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All Journal Journal Information System Development bit-Tech Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Sains Informatika Terapan (JSIT) JURNAL LIGA ILMU SERANTAU Computer & Science Industrial Engineering Journal
Yenni, Yusli
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Jaringan Saraf Tiruan Menjaga Keamanan Data dengan Metode Bidirectional Associative Memory Yenni, Yusli
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.282

Abstract

System security is currently very much needed from irresponsible parties who damage and steal data. For this reason, a system is needed that is able to protect and secure data from criminal acts. The aim of this research is to provide data codes and keys using binary conversion using the Continuous BAM method. The Continuous BAM method, namely Bidirectional Associative Memory (BAM), has the ability to act as associative memory or content addressable memory, namely memory that can be called up using part of the information stored in it. Apart from that, the Bidirectional Associative Memory (BAM) Artificial Neural Network (ANN) has 2 layers and is fully connected from one layer to the other, so it is possible to have a reciprocal relationship between the input layer and the output layer. The final results of this research are 6 patterns that are entered into the search process, including number patterns 1, 3, 4, 5, 7, and 9 with input vector values of numbers 1 [16,-22], numbers 3 [20, 22], numbers 4 [16,-22], number 5 [20,6], number 7 [24,2] and number 9 [20,18], which do not match the pattern number 7. Requested target [1,-1], result obtained [1,1]. The Continuous Bam method can be used to detect patterns correctly and accurately.
ANALISIS BACKWARD CHAINING MENGIDENTIFIKASI VIRUS TANAMAN CABAI Yenni, Yusli; Mahendra, Ikhsa
Jurnal Liga Ilmu Serantau Vol. 1 No. 2 (2024): Edisi Agustus - Jurnal Liga Ilmu Serantau (JLIS)
Publisher : LPPM Universitas Ibnu Sina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jlis.v1i2.867

Abstract

Tanaman cabai salah satu komoditas sayuran yang banyak permintaan di kalangan Masyarakat. Tingkat produksi tanaman cabai di Sumatera barat dari tahun mengalami kenaikkan. Sehingga banyak Masyarakat Sumatera barat mulai membudidayakan tanaman cabai ini. Hal ini juga dimanfaatkan oleh Masyarakat pasaman bara khususnya daerah tandikek yang mulai memudidayakan tanaman cabai yang menjadi bagian penting dalam perekonomian local. Namun hal tersebut menimbulkan permasalah yang Dimana masih minim pengetahuan masyarak tentang penyakit tanaman cabai yang di akibatkan oleh virus. Sistem pakar merupakan suatu system yang dalam penyelesaian masalah menggunakan pola pikir seorang pakar. Pola pikir ini yang nanti di implementasikan kedalam suatu system yang nanti digunakan untuk memecahkan suatu masalah. Proses inferensi yang digunakan untuk memecahkan masalah dalam system pakar menggunakan pelacakan mudur (backward chaining) yang di ambil dari penalaran pengamatan dari hasil dengan mencari beberapa fakta-fakta yang mendukung sehingga mendapatkan Kesimpulan. Pengimplementasi ke system web dengan menggunakan Bahasa pemograman PHP dan database Mysql yang nantinya dapat di akses oleh Masyarakat sebagai media konsultasi.
Penerapan Data Mining Menggunakan Algoritma C4.5 Dalam Memprediksi Tingkat Perceraian Kecamatan Kuranji Kota Padang Berbasis Website Hannum, Karmila; Yenni, Yusli
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.795

Abstract

Divorce is one of the social problems that continues to increase and affects family harmony as well as children’s psychological development. Based on data from the Religious Court of Kuranji District, Padang City, during 2022–2024, divorce cases experienced fluctuations with a significant increase. This issue encourages the need for a system that can help predict divorce rates as a basis for decision-making. This study applies data mining using the C4.5 algorithm to classify the factors causing divorce, such as continuous disputes, physical abandonment, economic issues, and other factors. The C4.5 algorithm was chosen because it can generate decision tree models that are easy to interpret and effective for prediction. This research produced a web-based system that provides predictive information about divorce rates, which is expected to assist the religious court and government in anticipating and reducing divorce cases. The results show that the implementation of the C4.5 algorithm can provide more accurate solutions in predicting divorce rates by utilizing available data and has the potential to be a useful decision support tool.
Co-Authors Andreas, Yose Dedy Saputra Fajrin, Rifaldi Fariska, Muhammad Yogi Febrianto, Wahyu Gultom, Heman Hottua Hannum, Karmila Khaerini, Nini Mahendra, Ikhsa Nanda Jarti Ndruru, Ebitwan Famati Saro Rizki, Sestri Novia Utnasari, Intan Wicaksono, Dicky Eko