Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.

Volcanic eruption refers to a natural catastrophe on Earth that poses imminent danger to communities surrounding volcanoes. Therefore, ongoing monitoring of volcanic processes is crucial for effective analysis and observation of volcanic activities preceding an eruption. In response to this, the study presents a novel hybrid time series approach, integrated with machine learning techniques, to enhance the identification and classification of seismic events associated with volcanic eruptions. In this case, time series techniques, including STA/LTA, template matching, and autocorrelation, were implemented to facilitate the detection and classification process. The challenges, however, lie in addressing noise and ensuring accuracy in the analysis of seismic signals. To resolve this, a new hybrid time series method was proposed to improve signal analysis accuracy by integrating multiple time series techniques. In practice, the dataset was collected from Mount Merapi in Indonesia between 2019 and 2021, consisting of a compilation of seismic data categorized by event type, thus enhancing classification accuracy. On top of that, prior to implementing machine learning techniques for signal classification, the hybrid method was employed to efficiently remove noise, ensuring that genuine seismic events were clearly distinguished from spurious signals. Notably, the experimental learning rate was set at 0.01. The results demonstrated that the proposed hybrid method outperformed stand-alone time series techniques, achieving an accuracy of 0.93 to 0.95. This signifies the effectiveness of precise seismic event recognition and categorization, greatly enhancing the volcano monitoring system. Furthermore, the findings offer substantial improvements in the forecasting and risk mitigation associated with volcanic eruptions, hence, advancing reliable seismic analysis methodologies. Ultimately, the method enhances hybrid methods and machine learning for seismic event analysis and volcano monitoring. Doi: 10.28991/HIJ-2025-06-01-08 Full Text: PDF

The quadrotor is one kind of Unmanned Aerial Vehicles (UAV). Quadrotor has the ability to hover with minimal translational velocity approaching a stationary state. This capability is supported by the four rotors. These rotors are used to lift the Quadrotor to fly. These rotors are placed on all four sides of the tip of Quadrotor. In order to fly with good stability, we can use an IMU sensor (Inertial Measurement Unit). Imu sensor consists of some DOF (degrees of freedom) sensors, such as 3-axis accelerometer sensor, 3-axis gyroscope sensor, 3-axis magnetometer sensor, and so on in accordance with the needs of flight. To test the stability of Quadrotor can be done by utilizing the video and image processing methods. This processing act as the ’eyes’ of Quadrotor. Sobel method as one of the image processing algorithms can be used to read the edges of the object. This method can measure the level of stability fly. But before reading the results of the edge must first be converted to black and white format. Otsu method can be used to perform the conversion. Then we find the center point of the result of the conversion of the object being viewed. This point can be used to read the movement of Quadrotor. It is used to determine the position of the quadrotor movement on vertical and horizontal axes. The position can be used as input to control the quadrotor flight stability.

The large amount of coal production in Indonesia generates a significant amount of data that can be used to understand the rank of the coal. To effectively process and interpret this data, our study employs the use of big data techniques, including big data management and big data analysis. Big data management allows us to organize and understand the data patterns, while big data analysis is used to gain insights and knowledge about the data, such as coal rank analysis and identifying the type of coal. Our study uses a python-based approach to define variables and automatically classify the coal rank based on the threshold values obtained from the two basic analyses described earlier. Our results show that this method is able to accurately classify the coal according to the given threshold. We found that according to the Indonesian Coal Standardization based on Pusat Sumber Daya Mineral Batubara dan Panas Bumi (PSDBMP) standard, the calorific value (in adb) is dominated in low to medium calories for 14 boreholes. The coal rank in American Standard Testing and Material (ASTM) analysis is dominated by Lignite A and B for 14 boreholes. The last analysis, according to the atomic ratio, shows that the coal can be classified as Lignite and Subbituminous Coal. Thus, by implementing the big data concept, we can easily analyze the coal classification with comprehensive and large amount of data.

Documents on the Internet have increased in number exponentially; this has resulted in users having difficulty finding documents or information needed. Special techniques are needed to retrieve documents that are relevant to user queries. One technique that can be used is Information Retrieval (IR). IR is the process of finding data (generally documents) in the form of text that matches the information needed from a collection of documents stored on a computer. Problems that often appear on IRs are incorrect user queries; this is caused by user limitations in representing their needs in the query. Researchers have proposed various solutions to overcome these limitations, one of which is to use the Expansion Query (QE). Various methods that have been applied to QE include Ontology, Latent Semantic Indexing (LSI), Local Co-Occurrence, Relevance Feedback, Concept Based, WordNet / Synonym Mapping. However, these methods still have limitations, one of them in terms of displaying the connection or relevance of the appearance of words or phrases in the document collection. To overcome this limitation, in this study we have proposed an approach to QE using the FP-Growth algorithm for the search for frequent itemset and Association Rules (AR) on QE. In this study, we applied the use of AR to QE to display the relevance of the appearance of a word or term with another word or term in the collection of documents, where the term produced is used to perform QE on user queries. The main contribution in this study is the use of Association rules with FP-Growth in the collection of documents to look for the connection of the emergence of words, which is then used to expand the original query of users on IR. For the evaluation of QE performance, we use recall, precision, and f-measure. Based on the research that has been done, it can be concluded that the use of AR on QE can improve the relevance of the documents produced. This is indicated by the average recall, precision, and f-measure values produced at 94.44%, 89.98%, and 92.07%. After comparing the IR process without QE with IR using QE, an increase in recall value was 25.65%, precision was 1.93%, and F-Measure was 15.78%.

The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process.