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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Elektronik Informatics and Digital Expert (INDEX) Journal of Informatics and Communication Technology (JICT) Industrial Research Workshop and National Seminar SisInfo : Jurnal Sistem Informasi dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Riwayat: Educational Journal of History and Humanities
Kasyidi, Fatan
Program Studi Teknik Informatika, Universitas Jenderal Achmad Yani, Cimahi
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks Armandika, Fladio; Djamal, Esmeralda Contessa; Nugraha, Fikri; Kasyidi, Fatan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2048

Abstract

Movement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training.
Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network Besti, Agung; Ilyas, Ridwan; Kasyidi, Fatan; Djamal, Esmeralda Contessa
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2051

Abstract

One development of Natural Language Processing is the semantic classification of sentences and documents. The challenge is finding relationships between words and between documents through a computational model. The development of machine learning makes it possible to try out various possibilities that provide classification capabilities. This paper proposes the semantic classification of sentence pairs using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Each couple of sentences is turned into vectors using Word2Vec. Experiments carried out using CBOW and Skip-Gram to get the best combination. The results are obtained that word embedding using CBOW produces better than Skip-Gram, although it is still around 5%. However, CBOW slows slightly at the beginning of iteration but is stable towards convergence. Classification of all six classes, namely Equivalent, Similar, Specific, No Alignment, Related, and Opposite. As a result of the unbalanced data set, the retraining was conducted by eliminating a few classes member from the data set, thus providing an accuracy of 73% for non-training data. The results showed that the Adam model gave a faster convergence at the start of training compared to the SGD model, and AdaDelta, which was built, gave 75% better accuracy with an F1-Score of 67%.
Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks Permana, Aldi Sidik; Djamal, Esmeralda Contessa; Nugraha, Fikri; Kasyidi, Fatan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2055

Abstract

Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely 'right', 'left', 'grab', and 'phone'. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed.
Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks Fladio Armandika; Esmeralda Contessa Djamal; Fikri Nugraha; Fatan Kasyidi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2048

Abstract

Movement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training.
Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network Agung Besti; Ridwan Ilyas; Fatan Kasyidi; Esmeralda Contessa Djamal
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2051

Abstract

One development of Natural Language Processing is the semantic classification of sentences and documents. The challenge is finding relationships between words and between documents through a computational model. The development of machine learning makes it possible to try out various possibilities that provide classification capabilities. This paper proposes the semantic classification of sentence pairs using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Each couple of sentences is turned into vectors using Word2Vec. Experiments carried out using CBOW and Skip-Gram to get the best combination. The results are obtained that word embedding using CBOW produces better than Skip-Gram, although it is still around 5%. However, CBOW slows slightly at the beginning of iteration but is stable towards convergence. Classification of all six classes, namely Equivalent, Similar, Specific, No Alignment, Related, and Opposite. As a result of the unbalanced data set, the retraining was conducted by eliminating a few classes member from the data set, thus providing an accuracy of 73% for non-training data. The results showed that the Adam model gave a faster convergence at the start of training compared to the SGD model, and AdaDelta, which was built, gave 75% better accuracy with an F1-Score of 67%.
Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks Aldi Sidik Permana; Esmeralda Contessa Djamal; Fikri Nugraha; Fatan Kasyidi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2055

Abstract

Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely 'right', 'left', 'grab', and 'phone'. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed.
Peringkasan Otomatis Makalah Menggunakan Maximum Marginal Relevance Wildan Pratama; Ridwan Ilyas; Fatan Kasyidi
Informatics and Digital Expert (INDEX) Vol. 3 No. 1 (2021): INDEX, MEI 2021
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v3i1.677

