Publication Search

Abstract. This research aims to analyze the effectiveness and reliability of production machines in the process of making public street lighting poles (PJU) at PT. XYZ The main problem faced by the company is high machine downtime so that production targets are not achieved. Therefore, a method is needed that is able to measure machine effectiveness as a whole and identify the main causes of production losses. The method applied includes Overall Equipment Effectiveness (OEE) to measure machine effectiveness based on three components, namely availability, performance and quality. The Total Productive Maintenance (TPM) approach is used to identify factors causing low effectiveness through Six Big Losses analysis. Apart from that, Mean Time To Repair (MTTR) and Mean Time Between Failure (MTBF) are calculated to determine the level of machine reliability. The data used includes machine working hours, downtime, operating time, production quantities, defective products, as well as machine damage and repair data. The analysis results are expected to show the level of machine effectiveness and identify the dominant factors causing downtime. Based on these results, improvement proposals are prepared to reduce downtime, increase machine reliability and improve production productivity Keywords: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime, Efektivitas   Abstrak. Penelitian ini bertujuan untuk menganalisis efektivitas dan keandalan mesin produksi pada proses pembuatan tiang penerangan jalan umum (PJU) di PT. XYZ Permasalahan utama yang dihadapi perusahaan adalah downtime mesin yang tinggi sehingga target produksi tidak tercapai. Oleh karena itu, diperlukan metode yang mampu mengukur efektivitas mesin secara menyeluruh dan mengidentifikasi penyebab utama kerugian produksi. Metode yang diterapkan meliputi Overall Equipment Effectiveness (OEE) untuk mengukur efektivitas mesin berdasarkan tiga komponen, yaitu availability, performance, dan quality. Pendekatan Total Productive Maintenance (TPM) digunakan untuk mengidentifikasi faktor penyebab rendahnya efektivitas melalui analisis Six Big Losses. Selain itu, dilakukan perhitungan Mean Time To Repair (MTTR) dan Mean Time Between Failure (MTBF) untuk mengetahui tingkat keandalan mesin. Data yang digunakan mencakup jam kerja mesin, downtime, waktu operasi, jumlah produksi, produk cacat, serta data kerusakan dan perbaikan mesin. Hasil analisis diharapkan dapat menunjukkan tingkat efektivitas mesin dan mengidentifikasi faktor dominan penyebab downtime. Berdasarkan hasil tersebut, disusun usulan perbaikan untuk mengurangi downtime, meningkatkan keandalan mesin, dan memperbaiki produktivitas produksi Kata kunci: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime Mesin, Efektivitas Mesin

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

Information technology services have an important role in supporting operational activities and digital services at PT Pertamina Patra Niaga Regional Sumbagsel through the MyPertamina application. However, several service disruptions are still encountered, such as server downtime, database errors, system bugs, login failures, and delays in transaction verification, which affect service quality and operational effectiveness. This study aims to analyze the maturity level of information technology services using the ITIL V4 framework, focusing on Incident Management and Problem Management practices. The research employed a mixed methods approach with descriptive analysis through observation, interviews, questionnaires, SWOT analysis, RACI mapping, maturity level assessment, and gap analysis. The results indicate that Incident Management obtained a maturity value of 3.09 and Problem Management obtained 2.91, both categorized at Level 3 (Defined Process). These findings show that service management processes have been implemented and documented properly, although several aspects still require improvement, particularly in monitoring, documentation, root cause analysis, and process evaluation. Therefore, the implementation of ITIL V4 practices is expected to improve the effectiveness, consistency, and quality of information technology service management within the organization.

