Evaluasi Keamanan Fitur Tarik Tunai Cardless pada Aplikasi BRImo Menggunakan PIECES

Financial technology (fintech) merupakan perkembangan teknologi yang memudahkan transaksi keuangan di era digital saat ini. Salah satu aplikasi fintech dalam layanan mobile banking yaitu aplikasi BRI Mobile (BRImo). Pengguna aplikasi BRImo masih belum sepenuhnya percaya terhadap keamanan fitur BRImo ketika menarik uang tunai tanpa kartu di Automatic Teller Machine (ATM) karena adanya cybercrime seperti kasus penipuan online. Penelitian ini dilakukan untuk menganalisa keamanan dan kepuasan pengguna terhadap fitur tarik tunai cardless pada aplikasi BRImo. Penelitian ini menggunakan metode framework PIECES dengan lima aspek yaitu Performance, Information and Data, Control and Security, Efficiency, serta Service. Pengumpulan data dilakukan dengan menyebarkan kuesioner dan diolah dengan bantuan software Smart Partial Least Squares (Smart-PLS) versi 3.0. Hasil yang diperoleh dari penelitian menunjukkan bahwa kualitas layanan serta informasi data pada fitur tarik tunai cardless BRImo memiliki pengaruh yang signifikan terhadap Control and Security serta kepuasan pengguna BRImo.Financial technology (fintech) is a technological development that makes it easier for us in the current digital era. One of the fintech applications in mobile banking services is the BRI Mobile (BRImo) application. BRImo application users still do not fully trust the security of the BRImo feature when withdrawing cash without an Automatic Teller Machine (ATM) card due to cybercrime, such as cases of online fraud. So in this study, we will analyze security and user satisfaction with the cardless cash withdrawal feature in the BRImo application. This study uses the PIECES framework method with six aspects: Performance, Information and Data, Control and Security, Efficiency, and Service. Data was collected by distributing questionnaires and processing data with the help of software version 3.0, namely Smart Partial Least Squares (Smart-PLS). The results from the study indicate that the quality of service and data information on the BRImo cardless cash withdrawal feature significantly influence Control and Security and BRImo user satisfaction

Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset

Detecting fraud in the healthcare insurance dataset is challenging due to severe class imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques have been applied to address this problem, such as oversampling and undersampling methods. However, there is a lack of comparison and evaluation of these sampling methods. Therefore, the research contribution of this study is to conduct a comprehensive evaluation of the different sampling methods in different class distributions, utilizing multiple evaluation metrics, including , , , Precision, and Recall. In addition, a model evaluation approach be proposed to address the issue of inconsistent scores in different metrics. This study employs a real-world dataset with the XGBoost algorithm utilized alongside widely used data sampling techniques such as Random Oversampling and Undersampling, SMOTE, and Instance Hardness Threshold. Results indicate that Random Oversampling and Undersampling perform well in the 50% distribution, while SMOTE and Instance Hardness Threshold methods are more effective in the 70% distribution. Instance Hardness Threshold performs best in the 90% distribution. The 70% distribution is more robust with the SMOTE and Instance Hardness Threshold, particularly in the consistent score in different metrics, although they have longer computation times. These models consistently performed well across all evaluation metrics, indicating their ability to generalize to new unseen data in both the minority and majority classes. The study also identifies key features such as costs, diagnosis codes, type of healthcare service, gender, and severity level of diseases, which are important for accurate healthcare insurance fraud detection. These findings could be valuable for healthcare providers to make informed decisions with lower risks. A well-performing fraud detection model ensures the accurate classification of fraud and non-fraud cases. The findings also can be used by healthcare insurance providers to develop more effective fraud detection and prevention strategies

Sistem Pendukung Keputusan Menyeleksi Saham LQ45 untuk Generasi Milenial Menggunakan Metode SAW

