The reliance on technology to support various aspects of human life has become very relevant and significant. The steadily increasing volume of data (called big data) plays an important role in machine learning, Internet of Things (IoT) and other intelligence applications. The interaction between big data and operations in various aspects of human life is a concern, this systematic literature review provides a descriptive aspect of the literature review, the research results offer a comprehensive overview of existing big data implementations ranging from 2019 to 2023. Other than this, we discuss the general challenges for researchers in using various big data sources in different crises. This study, especially revealing the implementation and challenges of big data in general, is expected to provide a knowledge base for researchers to delve deeper and more specifically.

Admission of New Students covers the entire process, from registration and administrative selection to graduation announcement. This activity is an annual routine that is the first step in finding quality prospective students. Therefore, there is a need for a valuable and user-friendly online PMB website. Based on these problems, it is necessary to assess the success of implementing the PMB Online system at Amikom Purwokerto University using the Technology Acceptance Model (TAM) approach. This system development method refers to TAM, which emphasizes user perceptions of two main variables: usefulness and ease of use. Variables that describe user acceptance of the PMB Online system include Perceived Ease of Use, Perceived Usefulness, Perceived Enjoyment, Attitude Towards Using, and Intention to Use. The results of this study indicate that Perceived Ease of Use has a positive effect on Perceived Usefulness, and Perceived Usefulness has a positive effect on Attitude Towards Using and Intention to Use. Meanwhile, Perceived Enjoyment also has a positive effect on Attitude Towards Using. The results of this study are expected to identify weaknesses and improve certain aspects to optimize the implementation of PMB Online at Amikom Purwokerto University.

The rapid development of information technology and good technology services is an expectation for all people, organisations, institutions, and universities in order to support activities, facilitate their activities and business processes. A business organisation needs to adapt to the current development of information technology. IT service management is a method of managing information technology systems that is centred on the consumer perspective of information technology services on the company's business. Company XYZ has implemented information technology that is intended for users to be able to carry out service management activities as well as processing company administrative data. Service Operation is a lifecycle phase that includes all the day-to-day operations of IT service management. Based on the results of research on 3 processes, namely Event Management, Request Fulfilment and Problem Management, it is necessary to increase the need for adequate hardware, software and infrastructure in meeting the needs of Company XYZ in working more effectively. Then the need for proper application of SOPs and modules to employees is somewhat more efficient in using information technology.

Skin cancer is a type of cancer that can cause death, where skin cancer is included in the 15 common cancers that occur in Indonesia. The number of skin cancer sufferers was around 6,170 cases of non-melanoma skin cancer and 1,392 cases of melanoma skin cancer in 2018 in Indonesia. Therefore, research related to skin cancer classification is increasing. This is done as an initial step in detecting whether a lesion can be said to be cancerous or not. The deep learning approach has certainly shown promising results in carrying out classification, so this research proposes a deep learning-based method used for skin cancer classification. The proposed approach involves Convolutional Neural Networks with the ISIC 2017 dataset. The models used for skin cancer classification are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, and NASNetMobile. The highest accuracy of the single model produced reached 69.3% using the MobileNetV2 model. An ensemble model combining the five models was also tested and produced the highest accuracy compared to other single models with an accuracy result of 80.6%.

Waste management poses a significant challenge in densely populated urban areas of Indonesia, particularly in the Special Region of Yogyakarta, which grapples with both a large populace and robust tourism industry. The region witnesses a staggering 11.53% annual uptick in waste accumulation, exacerbating the strain on existing waste management infrastructure. Addressing this pressing issue necessitates a comprehensive analysis to forecast waste generation trends, enabling the formulation of effective management strategies. This study employs the CRISP-DM framework, facilitating a structured approach to analysis and informed decisionmaking. Utilizing a time series dataset spanning from 2016 to 2022, detailing waste generation across various districts and cities, the research employs the ARIMA model for predictive analysis. This model, renowned for its suitability in time series forecasting, emerges as the preferred choice. Projections derived from the ARIMA model reveal a notable surge in waste generation across the Special Region of Yogyakarta from 2023 to 2025. Specifically, it is anticipated that the aggregate waste output will escalate from 638 thousand tons in 2022 to 642 thousand tons in 2023, with an anticipated annual increase ranging between 5 to 7 thousand tons thereafter. These forecasts underscore the urgency of implementing proactive measures to mitigate the burgeoning waste management challenges facing the province.

Accurately assessing the life and operating status of transformers has important guiding significance for the formulation of maintenance strategies for power grid companies, and at the same time plays a key role in the risk management of power grid companies. However, the traditional methods for predicting the remaining life of the equipment have the problems of insufficient accuracy or long data training time. In order to achieve a more accurate assessment of the life and status of the transformer, a random forest-based transformer life prediction method is constructed in this paper. Relying on the theory of big data analysis, by mining and analyzing the accumulated data of massive transformers, the life prediction model of the transformer is established and the characteristic parameters affecting the life of the transformer are extracted to predict the life of the transformer. The experimental data research demonstrates that the model can be accurate and effective Predicting the life of transformers has higher prediction accuracy than traditional methods, providing method references for asset management and risk management of power grid companies.

Music serves as a powerful and immediate avenue for the expression of emotions, and a nuanced understanding of musical compositions is crucial for accurately interpreting and appreciating them. This research centers on the examination of robust Pitch Class Profile (PCP) features and Support Vector Machine (SVM) in the realm of music analysis. The initial phase of the study delves into the exploration of pertinent concepts and a myriad of resilient Constant-Q Transform (CQT) methods used in describing chord spectra for audio analysis. Subsequently, the paper elucidates the intrinsic correlation between SVM and speech tonality, outlining the design of a comprehensive system for music chord recognition. Rigorous testing of the system's performance follows, with a particular emphasis on evaluating the recognition rate. The results of these tests underscore the significant enhancement in music chord recognition achieved by the system, highlighting the pivotal role played by robust feature optimization and SVM pattern in bolstering its efficacy. This research not only contributes to the theoretical understanding of music analysis but also provides practical insights into improving the accuracy of music chord recognition systems through innovative feature selection and machine learning techniques.