Abstract

Makalah merupakan karya tulis yang memuat pemikiran tentang suatu masalah atau topik tertentu yang ditulis secara sistematis disertai dengan analisis yang logis objektif. Membaca merupakan proses melihat serta memahami isi dari apa yang tertulis. Proses membaca dipengaruhi beberapa factor yang diantaranya jumlah dari bacaan, sehingga penyederhaan atau perinkasan suatu bacaan contoh seperti makalah maka akan menambah kecepatan membaca. Ringkasan merupakan suatu cara yang efektif untuk menyajikan suatu karangan yang panjang dalam bentuk yang singkat. Peringkasan makalah dilakukan untuk menyajikan jumlah bacaan lebih sedikit tanpa mengurangi informasi yang disampaikan dan mempertahankan dalam bentuknya yang singkat. Dalam membuat ringkasan kita diharuskan untuk membaca keseluruhan isi makalah tersebut terlebih dahulu, untuk kemudian meringkasnya. Hal ini tentu menjadi masalah dimana ringkasan dibuat dengan tujuan untuk meminimalkan waktu membaca tetapi tetap dapat memberikan teks yang isinya langsung mengarah pada tujuan utama atau ide pokoknya. Untuk memecahkan masalah tersebut diperlukan suatu perangkat atau aplikasi yang dapat meringkas teks secara otomatis. Beberapa penelitian terdahulu menggunakan graph-based summarization untuk meringkas dokumen tunggal bahasa Indonesia. Penelitian ini membangun sistem yang dapat meringkaskan makalah menjadi ringkasan menggunakan maximum marginal relevance. maximum marginal relevance dipilih karena mampu menilai kalimat dan merankingnya sebagai acuan untuk pembentukan ringkasan. Penelitian ini mendapatkan hasil ringkasan terbaik dengan pengujian Rouge-1 dengan rata-rata yang diraih 0.68.
Analisis Spasial Temporal Kerentanan Bencana Alam Dalam Mendukung Ketahanan Terhadap Bencana di Indonesia Asep Id Hadiana; Agus Komarudin; Eddie Khrisna Putra; Melina .; Rezky Yuniarti; Ridwan Ilyas; Fatan Kasyidi
Jurnal ICT : Information Communication & Technology Vol 20, No 2 (2021): JICT-IKMI, Desember 2021
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v20i2.379

Abstract

Gempa Bumi Besar dan konsekuensi tragisnya belakangan ini di lokasi berbeda di seluruh dunia, termasuk dalam hal ini di Indonesia membenarkan kebutuhan darurat untuk memasukkan analisis risiko gempa dalam praktik manajemen penanggulangan bencana.  Mengintegrasikan analisis dampak risiko gempa bumi dalam praktik manajemen penanggulangan bencana menjadi hal yang sangat penting. Studi ini menyajikan pendekatan yang sederhana dan ilmiah melalui analisis spasial temporal dari data gempa bumi yang ada di Indonesia selama kurun waktu 2020. Dengan mengambil dataset mengenai kejadian gempa bumi di Indonesia yang terjadi dalam kurun waktu 2020, dapat dilihat bahwa hampir seluruh wilayah di Indonesia rentan terhadap bencana gempa bumi, dimana kekuatan gempa bumi rata-rata ada di kisaran 4 sampai 4.5 skala richter.Temuan penelitian ini diharapkan dapat membantu otoritas manajemen bencana di Indonesia dalam mengidentifikasi zona risiko, memvisualisasikan risiko bahaya untuk interpretasi yang lebih mudah, optimalisasi sumber daya dengan menargetkan kerentanan, dan memutuskan intervensi perencanaan dan pengendalian atas dampak bencana yang mungkin terjadi.
Use Case Framework of Computerized Production Monitoring Processes in Textile Industry Irma Santikarama; Faiza Renaldi; Fatan Kasyidi; Agya Java Maulidin
Journal of Applied Informatics and Computing Vol 6 No 1 (2022): July 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i1.3977