Stocks as an investment instrument are in great demand by millennials because of their lucrative returns. LQ45 is an index introduced by IDX which is selected based on liquidity and market capitalization. However, in LQ45 not all existing shares can be purchased by the millennial generation because the price is quite expensive and these stocks still need to be selected so that they get the stocks that have the best quality but at prices that are friendly to millennials. To support this, we need a system that can support proper decision making so that it can get the best alternative in making decisions. This study uses the SAW method in which this method is able to select existing alternatives based on predetermined categories. This study determines each weight value in each attribute, then ranks it so as to produce the best alternative from the available alternatives. The results showed that HMSP shares with a value of 0.870 are the best alternative as stocks for the millennial generation. This research can be a tool in making decisions before buying stock

Utilization of Social Network Analysis (SNA) in Knowledge Sharing in College

Campus competition in Central Java creates superior and empowered human resources to make XYZ campus optimize the Knowledge Sharing process. In optimizing the Knowledge Sharing process on the XYZ campus through interaction and communication between students in the study program. This study aims to identify the Knowledge Sharing collaboration of students on the XYZ campus in three study programs with 100 respondents using the Social Network Analysis (SNA) method. The parameters used in this study include density, degree centrality, closeness centrality, betweenness centrality, and clicks (subgroups). Based on the analysis of the results obtained by the level of density level of 4.7% or weak ties because under 50%. Actor 98 has the highest degree of centrality with outdegree value 32 and indegree 7, while actor 65, which has the highest closeness centrality with inCloseness value 16,952 and outCloseness value 1,020. Actor 15 also has the highest centrality betweenness with an amount of Betweenness 2750,148 and nBetweenness 28,346. In this study, it can be concluded that there is collaboration in the Knowledge Sharing of students on the XYZ campus from each divided into three study programs, namely, informatics engineering, accounting computerization, and graphic design.Campus competition in Central Java creates superior and empowered human resources to make XYZ campus optimize the Knowledge Sharing process. In optimizing the Knowledge Sharing process on the XYZ campus through interaction and communication between students in the study program. This study aims to identify the Knowledge Sharing collaboration of students on the XYZ campus in three study programs with 100 respondents using the Social Network Analysis (SNA) method. The parameters used in this study include density, degree centrality, closeness centrality, betweenness centrality, and clicks (subgroups). Based on the analysis of the results obtained by the level of density level of 4.7% or weak ties because under 50%. Actor 98 has the highest degree of centrality with outdegree value 32 and indegree 7, while actor 65, which has the highest closeness centrality with inCloseness value 16,952 and outCloseness value 1,020. Actor 15 also has the highest centrality betweenness with an amount of Betweenness 2750,148 and nBetweenness 28,346. In this study, it can be concluded that there is collaboration in the Knowledge Sharing of students on the XYZ campus from each divided into three study programs, namely, informatics engineering, accounting computerization, and graphic design

Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia

Forest and land fires are national and international problems. The frequency of fires in one of Indonesia's provinces, Riau, is a significant problem. Knowing where to repair the burn is essential to prevent more massive fires. Fires occur because of a fire triangle, namely fuel, oxygen, and heat. The third factor can be seen through remote sensing. Using the Landsat-8 satellite, named the Enhanced Vegetation Index (EVI) variable, Normalized Burn Area (NBR), Normal Difference Humidity Index (NDMI), Normal Difference Difference Vegetation Index (NDVI), Soil Adapted Vegetation Index (SAVI), and Soil Surface Temperature (LST). DBSCAN, as a grouping algorithm that can group the data into several groups based on data density. This is used because of the density of existing fire data, according to the character of this algorithm. The selected cluster is the best cluster that uses Silhouette Coefficients, eps, and minutes value extracted from each variable, so there is no noise in the resulting cluster. The result is more than 0, and the highest is the best cluster result. There are 5 clusters formed by clustering, each of which has its members. This cluster is formed enough to represent the real conditions, cluster which has a high LST value or has an NBR value. A high  LST value indicates an increase in the area's temperature; a high NBR value indicates a fire has occurred in the area. The combination of LST and NBR values indicates the area has experienced forest and land fires. This study shows that DBSCAN clustered fire and surface temperature data following data from the Central Statistics Agency of Riau Province. Proven DBSCAN can cluster satellite imagery data in Riau province into several clusters that have a high incidence of land fires

Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset

Detecting fraud in the healthcare insurance dataset is challenging due to severe class imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques have been applied to address this problem, such as oversampling and undersampling methods. However, there is a lack of comparison and evaluation of these sampling methods. Therefore, the research contribution of this study is to conduct a comprehensive evaluation of the different sampling methods in different class distributions, utilizing multiple evaluation metrics, including 〖AUC〗_ROC, G-mean, 〖F1〗_macro, Precision, and Recall. In addition, a model evaluation approach be proposed to address the issue of inconsistent scores in different metrics. This study employs a real-world dataset with the XGBoost algorithm utilized alongside widely used data sampling techniques such as Random Oversampling and Undersampling, SMOTE, and Instance Hardness Threshold. Results indicate that Random Oversampling and Undersampling perform well in the 50% distribution, while SMOTE and Instance Hardness Threshold methods are more effective in the 70% distribution. Instance Hardness Threshold performs best in the 90% distribution. The 70% distribution is more robust with the SMOTE and Instance Hardness Threshold, particularly in the consistent score in different metrics, although they have longer computation times. These models consistently performed well across all evaluation metrics, indicating their ability to generalize to new unseen data in both the minority and majority classes. The study also identifies key features such as costs, diagnosis codes, type of healthcare service, gender, and severity level of diseases, which are important for accurate healthcare insurance fraud detection. These findings could be valuable for healthcare providers to make informed decisions with lower risks. A well-performing fraud detection model ensures the accurate classification of fraud and non-fraud cases. The findings also can be used by healthcare insurance providers to develop more effective fraud detection and prevention strategies

IPv6 flood attack detection based on epsilon greedy optimized Q learning in single board computer

Internet of things is a technology that allows communication between devices within a network. Since this technology depends on a network to communicate, the vulnerability of the exposed devices increased significantly. Furthermore, the use of internet protocol version 6 (IPv6) as the successor to internet protocol version 4 (IPv4) as a communication protocol constituted a significant problem for the network. Hence, this protocol was exploitable for flooding attacks in the IPv6 network. As a countermeasure against the flood, this study designed an IPv6 flood attack detection by using epsilon greedy optimized Q learning algorithm. According to the evaluation, the agent with epsilon 0.1 could reach 98% of accuracy and 11,550 rewards compared to the other agents. When compared to control models, the agent is also the most accurate compared to other algorithms followed by neural network (NN), K-nearest neighbors (KNN), decision tree (DT), naive Bayes (NB), and support vector machine (SVM). Besides that, the agent used more than 99% of a single central processing unit (CPU). Hence, the agent will not hinder internet of things (IoT) devices with multiple processors. Thus, we concluded that the proposed agent has high accuracy and feasibility in a single board computer (SBC)

Soft System Methodology (SSM) Analysis to Increase the Number of Prospective Students

The competition between campus, whether it’s a public college and private college in Central Java, is very tight with the increasing number of interested students for prospective students from various regions. The close competition requires many campuses to compete to provide the best facilities and services. The research objective is expected to support the "XY" university promotion strategy to help the university in the knowledge capture process. Data collection was carried out using the group discussion forum (FGD) method with a structured interview process for university leaders, university officials, marketing departments, and students. The technique used in this study is a soft system methodology (SSM). The results of this study model knowledge capture (KC) on the "XY" university promotion strategy and produce knowledge documentation that provides benefits in making policy strategies and has an impact on increasing the number of prospective new college students by optimizing digital marketing.The competition between campus, whether it’s a public college and private college in Central Java, is very tight with the increasing number of interested students for prospective students from various regions. The close competition requires many campuses to compete to provide the best facilities and services. The research objective is expected to support the "XY" university promotion strategy to help the university in the knowledge capture process. Data collection was carried out using the group discussion forum (FGD) method with a structured interview process for university leaders, university officials, marketing departments, and students. The technique used in this study is a soft system methodology (SSM). The results of this study model knowledge capture (KC) on the "XY" university promotion strategy and produce knowledge documentation that provides benefits in making policy strategies and has an impact on increasing the number of prospective new college students by optimizing digital marketing

