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ANT COLONY OPTIMIZATION UNTUK MENYELEKSI FITUR DAN KLASIFIKASI ARTIKEL nugroho, arief kelik; Permadi, Ipung
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Universitas Muria Kudus

Algoritma koloni semut dapat diterapkan dalam pengklasifikisian dengan menerapkan mekanisme perilaku semut dalam mencari sumber makanan. Semut memberikan kemungkinan hasil terbaik/optimal berdasar intensitas pheromone. Himpunan fitur pada artikel diseleksi berdasarkan topik atau jenis kelas yang diinputkan dalam sistem kemudian dievaluasi dengan mengecek kesesuaian masing-masing semut. Berdasarkan 10 percobaan yang telah dilakukan, percobaan dengan hasil terbaik didapatkan pada percobaan pertama dan terakhir yang menyeleksi/memilih 66 fitur yang artinya berhasil mengurangi fitur sebanyak 96,34% dengan akurasi pelatihan 52%.

Image Quantization in Psoriasis Using K-Mean Clustering Nugroho, Arief Kelik
SENATIK STT Adisutjipto Vol 4 (2018): Transformasi Teknologi untuk Mendukung Ketahanan Nasional [ ISBN 978-602-52742-0-6 ]
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Along with technology that is very fast image processing with high quality can be presented and displayed in many ways. The image has detailed information that can cause problems during processing. Image quantization is done as a preprocessing stage to reduce the number of colors in the image so that the resulting image approaches the original image. The purpose of this study is to group clusters of characteristics with the same image value.

Algoritma Iterative Dichotomizer 3 (ID3) Pengambilan Keputusan Nugroho, Arief Kelik; Iskandar, Dadang
Dinamika Rekayasa Vol 11, No 2 (2015): Dinamika Rekayasa - Agustus 2015
Publisher : Jenderal Soedirman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.dr.2015.11.2.71

Tujuan makalah ini adalah menerapkan metode yang dapat digunakan dalam proses klasifikasi untuk memberikan keputusan. Model klasifikasi dapat digambarkan dalam berbagai bentuk, salah satunya adalah dengan menggunakan konsep pohon (tree). Pohon (tree) merupakan salah satu konsep teori graf yang paling penting. Pemanfaatan pohon dalam kehidupan sehari-hari adalah untuk menggambarkan hierarki dan memodelkan persoalan. Iterative dichotomiser 3 ( ID3 ) merupakan suatu metode dalam learning yang akan membangun sebuah pohon keputusan untuk mencari solusi dari persoalan. Pohon keputusan yang dihasilkan dengan menggunakan proses pencarian nilai terbaik (the best classifier) akan dijadikan seagai akar (root). Dalam penelitian ini akan dibahas model klasifikasi menggunakan Decision Tree dengan algoritma Interactive Dichotomicer 3 (ID3) berdasarkan pada kasus.

ANT COLONY OPTIMIZATION UNTUK MENYELEKSI FITUR DAN KLASIFIKASI ARTIKEL nugroho, arief kelik; Permadi, Ipung
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Algoritma koloni semut dapat diterapkan dalam pengklasifikisian dengan menerapkan mekanisme perilaku semut dalam mencari sumber makanan. Semut memberikan kemungkinan hasil terbaik/optimal berdasar intensitas pheromone. Himpunan fitur pada artikel diseleksi berdasarkan topik atau jenis kelas yang diinputkan dalam sistem kemudian dievaluasi dengan mengecek kesesuaian masing-masing semut. Berdasarkan 10 percobaan yang telah dilakukan, percobaan dengan hasil terbaik didapatkan pada percobaan pertama dan terakhir yang menyeleksi/memilih 66 fitur yang artinya berhasil mengurangi fitur sebanyak 96,34% dengan akurasi pelatihan 52%.

