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This study was conducted to analyze the causes of occupational accidents and to identify potential hazards in the material cutting process at PT Kanaan Global Indonesia using the Job Safety Analysis (JSA) approach. The high number of occupational accidents, totaling 15 cases during the period of December 2024–May 2025, indicates the existence of gaps in occupational safety control in this area. Data were collected through direct observation, interviews with workers, and the collection of historical data from the Occupational Health and Safety (OHS) unit. Risk analysis was carried out using a risk matrix to determine likelihood and severity values, followed by JSA for each work stage. The results identified 25 potential hazards, consisting of 4 risks in the extreme category, 9 in the high category, 7 in the medium category, and 5 in the low category. High-risk hazards were predominantly found in roll cutting and cutting press machines, which are characterized by mechanical hazards. Control recommendations focus on the implementation of engineering controls such as the installation of guarding systems and safety light curtains, improvements in safe work procedures, and increased discipline in the use of personal protective equipment (PPE). These findings demonstrate that the JSA method provides a comprehensive overview of risk sources and serves as an effective basis for formulating occupational accident control strategies within the company.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

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.

This research investigates the development of integrated operational systems connecting terminal and ship operations for docking and berthing time optimization through systematic analysis of historical data. Port efficiency depends critically on minimizing vessel turnaround time, with berth allocation, docking procedures, and cargo operations coordination determining overall port productivity and competitiveness. Through qualitative analysis involving port operators, terminal managers, ship agents, harbor masters, and operations research specialists, this study examines how historical operational data can inform intelligent coordination systems improving berthing efficiency. Results demonstrate that data-driven integration systems incorporating predictive analytics, automated scheduling, and coordinated workflows can reduce average berth turnaround time by 15-30%, improve berth utilization by 20-35%, and decrease operational conflicts by 40-60% through optimized allocation and proactive coordination. Key implementation challenges include data quality and availability, system integration complexity, organizational coordination barriers, and resistance to automated decision support. Findings reveal that historical data-based optimization represents transformative advancement from experience-based scheduling to evidence-driven operational planning supporting port efficiency enhancement, capacity maximization, and service reliability improvement. This research contributes to port operations literature by providing practical frameworks for data-driven berthing optimization applicable to diverse port operational contexts.

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 development of smart cities has become a strategic priority for local governments seeking to enhance citizens’ quality of life, strengthen sustainable development, and improve public space management. Kambang Iwak Park in Palembang represents one of the city’s major urban green spaces that has undergone significant revitalization and serves as a case study for smart city implementation in public areas. This study analyzes the application of Cohen’s six smart city dimensions—Smart People, Smart Living, Smart Government, Smart Economy, Smart Mobility, and Smart Environment—and their relationship with Carmona’s six urban design dimensions. This qualitative–descriptive research utilizes visual observations, historical data, and facility documentation extracted from the provided presentation. The findings indicate that Kambang Iwak Park effectively integrates several smart city dimensions, particularly Smart Living, Smart Environment, and Smart Mobility. Nonetheless, issues such as irregular parking, insufficient smart services, and poorly organized street vendors remain challenges. The study concludes that integrating smart city principles with urban design concepts enhances public space quality and supports sustainable urban development in Palembang.

This study aims to determine how to optimize container loading and unloading activities at PT. Pelindo Multi Terminal Sibolga Branch. Container loading and unloading activities are one of the main activities in port operations that greatly affect the efficiency and productivity of maritime logistics. PT. Pelindo Multi Terminal Sibolga Branch as one of the port service providers has an important role in ensuring the flow of goods in the West Coast of North Sumatra. This study is to analyze and identify factors that affect container loading and unloading performance as well as optimization strategies to improve time, costs, and resource utilization. The methods used in this study include field observations, interviews with related parties, and analysis of historical data on operational activities. The results of the study indicate that the main obstacles come from limited equipment, lack of coordination between parties, and low utilization of information technology. Suggested optimization efforts include the addition of heavy equipment for unloading, increasing workforce training, and digitalizing monitoring and scheduling systems.  

