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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Tecnoscienza Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
Asmaul Husnah Nasrullah
Universitas Ichsan Gorontalo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mathematics Other

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Penerapan Metode C4.5 untuk Klasifikasi Mahasiswa Berpotensi Drop Out Nasrullah, Asmaul Husnah
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.12 KB)

Abstract

The quality of education in universities can be seen from the high level of student success and the low failure of students. One indicator of student failure is the case of Drop Out (stop study). The problem of Drop Out becomes something interesting to study, because this can affect the quality of education. Faculty of Economics UNISAN Gorontalo is a favorite Faculty in UNISAN Gorontalo so it has a number of students of approximately 1000 students until 2017. But the ratio of the number of graduate students and not pass unbalanced. So as to produce the number of students Drop Out approximately 200 students per year. To solve the problem, we proposed a new model by utilizing a C4.5 computation method, in order to produce a pattern based on the results of the correct classification in determining the potential Drop Out students. The results obtained from the application of method C4.5 in this research is the discovery of 17 rules that can be used as a pattern to determine the potential students Drop Out.
IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI PRODUK LARIS Asmaul Husnah Nasrullah
JURNAL ILMU KOMPUTER Vol 7 No 2 (2021): Edisi September
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v7i2.203

Abstract

Decision Tree C4.5 algorithm is an algorithm that can be used to make a decision tree. Decision tree (Decision Tree) is one method that is quite easily interpreted by humans. However, this algorithm has never been tested for product classification using private data (stock data and sales of goods at PT Cipta Karya Gorontalo). Therefore this study aims to test the accuracy of C4.5 in classifying best-selling products (private data). As a result of the evaluation of product classification models using Decision Tree C4.5 obtained from this study amounted to 90% and AUC value of 0.709 where this value is included in the Good Classification. It can be used as a data mining classification method Decision Tree C4.5 algorithm is accurate in classifying hot-selling products. Keywords— Decision Tree, C4.5, Classification, Best-Selling Product
Quick Response Code Absensi Guru Menggunakan Secure Hashing Algorithm (SHA) Agriyanto Asiking; Asmaul Husnah N; Irma Surya Kumala Idris
JURNAL TECNOSCIENZA Vol. 6 No. 2 (2022): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v6i2.705

Abstract

Sistem Absensi guru yang diterapkan di sekolah masih dilakukan secara manual, yaitu guru menandatangani buku absen yang telah disediakan. Hal ini dikhawatirkan dapat meningkatkan potensi penyebaran COVID 19 dikarenakan menggunakan peralatan absensi yang sama. Berdasarkan permasalahan tersebut penelitian absensi akan dibuat menggunakan teknologi antara Quick Response Code yang menggunakan Secure Hash Algorithm (SHA) dan Smartphone android sehingga mengurangi kontak fisik atau penggunaan benda yang disentuh oleh banyak orang secara bergantian. Penelitian ini mengimplementasikan algoritma kriptografi SHA-256 untuk pembuatan Quick Response Code absensi. Hasil enkripsi dari SHA-256 akan dikombinasi dengan algoritma BCRYPT untuk menghindari serangan decode hash SHA-256. Pengamanan Quick Response Code dengan menggunakan enkripsi SHA-256 lebih optimal dengan mengkombinasikan fungsi BCRYPT pada Message yang telah dienkripsi SHA-256, sehingga menghindari serangan decode hash SHA-256
Penerapan Metode Linier Regresi Untuk Prediksi Produksi Sayur-Sayuran Ayu Azhari Basahona; Rezqiwati Ishak; Asmaul Husna
Jurnal Cosphi Vol 3, No 2 (2019): Agustus-Desember 2019
Publisher : Teknik Elektro - Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (715.602 KB)

