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All Journal Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA Journal of Informatics and Technology JSM (Jurnal SIFO Mikroskil) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL EKONOMI MANAJEMEN AKUNTANSI Jurnal Teknik Informatika (JUTIF) Makara Journal of Technology
Sukmawati Nur Endah
Faculty Of Mathematics And Natural Science, Diponegoro University, Tembalang-Semarang 50239, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Color Space to Detect Skin Image: The Procedure and Implication Endah, Sukmawati Nur; Kusumaningrum, Retno; Wibawa, Helmie Arif
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12013

Abstract

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.
Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar Zulkarnaen, Harits Farras; Endah, Sukmawati Nur
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.15889

Abstract

Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.
TRANSITIF KLOSUR DARI GABUNGAN DUA RELASI EKUIVALENSI PADA SUATU HIMPUNAN DENGAN STRUKTUR DATA DINAMIS Endah, Sukmawati Nur
MATEMATIKA Vol 8, No 3 (2005): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (84.694 KB)

Abstract

A relation R on set A is an equivalence relation on A if and only if R is reflexive, symmetric and transitive. If two equivalence relations on set A are  combined, the combination of them is not surely an equivalence relation, because it is not surely transitive relation. In this paper is found the smallest transitive relation (transitive closure) of the combination, to be an equivalence relation. Then the steps to determine transitive closure are programmed in Pascal programming language with dynamic data structure that is multilist.
Sistem Temu Kembali Informasi pada Dokumen Teks Menggunakan Metode Term Frequency Inverse Document Frequency (TF-IDF) Harjanto, Dhony Syafe’i; Endah, Sukmawati Nur; Bahtiar, Nurdin
JURNAL SAINS DAN MATEMATIKA Volume 20 Issue 3 Year 2012
Publisher : JURNAL SAINS DAN MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3723.336 KB)

Abstract

Banyaknya informasi yang disimpan dalam dokumen teks mengakibatkan pengguna sistem informasi mengalami kesulitan untuk mendapatkan informasi yang diinginkan, maka diperlukan sebuah mesin pencarian yang dapat menentukan dan menemukan dokumen yang relavan sesuai dengan query pengguna. Penelitian ini menggunakan metode Term Frequency Inverse Document Frequency (TF-IDF) yang didasarkan pada kemunculan term pada tiap dokumen dan pengurangan dominasi term yang sering muncul di berbagai dokumen. Hasil Penelitian ini adalah program simulasi  Sistem Temu Kembali Informasi pada dokumen teks menggunakan Metode Term Frequency Inverse Document Frequency (TF-IDF) yang menghasilkan perhitungan pembobotan Term Frequency Inverse Document Frequency (TF-IDF) dan mendapatkan dokumen relevan yang teranking sesuai tingkat pembobotannya berdasarkan query masukan oleh pengguna.   Keywords: Mesin Pencarian, Query, Term Frequency, TF-IDF
Soybean Disease Detection with Feature Selection Using Stepwise Regression Algorithm: LVQ vs LVQ2 Muhamad, Nida; Endah, Sukmawati Nur; Sarwoko, Eko Adi; Sasongko, Priyo Sidik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 2, May 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (518.977 KB) | DOI: 10.22219/kinetik.v5i2.919

Abstract

ndonesia's soybean needs increase from year to year. But according to data from the Badan Pusat Statistik (BPS) the amount of national soybean productivity is still low, so the fulfillment of soybean needs is done by importing soybeans from several countries such as China, Ukraine, Canada, Malaysia, and the United States. Low soybean productivity is caused by several factors. One of the causes is disease. This study aims to create a soybean disease detection by applying Learning Vector Quantization 2 (LVQ2) neural network algorithm(ANN) and Stepwise Regression Algorithm attribute selection. The attribute variables used consisted of 35 symptoms of the disease in soybean crop data. The data used in this study is a soybean dataset taken from University of California Irvine Machine Learning Repository as much as 200 data. The distribution of training data and test data is done by the k-fold cross validation method with a value of k = 10. The result of the study shows that the best paramater use in lVQ2. The results showed that the best parameters in LVQ2 is learning rate (α) value of 0.3; epsilon 0.04; and maximum epoch 100. While the best attribute selection uses the parameter p to enter and p to remove of  0.15 which produces 17 selected attributes such as date, plant stand, precipitation, leaves, leaf spot halo, leaf spot margins, leafspot size, leaf mildew, stem canker, stem fungi, external decay, fruit pods, fruit spots, seeds, mold growth, seed discolor, roots. The best results in this study resulted in an accuracy of 90.5%, 9.5% error rate, 90.5% sensitivity, and 98.94% specificity
Pengembangan Aplikasi Mobile Deteksi Dini Penyakit dan Hama Pada Tanaman Palawija Endah, Sukmawati Nur; Sarwoko, Eko Adi; Sasongko, Priyo Sidik; Sutikno, Sutikno
Informatika Pertanian Vol 28, No 1 (2019): Juni 2019
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/ip.v28n1.2019.p49-66