The implementation of predictive maintenance supported by SAP Plant Maintenance (SAP PM) at PT Xyz has proven to be effective in reducing machine downtime, lowering maintenance costs, and improving asset reliability. The integration of SAP PM with Industry 4.0 technologies such as IoT sensors, AI-based analytics, and real-time notification systems strengthens operational efficiency and ensures continuous performance. Empirical results show improvements in key performance indicators, including a 20-25% reduction in downtime, a 30% reduction in maintenance costs, an increase in asset availability to 97%, an MTBF extension of up to 511 hours, and an OEE rate of 92.1%. These findings highlight the strategic role of digital predictive maintenance in increasing competitiveness and supporting long-term sustainability in manufacturing operations.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

Penelitian ini menganalisis pengaruh topologi star terhadap performa jaringan komputer di era digital, di mana akses internet Indonesia mencapai 72,78% menurut BPS (2024), menuntut infrastruktur andal dan efisien. Tujuan utama adalah mengevaluasi keunggulan seperti kemudahan pemeliharaan, performa stabil, serta keamanan terpusat, sekaligus keterbatasan kritis berupa single point of failure, biaya kabel tinggi, dan skalabilitas terbatas, dengan mitigasi melalui protokol redundansi FHRP. Metode yang digunakan adalah studi kepustakaan digital melalui analisis literatur dari jurnal, e-book, dan sumber daring. Hasil menunjukkan topologi star unggul untuk LAN skala kecil-menengah dengan throughput tinggi dan deteksi kesalahan mudah, meskipun rentan kegagalan pusat; integrasi FHRP berhasil meminimalkan downtime di bawah 1% serta mencapai uptime 99,9%. Secara keseluruhan, topologi star dikombinasikan redundansi menjadi solusi optimal untuk lingkungan enterprise modern, dengan saran penggunaan switch stackable, monitoring SNMP, dan VLAN untuk meningkatkan skalabilitas serta keamanan.

Penelitian ini menganalisis pengaruh topologi star terhadap performa jaringan komputer di era digital, di mana akses internet Indonesia mencapai 72,78% menurut BPS (2024), menuntut infrastruktur andal dan efisien. Tujuan utama adalah mengevaluasi keunggulan seperti kemudahan pemeliharaan, performa stabil, serta keamanan terpusat, sekaligus keterbatasan kritis berupa single point of failure, biaya kabel tinggi, dan skalabilitas terbatas, dengan mitigasi melalui protokol redundansi FHRP. Metode yang digunakan adalah studi kepustakaan digital melalui analisis literatur dari jurnal, e-book, dan sumber daring. Hasil menunjukkan topologi star unggul untuk LAN skala kecil-menengah dengan throughput tinggi dan deteksi kesalahan mudah, meskipun rentan kegagalan pusat; integrasi FHRP berhasil meminimalkan downtime di bawah 1% serta mencapai uptime 99,9%. Secara keseluruhan, topologi star dikombinasikan redundansi menjadi solusi optimal untuk lingkungan enterprise modern, dengan saran penggunaan switch stackable, monitoring SNMP, dan VLAN untuk meningkatkan skalabilitas serta keamanan.

Penelitian ini mengkaji transformasi strategis manajemen risiko finansial melalui penerapan Digital Twins (DT) dengan pendekatan Systematic Literature Review (SLR). DT berevolusi dari sekadar model manufaktur menjadi sistem cerdas yang mampu memprediksi perilaku entitas finansial secara real-time. Hasil kajian menunjukkan bahwa DT mendukung kerangka Prevention, Preparedness, Response, Recovery (PPRR), memperkuat resiliensi rantai pasok, serta meningkatkan efektivitas stress testing perbankan sesuai regulasi Basel III. Selain itu, DT berperan dalam deteksi penipuan, akuntansi karbon, dan simulasi kebijakan makroekonomi. Studi ini menegaskan bahwa DT mampu meningkatkan akurasi prediksi risiko hingga 95% dan mengurangi downtime operasional sebesar 20%. Namun, keterbatasan standar global, integrasi data lintas sistem, serta hambatan regulasi masih menjadi tantangan utama. Agenda riset masa depan diarahkan pada pengembangan interoperabilitas global, integrasi teknologi AI, serta evaluasi ROI jangka panjang untuk memperkuat ketahanan finansial berbasis DT.

