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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia)
Rismiyati Rismiyati
Universitas Diponegoro
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Public Health Social Sciences

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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.
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.
Indonesian continuous speech recognition optimization with convolution bidirectional long short-term memory architecture Sukmawati Nur Endah; Rismiyati Rismiyati; Priyo Sidik Sasongko; Anwar Petrus F Noiborhu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.24994

Abstract

Speech recognition can be defined as converting voice signals into text or lines of words by using algorithms implemented in computer programs. There are several types of speech recognition, including recognition for isolated word speech, continuous speech, spontaneous speech, and conversational speech. Research on continuous speech recognition, especially in Indonesian, has been developed using both stochastic methods such as Hidden Markov model (HMM) and deep learning methods. Currently, deep learning approaches are more widely used in speech recognition applications. This research optimizes Indonesian speech recognition by adding convolution layers to the bidirectional long short-term memory (Bi-LSTM) architecture. The goal of this research is to find the best architecture so that better Indonesian continuous speech recognition results can be obtained. The dataset used in this research was created by the intelligent systems research group in the Department of Informatics at Universitas Diponegoro. All speakers who participated in this dataset came from five ethnic groups in Indonesia, representing the dialects of their respective ethnic groups. The research results show that by adding a convolution layer to the Bi-LSTM architecture, speech recognition performance increases significantly with an average word error rate (WER) reduction of 15.56% compared to using only the Bi-LSTM architecture.
Automatic Speech Recognition for Javanese Language using Wav2Vec 2.0 with Finetuning Johanes Setiawan; Ardytha Luthfiarta; Adhitya Nugraha; Rismiyati Rismiyati; Bastiaans, Jessica Carmelita; Yohanes Deny Novandian
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.1-9

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem pengenalan suara untuk bahasa Jawa dengan memanfaatkan model Wav2Vec 2.0 melalui proses finetuning. Bahasa Jawa, sebagai salah satu bahasa daerah dengan lebih dari 80 juta penutur, memiliki tantangan tersendiri dalam pengenalan suara akibat keterbatasan data dan kompleksitas linguistiknya. Penelitian ini menggunakan dataset audio yang diambil dari OpenSLR dan diterapkan pada dua varian model, yaitu wav2vec2-base dan wav2vec2-large, yang masing-masing memiliki jumlah parameter 94,4 juta dan 315 juta. Proses finetuning dilakukan untuk meningkatkan akurasi sistem dalam mengenali variasi suara bahasa Jawa. Evaluasi dilakukan menggunakan metrik Word Error Rate (WER) dan evaluation loss, dengan hasil akhir menunjukkan bahwa model wav2vec2-base memiliki WER sebesar 15,02% dan model wav2vec2-large sebesar 15,57%. Hasil ini menunjukkan efektivitas pendekatan finetuning dalam meningkatkan performa pengenalan suara bahasa Jawa.
Co-Authors Adhitya Nugraha Anwar Petrus F Noiborhu Ardytha Luthfiarta Bastiaans, Jessica Carmelita Dinar Mutiara Kusumo Nugraheni Edy Suharto Eko Adi Sarwoko Guruh Aryotejo Iffa Zainan Nisa Johanes Setiawan Khadijah, Khadijah Priyo Sidik Sasongko Retno Kusumaningrum Sukmawati Nur Endah Yohanes Deny Novandian