1 10 4 https://repository.horizon.ac.id/files/original/5c51cfdafe9ec757f3af2e0a578b6071.pdf 3bd1b3e3bf09297aab3d4871a46204ba Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 2 MARCH 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementation of Big Data Analytics and its Challenges in Digital Transformation Era: A Literature Review Subject The topic of the resource Big Data, Big Data Analytics, Implementation, Challenges, Systematic Literature Review Description An account of the resource 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. Creator An entity primarily responsible for making the resource Firsty Larastiyana Susanto1,*, Agustin Maulani2, Siti Nuran Nuri3 Date A point or period of time associated with an event in the lifecycle of the resource 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/93ab1f2e2887fa0aa934b285920b01e3.pdf 1748cb3f0099ea59d3fed15aee47e7a5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 2 MARCH 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Evaluation of Online Admissions Information System Utilizing the Technology Acceptance Model Subject The topic of the resource PMB Online, Technology Acceptance Model, Management Information Systems. Description An account of the resource 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. Creator An entity primarily responsible for making the resource Chuan De Lian1,*, Nauval Dwi Yuzhak2 Date A point or period of time associated with an event in the lifecycle of the resource 2024 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/c86c9ac45740ac8a7f5f7d07e0732351.pdf 5ec30c58db9f5f3c5f9c1e19532292be Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 2 MARCH 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of Waste Generation in Yogyakarta Special Region Province Using ARIMA Model Subject The topic of the resource ARIMA, CRISP-DM, Time Series, Prediction, Waste Description An account of the resource 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 decision-making. 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. Creator An entity primarily responsible for making the resource Resti Nur Azizah1,*, Ranggi Praharaningtyas Aji2 Date A point or period of time associated with an event in the lifecycle of the resource 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/3a415e43bc52d89009d6348df4831176.pdf 97d7ec3221723bf14cfb678f78567e5c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 2 MARCH 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence Subject The topic of the resource Skin Cancer, CNN, InceptionV3, EfficientNetB0, ResNet50, MobileNetV2 dan NASNetMobile Description An account of the resource 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%. Creator An entity primarily responsible for making the resource Sabda Norman Hayat1*, Lulu’ul Watef2, Rarasmaya Indraswari3 Date A point or period of time associated with an event in the lifecycle of the resource 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text