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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding Semnastek English Review: Journal of English Education EMITTER International Journal of Engineering Technology Fountain of Informatics Journal JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika DIKLUS: Jurnal Pendidikan Luar Sekolah Jurnal Teknoinfo KOMPUTIKA - Jurnal Sistem Komputer Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Applied Technology and Computing Science Journal Jurnal Mantik Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PELS (Procedia of Engineering and Life Science) SAINTEK Journal of Information System and Application Development

Oddy Virgantara Putra

Universitas Darussalam Gontor

Author-ID : 2370495

Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Classification of Calligraphy Writing Types Using Convolutional Neural Network Method (CNN) Oddy Virgantara Putra; Aziz Musthafa; Muhammad Nur; Muhamad Rido
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

Calligraphy is the art of beautiful Arabic writing in which a series of letters are formed in appropriate proportions, maintaining distance and accuracy containing verses from the Qur'an or Hadith. There is a challenge to recognize the type of calligraphy using machine learning. This study aims to classify the types of calligraphy writing for ordinary people who do not understand the differences between each type of calligraphy writing. This study builds a model using the Convolutional Neural Network (CNN) method. The image used will go through a noise cleaning, resizing, and cropping process. This method is to carry out the process of classifying the type of calligraphy using a dataset consisting of 230 of 2 different types of calligraphy, namely the Naskhi and Riq'ah types. 80% is used as training data and 20% for test data. In the modeling process there are two convolutional layers and two MaxPooling layers followed by a Fully connected layer. The CNN modeling results used to test the built data have an average percentage result of 89% accuracy from the training data used. For further research, it can be developed with other types of calligraphy.

Outlier Detection On Graduation Data Of Darussalam Gontor University Using One-Class Support Vector Machine Oddy Virgantara Putra; Triana Harmini; Ahmad Saroji
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

Outlier detection is an important field of study because it is able to detect abnormal data distribution from a set of data. In the student graduation data, there are students with high semester GPA but do not graduate on time but students with low semester GPA can graduate on time. This study aims to detect outlier values in student graduation data for the 2020-2021 class. Factors (attributes) used in this study are Student Academic Support Credit Scores (AKPAM) and Social Studies from semester one to semester six. The dataset used is 1204 graduates. The outlier detection method used is One-Class Support Vector Machine (SVM). One-class SVM is a derivative of SVM method that detects outliers based on data outside the specified class. The results of outlier detection using One-Class SVM method with three types of kernels produce the following reference values: kernel 'rbf' n by 91.4%, kernel 'linear' by 90% and kernel 'poly' by 90%. After normalization using MinMaxScaler the reference value increased by 2% in each kernel. The results of testing the One-Class SVM method get an average 90.3%, thus it can be concluded that the One-Class SVM method is feasible to be used as an outlier detection method.

Mad Reading Law Classification Using Mel Frequency Cepstal Coefficient (MFCC) and Hidden Markov Model (HMM) Oddy Virgantara Putra; Faisal Reza Pradana; Jordan Istiqlal Qalbi Adiba
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

The COVID-19 pandemic is a disaster that hit the world at this time, all activities are limited. This pandemic has also greatly impacted the process of teaching and evaluating the reading of the Koran which was carried out using the talaqqi and musyafahah methods. Machine Learning research has been developed for the legal classification of Quran recitation. This study aims to be able to classify the law of recitation of recitation, especially in the law of Mad recitation of the letter Maryam verses 1 to 15. This study builds a model using the Mel Frequency Ceptral Coefficient (MFCC) feature extraction with the Hidden Markov Model (HMM) algorithm method. MFCC is used for feature extraction in voice that processes voice data in several stages, including pre-emphasis, frame-blocking, windowing, Fast Fourier Transform, Mel Frequency Wrapping, and Ceptral Liftering. HMM is used in speech recognition with standard sentence percentages. The dataset used in this study is voice data taken from the voice of the Quran reciter that has been recognized and has been affiliated. The test results on the model that has been built have an average percentage of 80% accuracy of the test data.

Raindrop Removal On A Single Image Using The Generative Adversarial Network Muhammad Rizal Muttaqin1; Oddy Virgantara Putra; Lukman Effendi
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

The presence of raindrops attached to the window glass or vehicle glass reduces visibility of the actual scene. The area covered by raindrops forms a round image and reflects light from the actual scene, this image is called Raindrop. In some cases, the camera's focus is blocked by Raindrop will result in a blurry image. The problem faced is that there is no actual landscape image, so to overcome this this research tries to adapt the research from Rui Qian and MaybeShewill-CV which uses the Generative Adversarial Network architecture, by adding the Raindrop and Groundtruh datasets from observations at Darussalam Gontor University. The purpose of this study is to remove raindrops from a single image. This is important to research because it provides updates and optimizes the results of previous research. This study shows the accuracy of PSNR 21.37 and SSIM 0.7561. The model managed to remove Raindrops from the image, but still couldn't match the Groundtruth image. Inability to handle raindrops due to lack of time to run a large number of epochs to produce PSNR values above 40 db and SSIM above 0.9. PSNR and SSIM values can continue to increase along with the addition of the dataset as well as the number of epoch training models carried out.

