Article Per Year (5 Year)
p-Index From 2019 - 2024
1.146
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Telematika : Jurnal Informatika dan Teknologi Informasi JAIS (Journal of Applied Intelligent System) E-Dimas: Jurnal Pengabdian kepada Masyarakat JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia)
Rismiyati Rismiyati
Universitas Diponegoro
Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Public Health Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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.
VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification Rismiyati Rismiyati; Ardytha Luthfiarta
Telematika Vol 18, No 1 (2021): Edisi Februari 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i1.4025

Abstract

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.
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.
Transfer Learning with Xception Architecture for Snakefruit Quality Classification Rismiyati Rismiyati; Ardytha Luthfiarta
Journal of Applied Intelligent System Vol 7, No 2 (2022): 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.v7i2.6797

Abstract

Machine learning has been greatly used in the field of image classification. Several machine learning techniques perform very well in this task. The development of machine learning technique in recent years are in the direction of deep learning. One of the main challenge of deep learning is that it requires the number of the samples to be extremely large for the model to perform well. This is because the number of feature that trainable parameter are huge. One of the solution to overcome this is by introducing transfer learning. One of the architecture that is currently introduced is Xception architecture. This architecture is claimed to outperform VGG16, ResNet50, and inception in terms of model accuracy and model size. This research aims to classify snakefruit quality by using transfer learning with Xception architecture. This is to explore possibility to achieve better result as Xception architecture generally perform better than other available architecture in transfer learning. The snakefruit quality is classified into two classes. Hyperparameter value is optimized by several scenario to determine the best model. The best performance is achieved by using learning rate of 0.0005, momentum 0.9 and dropout value of 0 or 0.25. The accuracy achieved is 94.44%.
Pembelajaran Digital Menggunakan Google Classroom Bagi Guru PAUD Dabin I Semarang Guruh Aryotejo; Eko Adi Sarwoko; Dinar Mutiara Kusumo Nugraheni; Edy Suharto; Rismiyati Rismiyati
JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Vol 4, No 2: Mei (2023)
Publisher : ICSE (Institute of Computer Science and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jpkmi.v4i2.584

Abstract

Abstrak: Pandemi Covid-19 saat ini sedang berlangsung di seluruh dunia termasuk di Indonesia. Pendidikan melalui daring atau online di Indonesia, dari PAUD sampai perguruan tinggi, sudah mulai dilakukan pada Bulan Maret 2020. PAUD di Kota Semarang saat ini berkembang dengan cepat dari berbagai skala. Implementasi Teknologi Informasi di bidang pendidikan melalui daring adalah dengan menggunakan media sosial atau Learning Management System (LMS). Whatsapp adalah salah satu media sosial yang sering digunakan dalam pendidikan melalui daring. Penggunaan Whatsapp memunculkan beberapa masalah seperti file tugas tidak bisa diunduh oleh orang tua, terhapus secara tidak sengaja dari gadget guru dan ukuran file yang terlalu besar untuk gadget orang tua/siswa. Pengabdian kepada masyarakat ini akan dilakukan penyuluhan kepada guru PAUD DABIN I tentang penggunaan Google Classroom sebagai LMS untuk menciptakan proses pembelajaran luring yang tidak monoton dan mengurangi permasalahan-permasalahan yang ada selama menggunakan aplikasi Whatsapp.Abstract: The Covid-19 pandemic is currently taking place all over the world, including Indonesia. Education through online or online in Indonesia, from PAUD to tertiary institutions, has begun in March 2020. PAUD in Semarang City is currently growing rapidly from various scales. The implementation of Information Technology in the field of education through online is by using social media or the Learning Management System. Whatsapp is one of the social media that is often used in online education. The use of Whatsapp raises several problems such as the assignment file cannot be downloaded by the parents, accidentally deleted from the teacher's gadget and the file size is too large for the parent/student gadget. Community service at this time will be counseling to PAUD DABIN I teachers about using Google Classroom as an LMS to create an offline learning process that is not monotonous and reduces the problems that exist while using the Whatsapp application.
Co-Authors Ardytha Luthfiarta Dinar Mutiara Kusumo Nugraheni Edy Suharto Eko Adi Sarwoko Guruh Aryotejo Helmie Arif Wibawa Iffa Zainan Nisa Khadijah, Khadijah Priyo Sidik Sasongko Ragil Saputra Retno Kusumaningrum Satriyo Adhy Sukmawati Nur Endah