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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 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

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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.
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%.
Sistem Pengelolaan Tumbuhan Adiwiyata Berbasis Android di SMA Negeri 2 Semarang Triatmaja, Yudha Kusuma; Sasongko, Priyo Sidik
Prosiding Sains Nasional dan Teknologi Vol 11, No 1 (2021): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 11 2021
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v1i1.5082

Abstract

SMA Negeri 2 Semarang, merupakan salah satu Sekolah Menengah Atas Negeri yang ada di Semarang yang sedang mengadakan program Adiwiyata untuk menanam tumbuhan sebagai gerakan penghijauan di lingkungan sekolah. Program ini masih menggunakan cara konvensional berupa kertas dalam mengelola data tumbuhan. Hal tersebut bisa dibilang kurang efektif dalam mengelola data karena dalam proses yang cukup memakan waktu dan menghabiskan banyak biaya. Oleh karena itu, sistem pengelolaan data tumbuhan berbasis Android dibangun untuk mempermudah pengelolaan tumbuhan di SMA Negeri 2 Semarang. Sistem akan dikembangkan menggunakan model proses Waterfall dengan pendekatan Object Oriented Analysis Design (OOAD) berbasis Android, dengan menggunakan Bahasa pemrograman Java dengan Firebase sebagai pengelolaan basis datanya. Berdasarkan hasil pengujian yang telah dilakukan dengan menggunakan metode Blackbox. Sistem diharapkan dapat membantu untuk mengelola data tumbuhan di SMA Negeri 2 Semarang.
Sistem Informasi Pengarsipan Surat Berbasis Web Di Dinas Sosial Pemberdayaan Perempuan Dan Perlindungan Anak Kabupaten Blora Prasetyo, Syalwa Dea Putri; Sasongko, Priyo Sidik
Prosiding Sains Nasional dan Teknologi Vol 11, No 1 (2021): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 11 2021
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v1i1.5102

Abstract

Dinas Sosial Pemberdayaan Perempuan dan Perlindungan Anak merupakan unsur pelaksana urusan pemerintahan yang menjadi kewenangan Daerah dengan tugas bantuan di bidang sosial. Pelayanan sosial yang dilakukan Dinas Sosial P3A erat kaitannya dengan pengelolaan data surat yang biasa disebut dengan arsip surat. Namun, arsip surat masih dibukukan secara manual menggunakan tulisan tangan. Oleh karena itu, dibuatlah sebuah Sistem Informasi Pengarsipan Surat Berbasis Web yang dirancang untuk memudahkan dalam mencari, mengelola, dan membuat laporan surat masuk, surat keluar, dan surat perjalanan dinas. Perancangan Sistem ini menggunakan model Waterfall dengan pendekatan Object Oriented Analysis and Design (OOAD) berbasis website, dengan bahasa pemrograman PHP, Framework Laravel versi 8.0, dan MySQL. Sistem yang telah selesai tahap pengembangan diuji, sehingga sistem dapat dinyatakan telah memenuhi spesifikasi yang telah disepakati dan sistem berhasil diterima.
Sistem Pengelolaan Katalog UMKM Berbasis Android di Dinas Koperasi dan Usaha Mikro Kabupaten Blitar Vebriana, Melanie Safira; Sasongko, Priyo Sidik
Prosiding Sains Nasional dan Teknologi Vol 11, No 1 (2021): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 11 2021
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v1i1.5071