Classification of Book Collections Based on DDC 23 Using Text Mining Algorithm at UNIDA Gontor Library Muhammad Alwi; Oddy Virgantara Putra; Dihin Muriyatmoko1
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

The collection of books in a library is a means of information that has become the main actor as a supporter of the existence of a library. UNIDA Gontor library uses the 23rd edition of the Dewey Decimal Classification (DDC 23) classification system, as a reference for the classification numbering system for each book collection. However, in the classification numbering there is no automatic system that helps librarians in assigning classification numbering to each collection. So it is necessary to select a suitable model system to be applied to the automatic classification system. The data used in this study is in the form of blurb data on each collection of Indonesian public books in the UNIDA Gontor Library. In this study, four methods of text mining algorithms were applied to be tested and compared. The algorithm used in testing this research is Multinomial Nb, Logistic Regression, Random Forest, and Support Vector Classifier. From the test results, the highest accuracy results are the Support Vector Classifier algorithm of 72%, while the Logistic Regression algorithm is 69%, Random Forest 69%, and Multinomial Nb 59%. Further research is recommended to apply the support vector classifier algorithm into the UNIDA Gontor library information system.

Implementation Of Augmented Reality Procedures For Prayer Using Marker-Based Tracking Method Adi Darmanto; Faisal Reza Pradhana; Dihin Muriyatmoko; Oddy Virgantara Putra; Lukman Effendi
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

Prayer learning activities at PAS Baitul Qur'an Ponorogo Kindergarten have so far been carried out with face-to-face practice between teachers and students. However, the teachers teach the proper way of praying during the pandemic. This study aims to introduce Augmented Reality (AR) technology to teachers and children as a medium for learning to pray. From this research resulted in the application "Let's Learn Prayer" which contains guidance on how to pray and pray in the form of three-dimensional animation. In addition, this application is equipped with a card (marker) that represents each prayer movement. The animation of the prayer can move when the user does the marking. As a result, animations can appear on the smartphone screen. This application development stage uses Waterfall model steps and marker-based tracking. The use of AR applications for prayer learning can increase students' interest and understanding in implementing prayer movements

HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning Oddy Virgantara Putra; Niken Trisnaningrum; Niken Sylvia Puspitasari; Agung Toto Wibowo; Ema Rachmawaty
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p06

Rice is one of the global most critical harvests, and a great many people eat it as a staple eating routine. Different rice plant diseases harm, spread, and drastically reduce crop yields. In extreme situations, they may result in no grain harvest at all, posing a severe threat to food security. In this paper, to amplify the recognition ability for rice leaf disease (RLD) classification, we proposed hierarchical transfer learning (HTL) methods incorporating ensemble models containing two-step. In the first step, an ensemble combining MobileNet and DenseNet was addressed to tackle the diseased leaf problem. Consequently, DenseNet and XceptionNet were fused to identify three RLDs. Here, we compare our models with state-of-the-art deep learning models such as ResNet, DenseNet, InceptionNet, Xception, MobileNet, and EfficientNet. Our framework at top-notch with 89 % and 91 % for accuracy. In future works, RLD segmentation is suggested to pinpoint the illness and quantify the afflicted region.

Implementasi Metode Design Thinking pada Perancangan UI/UX Situs Olah-Oleh TPS3R Kota Batu Zsaffa Aulia Putri Prasetyo; Oddy Virgantara Putra; Triana Harmini
ikraith-informatika Vol 7 No 2 (2023): IKRAITH-INFORMATIKA Vol 7 No 2 Juli 2023
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v7i2.2245

Pendirian TPS3R atau Tempat Pembuangan Sementara – Reuse, Reduce, Recycle – adalah sebuahsolusi inovatif untuk mengatasi permasalahan sampah, khususnya di kota Batu. Akan tetapi, pendiriannyabukan tanpa masalah. Sistem administrasi manual adalah salah satunya. Untuk itu, maka diusulkanperancangan situs Olah-Oleh TPS3R Batu menggunakan metode Design Thinking dengan penerapan 5elemen yang wajib dipenuhi pada pembangunan UX. Tujuan penelitian ini adalah membuat Minimum ViableProduct (MVP) dari situs Olah-Oleh TPS3R Batu. Fitur yang dikembangkan berfokus pada fitur reservasipenyetoran sampah, showcase produk keluaran TPS3R Batu, berita terkait perkembangan TPS3R Batu danpenukaran poin digital. Pengujian sistem dilakukan dengan pertimbangan aspek learnability dan satisficationdengan pendekatan evaluasi System Usability Scale (SUS). Skor final yang didapatkan adalah 86, yang manatermasuk pada kategori B “Excellent” yang merepresentasikan kemudahan pengguna dalam penggunaansitus ini. Pengguna berhasil menyelesaikan skenario yang telah ditetapkan dan mencapai tujuannya dalampenggunaan situs Olah-Oleh TPS3R Batu.

