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HANIF AL FATTA
STMIK Amikom Yogyakarta
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MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK Constantin Menteng; Arief Setyanto; Hanif Al Fatta
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 17 No. 2 (2023): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v7i2.8151

Abstract

Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network.
ANALISIS SENTIMEN PADA OPINI PENGGUNA APLIKASI QASIR MENGGUNAKAN SUPPORT VECTOR MACHINE DAN RANDOM FOREST Dhana Aulia Ayu Kurniawan; Ema Utami; Hanif Al Fatta
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.83

Abstract

Qasir is an Android-based Point-Of-Sale (POS) application that can be accessed for free on the Google Playstore. With so many POS applications available, users will be more selective in choosing the application to use. One aspect that can influence the decision to choose an application is the opinion on the application. Opinion is information obtained after using the application that can contain criticism or suggestions. So based on this the user can conclude how other users use the application. Besides being useful for users, opinions if processed properly will produce information that can be used for evaluation for the development team. To analyze and find relationships between owned data, you can use Data Mining. This research will use the Support Vector Machine and Random Forest methods, but each method has its advantages and disadvantages so that the accuracy of the two methods will be compared. The results obtained are that the Support Vector Machine has the highest accuracy value with 80.63% while the Random Forest is 80.21%.
Prediksi Jumlah Kunjungan Wisatawan Kabupaten Lombok Barat Menggunakan Algoritma Long Short Term Memory M. Imam Budi Laksamana; Ema Utami; Hanif Al Fatta
TAFAQQUH Vol. 6 No. 2 (2021): Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah
Publisher : STIS DAFA MATARAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70032/4k3jrv73

Abstract

Kabupaten Lombok Barat merupakan salah satu wilayah di Indonesia yang memiliki daya tarik tersendiri bagi wisatawan lokal maupun internasional. Salah satu sektor yang paling terdampak besar terhadap intensitas kunjungan wisata adalah hotel. Untuk meningkatkan diperlukan upaya yang tepat untuk memelihara objek wisata sehingga dapat menjadi daya tarik bagi wisatawan. Dalam upaya pemeliharaan objek wisata, Dinas Pariwisata Lombok Barat perlu melakuakan analisa dan prediksi kedatangan wisatawan lokal maupun internasional, dalam prosesnya analisa dan prediksi, pemerintah kabupaten Lombok Barat melakukan pengumpulan data kunjungan wisatawan dari setiap pintu masuk objek wisata yang dimana pada prosesnya memerlukan waktu yang cukup lama dan membutuhkan sumber daya manusia yang cukup tinggi. Untuk mengatasi permasalahan tersebut dilakukan proses prediksi menggunakan sistem komputasi dengan machine learning agar nantinya waktu yang dibutuhkan dalam analisa dan prediksi menjadi lebih singkat dan kebutuhan akan sumber daya manusia yang tinggi bisa teratasi. Metode yang akan diterapkan dalam prediksi adalah Long Short Term Memory (LSTM), atribut dan nilai yang digunkan dalam model LSTM adalah nilai input layer 1, lalu nilai epochs 100 dan batch size 1, berdasarkan hasil pengujian yang dilakukan pada penelitian ini, Long Short Term Memory (LSTM) memiliki performa yang kurang baik dalam memprediksi jumlah kunjungan wisata kabupaten Lombok Barat menggunakan data rentang waktu bulanan dari tahun 2017-2021, hal ini dibuktikan dengan hasil uji evaluasi yang dilakukan dengan mencari nilai Root Mean Square Error (RMSE), dimana hasil model prediksi akan dikatakan baik jika memiliki nilai error yang lebih kecil. dimana nilai Root Mean Square Error (RMSE) yang dihasil dalam penelitian ini cukup tinggi yaitu 10479,30.
Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan Nur Fitrianingsih Hasan; Kusrini Kusrini; Hanif Al Fatta
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 2 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i2.1607

Abstract

The inhibition of the production and distribution of bottled water has become a serious problem in the survival of the community and the company, so there is a need for a solution to this problem both short-term and long-term solutions. One of the things that can be done by the company or management is that the right amount of production and distribution is by forecasting sales. Sales forecasting is the process of predicting which products will be sold in the future made based on data that has ever happened. This paper aims to determine the level of accuracy of the use of Backpropagation ANN in estimating the sales of bottled water.The ANN architecture used is 12-10-1 with the MSE value of 0,00043743 and the MAPE value of 6.88%. Forecasting sales results of Robong Holo 600ml brand using Backpropagation ANN for 2019 is 2271 pcs in January, 2019 pcs in February, 1358 pcs in March, 917 pcs in April, 462 pcs in May, 324 pcs in June, 739 pcs in July, 370 pcs in August, 367 pcs in September, 1073 pcs in October, 765 pcs in November and 1388 pcs in December. Keywords— AMDK,Backpropagation,Jaringan Syaraf Tiruan,Penjualan,Peramalan
Co-Authors Abidarin Rosidi Arief Setyanto Bety Wulan Sari Constantin Menteng Dhana Aulia Ayu Kurniawan Dimas Setiawan Donni Prabowo Ema Utami Fatimah Nur Arifah Hadi Sucipto Hasan, Nur Fitrianingsih Kusrini . Kusrini Kusrini M Suyanto M. Imam Budi Laksamana Riska Dwi Handayani Sofyan Pariyasto Sri Lestari Rahayu Wahyu Wijaya Widiyanto Wira Dimuksa Wiwi Widayani