SISTEM PENDUKUNG KEPUTUSAN PEREKOMENDASIAN OLI MENGGUNAKAN FUZZY MADM Nugroho, Arief Kelik; Permadi, Ipung; Hanifa, Aini
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol 9, No 1 (2020)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v9i1.22959

Perekomendasian oli secara manual, tanpa menggunakan perhitungan yang akurat cenderung bersifat subyektif serta cukup sulit mengenali karakteristik oli yang paling tepat untuk jenis motor tertentu. Proses analisis data transaksi secara manual berdasarkan pada pengamatan akan mempengaruhi kualitas mesin. Sebagai contoh untuk memberikan rekomendasi oli terbaik bagi seorang konsumen, maka sebuah perusahaan/bengkel sepeda motor harus melihat data transaksi ganti oli yang lalu untuk mendapatkan data tentang oli yang digunakan untuk mengganti oli motor konsumen tersebut. . Penggunaan perangkat komputer dapat digunakan sebagai pendukung keputusan menjadi lebih cepat, tepat, dan akurat. Proses perekomendasian oli terbaik bagi kendaraan bermotor menggunakan metode fuzzy. Hasil perhitungan diperoleh direkomendasikan A3=0.72, A2 = 0.66, A1= 0.55, A4= 0.40.

OPTIMIZING COURSE SCHEDULING FACULTY OF ENGINEERING UNSOED USING GENETIC ALGORITHMS Nugroho, Arief Kelik; Permadi, Ipung; Yasifa, Ana Romadhona
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 7 No 2 (2022): JITK Issue February 2022
Publisher : LPPM Nusa Mandiri

In carrying out an activity regularly and smoothly, it is necessary to make an activity schedule that can manage the time of one activity with another so that unwanted things do not happen such as the same time, the same place, and others. Making a schedule of activities is quite easy to do if there are not too many entities involved and if the entities are not tied to each other, but for larger cases, creating a schedule of activities manually will take quite a lot of time and can result in errors in the schedule or shortages. effectiveness in the resulting schedule. This is commonly experienced in making course schedules at universities because there are a lot of course data and lecturers can teach several courses at once and at different times, therefore in making course schedules can be done by applying genetic algorithms so that the time required needed in making the course schedule shorter and the results obtained can be more optimal than the results of making the course schedule manually. In this study, the optimal course schedule was obtained in the 31st generation using data on rooms, courses, study time, lecturers, and departments so that one chromosome has 154 gen, then the population length is made up to 9 individuals or chromosomes, the mutation rate is set at 0.1, and the method used in the individual selection stage is the tournament selection method where the tournament size is set at 3. The fitness value is taken so that a schedule is said to be optimal, i.e. if the fitness value is equal to 1 because then it shows that there are no errors or problems (such as time, lecturers, conflicting rooms) that occur in the schedule.

Improvement Of Image Quality Using Convolutional Neural Networks Method Nugroho, Arief Kelik; Permadi, Ipung; Faturrahim, Muhammad
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.30892

Abstract. Purpose: This desire for high resolution stems from two main application areas, namely improving pictorial information for human interpretation and assisting automatic machine perception in representing images or videos. Image resolution describes the detail contained in an image, the higher the resolution, the more detail there is. The resolution of a digital image can be classified into various types, namely pixel resolution, spatial resolution, temporal resolution, and radiometric resolution. In this context, we are interested in spatial resolution.Methods: Elements of a digital image consist of a collection of small images called pixels. Spatial resolution refers to the pixel density of an image and is measured in pixels per unit area. A quality digital image is determined by the size of the resolution it has. A low resolution or low-resolution is a drawback of a digital image because the information contained in the image means little compared to a high-resolution image.Result: Therefore, in this study, a digital image processing program was created in the form of Image Super-Resolution with the Convolutional Neural Network method to utilize low-resolution images to produce high-resolution images. With a fairly short training process, namely 6050 datasets with 100 CNN epochs, the average PSNR image is 5% higher.Novelty: Image quality can be improved by changing the parameters in the CNN method so that image quality can be improved.