Segmentasi Semantik pada Citra Hama Leafblast Menggunakan Unet dan Optimasi Hyperband Moch. Nasheh Annafii; Oddy Virgantara Putra; Triana Harmini; Niken Trisnaningrum
Prosiding Seminar Sains Nasional dan Teknologi Vol 12, No 1 (2022): VOL 12, NO 1 (2022): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v12i1.7230

Padi menjadi konsumsi primer di Indonesia. Penyakit padi menjadi salah satu faktor yang menyebabkan menurunnya jumlah produksi padi. Meningkatnya konsumsi beras menjadi masalah dengan menurunnya jumlah produksi padi pada tahun 2021. Luasnya lahan dan lambatnya proses identifikasi keparahan menjadikan kurang maksimalnya penanganan penyakit padi, yang berujung tidak maksimalnya hasil panen bahkan terancam gagal panen. Penelitian ini berupaya untuk mensegmen daun padi yang terkena hama leafblast dengan model yang dioptimalkan. Penelitian ini menggunakan metode algoritma Convolutional Neural Network dengan model UNet yang ditingkatkan dengan optimasi model Hyperband optimization. Dengan banyaknya penelitian mengenai UNet, UNet menjadi populer dan berkembang dengan pesat. Perkembangan yang pesat ini ditandai dengan banyaknya penelitian yang menggunakan UNet dan banyaknya modifikasi yang terus dikembangkan. Dataset yang digunakan pada penelitian ini merupakan murni hasil observasi peneliti dan telah divalidasi oleh ahli, dengan total 300 data asli dan data label. Dalam model yang digunakan, digambarkan terdapat bagian encoder dan decoder yang masing masing memiliki beberapa blok konvolusi. Hasil yang diperoleh dari model yang sudah dioptimasi terbukti 3 kali lebih ringan dengan perbandingan jumlah parameter yang cukup signifikan dan hasil valuasi akurasi mencapai 97.72%.

Lightweight convolutional neural network for khat naskhi and riq'ah classification Muhamad Taufiq Riza; Oddy Virgantara Putra; Taufiqurrahman
Jurnal Mantik Vol. 7 No. 1 (2023): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i1.3651

Arabic writing has various types of khat that are complex and different from each other, so it requires proper classification to identify the type of khat used. This research uses the Lightweight Convolutional Neural Network (CNN) classsification method to recognize the types of khat naskhi and riq'ah on Arabic writing datasets. The evaluation results show that this classification model has an accuracy of 98.75% on training data and 100% on validation data, with a relatively fast processing time of 2s 375ms per step so that the model can be implemented well in systems that require high data processing speed and also devices that have limited resources. These results show that the classification model using the Lightweight CNN layer can be used as an effective alternative in classifying types of Arabic writing, especially in recognizing certain types of khat such as naskhi and riq'ah. Furthermore, this research can be developed using a larger and more diverse dataset, as well as evaluated and compared with other classification models to improve the performance of the model in recognizing more complex types of Arabic writing.

Co-Authors Achmad, Dellyan Adi Darmanto Agung Toto Wibowo Ahmad Saroji Ardyono Priyadi azis isrofi Aziz Musthafa Dahlan, Muhammad Alwi Diah Puspito Wulandari Dihin Muriatmoko Dihin Muriyatmoko Eko Mulyanto Yuniarno Ema Rachmawaty Faisal Reza Pradana Faisal Reza Pradhana Faishal Reza Pradhana Farah Zakiyah Rahmanti Febri Vicky Allen Harris Abdillah Faqih Havidz Muhammad Iqbal Hesti Rokhaniyah, Hesti Imam Fauzi Annur Imron Rosyadi Jordan Istiqlal Qalbi Adiba Jumhurul Umami Jumhurul Umami, Jumhurul Kukuh Prasetyo Wibowo Kurniawan, Widya Lukman Effendi Lukman Effendi, Lukman Mauridhi Hery Purnomo Moch. Iskandar Riansyah Moch. Nasheh Annafii Muhamad Rido Muhamad Taufiq Riza Muhammad Alwi Muhammad Arsyad Maulana Muhammad Fakhruzzaini Muhammad Firdaus Muhammad Nur Muhammad Rizal Muttaqin1 Muhammad Siddiq, Muhammad Muhammad Zaim Mustaqim Niken Sylvia Puspitasari Pradana, Kelvin Raja Syam Abbas Shagir Reyhan Nafis Ibrahim Shoffin Nahwa Utama Taufiqurrahman Tri Arief Sardjono Triana Harmini Trisnaningrum, Niken Wijaya, Akhmad Trisna Yoke Suryadarma Zsaffa Aulia Putri Prasetyo

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