Abstract

Use cases are a description of system functions resulting from needs analysis and obtained from interviews and observations. In standard practices, this stage is also known as the most time-consuming stage. Although every use case produced in software development is unique, there is always a similarity in its function to systems made previously in other organizations. These similarities are studied to reduce time in the process during the requirements analysis stage. Many studies have built and used a Use Case Framework (UCF) to be used together by software developers. So far, UCF has been owned by the banking industry in mapping use case standards in ATMs, health in standardizing use cases in electronic medical records, libraries in standardizing information retrieval, and mapping processes in crowdfunding. This research adds to the list of the latest UCFs produced, namely in the related textile industry, in standardizing the functions that exist in computer-based production monitoring systems. It is based on the fact that there are many textile companies globally, with more than 1.000 of them are established in Indonesia. This study investigated eight Indonesian textile companies to obtain information data to determine what functions are required, t. The data collection techniques used were interviews and observation. More stages were carried out in this study afterward, namely defining Actor Analysis and Functional Methods, Combining Analysis, Classification of Use Cases, Describing Use Case Scenarios, and Visualizing Frameworks. The data analysis results obtained from each company, we managed to define 10 main use cases, 4 supporting use cases, and four specific use cases. This study’s products can help provide a reference in using case design to create a computer-based textile company monitoring system.
MESIN PENTERJEMAH BAHASA INDONESIA-BAHASA SUNDA MENGGUNAKAN RECURRENT NEURAL NETWORKS Yustiana Fauziyah; Ridwan Ilyas; Fatan Kasyidi
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1930

Abstract

Penterjemah merupakan suatu proses dimana suatu bahasa diubah ke dalam bahasa lain. Penterjemah pada Penelitian lalu dilakukan dengan menggunakan pendekatan Phrase-based Statistical Machine Translation (PSMT). Penelitian ini membangun sebuah penerjemah Bahasa Indonesia ke Bahasa Sunda. Adapun tahapan yang digunakan dimulai dari pra proses menggunakan text preprocessing dan word embedding Word2Vec dan pendekatan yang digunakan yaitu Neural Machine Translation (NMT) dengan arsitektur Encoder-Decoder yang didalamnya terdapat sebuah Recurrent Neural Network (RNN). Pengujian pada penelitian menghasilkan nilai optimal oleh GRU sebesar 99,17%. Model dengan menggunakan Attention mendapat 99.94%. Penggunaan model optimasi mendapat hasil optimal oleh Adam 99.35% dan hasil BLEU Score dengan optimal bleu 92.63% dan brievity penalty 0.929. Hasil dari mesin penterjemah menghasilkan prediksi pelatihan dari Bahasa Indonesia ke Bahasa Sunda apabila input kalimat sesuai dengan korpus dan hasil terjemahan kurang sesuai ketika input kalimat berbeda dari korpus.
Co-Authors Ade Kania Ningsih Ade Kania Ningsih AGIEL FADILLAH HERMAWAN Agung Besti Agus Komarudin Agya Java Maulidin Aldi Sidik Permana Anggoro, Sigit Armandika, Fladio Besti, Agung Djamal, Esmeralda Contesa Djuniardi Suhardinata Eddie Khrisna Putra Erna Sesarliana* Esmeralda Contessa Djamal Faiza Renaldi Fajri Rakhmat Umbara Fajri Rakhmat Umbara FAJRI RAKHMAT UMBARA FAJRI RAKHMAT UMBARA fery bayu aji Fikri Nugraha FIQRI FAKHRUL GUNAWAN Fladio Armandika Hadiana, Asep Id Irma Santikarama Janjan Nurjaman Melina Melina Muhamad Rizal Firmansyah NIDA MUTHI ANNISA Nugraha, Fikri Permana, Aldi Sidik Puspita Nurul Sabrina Rafli Firdaus Reza Dwi Putra Rezky Yuniarti Ridwan Ilyas RINDU TEGAR SENJAWATI Rizky Ryan Dwi Yudistira Seffiera Fauziah Wildan Pratama Yoga Esa Mahendra Yustiana Fauziyah