Article Per Year (5 Year)
p-Index From 2021 - 2026
1.893
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Fundamental Mathematics and Applications (JFMA)
Priyo Sidik Sasongko
The Informatics Departement, Faculty Of Mathemathics And Sciences, Diponegoro University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
FUZZY MODEL BASED SOLVING NONLINEAR SYSTEMS WITH CASE STUDY OF TRUCK-TRAILER SYSTEM sasongko, priyo Sidik; indriyati, Indriyati; sarwoko, Eko adi
MATEMATIKA Vol 12, No 2 (2009): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (6.365 KB)

Abstract

 In this paper, we consider the fuzzy controller problem for nonlinear system using fuzzy models. The controller is constructed using a design model of the dynamical process to be controlled. The design model obtained from the truth model using a fuzzy modeling approach. The Takagi-Sugeno fuzzy model is adopted for fuzzy modeling of the nonlinear system. The truth model represents a detailed description of the process dynamic. The model is used in a simulation to evaluate the performance of the controller design. Stabilization of the closed-loop discrete Takagi-Sugeno systems using the well-known PDC (Paralel Distributed Compensation) technique is investigated. The design procedure we adopt is to convert the design of the controller to a Linear Matrix Inequality (LMI) problem so that the stability of the whole system can be assured.  
Verifikasi Kepemilikan Citra Medis dengan Kriptografi RSA dan LSB Watermarking Putra, Satya Sandika; Sasongko, Priyo Sidik; Bahtiar, Nurdin
JURNAL SAINS DAN MATEMATIKA Volume 19 Issue 3 Year 2011
Publisher : JURNAL SAINS DAN MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4276.608 KB)

Abstract

Di  dalam  dunia  medis,  penyembunyian  informasi  untuk perlindungan  hak  cipta sangat  diperlukan.  Teknik  penyembunyian  informasi  biasa  disebut  dengan watermarking. Metode yang digunakan adalah dengan menyisipkan pesan teks ke dalam sebuah data citra medis. Perlindungan informasi di dalam data citra medis seorang pasien perlu dilakukan agar tidak terjadi kesalahan informasi kepemelikan data  medis  pasien  satu  dengan  yang  lainnya.  Informasi  yang  disembunyikan  di dalam  citra  medis  berupa  teks  yang  sebelumnya  telah dilakukan  enkripsi  atau pengacakan pesan. Salah satu metode untuk menyembunyikan pesan teks adalah dengan memanfaatkan  Least Significant Bit  (LSB), yaitu dengan mengubah nilai bit  terakhir  pada  citra  medis.  Karena  hanya  bit-bit terakhir  yang  diubah,  maka citra medis yang telah tersisipi pesan sangat miripdengan citra aslinya, perubahan nilai-nilai  piksel  pada  citra  medis  tidak  begitu  terlihat.  Untuk  mengekstrak kembali pesan teks yang disisipkan menggunakan private key (kunci rahasia) yang sebelumnya telah ditentukan secara acak. Citra medis dan pesan teks hasil ekstrak sama dengan citra medis dan pesan teks sebelum dilakukan penyisipan. Kata kunci : watermarking, citra medis, enkripsi,  private key, Least Significant Bit
Sistem Pakar Identifikasi Modalitas Belajar Siswa Dengan Implementasi Algoritma C4.5 Soewono, Rachmawati; Gernowo, Rachmat; Sasongko, Priyo Sidik
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 4, No 1 (2014): Volume 4 Nomor 1 Tahun 2014
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1984.46 KB) | DOI: 10.21456/vol4iss1pp20-27

Abstract

C4.5 Algorithm is one of the classification technique in machine learning which is used in data mining process by build a decision tree which is represent in the rules. The aims of classification technique in data mining is to recognize the regularity of the pattern and the relation in a huge dataset by historical data collection. Students’ modalities measurement which is done by the questionnaire is produce historical data which is potentially to be processed to generate the classification that can be converted in rules. The expert acquisition and the C4.5 algorithm classification rules are used as knowledge base in the expert system. Therefore this research is done to build an expert system of the student’s modalities identification by implementing C4.5 algorithm that can produce seven categories of modalities classification, they are : visual, auditory, kinesthetic, visual-auditory, visual-kinesthetic, auditory-kinesthetic and visual-auditory-kinesthetic which has good in accuracy. The accuracy of the C4.5 algorithm classification and the expert system testing prediction is 80%. Keywords : Expert system; Decision tree; C4.5 Algorithm; Modalities.  
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.
The Comparison of Imbalanced Data Handling Method in Software Defect Prediction Khadijah, Khadijah; Sasongko, Priyo Sidik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 3, August 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i3.1049

