Article Per Year (5 Year)
p-Index From 2019 - 2024
2.921
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Perfecting a Video Game with Game Metrics Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate : Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi
Moch Arief Soeleman
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa Saiful Ulya; M Arief Soeleman; Fikri Budiman
Techno.Com Vol 20, No 1 (2021): Februari 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i1.4215

Abstract

Klasifikasi adalah proses menemukan model atau fungsi yang menggambarkan dan membedakan kelas atau konsep data. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma klasifikasi berdasarkan pembelajaran dari data yang sudah terklasifiasi sebelumnya. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma yang sangat bagus dalam menangani beberapa kasus, salah satu kelebihan k-NN diantaranya adalah tangguh terhadap data training yang noisy dan sangat efektif apabila data trainingnya besar. Namun terdapat beberapa masalah pada algoritma k-NN diantaranya adalah penentuan nilai k untuk pemilihan jumlah tetangga terdekatnya sangat sulit, karena nilai k sangat peka atau sensitif terhadap hasil klasifikasi. Pada penelitian ini, akan dilakukan pemodelan klasifiasi dengan menggunakan algoritma k-NN yang difokuskan pada proses penentuan nilai k terbaik pada dataset IKG (Indeks Kesulitas Geografis) desa. Pada penelitian ini akan melakukan integrasi algoritma k-NN dengan menentukan nilai k optimal dengan optimize parameters berdasar algoritma genetika.
Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset Mursalim Mursalim; Purwanto Purwanto; M Arief Soeleman
Techno.Com Vol 20, No 1 (2021): Februari 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i1.4230

Abstract

Data merupakan hal penting diera sekarang begitu  juga dengan metode data mining yang dapat mengekstraksi data menghasilkan informasi. Klastering  1 dari 5 peran data mining yang berfungsi untuk mengelompokkan data berdasarkan tingkat kemiripan dan jarak minimum. Algoritma K-Means  termasuk algoritma yang populer banyak digunakan diberbagai bidang seperti bidang pendidikan, kesehatan, sosial, biologi, ilmu komputer. Seringkali metode K-Means dikombinasikan dengan metode optimasi seperti algoritma genetika untuk mengatasi permasalah pada K-Means yaitu sensitif dalam penentuan centroid awal .Namun metode algoritma genetika memiliki kekurangan yaitu mengalamai konvergen prematur sehingga hasil dari algorima genetika terjebak pada optimum lokal. Penelitian ini mengkombinasikan dynamic artificial cromosomes genetic algorithm dengan K-Means dalam menentukan nilai centroid awal pada k-means. Hasil eksperimen menunjukkan bahwa metode DAC GA + K-Means lebih unggul dibandingkan dengan K-Means dan GA + K-Means pada 2 dataset yang diuji dengan optimal nilai klaster sebanyak 2 dan 1 dataset sebanyak 3 klaster. Metode tersebut perolehan nilai DBI sebesar 0.138, 0.279 serta 0.382, nilai Sum Square Error sebesar 92.56, 332,39 dan 1280.68 serta nilai fitness yang tebentuk adalah 7.12, 3.57 dan 2.13.
Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP Tutik Khotimah; Abdul Syukur; M. Arief Soeleman
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 8, No 1 (2017): JURNAL SIMETRIS VOLUME 8 NO 1 TAHUN 2017
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (219.586 KB) | DOI: 10.24176/simet.v8i1.808

Abstract

Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.
Classification of Guarantee Types Using Leaf Feature Extraction with Minutiae and GLCM Using K-NN Method Muhammad Haris Zuhri; Adhe Irham Thoriq; Abdul Syukur; Affandy Affandy; Muslih Muslih; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.201

Abstract

Indonesia is a fertile area that has a sub-tropical climate that makes plants grow well in various parts of Indonesia. There are various variants of guava in Indonesia. Of the several types have differences including the structure of the fruit, tree and leaves. The focus of this research is to classify guava species based on leaf bone image using GLCM feature extraction, minutiae and shape extraction using the K-NN method. In this study using a dataset of 4 types of guava as many as 300 images, where each type of as many as 75 images. In the extraction process to get the leaf bone image in this study, there are several processes, namely preprocessing, grayscale image, binary image and morphology then only get the leaf bone image. After getting the extracted value, then the data is processed using the K-NN method. The highest accuracy in the K-NN method is at k1 = 92.42% with a standard deviation of 6.05% (micro average: 92.45%). Thus GLCM feature extraction, minutiae and shape extraction can potentially increase the level of accuracy in guava classification based on leaf bone images.
Classification of Toxic Plants on Leaf Patterns Using Gray Level Co-Occurrence Matrix (GLCM) with Neural Network Method Mohammad Faishol Zuhri; S. Kholidah Rahayu Maharani; Affandy Affandy; Aris Nurhindarto; Abdul Syukur; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.202

