Article Per Year (5 Year)
p-Index From 2020 - 2025
2.109
P-Index
This Author published in this journals
All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JAIS (Journal of Applied Intelligent System) Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Voice Of Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ekonomi, Teknologi dan Bisnis
Purwanto Purwanto
Dian Nuswantoro University
Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Model Hibrida Untuk Penjurusan Siswa SMA Purwanto Purwanto; Sutini Dharma Oetomo; Ricardus Anggi Pramunendar
Semantik Vol 3, No 1 (2013): Semantik 2013
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (259.19 KB)

Abstract

Penjurusan siswa di SMA merupakan rutinitas penting setiap tahun. Pada umumnya terdapat 2 jurusan  utama di setiap sekolah, yaitu jurusan IPA dan IPS. Pemilihan jurusan ini sangat penting karena berkaitan dengan jurusan fakultas yang dapat dipilih o leh siswa pada jenjang pendidikan selanjutnya. Oleh sebab itu, diperlukan model yang cocok dari variabel -variabel yang mempengaruhi penjurusan tersebut.  Dalam penelitian ini penulis mengusulkan model hibrida untuk penjurusan siswa di SMA. Model hibrida ini menggabungkan metode Logistic Regression dengan  Support Vector Machine (SVM).  SVM ini merupakan metode  yang lebih handal dibandingkan metode-metode analisis lainnya.Hasil yang didapat dari penelitian menunjukkan model hibrida Logistic Regression dengan SVM memiliki tingkat akurasi yang lebih tinggi dibandingkan dengan  metode SVM biasa.
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.
Whale Optimization Algorithm Bat Chaotic Map Multi Frekuensi for Finding Optimum Value Nur Wahyu Hidayat; . Purwanto; Fikri Budiman
Journal of Applied Intelligent System Vol 5, No 2 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i2.4432

Abstract

Optimization is one of the most interesting things in life. Metaheuristic is a method of optimization that tries to balance randomization and local search. Whale Optimization Algorithm (WOA) is a metaheuristic method that is inspired by the hunting behavior of humpback whales. WOA is very competitive compared to other metaheuristic algorithms, but WOA is easily trapped in a local optimum due to the use of encircling mechanism in its search space resulting in low performance. In this research, the WOA algorithm is combined with the BAT chaotic map multi-frequency (BCM) algorithm. This method is done by inserting the BCM algorithm in the WOA search phase. The experiment was carried out with 23 benchmarks test functions which were run 30 times continuously with the help of Matlab R2012a. The experimental results show that the WOABCM algorithm is able to outperform the WOA and WOABAT algorithms in most of the benchmark test functions. The increase of performance in the average of optimum value of WOABCM when compared to WOA is 2.27x10 ^ 3.
Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method Adhe Irham Thoriq; Muhamad Haris Zuhri; Purwanto Purwanto; Pujiono Pujiono; Heru Agus Santoso
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.v6i1.200

Abstract

Banana or Musa Paradisiaca is one type of fruit that is often found in Southeast Asia. The most popular is the Raja banana (Musa paradisiaca L.). The advantage of the plantain is that it has a fragrant aroma and is of medium size and has a very sweet taste that is appetizing when it is fully ripe. While the drawback of plantains is that they ripen quickly, if not handled properly, it can change the nutritional value and nutrients contained in plantains. In this study, the author focuses on identifying the level of ripeness of bananas using the image of a plantain fruit that is still intact and its skin. Processing of the image of the plantain fruit using HSI (Hue Saturation Intensity) color space transformation feature extraction. The tool used to extract the HSI (Hue Saturation Intensity) color space transformation feature is Matlab. The attribute values obtained from the extraction are the Red, Green, Blue values obtained from the RGB values. Hue, saturation and intensity attributes were obtained from HSI extraction. Classification of the level of ripeness of plantain fruit is done with the help of the rapidminer tool. The method used is K-NN. The results obtained from this test are the accuracy value of 91.33% with a standard deviation value of+/- 4.52% with a value of k=4. The RMSE value obtained is 0.276.
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.
Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling Viry Puspaning Ramadhan; Purwanto Purwanto; Farrikh Alzami
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1429