Abstract

Sayuran sangat penting sebagai sumber vitamin, mineral dan serat. Sayuran oleh masyarakat Indonesia dibudidayakan pada lahan kering baik sebagai tanaman utama maupun pada sistem tumpang sari. Masalah pada Produksi sayuran di Provinsi Gorontalo kadang naik kadang turun berdasarkan jenis sayuran maka dari itu pada penelitian ini akan di lakukan prediksi menggunakan metode Linier Regresi dan MAPE. Tujuan dari penelitian ini untuk memprediksi jumlah produksi sayuran berdasarkan jenis sayuran. Dari hasil penelitian yang dipilih ada bawang merah, cabai rawit, kangkung, terung, dan tomat. Hasil error yang di dapatkan untuk bawang merah 35.013 % dengan tingkat akurasi 64.987 % , hasil error yang di dapatkan untuk cabai rawit 15 % dengan tingkat akurasi 85 %, hasil error yang didapatkan untuk kangkung 18.253 % dengan tingkat akurasi 81.747 %, hasil error yang didapatkan untuk terung 85.638 % dengan tingkat akurasi 14.362 %, hasil error yang didapatkan untuk tomat 41.657 % dengan tingkat akurasi 58.343 %.
Pengelompokan Tingkat Kerusakan Hutan Menggunakan Algoritma K-Means Clustering Fadila Badu; Asmaul Husnah Nasrullah
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (869.663 KB) | DOI: 10.37195/balok.v1i2.276

Abstract

Kerusakan sumber daya hutan mengakibatkan penurunan kemampuan fungsi hutan dalam mendukung segala aspek kehidupan. Faktor yang mengakibatkan terjadinya tingkat kekritisan hutan, salah satunya adalah pertumbuhan penduduk yang begitu cepat, serta aktivitas pembangunan dalam berbagai bidang tentu saja akan menyebabkan ikut meningkatnya permintaan akan lahan. Oleh karenanya Dinas Kehutanan dan pertambangan Kabupaten Bone Bolango sangat memerlukan data yang akurat terhadap data kerusakan hutan yang terjadi setiap saat. Untuk itu penelitian ini bertujuan untuk merealisasikan penggunaan metode K-Means cluster yang mampu memberikan pengelompokan tingkat kerusakan hutan, sehingga dapat menjadi referensi bagi Dinas Kehutanan dan pertambangan Kabupaten Bone Bolango dalam membuat keputusan secara cepat dan tepat.Selaras dengan masalah yang dihadapi, peneliti memandang perlunya suatu tindakan Pengelompokan Tingkat Kerusakan Hutan. pengelompokkan tersebut dilakukan dengan menerapkan sebuah Metode K-Means Clustering. Dari hasil penelitian yang telah dilakukan menunjukkan metode K-Means mampu mengelompokkan tingkat kerusakan hutan dengan baik, hal itu dapat dilihat diperolehnya tiga kelompok kerusakan hutan yakni kerusakan sedang, menengah dan kerusakan tinggi. Kata kunci: Kerusakan, Hutan, K-Means, Clustering
Implementasi Metode Convolutional Neural Network Untuk Identifikasi Citra Digital Daun Asmaul Husnah Nasrullah; Haditsah Annur
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5962

Abstract

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.
Klasifikasi Penerimaan Beras Miskin (RASKIN) Menggunakan Metode K-Nearest Neighbor Mukti Ali Mohammad; Asmaul Husnah Nasrullah; Rofiq Harun
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 1 (2023): Edisi Mei 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i1.531

Abstract

Rice is the staple food of most of Indonesia's population. Rice for the poor is a staple food subsidy in the form of rice intended for poor families as an effort from the government to improve food security and provide protection to poor families. Therefore, in 2002 the Indonesian government launched the rice for the poor program as an implementation of the government's consistency. The rice for the poor program is stipulated in the Presidential Regulation of the Republic of Indonesia No. 15 of 2010 on the Acceleration of Poverty Reduction and Presidential Instruction No. 3 of 2010 on Equitable Development Programs. This program aims to reduce the expenditure burden of target households by meeting some of their basic needs in the form of rice. In addition, the program, rice for the poor, aims to increase and open access to family food through the sale of rice to beneficiary families with a predetermined amount. One of the efforts to overcome these problems is to implement one of the methods in data mining, namely classification, which can group data more accurately following the level of similarity of the data characteristics. In this research, data mining analysis is carried out with classification techniques using the K-Nearest Neighbor method. The variables applied in this study are government, education, income, housing conditions, employment, and government assistance card holders
Co-Authors Agriyanto Asiking Ayu Azhari Basahona Fadila Badu Haditsah Annur Irma Surya Kumala Idris Mukti Ali Mohammad Rezqiwati Ishak Rofiq Harun