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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 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

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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.
CONTENT-BASED IMAGE RETRIEVAL USING EXPRESSION SENSITIVITY BY FUZZY INFERENCE SYSTEM Sukmawati Nur Endah; Priyo Sidik Sasongko; Helmie Arif Wibawa
Jurnal Ilmiah Kursor Vol 8 No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i1.71

Abstract

Image retrieval can be divided into two types context-based and the content-based. Image retrieval based on the content refers to the image features such as color, texture, shape, semantics or sensations. This paper addresses the content-base image retrieval system based on expression sensitivity. It can be image or text query for input the system. Based on Itten theory, expression sensitivity consist of warm, cold, relax, anxious, and life. The research system uses two fuzzy inference system. Firstly, fuzzy inference system is used to decide image region of color. The image size is 256 x 256 pixel. Output the first fuzzy inference system is input for the second fuzzy inference system. The second fuzzy inference system is used to determined expression sensitivity of image. Degree of accuracy based on respondent from 50 images and 20 respondents is 42% for text query and 55% for image query. The further research, it can be used for other image such as medical image with certain criteria.
Pelatihan Computational Thinking bagi Guru SMP-SMK Muhammadiyah 2 Kota Semarang Helmie Arif Wibawa; Ragil Saputra; Priyo Sidik Sasongko; Satriyo Adhy; Rismiyati Rismiyati
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 11, No 2 (2020): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v11i2.3041

Abstract

Manusia mempunyai kemampuan bio-komputer yang bermanfaat dalam menyelesaiakan persoalan-persoalan yang dihadapi. Program berfikir yang dimiliki ini dapat dioptimalkan dengan menerapkan sebuah metode yang disebut dengan “Berpikir Komputatif” atau Computational Thinking (CT). CT adalah sebuah metode dalam menyelesaikan persoalan dengan menerapkan teknik ilmu komputer (informatika). Ketika pendekatan CT diterapkan dalam proses pembelajaran maka akan dapat membantu siswa untuk dapat melihat hubungan antara mata pelajaran, dan kehidupan di dalam dengan di luar kelas. Pengabdian ini berupaya untuk mensosialisasikan dan melakukan pelatihan dan pembinaan ke sekolah-sekolah mengenai metode CT. Tujuan yang diharapkan adalah metode CT ini dapat diimplementasi dalam proses belajar di sekolah yang nantinya akan membantu siswa untuk lebih berpikir secara komputatif. Selain itu juga diharapkan para guru dapat mempersiapkan para siswa untuk bersaing dalam Bebras Challenge Indonesia sebagai ajang kompetisi CT. Kegiatan ini meliputi pemaparan CT, pembahasan soal-soal dengan metode CT, dan pengenalan terhadap Bebras Challenge.
LOGISTIC MODEL DEVELOPMENT OF COVID-19 SPREAD WITH PHYSICAL DISTANCING INTERVENTION Okky Widya Arditya; Widowati Widowati; Sutimin Sutimin; R. Heru Tjahjana; Priyo Sidik Sasongko
Journal of Fundamental Mathematics and Applications (JFMA) Vol 4, No 1 (2021)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1664.587 KB) | DOI: 10.14710/jfma.v4i1.8385

Abstract

In early 2020, covid-19 spread fast in the worldwide and cause the high death. The disease started from the Asian region which resulted in a viral pandemic in 2020. In order to anticipate the increasing of the cases, a strategy is needed to inhibit its transmission. The mathematical model approach is important tool for predicting of covid-19 spread in populations. In this paper we propose and analyze the dynamical behaviour of a developed logistic model by considering the effect of the contact patterns in reducing the covid-19 spread process. To verify the developed logistic model, numerical simulation was given with case study of covid-19 spread for patients under supervision in Central Java Province, Indonesia. Based on simulation results, it was found that physical distancing can reduce the growth of the covid-19 spread for patient under supervision. It can be seen from the number of covid-19 spread for patients under supervision with physical distancing intervention smaller compared to without physical distancing intervention.
Optic Disc Detection on Retina Image using Extreme Learning Machine Wibawa, Helmie Arif; Sutikno, Sutikno; Sasongko, Priyo Sidik
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12123

Abstract

Optic disk detection on retina image has become one of many initial steps in evaluation of Diabetic Macular Edema (DME) severity. As much as early the step is, the result of the step is extremely essential. This article discusses the optic disk detection on retina image based on the color histogram value. The detection is done by using color histogram value which is taken from window sliding process with the size of 50x50 pixels. First, the candidates of optic disc were detected using Extreme Learning Machine towards the histogram value. Then the optic disc was selected form the candidates of optic which has highest average intensity. 4 retina image datasets were employed in the evaluation, including Drions dataset, DRIVE dataset, DiaretDB1 dataset, and Messidor dataset. The result of evaluation then validated by medical expert. The model outcome reaches the accuracy as much as 85,39 % for DiaretDB1 dataset, 95% for DRIVE dataset, 98,18% for Drions and 99% for Messidor dataset.
Improved inception-V3 model for apple leaf disease classification Sirait, Dheo Ronaldo; Sutikno, Sutikno; Sasongko, Priyo Sidik
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp161-167

