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The amount of rainfall can be used to estimate the runoff that enters a reservoir. Runoff is influenced by land use, and soil type greatly affects the amount of runoff that will occur. This study discusses the development of a hydrological model with the application of the Hydrologic Engineering Center (HEC-HMS) in the Karangmumus watershed using soil data that has been verified in the field and divided into soil zones based on soil permeability testing in the laboratory. With the help of Geographic Information System (GIS) and Geospatial Hydrological Model (HEC-GeoHMS) applications, it is possible to identify the flow of the Karangmumus watershed and the Lempake Dam in Kalimantan by simulating the rain runoff process. The hydrological model was developed in the HEC-HMS by recording daily rainfall events from 2009 to 2019. With a daily period, then, the zoning soil type data was entered based on the results of soil permeability testing with the help of the application of the soil conservation curve method (SCS-CN), then discharge transformation, and calculation of water loss, including routing with Muskingum and SCS-Hydrograph applications. Based on the distribution of the CN value, the theoretical runoff is calculated and then calibrated with the observed discharge in 2017 and 2018, and then validated with the observed discharge in 2019, showing good results with a coefficient of determination between 0.89 to 0.92. Doi: 10.28991/CEJ-2023-09-01-06 Full Text: PDF

The live forensics method was used to acquire lawful digital evidence data from device memory in the WhatsApp application, particularly for desktop-based WhatsApp. There has been little research on live forensics on desktop-based WhatsApp applications. These studies involve mimicking crime cases in cyberspace using the Instant Messenger application. Much of the acquisition process is completed only once, even though many possible conditions may arise during the purchase process. Investigators or experts can employ digital evidence data discovery to identify crimes that have occurred. The stages of research carried out in detecting digital evidence are data collecting, the examination process, and the acquisition of analysis and reporting outcomes. During the data-gathering phase, a case simulation dataset was obtained. The examination process stage results in the integrity of the duplicated data; data reduction is performed on data related to fundamental operating system components, influential application features, and incomplete data. According to the investigation findings, there are difficulties in looking for digital evidence, and the features of each digital evidence vary. The simulation file contained many reports on the finds of digital evidence. As a data acquisition method, the characteristics of live forensics are limited to the data retrieval process in RAM. Based on these findings, it is possible to conclude that the data collection and examination processing were completed effectively. The analysis results were acquired, and the report was presented with the indicated digital evidence. Further study can be paired with chip-off procedures on RAM devices for data recovery.

There are many methods used in resolving data clustering. One of them is the Fuzzy C-Means (FCM) method, which is a reliable method to solve clustering problems in the East Java region. This study aims to determine the optimum cluster in the East Java region which can help the government to identify problems and assist policymaking in regencies/cities in East Java province. The research process uses data from the central statistical agency, namely the unemployment rate and poverty rate from 2010 to 2015. In this study, the Davies Bouldin Index (DBI) is used as a cluster validation test for determining the optimum cluster. Unemployment rate and poverty rate data were analyzed using RStudio. From the calculation of the FCM method and also the determination of the optimum cluster results obtained in 2 clusters with a DBI value of 1.2759, 3 clusters with a DBI value of 0.9937, 4 clusters with a DBI value of 0.8737. The optimum cluster is in 4 clusters with a minimum DBI value.