Abstract

Meningkatnya jumlah penduduk di Indonesia berdampak pada kebutuhan pangan, salah satu sumber pangan selain padi adalah tanaman palawija. Tanaman palawija memiliki peranan dalam mewujudkan impian Indonesia menjadi Lumbung Pangan Dunia di tahun 2045. Namun, penyakit dan serangan hama dapat menurunkan kwalitas dan kwantitas hasil produk tanaman palawija. Maka, diperlukannya identifikasi dan penanganan terhadap penyakit hama pada tanaman palawija agar mutu dan kwalitas produk tetap terjaga dan hasil produk melimpah. Penelitian ini mengusulkan pengembangan sebuah aplikasi mobile mengenai deteksi penyakit dan hama palawija berdasarkan gejala yang timbul. Pengujian telah dilakukan baik pengujian fungsionalitas sistem maupun usability testing terhadap aplikasi yang diberi nama Online at Sawat (OAS). Hasil pengujian menunjukkan bahwa OAS telah memenuhi requirement yang dibutuhkan dan mempunyai hasil usability test yang baik. Hasil penelitian ini diharapkan dapat membantu petani palawija khususnya jagung dan kedelai agar hasil panennya terjaga dan pemerintah secara tidak langsung untuk mewujudkan Indonesia sebagai Lumbung Pangan Dunia.
Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar Zulkarnaen, Harits Farras; Endah, Sukmawati Nur
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.15889

Abstract

Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.
Color Space to Detect Skin Image: The Procedure and Implication Endah, Sukmawati Nur; Kusumaningrum, Retno; Wibawa, Helmie Arif
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12013

Abstract

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr,  CIE XYZ, CIE L*a*b*, CIE  L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.
Suitability analysis of rice varieties using learning vector quantization and remote sensing images Annisa Apriliani; Retno Kusumaningrum; Sukmawati Nur Endah; Yudo Prasetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12234

Abstract

Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness. The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO. While the representative features that can be extracted from remote sensing images and related to agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and the features combination of NDWI and BI values (in standard deviation). The architecture generates accuracy value of 56%.
Solid waste classification using pyramid scene parsing network segmentation and combined features Khadijah Khadijah; Sukmawati Nur Endah; Retno Kusumaningrum; Rismiyati Rismiyati; Priyo Sidik Sasongko; Iffa Zainan Nisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.18402

Abstract

Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed  to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
Co-Authors Adi Wibowo Afry Rachmat Amalia Rachma Yanti Andika Putra Pratama Angga Pradana Saputra Annisa Apriliani Arif Lutfiyanto Aris Sugiharto Beta Noranita Beta Noranita Dinar Mutiara KN eko adi sarwoko Eko Adi Sarwoko Eko Prasetiawan Erlangga Erlangga Fachry Abda El Rahman Fitriana Prasari Dewi Frediansah Frediansah Ganang Surya Atmaja Ganis Khufad Arridho Hanifah Awliya Harjanto, Dhony Syafe’i Helmie Arif Wibawa Helmie Arif Wibawa Iffa Zainan Nisa Indriyati Indriyati Irfan Pradipta Kabul Kurniawan Khadijah Khadijah Khadijah, Khadijah Kushartantya Kushartantya Muhamad, Nida Muhammad Muhammad Muhammad Rahmat Widyanto, Muhammad Rahmat Nisa, Iffa Zainan Nurdin Bahtiar Nurdin Bahtiar Panji Wisnu Wirawan Perawaty Panjaitan Priyo Sidik Sasongko Priyo Sidik Sasongko Ragil Saputra Raina Stefani Budi Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Rizka Ella Setyani Roby Hanintyo Nursio Sakti Samsul Ma’arif Sanggam Andreas Harahap Satriyo Adhy Satriyo Adhy Siradjuddin, Indah Agustien Situmeang, Axelliano Rafael Suryo Hartanto Sutikno . Sutikno, Sutikno Tarzan Basaruddin, Tarzan Yudo Prasetyo Zulkarnaen, Harits Farras Zulkarnaen, Harits Farras