Abstract

Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
Implementation of Feature Selection Chi-Square to Improve the Accuracy of the Classification Model Using the Random Forest Algorithm on Coronary Artery Disease Mahendra, Ida Bagus Satya; Widiharih, Tatik; Nugroho, Fajar Agung; Sasongko, Priyo Sidik
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.7858

Abstract

Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predict whether a person has heart disease or not. How well a model performs classification can be determined from its accuracy value, but this accuracy value can still be improved. Increasing the accuracy value can be done by performing Feature Selection. The research object used in this research is a dataset about coronary heart disease obtained from the Kaggle website. The classification method used in this modeling is the Random Forest algorithm to classify whether a person has coronary heart disease or not. The Random Forest Algorithm is a classification algorithm consisting of Decision Trees for classifying. The Random Forest algorithm is used because it has been proven to produce good accuracy in several previous studies. The Feature Selection method used in this modeling is the Chi-Square hypothesis test to determine whether there is an effect of each independent variable on the dependent variable. This research compared the value of modeling accuracy without using Feature Selection with modeling using Feature Selection. The result of this study is that the model without Chi-Square Feature Selection produced an accuracy value of 96,05% and the model with Chi-Square Feature Selection produced an accuracy value of 97,33%.
Penerapan Convolutional Neural Network Untuk Klasifikasi Tingkat Keparahan Retinopati Diabetik Pada Penderita Diabetes Melitus Syahrul, Fithra Hayati; Sasongko, Priyo Sidik
Jurnal Masyarakat Informatika Vol 13, No 1 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.1.42354

Abstract

Retinopati Diabetik adalah penyakit yang dapat menganggu pembuluh darah retina yang menjadi penyebab kebutaan bagi penderita Diabetes Melitus. Jika penyakit ini terlambat ditangani maka penderita dapat mengalami kebutaan. Perawatan dan pemeriksaan yang tepat dapat membantu mencegah meningkatnya keparahan Retinopati Diabetik. Pemeriksaan secara manual oleh dokter mata dalam mendiagnosis penyakit ini membutuhkan waktu yang relatif lama, sehingga diperlukan sistem untuk mengklasifikasikan tingkat keparahan Retinopati Diabetik. Sistem yang dirancang pada penelitian ini menggunakan metode Convolutional Neural Netwok untuk klasifikasi tingkat keparahan Retinopati Diabetik. Tingkat keparahan Retinopati Diabetik dibagi menjadi 5 kelas yaitu NO DR, Mild, Moderate, Severe, dan Proliferative DR. Penelitian Penerapan Convolutional Neural Netwok untuk Klasifikasi Tingkat Keparahan Retinopati Diabetik pada Penderita Diabetes Melitus menggunakan citra berukuran 64 x 64 x 3 dengan channel RGB. Tahap pra-pengolahan citra yang dilakukan adalah pengubahan ukuran citra. Arsitektur CNN yang digunakan terdiri dari 5 blok dimana masing-masing blok berisi batch normalization layer, convolution layer, max pooling layer menggunakan parameter learning rate 0.0005. Hasil evaluasi model 652 data uji menunjukkan akurasi terbaik sebesar 91.10%.
Co-Authors A.B. Susanto Adzani, Wildan Azka Anak Agung Gede Sugianthara Anita Ika Nurcahyani, Anita Ika Aprilianti, Ika Nur Edy Suharto Eko Adi Sarwoko Fajar Agung Nugroho Frediansah Frediansah Fuady, Zakial Helmi Arif Wibawa Helmie Arif Wibawa Iffa Zainan Nisa Indra Waspada Indriyati Indriyati Khadijah Khadijah Khadijah, Khadijah Mahendra, Ida Bagus Satya Muhamad, Nida Mukaroh, Afifatul Nandhista, Dhimas Nurdin Bahtiar Okky Widya Arditya Prajanto Wahyu Adi, Prajanto Wahyu Prasetyo, Syalwa Dea Putri Rachmat Gernowo Rachmawati Soewono, Rachmawati Ragil Saputra Rahman, Gohar Redemtus Heru Tjahjana Retno Kusumaningrum Rismiyati Rismiyati Riyandika Andhi Saputra Sabda Dewa, Jason Al Hilal Satriyo Adhy Satya Sandika Putra Sekar Ratnaningtyas Sirait, Dheo Ronaldo Siti Suryani Sukmawati Nur Endah Sukmawati Nur Endah Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno, Sutikno Sutimin Sutimin Syahrul, Fithra Hayati Tatik Widiharih Triatmaja, Yudha Kusuma Utomo, Zidan Rafindra Vebriana, Melanie Safira Widowati, Widowati Yadanur Alim