Geographic Information System for Mapping the Spread of COVID-19 in the city of Salatiga

The city of Salatiga, located in the province of Central Java, is one of the cities affected by the spread of COVID-19 in Indonesia. The first COVID-19 case in the city of Salatiga was confirmed on 31 March 2020 until now it has reached more than eighty people. The increasing number of cases and the lack of information on the spread of COVID-19 and the information provided are static. This information, this research aims to build and utilize the WebGIS application as one of the information for the spread of COVID-19 in Salatiga. Mapping the area in this application uses a shapefile file and is converted to a GeoJSON file. It uses Blogspot as web hosting and javascript leaflets to display GIS maps and designs and uses the Exponential Smoothing method to forecast COVID-19 cases and use the web equal 4.0 method for website testing. The results of this study to provide information about the spread of COVID-19 in the city of Salatiga.  Interactive map and forecasts of COVID-19 cases in the city of Salatiga. In this study, the single exponential smoothing has the smallest MAPE value, namely 35.2360. It results in a prediction on July 20 to 26, 2020, which has decreased to 1 positive case consisting of the lowest number in this forecasting is -2. The highest number shows four positive cases. And the website testing using the webqual 4.0 standard, respondents agreed with the website's usability and information quality, and it was sufficient for website service interaction

Deteksi Cacat pada Isolasi Trafo secara Visual Menggunakan Algoritma Convolutional Neural Network (CNN)

Isolasi trafo adalah bahan dielektrik yang memiliki fungsi untuk memisahkan dua atau lebih penghantar listrik yang bertegangan. Kerusakan pada isolasi trafo akan menyebabkan gangguan kinerja trafo sehingga dapat membuat trafo mengalami kegagalan operasi atau bahkan kerusakan. Penelitian ini membangun suatu sistem yang dapat mengklasifikasikan gambar isolasi trafo cacat dan normal. Metode Convolutional Neural Network diimplementasikan dalam pembuatan model. Metode penelitian dimulai dengan melakukan perencanaan penelitian, pengumpulan dataset, preprocessing data, pembangunan model klasifikasi, training model, serta testing dan evaluasi. Berdasarkan hasil uji dengan standardisasi data ukuran 180 x 180 x 3 piksel menghasilkan accuracy 0.9913 untuk training, 0.9884 untuk testing, dan 1.00 untuk evaluasi. Hasil uji dengan standardisasi data ukuran 240 x 240 x 3 piksel menghasilkan accuracy 0.9798 untuk training, 0.9651 untuk testing, dan 0.94 untuk evaluasi. Berdasarkan penelitian yang telah dilakukan menunjukkan bahwa perbedaan standardisasi data dapat memengaruhi hasil dari performa model.Transformer insulation is a dielectric material that has the function of selling two or more voltage electrical conductors. Damage to the transformer insulation will cause interference with the performance of the transformer so that it can cause the transformer to experience operational failure or even damage. This research builds a system that can classify defective and normal transformer insulation images. The Convolutional Neural Network method is implemented in model building. The research method begins with conducting research planning, dataset collection, data preprocessing, classification of development models, training models, as well as testing and evaluation. Based on the test results with standardized data size 180 x 180 x 3 pixels, it produces an accuracy of 0.9913 for training, 0.9884 for testing, and 1.00 for evaluation. Test results with standardized data size 240 x 240 x 3 pixels produce an accuracy of 0.9798 for training, 0.9651 for testing, and 0.94 for evaluation. Based on the research that has been done, shows that differences in data standardization can affect the results of the model performance