Abstract

Poisonous plants are plants that must be avoided and not consumed by humans, because the presence of poisonous plants is also often found in the surrounding environment without realizing it. Because of the lack of knowledge to classify poisonous plant species, it will be more difficult to find out. With the help of a computer system, it will be easier to identify the types of poisonous plants. There are 3 types of poisonous plants that will be used in this study, namely cassava, jatropha, and amethyst. There are also 3 types of non-toxic plants with almost the same morphology as a comparison, namely cassava, figs, and eggplant. In this study, researchers tried to classify poisonous plant species using leaf pattern features that would be extracted using shape features and Gray Level Co-occurrence Matrix (GLCM). The value taken from the shape feature is the values ​​of area, width, diameter, perimeter, slender, and round. While the value of contrast, entropy, correlation, energy, and homogeneity for Gray Level Co-occurrence Matrix (GLCM) attributes. To classify data using Neural Network with RapidMiner application. From this study, it is known that from 300 total datasets used, the highest accuracy is 96.13% using the Neural Network method. With an AUC value of 0.986 and is included in the very good category.
Seam Cerving and Salient Detection for Thumbnail Photos Much Chafid; Abdul Syukur; Moch Arief Soeleman; Affandy Affandy
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.204

Abstract

Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scaling. However, the seam carving method still cannot protect the object that is considered the most important. In overcoming this weakness, we can use a combination of seam carving algorithm with salient detection. In this research, we will improve the two methods which function as thumbnail maker. The results of the salient detection of the most important areas of the image will be detected and as a reference in resizing the image (seam carving) The dataset uses 200 images. The accuracy value is calculated by distributing questionnaires to 100 respondents and producing an acceptance rate of 78% so that the results are Very Natural/Natural.
Pelatihan dan Pendampingan Peningkatan Keahlihan Modelling Character 3D untuk Siswa dan Guru di SMK ST. FRANSISKUS SEMARANG Muslih Muslih; Ajib Susanto; Ali Muqoddas; Elkaf Rahmawan Pramudya; M. Arief Soeleman
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 4, No 3 (2021): September 2021
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v4i3.190

Abstract

Perkembangan saat ini teknologi informasi dan komunikasi (TIK) berjalan sangat begitu cepat yang diiringi dengan perkembangan bidang lainnya seperti teknologi telekomunikasi, termasuk juga pada jaringan komputer dan aplikasi 3D, seperti saat ini yang sedang ada wabah virus Corona (Covid-19) informasi dengan 3D sangatlah membantu. Kurikulum di SMK dengan DUDI (dunia usaha dan industri) diselaraskan agar lulusan SMK dapat siap pakai dengan lapangan pekerjaan yang tersedia. Tujuan pelatihan dan pendampingan agar Guru dan siswa dapat meningkatkan keahlian modelling character 3D di lingkungan Sekolah Menengah Kejuruan (SMK) ST. Fransikus Semarang. Implementasi kegiatan dengan melakukan pelatihan dan pendampingan melalui virtual secara bertahap dengan memberikan contoh dana penugasan modelling character 3D sehingga hasil karya multimedia lebih interaktif dan menarik.
PERANAN PENGAJAR DALAM KOLABORASI PENYAMPAIAN MATERI PELATIHAN IT TERHADAP SISWA Moch Arief Soeleman; Pulung Nurtantio Andono; - Pujiono; Noor Ageng Setiyanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 1, No 2 (2018): Juli 2018
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.761 KB) | DOI: 10.33633/ja.v1i2.24

Abstract

Siswa-siswi asuh dari Lembaga Amil Zakat Universitas Dian Nuswantoro, merupakan lembaga zakat yang dibentuk oleh karyawan muslim Universitas Dian Nuswantoro yang bertujuan memberikan bantuan uang sekolah setiap bulan untuk membayar biaya sekolah bagi siswa siswi sekolah menengah pertama dan sekolah menengah atas di kota Semarang yang tidak mampu untuk tetap bisa menyelesaikan sekolahnya. Motivasi bantuan sekolah untuk anak-anak binaan Lembaga Amil Zakat Udinus yang tidak mampu secara fiansial tetapi memiliki semangat dan kemampuan baik dalam akademik sehingga harus diberikan dukungan moril dan materiil agar tetap bisa meneruskan sekolahnya. Dengan memperhatikan  kondisi  diatas maka salah satu pendekatan yang perlu diambil adalah  dengan  peningkatan  kualitas  pengetahuan siswa siswi penerima LAZ agar tidak gagap terhadap teknologi atau tidak memiliki kemampuan  dasar dalam menguasai dunia teknologi informasi. Sebagai seorang pelajar menggunakan teknologi informasi merupakan hal yang sangat minim, hal ini dikarenakan ketidak mampuan mereka memiliki peralatan teknologi informasi yang baik seperti komputer, laptop atau alat digital mobile. Disamping hal tersebut disekolah mereka masing-masing belum tentu mendapat pengetahuan teknologi informasi  yang cukup karena tidak bersekolah ditempat yang baik fasilitasnya.
Employee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest Aris Nurhindarto; Esa Wahyu Andriansyah; Farrikh Alzami; Purwanto Purwanto; Moch Arief Soeleman; Dwi Puji Prabowo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1345

Abstract

Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%. Thus with those result, we can conclude that we can obtain better prediction using 21 features for employee attrition and performance which help the higher management in making decisions.
Co-Authors Abdul Syukur Abdul Syukur Adhe Irham Thoriq Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Ari Yahya A Arry Maulana Syarif Barata, Mula Agung Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Noersasongko Edi Sugiarto Elkaf Rahmawan P Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Imran, Bahtiar Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muh Nasirudin Karim Muhammad Haris Zuhri Muhammad Munsarif Muljono Muljono Muljono Muljono Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Pulung Nurtantio Andono Purwanto Purwanto R. Anggi Pramunendar Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Tutik Khotimah Widyatmoko Karis