Image Quantization in Psoriasis Using K-Mean Clustering Nugroho, Arief Kelik
SENATIK STT Adisutjipto Vol 4 (2018): Transformasi Teknologi untuk Mendukung Ketahanan Nasional [ ISBN 978-602-52742-0-6 ]
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v4i0.162

Along with technology that is very fast image processing with high quality can be presented and displayed in many ways. The image has detailed information that can cause problems during processing. Image quantization is done as a preprocessing stage to reduce the number of colors in the image so that the resulting image approaches the original image. The purpose of this study is to group clusters of characteristics with the same image value.

Image dermoscopy skin lesion classification using deep learning method: systematic literature review Nugroho, Arief Kelik; Wardoyo, Retantyo; Wibowo, Moh Edi; Soebono, Hardyanto
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6077

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Implementasi Algoritma Genetika dalam optimasi Performa Truk Sampah Menggunakan Aplikasi Trash Queen Nugroho, Arief Kelik; Faza, Muhammad Naufal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023105193

Kebutuhan akan kendaraan dapat dibilang sebagai salah satu kunci dari berjalannya ekonomi dunia. Akan tetapi, efisiensi desain spesifikasi kendaraan masih menjadi topik hangat di kalangan desainer otomotif. Hal ini karena meskipun ada jutaan kendaraan di seluruh dunia, tidak semua desain kendaraan dapat menunjukkan performa yang optimal di segala keadaan. Aspek spesifikasi kendaraan yang memiliki pengaruh paling besar antara lain daya mesin, ukuran ban, dan berat kendaraan. Oleh karena itu, dibutuhkan suatu optimasi desain spesifikasi kendaraan yang hanya membutuhkan faktor ukuran ban dan berat kendaraan, di mana berat kendaraan tersebut dapat direpresentasikan oleh dua faktor besar, yaitu kapasitas kargo dan kapasitas bahan bakar. Dalam penelitian ini ditelusuri kemungkinan optimasi spesifikasi truk sampah berdasarkan faktor-faktor tersebut dalam tujuannya mengumpulkan sampah dan kembali ke sentra pengumpulan sampah tanpa kehabisan bahan bakar menggunakan aplikasi Trash Queen. Trash Queen memanfaatkan algoritma genetika untuk menjalankan simulasi secara berulang-ulang hingga didapatkan solusi yang optimal. Truk sampah yang kehabisan bahan bakar tanpa mampu mengantarkan sampah akan dianggap gagal karena tidak mampu mencapai tujuannya. Pada penelitian ini, ditemukan bahwa dalam 30 generasi fitness terbaik tiap generasi telah naik sebanyak 68.4% dengan trend ukuran ban yang makin kecil, kapasitas bahan bakar yang makin kecil, dan kapasitas kargo yang makin besar. AbstractThe need for vehicles can be said to be the key in the continuity of the world’s economy. Even so, the efficiency of vehicle design specifications remain a hot topic among automotive designers. This is caused by the many millions of vehicles all across the globe, yet not all of them are able to perform to factory standards at optimal efficiency due to variations in different situations. The parts that have the most significant roles in a vehicle’s specification with respect to efficiency includes engine power, wheel size, and the weight of the vehicle itself. That is why a design optimization where it only accounts for the easier to find parts of a vehicle’s specification is needed, the wheel size and the weight of the vehcile which can be represented by two major factors: cargo capacity and fuel capacity. This research aims to explore the possibilities of optimizing a trash truck’s specifications in its conquest to collect trash and return to a trash collecting centre to deliver them without running out of fuel using the Trash Queen app based on those factors. Trash Queen utilizes genetic algorithm to run simulations repeatedly until an optimal solution is obtained. Trash trucks that run out of fuel before being able to deliver any trash will be considered as failed trucks due to being unable to accomplish their set goal. In this research, it was found that in just 30 generations, the best fitness result of each generation has risen by 68.4% with wheel sizes trending to a smaller size, fuel capacity to a smaller size, and cargo capacity to a larger size.

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