Abstract

Software testing is a crucial process in software development life cycle which will affect the software quality. However, testing is a tedious task and resource consuming. Software testing can be conducted more efficiently by focusing this activitiy to software modules which is prone to defect. Therefore, an automated software defect prediction is needed. This research implemented Extreme Learning Machine (ELM) as classification algorithm because of its simplicity in training process and good generalization performance. Aside classification algorithm, the most important problem need to be addressed is imbalanced data between samples of positive class (prone to defect) and negative class. Such imbalance problem could bias the performance of classifier. Therefore, this research compared some approaches to handle imbalance problem between SMOTE (resampling method) and weighted-ELM (algorithm-level method).The results of experiment using 10-fold cross validation on NASA MDP dataset show that including imbalance problem handling in building software defect prediction model is able to increase the specificity and g-mean of model. When the value of imbalance ratio is not very small, the SMOTE is better than weighted-ELM. Otherwise, weighted-ELM is better than SMOTE in term of sensitivity and g-mean, but worse in term of specificity and accuracy.Software testing is a crucial process in software development life cycle which will affect the software quality. However, testing is a tedious task and resource consuming. Software testing can be conducted more efficiently by focusing this activitiy to software modules which is prone to defect. Therefore, an automated software defect prediction is needed. This research implemented Extreme Learning Machine (ELM) as classification algorithm because of its simplicity in training process and good generalization performance. Aside classification algorithm, the most important problem need to be addressed is imbalanced data between samples of positive class (prone to defect) and negative class. Such imbalance problem could bias the performance of classifier. Therefore, this research compared some approaches to handle imbalance problem between SMOTE (resampling method) and weighted-ELM (algorithm-level method).The results of experiment using 10-fold cross validation on NASA MDP dataset show that including imbalance problem handling in building software defect prediction model is able to increase the specificity and g-mean of model. When the value of imbalance ratio is not very small, the SMOTE is better than weighted-ELM. Otherwise, weighted-ELM is better than SMOTE in term of sensitivity and g-mean, but worse in term of specificity and accuracy.
Detection of Ship Using Image Processing and Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.7357

Abstract

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.
Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network Sutikno Sutikno; Indra Waspada; Nurdin Bahtiar; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3486

Abstract

One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.
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.
Software Defect Prediction Using Synthetic Minority Over-sampling Technique and Extreme Learning Machine Khadijah Khadijah; Priyo Sidik Sasongko
Journal of Telematics and Informatics Vol 7, No 2: JUNE 2019
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v7i2.

Abstract

Software testing is one of the crucial processes in software development life cycle which will influence the software quality. One of the strategies to help testing process is predicting the part or module of software which is prone to defect. Then, the testing process can be more focused to those parts. In this research a classifier model for predicting software defect was built. One of the most important problems in software defect prediction is imbalance data distribution between samples of positive class (prone to defect) and of negative class. Therefore, in this research SMOTE is implemented to handle imbalance data problem and extreme learning machine is implemented as a classification algorithm. As a comparison to SMOTE-ELM, a modification of ELM which directly copes with imbalance problem, weighted-ELM, is also observed. This research used NASA MDP dataset PC1, PC2, PC3 and PC4. The results of experiment using 10-fold cross validation show that directly classification using ELM obtain the worse result compared to SMOTE-ELM and weighted-ELM. When the value of imbalance ratio is not very small, the SMOTE-ELM is better than weighted-ELM. When the value of imbalance ratio is very small, the g-mean of weighted-ELM is higher than the g-mean of SMOTE-ELM, but the accuracy of weighted-ELM is lower than the accuracy of SMOTE-ELM. Therefore, in this software defect prediction case it can be concluded that SMOTE is effective to increase the generalization performance of classifier in minority class as long as the value of imbalance ratio is not very small.
Co-Authors Eko Adi Sarwoko Fajar Agung Nugroho Helmie Arif Wibawa Iffa Zainan Nisa Indra Waspada Indriyati Indriyati Jason Al Hilal Sabda Dewa Khadijah Khadijah Khadijah, Khadijah Mahendra, Ida Bagus Satya Muhamad, Nida Nisa, Iffa Zainan Nurdin Bahtiar Okky Widya Arditya Prasetyo, Syalwa Dea Putri Rachmat Gernowo Rachmawati Soewono, Rachmawati Ragil Saputra Redemtus Heru Tjahjana Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Satriyo Adhy Satya Sandika Putra Sirait, Dheo Ronaldo Sukmawati Nur Endah Sukmawati Nur Endah Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno, Sutikno Sutimin Sutimin Tatik Widiharih Triatmaja, Yudha Kusuma Vebriana, Melanie Safira Widowati, Widowati Wildan Azka Adzani