Abstrak: FIF adalah salah satu Lembaga keuangan yang menyediakan berbagai macam alternatif pinjaman uang bagi nasabah. Sejatinya dalam pemberian kredit kepada nasabah pihak Lembaga keuangan mengalami berbagai masalah atau resikko. Salah satu masalah atau resiko yang dialami Lembaga Keuangan dalam pemberian kredit adalah perilaku nasabh yang macet dalam pembayaran kredit yang pada akhirnya menyebabkan kredit macet. Hal ini merupakan masalah yang serius yang perlu diperhatikan oleh pihak penyedia layanan keuangan untuk lebih berhati-hati dalam menentukan nasabah karena dalam pemberian kredit sangat beresiko khusuusnya pada PT FIF Goup Cabang Arjawinangun. Teknik Pengambilan data yang digunakan dalam pembuatan tugas akhir ini adalah dengan menggunakan observasi, wawancara, studi dokumentasi, dan data nasabah PT FIF Goup Cabang Arjawinangun. Sementara itu Teknik pengolahan data menggunakan prinsip tahapan knowledge discovery in database (KDD) yang terdiri dari data, Data Cleaning, Data Information, Data mining, Patternevalution, knowledge. Sementara itu atribut yang digunakan adalah dari nomort NIK, Kelancaran, Prediksi, Confident macet, confident lancer asset, dan omset perbulan dari nasabah. Metode K-NN dengan jumlah dataset sebanyak 296 data menghasilkan nilai akurasi sebesar 71%.   Kata kunci: Kredit, K-Nearest Neighbor (KNN), Prediksi.   Abstract: FIF is a financial institution that provides various kinds of money loan alternatives for customers, one of which is through the provision of loans in the form of credit to customers. In fact, in providing credit to customers, financial institutions experience various problems or risks. One of the problems or risks experienced by financial institutions In the provision of credit is the behavior of customers who are bad in credit payments which ultimately causes bad credit. This is a serious problem that financial service providers need to pay attention to to be more careful in determining customers because in providing credit is very risky, especially at PT FIF Goup Cabang Arjawinangun The data collection technique used in the making of this final project is to use observation, interviews, study documentation, and customer data of PT FIF Goup Cabang Arjawinangun Meanwhile, data processing techniques use the principles of knowledge discovery in databases (KDD) stages consisting of data, data cleaning, data transformation, data mining, pattern evolution, knowledge. Meanwhile, the attributes used are the NIK number, fluency, prediction, bad confidence, smooth confidence, assets, and turnover per month from customers. The K-NN method with a total dataset of 296 data yields an accuracy value of 71%.   Keywords: Credit, K-Nearest Neighbor (KNN), Prediction.

The purpose of this research is to detect and store data on theft of mosque charity boxes using the Internet of Things (IoT) by activating short message notifications on smartphones and database servers through a web framework. The focus of this research is the use of Arduino R3 as a microcontroller that regulates the detection of theft of a mosque’s charity box with a passive infrared sensor HC-SR501, a magnetic switch sensor MC-38, and sending short message notifications and storing detection results to a database server. Arduino R3 is used as a microcontroller with a program that can be recycled using the Arduino IDE, while the HC-SR501 passive infrared sensor is used to detect human movement with changes in body heat, through infrared rays emitted by a Fresnel lens, then identified by the pyroelectric sensor made from gallium nitride (GaN), cesium nitrate (CsNO3) and lithium tantalate (LiTaO3), a change in heat temperature then activates the buzzer when a hot object is detected. Whereas the MC-38 magnetic switch sensor functions to turn on the buzzer when the mosque charity box is forcibly opened by a thief with a normally closed and normally open angle. The results obtained from this study are testing the MC-38 magnetic sensor has two working modes, namely normally close when the two beam blades fuse within a distance of < 3 cm, above a distance of > 3 cm, normally open, meaning that the conditions are not safe. While the test results of the HC-SR501 PIR sensor have the ability to detect body movements with a minimum reading distance of 30 cm and a maximum of 10 m, with LOW results if not detected and High if detected by human objects.

Abstract- Fisheries development as part of national economic development has the aim, among others, to improve the standard of living and welfare of the fishing community. The effort to realize the Champion fisherman and Fish Warehouse program to encourage fish production in the West Java provincial government of West Jawa through the Department of Marine Affairs and Fisheries seeks to develop fishery potential through the SMART FISHING assistance program. So that the aid program is properly, an application is made to determine the fisherman who are entitled to receive assistance. The purpose of this study is producing a SAFIA application using the SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE method based on a web framework that can facilitate the Department of Marine Affairs and Fisheries of West Java to determine the best group of fisherman according to the criteria for receiving assistance. The Result of making this research is how the SAFIA application works with accuracy

The use of websites as a medium for disseminating information is a matter that is familiar in this era of digital technology. Telkom University which is a tertiary institution in Indonesia which often carries digital themes in its campus certainly does not want to be left behind in the use of websites as a medium for disseminating information and marketing for the community. This study aims to analyze the quality of the information system on the smb.telkomunivesity.ac.id New Student Selection website which is used as an information center for admission of new students as well as registration for Telkom University entrance selection with indicators of McCall's information system quality factors. Based on the results of the study, the smb.telkomuniversity website is concluded to be a good quality website and can provide benefits for its users

Critical thinking in mathematics can be defined as the processes and abilities used to understand concepts, apply, synthesize and evaluate the information generated. Critical thinking in mathematics is a skill for higher order thinking. It is understood that logical thought plays a part in spiritual growth, social progress, behavioral growth, cognitive development and science progress. This study aims to classify critical thinking skills. The method used to determine the classification of critical thinking skills is to use the Neural Network algorithm. A method which has the potential to classify structured data is the neural network. In this study, a neural network algorithm model was developed. A technology that has the potential to identify structured data is the neural network. In this study, a neural network algorithm model was created. With this neural network model, it can be seen the classification of critical thinking skills. The amount of data used as data in this analysis was 150 in the form of school data and as many as 40 measures were measured in the form of math scores. On the basis of the research results, it was found that the neural network model based on Particle Swarm Optimization achieved an accuracy value of up to 93.33 percent with a 2 percent variance, tested using the k-cross-validation method.