Abstract

The use of the internet globally, especially on the use of social media, includes Indonesia as one of the most active users in the world. The amount of information that can be obtained can be used to be processed into useful information, for example, information about the public sentiment on a particular topic. Tracking and analyzing tweets can be a method to find out people's thoughts, behavior, and reactions regarding the impact of Covid-19. The key to sentiment analysis is the determination of polarity, which determines whether the sentiment is positive or negative. The word negation in a sentence can change the polarity of the sentence so that if it is not handled properly it will affect the performance of the sentiment classification. In this study, the implementation of negation handling on sentiment analysis of Indonesian people's opinions regarding COVID-19 on Twitter has proven to be good enough to improve the performance of the classifier. Accuracy results obtained are 59.6% compared to adding negation handling accuracy obtained is 59.1%. Although the percentage result is not high, documents that include negative sentences have more meaning than negative sentences. However, for the evaluation using the MCC evaluation matrix, the results were quite good for the testing data. For the results of the proposed method whether it is suitable for data that has two classes or three classes when viewed from the results of the evaluation matrix, the proposed method is more suitable for binary data or data that has only two classes.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN) Muh Nasirudin Karim; Ricardus Anggi Pramunendar; Moch Arief Soeleman; Purwanto Purwanto; Bahtiar Imran
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1317.209-217

Abstract

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Semantic segmentation of pendet dance images using multires U-Net architecture Hendri Ramdan; Moh. Arief Soeleman; Purwanto Purwanto; Bahtiar Imran; Ricardus Anggi Pramunendar
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1316.329-338

Abstract

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
Comparison of Grid Search and Evolutionary Parameter Optimization with Neural Networks on JCI Stock Price Movements during the Covid 19 Wresti; Gunawan; Purwanto; Catur Supriyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4402

Abstract

This study aims to determine the effect of covid 19 on the movement of the JCI Stock Price by testing various combinations of the input variables of closed price stock data on the JCI. The analysis is carried out to find the best RMSE value from the combination of these input variables using the neural network method. The best RMSE results are compared using the optimization of grid search and evolutionary parameters. The data used in this study was taken from the Yahoo.finance.com page on the JCI Historical Data, during the covid pandemic, from 12/11/2019 to 12/30/2021. The data obtained are 509 records. The input variable used is the closing price data (closed price) as a target. The preprocessing data used are data cleansing, filtering, and windowing until seven days before. The results obtained an RMSE value of 0.104 five days before Close t (P=5), training cycle 9000. Momentum 0.9 and learning rate 0.2 is then optimized using the grid search parameter to produce RMSE 0.101, training cycle 100. Learning rate 1 and momentum 0.1 are then compared with evolutionary parameters, which make RMSE 0.103 at learning rate 0.029, momentum 0.68, and training cycle 86. Based on this research, optimizing grid search parameters produces better RMSE than evolutionary parameter optimization. This small RMSE result shows that investors are still safe to invest.
Pengenalan Objek Kendaraan Bermotor Berbasis Framework YOLO Dengan Metode Convolutional Neural Network Hendri Julian Pramana; Purwanto Purwanto; Pujiono Pujiono
Jurnal VOI (Voice Of Informatics) Vol 12, No 1 (2023)
Publisher : STMIK Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengenalan dan penilaian visual terhadap objek kendaraan bermotor dengan pedekatan visi komputer, berupa pembelajaran mendalam menggunakan framework You Only Look Once (YOLO) dengan memanfaatkan darknet model sebagai metode Convolutional Neural Netwok (CNN) untuk mendeteksi dan mengklasifikasikan objek kendaraan bermotor tersebut. Pelatihan model dilakukan dengan 2 (dua) jenis framework yolo yaitu yolov3 dan yolov3-tiny terhadap 2000 data gambar objek kendaraan bermotor. Sedangkan untuk pengujian menggunakan video kendaran di jalan raya yang berdurasi 30 detik. Dari pengujian yang dilakukan, didapatkan hasil yang cukup baik dengan nilai mean average precission (mAP) sebesar 84,66% untuk pemodelan yolov3 dan 79.6% untuk pemodelan yolov3-tiny.
Co-Authors Adhe Irham Thoriq Alzami, Farrikh Anas Syaifudin Catur Supriyanto Dwi Puji Prabowo, Dwi Puji Edi Noersasongk Edi Noersasongko Esa Wahyu Andriansyah Fandi Yulian Pamuji Fikri Budiman Gunawan Hendri Ramdan Heribertus Himawan Heru Agus Santoso Imran, Bahtiar Julius Warih Angkasa Karim, Muh Nasirudin Moch Arief Soeleman Moh. Arief Soeleman Muhamad Haris Zuhri Muhammad Misdram Mula Agung Barata Muljono, - Mursalim Mursalim Nur Wahyu Hidayat Nurhindarto, Aris Pramana, Hendri Julian Pujiono Pujiono Ricardus Anggi Pramunendar Sutini Dharma Oetomo Viry Puspaning Ramadhan Wresti Andriani