This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

Steam turbines remain a core technology in thermal power generation and continue to evolve through advances in aerothermal design, materials, control strategies, and digital maintenance. This paper presents a systematic literature review (SLR) of recent international studies published between 2020 and 2025 to synthesize current developments in steam turbine performance and thermal efficiency improvement. Article identification was conducted through SCOPUS using keywords related to turbine efficiency, blade/nozzle optimization, failure analysis, and material enhancement. The selected studies were analyzed thematically across four domains: (1) design and optimization using CFD/FEA, (2) material and structural resilience, (3) operational performance under variable/part-load conditions, and (4) integration with hybrid renewable systems and predictive maintenance. The reviewed evidence indicates that CFD-based nozzle/blade optimization and advanced control approaches can yield measurable efficiency improvements (approximately 2–7.3%), while material innovations and enhanced cooling strategies improve durability by mitigating thermal stress and fatigue risks. In parallel, digitalization through IoT-based predictive maintenance and additive manufacturing is increasingly reported as a pathway to reduce downtime and accelerate component production. However, recurring gaps include limited real-world validation, insufficient studies in humid/tropical environments, and a lack of long-term economic/lifecycle assessments. Future work should prioritize experimental or field verification, region-specific performance studies, and integrated techno-economic evaluation to support broader deployment of high-efficiency steam turbine systems.

Unexpected downtime of a 2 kg/hour coffee grinder is crucial in cafe operations, thus less guaranteeing the availability of the grinder. The purpose of component replacement and repair planning is to obtain a prediction of the maintenance and repair schedule and costs in the 2026 period. The component replacement planning method includes collecting previous maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component life, predicting technician costs, predicting supporting work equipment and supporting materials to be used in maintenance, predicting the time to replace spare parts or reinstall components after repair, estimating maintenance and repair costs for the 2026 period, and calculating the ratio of maintenance costs to profits. The results of component replacement and repair planning obtained maintenance costs for the 2026 period are IDR 2,350,000, - with an estimated coffee grinder rental rate of IDR 25,000/hour which has the potential to be rented for 1440 hours/year, and the ratio of maintenance costs to profits is 6.5% which implies that the coffee grinder with a capacity of 2 kg / hour is still suitable for use for the next few years and still has the opportunity to make a profit.

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.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

Corrective maintenance is one of the maintenance strategies performed after system failures or malfunctions occur, particularly in diesel power plants (PLTD) that play a crucial role in supplying electricity to remote areas such as Jayapura. This study aims to identify the types of failures occurring in diesel engines at the Jayapura PLTD and to evaluate the corrective maintenance actions implemented. Data were collected through direct observation during an internship program, interviews with technicians, and analysis of historical maintenance and failure records. The results indicate that the most common failures occurred in the lubrication system, fuel system, and cooling system. Corrective actions included component repairs, spare part replacements, and system adjustments. The application of timely and appropriate corrective maintenance significantly reduced machine downtime and improved the reliability of the power generation system. This study recommends integrating corrective and preventive maintenance strategies to maximize operational efficiency of the PLTD.

This study analyzes the maintenance process of the Roll Bending machine used in the wind tower production line at PT Kenertec Power System, Cilegon, Indonesia. The Roll Bending machine plays a crucial role in shaping steel plates into cylindrical shell components, which are later assembled into wind tower sections. The objective of this research is to identify maintenance patterns, types of failures, and improvement strategies to ensure machine reliability and operational efficiency. The research employed observation, interviews with maintenance personnel, and documentation review to collect relevant data. The findings show that the machine experienced multiple failures, mostly related to hydraulic system leaks, PLC programming errors, and component wear such as cylinders, seals, and gear pumps. A significant increase in corrective maintenance activities occurred between August 2023 and April 2024, particularly in February 2024, indicating the need for a more consistent predictive maintenance strategy. The implications of this study highlight that optimized maintenance scheduling and monitoring are essential to reduce downtime, avoid production delays, and maintain product quality. This research is expected to support maintenance decision-making and contribute to the improvement of industrial machine reliability in wind tower manufacturing operations.

Damage to rotable axle components on CAT KT4 Loaders in the mining industry is often the main cause of operational downtime. This study aims to analyze the frequency of damage to axle components and evaluate the effectiveness of preventive maintenance to reduce downtime duration and repair costs. Damage data collected from January to August 2024 were quantitatively analyzed to identify the most frequent damage patterns, especially seal leaks and brake modules. The results show that the implementation of preventive maintenance, such as regular seal replacement and hydraulic pressure checking, can significantly reduce the frequency of breakdowns and improve operational efficiency. The research also provides recommendations on maintenance strategies that can be implemented to extend component life and reduce overall downtime.