Abstract

Dinas Koperasi dan Usaha Mikro Kabupaten Blitar merupakan penyelenggara urusan pemerintahan dan pelayanan umum di bidang Koperasi dan Usaha Mikro (UM). Dalam pengelolaan data UMKM-nya, Dinas Koperasi dan Usaha Mikro Kabupaten Blitar masih menggunakan cara manual, yaitu dengan menggunakan Microsoft Excel dan disebarkan ke masyarakat melalui brosur atau pamflet. Hal tersebut bisa dibilang kurang efektif dan efisien karena prosesnya memakan banyak waktu dan banyak biaya dalam mengelola data dan memublikasikannya ke masyarakat. Pada masa sekarang ini, masyarakat juga jarang ada yang mau membaca brosur atau pamflet yang diberikan, mereka lebih sering menggunakan smartphone untuk mendapatkan informasi. Oleh karena itu, sistem pengelolaan katalog ini dibangun untuk memudahkan pengelolaan data UMKM yang ada di Kabupaten Blitar dan dalam publikasi ke masyarakat. Sistem dikembangkan menggunakan model waterfall berbasis Android, dengan menggunakan bahasa pemrograman PHP, bahasa pemrograman JavaScript dengan Framework React Native, serta MySQL sebagai pengelolaan basis datanya. Dengan adanya Sistem Pengelolaan Katalog UMKM ini diharapkan dapat mempermudah pengelolaan data UMKM di Dinas Koperasi dan Usaha Mikro Kabupaten Blitar.
Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments Nisa, Iffa Zainan; Endah, Sukmawati Nur; Sasongko, Priyo Sidik; Kusumaningrum, Retno; Khadijah, Khadijah; Rismiyati, Rismiyati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 5: Oktober 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022954868

Abstract

Sampah merupakan salah satu permasalahan global yang dihadapi seluruh dunia termasuk Indonesia. Apabila tidak dikelola dengan baik, jenis dan volume sampah yang semakin meningkat dapat berdampak buruk pada lingkungan dan kesehatan manusia. Pemilahan sampah merupakan langkah awal dalam melakukan berbagai jenis pengolahan sampah. Pemilahan sampah secara manual tidak mudah dilakukan mengingat jumlahnya yang amat besar, sehingga otomatisasi pemilahan sampah diperlukan. Penelitian ini mengusulkan klasifikasi citra sampah menggunakan Support Vector Machine (SVM) dengan ekstraksi fitur Gray Level Co-Occurrence Matrix (GLCM) dan Color Moments serta mengoptimalkan kinerja terbaik dalam proses klasifikasinya. Dataset TrashNet digunakan untuk mengevaluasi metode yang diusulkan. Beberapa parameter penting yang digunakan dalam penelitian ini adalah orientasi sudut GLCM, parameter C (soft margin) pada SVM, dan parameter ???? pada Radial Basis Kernel (RBF). Pembagian data dilakukan menggunakan 10-Fold Cross Validation. Hasil penelitian menunjukkan bahwa kombinasi fitur GLCM dengan orientasi sudut 45° dan Color Moments memberikan rata-rata akurasi terbaik sebesar 78,87% dengan menggunakan parameter C bernilai 32 dan parameter γ bernilai 4. Hasil pengujian terbaik diperoleh pada fold ke-3 dengan akurasi sebesar 85,43% yang digunakan sebagai skenario pengujian data baru. Pengujian terhadap 30 citra sampah baru menggunakan model terbaik memperoleh akurasi sebesar 70%. AbstractWaste is one of the global problems faced by the whole world, including Indonesia. Improper waste management can harm the environment and interfere with health. Waste management involved several steps in handling waste, the first one being waste sorting. In Indonesia, waste sorting is still performed manually. Manual waste sorting is not easy to do because the waste amount is very large. Therefore, automatic waste detection technology is needed to support more optimal waste sorting. This study proposes waste image classification using Support Vector Machine (SVM) with Gray Level Co-Occurrence Matrix (GLCM) and Color Moments as the features. The TrashNet dataset is used to evaluate the proposed method. In addition, 30 additional waste image outside trashnet is used as testing data. Some of the important parameters that are tuned in this study are the angle orientation of the GLCM, C (soft margin) parameter on the SVM, and ???? parameter on the Radial Base Kernel (RBF). Data splitting is done using 10-Fold Cross Validation. The results showed that the combination of GLCM features with 45° angle orientation and Color Moments gave the best average accuracy of 78.87% using C parameter with a value of 32 and γ parameter with a value of 4. The best test results were obtained in the third fold with an accuracy of 85, 43%. This result is used to test the 30 test image outside the TrashNet dataset, and achieve accuracy of 70%.
Pelatihan Computational Thinking bagi Guru SMP-SMK Muhammadiyah 2 Kota Semarang Wibawa, Helmie Arif; Saputra, Ragil; Sasongko, Priyo Sidik; Adhy, Satriyo; 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.
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