Pengajuan aplikasi kredit oleh calon nasabah sekarang sangatlah mudah, hal ini dikarenakan pengajuan kredit bisa dilakukan semua orang sepanjang memenuhi syarat tertentu. Pemberian kredit kepada nasabah adalah kegiatan rutin yang mempunyai risiko tinggi, hal ini bisa menyebabkan kerugian pada perusahaan dan mengakibatkan kredit macet. Persaingan perusahaan penyedia kredit menjadi sangat pesat dan prediksi konsumen kredit adalah hal yang sangat penting. Analisis terhadap data kredit diperlukan dengan tujuan untuk meminimalisasi risiko nasabah yang terlambat membayar kredit, kegiatan ini sangatlah penting karena salah satu penyebab terjadinya kredit macet bisa disebabkan oleh kurang cermatnya perusahaan dalam pemberian kredit. Masalah ini sebenarnya dapat diatasi dengan cara mengidentifikasi dan memprediksi nasabah dengan baik sebelum memberikan pinjaman dengan cara memperahitkan data historis pinjaman. Teknik prediksi dalam pengambilan keputusan telah banyak digunakan oleh perusahaan-perusahaan besar. Penelitian ini menerapkan algoritma naive bayes untuk memprediksi dan mengklasifikasi nasabah mana saja yang bermasalah dan tidak bermasalah, dan diharapkan mampu meningkatkan akurasi dalam menganalisa kelayakan kredit. Kata kunci : Analisa kredit, penilaian kredit, naive bayes.
Abstack: Data stored in storage media is often lost or opened by certain parties who are not responsible, so that it is very detrimental to the owner of the data, it is necessary to secure data so that the data can be locked so that it cannot be opened by irresponsible parties. The RC5 and RC6 algorithms are digestive massage algorithms or sometimes also known as the hash function which is an algorithm whose input is a message whose length is not certain, and produces an output message digest from its input message with exactly 128 bits in length. RC6 password is a protection for the user in securing data on a PC or computer. Based on the results of the conclusions taken: For the experiments carried out on the RC5 algorithm the execution time for the generation of keys (set-up key) is very fast, which is about 9-10 ns, a trial carried out on the RC6 algorithm execution time for the key generator (set up key ) faster than 10-11 ns. In the encryption and decryption process, the execution time depends on the size or size of the plaintext file. The larger the size of the plaintext file, the longer the execution time.Abstrak : Data yang tersimpan dalam media penyimpanan sering hilang atau dibuka oleh pihak-pihak tertentu yang tidak bertanggung jawab, sehinga merugikan sekali bagi pemilik data tersebut, untuk itu diperlukan suatu pengamanan data agar data tersebut dapat terkunci sehingga tidak dapat dibuka oleh pihak yang tidak bertanggung jawab.. Algoritma RC5 dan RC6 merupakan algoritma massage digest atau kadang juga dikenal dengan hash function yaitu suatu algoritma yang inputnya berupa sebuah pesan yang panjangnya tidak tertentu, dan menghasilkan keluaran sebuah message digest dari pesan inputnya dengan panjang tepat 128 bit. Password RC6 merupakan salah satu perlindungan kepada user dalam pengamanan data yang berada dalam sebuah Pc atau computer. Berdasarkan hasil pengujian diambil kesimpulan : Untuk uji coba yang dilakukan pada algoritma RC5 waktu eksekusi untuk pembangkitan kunci (set up key) sangat cepat sekali yaitu sekitar 9-10 ns, uji coba yang dilakukan pada algoritma RC6 waktu eksekusi untuk pembangkit kunci (set up key) lebih cepat sekali yaitu 10-11 ns, Pada proses enkripsi dan dekripsi, waktu eksekusi tergantung dari besar atau kecilnya ukuran file plaintext.s emakin besar ukuran file plaintext maka semakin lama waktu eksekusinya.
In everyday life we often encounter emergency situations such as accidents, drowning victims, fires, crimes and so on. Certain community may have had socialization or learned how to handle or first aid the emergencies, but not all societies know how to do first aid for emergencies.This research is aimed at building an Android-based emergency call application system for Bengkulu Region so that people can dial important phone numbers and public services when there is a crime or accident. The benefits of this application is as a medium to facilitate users of mobile devices based on Android in making emergency public service calls. From the problems that occur then, the problem of this research is How to Create Android-Based Emergency Call Applications For Bengkulu Region Using Location Based Services. The purpose of this research is to Design and Build Android-Based Emergency Call Applications for Bengkulu Region Using LBS Technology and create easily and quickly Emergency safe Calling System. The place of research is conducted on public service in Bengkulu city and is independently created. Data collection is done by using interview, observation and literature study method. The results showed that Applications that are created can help people to face emergencies so they do not get constrained when looking for agencies contact information that provide assistance services in times of emergency.Keywords : Applications, Emergency Calls, LBS
Chili is one of the most essential horticultural plants in Indonesia. In addition to the lack of supply of plants, the price of chili on the market has increased dramatically. The shortage is affected by unpredictable climate changes, which have to result in many chili plants suffering from crop failure. It was because the disease infects chili plants so that harvests are decreased. This work would incorporate Deep Learning for image processing in Disease Detection Systems. This disease detection method will be used to help users, in particular chili farmers, identify whether or not the leaves of their chili plants are contaminated with the disease. This system would take a picture of chili leaf using a Raspberry Pi camera and implement image processing on the chili leaf image to collect valuable information on the image to find out whether or not the chili leaf is contaminated with the disease. The purpose of this research is to make a desktop application for a disease detection system that has the ability to detect whether or not a chili leaf is infected by several diseases, display the condition of the chili leaves, display the type of disease that infects the chili leaves (if any), and provide a percentage probability of the system in detecting the image of the chili leaves correctly (whether it is healthy chili leaves or sick chili leaves). The system reaches 100 percent accuracy with good brightness and distance less than 1 meter, while the system reaches 68.8 percent accuracy with poor brightness and distance greater than or equal to 100 percent. Keywords— chili leaf; deep learning; disease detection; raspberry pi
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