a more adaptive organization to change. One example of the implementation of this synergy can be seen in PT X Jakarta, which experienced a 28% increase in output and employee satisfaction reached 85%. However, the main challenge faced is resistance to change, which can be overcome through effective communication. The practical implications of this study include recommendations for holistic organizational transformation, especially in Indonesia, to face the challenges of global disruption.

This study presents a meta-analysis of open-access accounting information systems (AIS) literature in Indonesia during the digital transition period of 2015-2025. The primary objective is to identify and map the taxonomy of Independent Variables (X) and Dependent Variables (Y) predominantly used in academic and practical research. Through a systematic review of 15 key accredited articles with Digital Object Identifiers (DOI), this research finds that AIS success determinants (Variable X) have evolved from purely technical factors to integrative clusters encompassing Human Capital (competence, training), Organizational (culture, management commitment), and Technological (infrastructure, internal control) aspects. Meanwhile, Dependent Variables (Y) have shifted from mere technical user satisfaction to strategic impacts such as financial report quality, operational efficiency, and MSME business performance. These findings indicate that AIS research in Indonesia is heavily influenced by public sector regulatory contexts and cloud technology adoption in the MSME sector. This report serves as a reference framework for future researchers to explore emerging variables such as artificial intelligence and cybersecurity behavior within the accounting ecosystem.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.  

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

Increasing traffic activity at unsignalized intersections often results in congestion and delays. The intersection of Jl. Imam Bonjol and Jl. Daya Nasional experiences these conditions, making performance evaluation necessary. This research tends to scrutinize the intersection performance under existing conditions and five-year projected conditions and to identify appropriate improvement measures. The analysis was carried out using the 2023 Indonesian Road Capacity Guidelines (PKJI 2023) and PTV VISSIM microsimulation software. Data on intersection geometry, vehicle speed, and traffic volume were collected through CCTV observations on weekdays and weekends. The results show that under existing conditions, the degree of saturation reached 1.170 with an average delay of 33.75 seconds (LOS D). In the five-year projection, the degree of saturation increased to 1.239 with an average delay of 53.69 seconds (LOS E). These findings indicate a decline in intersection performance, highlighting the need for alternative traffic management measures to improve operational performance and service levels.    

This study aims to analyze the application of the franchising system on business performance in the beverage franchise business through a case study of Mitra Minuman Siap Saji. The approach used is qualitative with a case study design, with data collection techniques in the form of in-depth interviews, operational observations, and supporting documentation. The results show that the implementation of standardized Standard Operating Procedures (SOPs), franchisor support in the form of training, raw material supplies, and periodic monitoring contribute significantly to improving business stability, product quality consistency, and customer satisfaction. However, there are limitations in flexibility and several communication obstacles that have the potential to affect the effectiveness of the partnership. The relatively strict contract structure also impacts partners' perceptions of local innovation space, although it is generally still viewed as providing business security and business model clarity. These findings emphasize that a balance between franchisor control and partner autonomy, accompanied by open communication and fair contract design, is a key factor in creating sustainable business performance in a franchising system.

This study aims to analyze the implementation of financial reporting and evaluate the economic performance of Koperasi Simpan Pinjam dan Pembiayaan Syariah (KSPPS) Nur Insani during the period from 2022 to 2023. A descriptive qualitative method was employed, utilizing secondary data from the Statement of Financial Position, Cash Flow Statement, and Operating Results Report published by the cooperative. The findings indicate that KSPPS Nur Insani has implemented a computerized financial recording system, which enhances accuracy, transparency, and operational efficiency. However, the cooperative experienced significant financial pressure in 2023, as indicated by decreases in cash and cash equivalents, total assets, and temporary syirkah funds, both short-term and long-term. These declines reflect weakened liquidity and reduced fundraising capacity from members. Despite these challenges, the cooperative succeeded in increasing its Net Operating Results (SHU), demonstrating effective revenue management and operational cost control. Overall, the profitability of KSPPS Nur Insani remains positive, yet strategic improvements are necessary, particularly in strengthening liquidity management, increasing funding sources, optimizing asset utilization, and enhancing digital system implementation to support better financial governance. These strategic efforts are expected to improve business sustainability and maintain member trust in the future.