In the automotive manufacturing industry, efficiency in quality control is a crucial factor to ensure consistent product quality. Conventional Quality Assurance (QA) processes using manual record-keeping often face challenges such as delayed reporting, human errors, and difficulty in tracking historical data. This study aims to design and implement a QA performance dashboard based on digital forms at PT Dharma Polimetal, Tbk, to enhance efficiency in production quality control. The research methodology includes direct field observation, collection of production and QA data, mapping of QA process flows, interactive dashboard interface design, and system trial implementation. The designed dashboard focuses on four main aspects: QA Incoming, QC Line, QC Gate, and Customer Handling, each containing measurable performance indicators and quality parameters. Initial implementation results indicate significant improvements in QA process monitoring, faster reporting of inspection results, and easier real-time data access for both production teams and management. The system enables early detection of potential quality issues, supports rapid decision-making, and facilitates internal and external audits. Moreover, the use of digital forms within the dashboard enhances data accuracy, minimizes human error, and creates structured historical records for long-term analysis. This study provides a tangible contribution to the digitalization of QA systems, strengthening sustainable quality control practices in the automotive industry, thereby ensuring consistent productivity and product quality.

Research on stock splits has been widely conducted in Indonesia and internationally, as stock splits are considered an important corporate action that can influence investor perception and stock performance. However, the motivations and consequences of stock splits remain diverse, ranging from efforts to increase stock liquidity, adjust market price ranges, attract new investors, or signal positive corporate prospects. This study aims to empirically reanalyze the effect of stock splits on trading volume and stock prices of companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. Specifically, the research investigates whether significant differences exist between trading activities and stock price levels before and after the stock split event. The data used in this study are historical in nature, consisting of stock split announcements, daily trading volume, and stock price movements surrounding the event period. To test the hypotheses, this research employs both the paired-sample t-test and the Wilcoxon signed-rank test as statistical tools. These tests are appropriate because they allow for the comparison of two related samples, namely the stock performance indicators before and after the split. The selection between the two methods depends on the distribution of the data, where the paired t-test is used if the data is normally distributed, while the Wilcoxon test is applied if the normality assumption is not met. This study is categorized as moderate TKT (Technology Readiness Level 4–6) because it uses secondary historical data and focuses on empirical statistical analysis rather than experimental or simulation-based approaches. By examining stock split events within the specified period, this research contributes to the understanding of whether stock splits in Indonesia are primarily cosmetic in nature or if they generate real economic impacts on liquidity and stock valuation. The findings are expected to provide useful insights for investors, market analysts, and policymakers in assessing the relevance and effectiveness of stock splits as a corporate strategy.

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.

Bitcoin is one of the most prominent digital assets in the modern financial era due to its high volatility and huge profit potential. However, its extreme price volatility also makes it a high-risk asset, so a reliable forecasting approach is needed to help investors make more rational decisions. This study aims to forecast Bitcoin price using the Moving Average (MA) method, specifically MA3, by utilizing monthly historical data of Bitcoin price in USD currency obtained from investing.com website. The MA3 method was chosen for its ability to smooth out short-term fluctuations and identify the direction of price trends. The forecasting process is performed by calculating the average of the last three months' prices for each point in time and compared to the actual price to evaluate its accuracy. The evaluation is done using various prediction error metrics, namely Error, Absolute Error, Squared Error, and Percentage Error. The results of the analysis show that the MA method provides a fairly representative picture of price trends and can be used as an early indicator in short-term investment strategies. Thus, the Moving Average method proves to be a simple but effective prediction tool, especially for novice investors in the dynamic crypto asset market.

The analysis in this study aims to evaluate the influence of Restaurant Tax, Parking Tax, and Groundwater Tax on the Regional Original Revenue (PAD) of Wonogiri Regency. A quantitative method was applied in this research, utilizing historical data collected from the Regional Financial Management Agency (BPKD) of Wonogiri Regency for the 2020–2024 period. The results indicate that the Restaurant Tax does not have a significant effect on PAD, with a significance value of 0.204. In contrast, both the Parking Tax and Groundwater Tax have a positive and significant impact on PAD, with significance values of 0.001 and 0.007, respectively. These findings suggest that although the number of restaurants has increased, the demand for restaurant services has not kept pace. Meanwhile, the growth in motor vehicle numbers and public awareness of groundwater tax obligations have contributed to the increase in PAD. This study recommends enhancing the effectiveness of tax management and improving public outreach regarding tax obligations by the local government.

This study aims to analyze the revenue budget of PT Securindo Packatama Indonesia in 2020. The purpose of this study is to evaluate the suitability between the planned budget and the realization of the company's revenue and to identify factors that influence revenue fluctuations. The type of research used is descriptive research with a quantitative approach. The results of the study indicate significant revenue fluctuations throughout the year which are influenced by operational activities, the number of projects, and the efficiency of vehicle use. There is a difference between the budget and the realization of revenue which indicates the need for improvement in budget preparation. Thus, the company is advised to conduct regular budget evaluations, use more accurate historical data, and improve the accuracy of operational projections.    