This study analyzes failures in the bending machine at PT. XYZ and determines maintenance priorities to reduce downtime and improve production efficiency. The company often faces repeated breakdowns, especially in hydraulic and control components, which negatively impact productivity. To address these issues, the research applies Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The study employs a descriptive qualitative approach using downtime and repair data from September 2024 to February 2025. FMEA was conducted to identify failure modes, effects, and causes, and to calculate the Risk Priority Number (RPN) as a basis for prioritization. FTA was then applied to trace root causes by mapping logical relationships among contributing factors leading to the top event. Recommendations were formulated with the 5W+1H method to propose preventive maintenance actions. The results indicate that the hydraulic valve is the most critical component, with an RPN value of 504 due to oil contamination. The main causes include damaged filters, improper oil usage, and lack of a cooling system. The hydraulic cylinder seal and back gauge were also found to contribute significantly to machine failures. FTA analysis revealed root causes such as inadequate maintenance procedures, unsuitable materials, and insufficient inspections. The proposed improvements involve regular replacement of oil filters, structured lubrication schedules, installation of oil coolers, and technician training to strengthen compliance with standard procedures. Overall, the integration of FMEA and FTA provides a systematic approach to identify critical components and root causes, enabling PT. XYZ to implement preventive strategies that minimize failures, reduce downtime, and improve bending machine performance sustainably.

In the modern computing era, servers face significant challenges in data storage due to hardware failures, cyber attacks, or human errors. The problem highlighted focuses on the impact of file systems on three critical aspects: data integrity (accuracy and consistency of data without corruption), data recovery (the ability to restore data after a failure), and failure resilience (fault tolerance, such as redundancy and journaling to prevent downtime). The main issue is that traditional file systems like FAT32 or NTFS are often susceptible to fragmentation, metadata loss, or long recovery times, which can lead to data loss of up to 20-30% on enterprise servers, especially in high-traffic environments like cloud computing.A simple problem-solving process is conducted through a straightforward comparative analysis approach: (1) A literature review of popular file systems (ext4, ZFS, Btrfs); (2) Failure simulations using tools like fsck and stress testing on virtual servers (e.g., via KVM or Docker); and (3) Measuring performance metrics with benchmarking tools like Bonnie++ for I/O throughput, recovery time, and error rates. This process is designed to be simple, requiring only a virtual lab setup without expensive hardware, and is analyzed quantitatively with descriptive statistics.The solution to the problem indicates that advanced file systems like ZFS or Btrfs provide significant improvements: data integrity is up to 95% more secure through automatic checksums, data recovery is achieved in minutes through snapshots and RAID integration, and failure resilience is higher with copy-on-write features. The main recommendation is to migrate to journaling-based file systems for servers, combined with automated backups, which can reduce the risk of downtime by up to 50%. This research provides practical guidance for system administrators to enhance server reliability without excessive additional costs.

PT. YGI, an automotive manufacturing company, uses the Press Injection GB-36 machine in its production process. However, this machine frequently experiences failures, causing high downtime and reduced production efficiency. This study aims to identify the types of failures, determine the most critical components, and propose a maintenance system using the Reliability Centered Maintenance (RCM) approach. The method used is descriptive qualitative, involving FMEA (Failure Mode and Effect Analysis), Logic Tree Analysis (LTA), and maintenance action selection. Data were collected through observations, historical failure documentation, and interviews. The analysis results indicate that components such as the injection nozzle, heating, and clamping are the most critical, contributing over 80% of total failures based on Pareto analysis and the highest RPN values in FMEA. Proposed maintenance actions include Condition Directed and Time Directed approaches. Additionally, the maintenance system is supplemented with standard operating procedures (SOP) and routine inspection schedules to improve machine reliability and reduce production downtime. This study is expected to enhance the efficiency and productivity of the Press Injection GB-36 machine at PT. YGI through the appropriate implementation of RCM.

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.