Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or Non-Potential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.

Security to enter a system has a very important role because as the main entrance to access data sources. But often lack the attention of the owners and managers of information systems. To reduce these weaknesses, one method that is widely used today is to use One-Time password, which is where the password we have becomes dynamic, meaning that at a certain time the password is always changing, the positive side is that it makes it difficult for others to steal our passwords because besides representative passwords that are difficult to understand and passwords are always changing. This study discusses One-Time Password installed on a mobile device where the password is randomized using a combination of two algorithms, namely SHA256 and Time-based One Time Password. The development of this login method can reduce the level of theft of passwords owned by users who are entitled to access information sources.

Steganografi adalah seni menyembunyikan informasi dan upaya untuk menyembunyikan keberadaan informasi yang disematkan. Steganografi berfungsi sebagai sebuah cara yang lebih baik untuk mengamankan pesan dari pada kriptografi, steganografi menyembunyikan isi pesan bukan mengacak pesan. Pesan asli disembunyikan di dalam citra gambar sedemikian rupa sehingga perubahan yang terjadi pada gambar tidak dapat diketahui perbedaannya dengan gambar tanpa pesan. Pada penelitian ini dikombinasikan algoritma RSA yang digunakan untuk mengenkripsi pesan rahasia dan teknik LSB digunakan untuk menyembunyikan pesan terenkripsi dengan tujuan untuk menghasilkan stego file yang lebih aman dan lebih baik secara kualitas. Berdasarkan hasil implementasi dan pengujian citra gambar yang dihasilkan sistem memiliki nilai diatas 40 dB sehingga kualitas citra gambar stego file memiliki kualitas yang baik.

Penelitian ini dilakukan untuk melakukan deteksi fitur yang ada pada wajah manusia, pendekatan yang digunakn dalam penelitian ini adalah Penerapan Haar Cascade Classification Model Untuk Deteksi Wajah, Hidung, Mulut, Mata Menggunakan Algoritma Viola-Jones sehingga sistem yang dihasilkan mampu untuk melakukan deteksi terhadap fitur-fitur yang ada pada wajah manusia yang meliputi Wajah, Hidung, Mulut, dan Mata. Dalam penerapan deteksi wajah, hidung, mulut dan mata ini dibangun menggunakan metode viola-jones yang terdiri dari metode haar-like feature, citra integral, adaboost, dan cascade of classifier. Teknologi pengenalan wajah, Deteksi wajah, hidung dan mulut juga menjadi hal yang penting dalam teknologi pengenalan wajah.  

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field