This research aims to analyze the compatibility of the sea sand export policy, as outlined in Government Regulation No. 26 of 2023 and its implementing regulations in Minister of Trade Regulations No. 20 and 21, with the principles of ecological justice. The research is driven by concerns over the ecological impacts of sea sand exploitation and the potential conflict between economic interests and environmental protection. Using a normative juridical approach and analysis of prevailing laws and regulations, the study finds that the current regulatory framework does not fully reflect the principles of ecological justice. The policy fails to ensure equitable protection for coastal communities and the marine environment. Government Regulation No. 26/2023 does not explicitly mandate Environmental Impact Assessments (AMDAL), does not emphasize conservation as a fundamental principle, and allows room for exploitation in vulnerable coastal and small island areas. Moreover, historical data reveals that similar policies in the past have led to coastal erosion, the loss of outermost islands, and state losses due to illegal practices. Therefore, without proper reassessment and alignment with environmental justice principles, this policy risks exacerbating ecological inequalities.

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.

Accurate sales forecasting is essential for stakeholders to make strategic decisions. This study aims to compare the performance of two deep learning models, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), in forecasting domestic motorcycle sales produced by AISI member manufacturers. The forecast is based on historical data from January 2021 to December 2024. The model was trained using time series data and the forecasting results for the period January to March 2025 were evaluated using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model produces lower MAE and MAPE values than CNN, which shows its superiority in providing more accurate and consistent predictions. On the other hand, the CNN model has lower RMSE and MSE values, thus being able to reduce large prediction errors. By comparing the results of LSTM, CNN, and actual data forecasting, the LSTM model is more suitable for forecasting motorcycle sales in Indonesia

This paper's goal is to undertake a critical evaluation of Nigerian democracy's speed, practices, patterns, priorities, issues, and future. Although Nigeria is still run by democratically elected officials, after a century as a political entity, Nigeria has yet to institutionalize democracy at the federal and state levels. After more than 50 years of political independence, the study examines some of the challenges Nigeria faces in institutionalizing democracy. These include the nation's colonial past mixed with the whims of deeply ingrained ethnicity; a smug and extravagant leadership; the military's constant meddling in the democratic process; electoral fraud; widespread poverty; and a high rate of illiteracy. According to the publication, corruption is the main cause of most of the aforementioned issues and has essentially taken on a life of its own in Nigeria. However, the study argues that despite the aforementioned, there is still hope for a politically secure and democratically viable People's enthusiasm to vote, the multi-party system's relative stability and sustainability, and the widespread recognition that the only legitimate and well-liked path to gaining political power is through the voting booth are what define the nation. The historical approach of data analysis—simple descriptive collation and analysis of historical data—is used in this paper, which draws its data from primary and secondary source materials. 

This article examines the history and development of the Islamization of education in Indonesia. This study aims to analyse Islamisation of education through Islamic education by looking at historical data that shape the patterns and characteristics of Islamic education in Indonesia, as well as identifying critical points that influence the direction of its development. The research uses the theory of historical institutionalism. Through a literature study with a qualitative approach, the research reveals that the development of Islamic education in Indonesia followed a strong path dependence pattern, where decisions in the colonial and early independence periods shaped a relatively stable institutional path. The findings show that the process of educational Islamization underwent institutional layering, where new elements were added without completely erasing the old structure. This research contributes to the theoretical understanding of the dynamics of institutional change in the context of Islamic education in Indonesia.

One of the biggest problems faced by several countries is the rapid increase in population growth, especially in an archipelagic country like Indonesia. One of the cities in North Sumatra Province that has experienced a significant increase in population is Medan City. This study discusses the population growth each year in Medan City. This study predicts the future population by combining historical data on population, birth rate, death rate, and migration using the Adams-Bashforth-Moulton approach in logistic modeling. This study shows the progress of the approach in predicting the population growth of Medan City. With an area of ​​265.10 thousand km2, and a population reaching 2,474,166 people in 2023. The Adams-Bashforth-Moulton method as a logistic growth model is very effective for decision making in predicting population growth in the city of Medan.

The purpose of this research is to develop a data search system for thesis and internship reports at the Faculty of Engineering Library of Gajah Putih University Takengon (UGP). This search engine will be created and used to help students and library employees access thesis and internship report information. Analysis of user needs, system design, creation of effective search algorithms, and evaluation of system performance are all topics that will be discussed in this thesis. Interviews with potential users, satisfaction surveys, and historical data collection of library usage are the methods used. It is expected that the results of this research will help library users find and retrieve thesis and internship report data and improve the accessibility and availability of academic information at the UGP Faculty of Engineering. When search engine technology is used, it is expected that the time required for Information will increase productivity, improve efficiency, and support the academic development of students at UGP.