Articles
<![CDATA[Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback ]]>
<![CDATA[Tanuwijaya, Evan]]>;
<![CDATA[Adam, Safri]]>;
<![CDATA[Anggris, Mohammad Fatoni]]>;
<![CDATA[Arifin, Agus Zainal]]>
<![CDATA[ Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January-June ]]>
Publisher : <![CDATA[Information Systems - Universitas Pesantren Tinggi Darul Ulum ]]>
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DOI: 10.26594/register.v5i1.1385
<![CDATA[Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna. Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user?s input queries thereby enabling the system retrieve relevant answers.]]>
<![CDATA[Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback ]]>
<![CDATA[Tanuwijaya, Evan]]>;
<![CDATA[Adam, Safri]]>;
<![CDATA[Anggris, Mohammad Fatoni]]>;
<![CDATA[Arifin, Agus Zainal]]>
<![CDATA[ Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January ]]>
Publisher : <![CDATA[Information Systems - Universitas Pesantren Tinggi Darul Ulum ]]>
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DOI: 10.26594/register.v5i1.1385
<![CDATA[Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna.  Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user’s input queries thereby enabling the system retrieve relevant answers.]]>
<![CDATA[Klasifikasi Bahasa Isyarat Amerika menggunakan Convolutional Neural Network ]]>
<![CDATA[Siswanto, Felicia Devina]]>;
<![CDATA[Lestari, Caecilia Citra]]>;
<![CDATA[Tanuwijaya, Evan]]>
<![CDATA[ JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 1 (2022) ]]>
Publisher : <![CDATA[Jurusan Informatika Universitas Tanjungpura ]]>
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DOI: 10.26418/justin.v10i1.47184
<![CDATA[Bahasa Isyarat adalah bahasa untuk orang - orang yang memiliki kesulitan mendengar maupun bicara. Tetapi bahasa isyarat bukanlah bahasa yang banyak digemari oleh masyarakat, sehingga orang yang memiliki disabilitas tersebut akan semakin kesulitan. Pada jurnal ini akan menjelaskan mengenai klasifikasi bahasa isyarat Amerika dengan menggunakan Convolutional Neural Network (CNN). Pada penelitian ini akan dilakukan beberapa penelitian menggunakan parameter berbeda seperti pada preprocessing, penelitian akan dilakukan dengan melihat parameter horizontal flip. Selanjutnya penelitian juga dilakukan dengan melihat epoch. Penelitian ini dilakukan untuk memantau akurasi dan akurasi validasi. Model yang dibuat pada penelitian ini nilai akurasi yang lebih tinggi saat memprediksi huruf v, dan n. Hasil nilai akurasi dari penelitian ini adalah 82.1%Sign Language is a language for people who have hearing and speech difficulties. But sign language is not a language that is favored by many people, so people with disabilities will find it increasingly difficult. This journal will explain the classification of American sign language using the Convolutional Neural Network (CNN). In this study, several studies will be carried out using different parameters such as in preprocessing, research will be carried out by looking at the horizontal flip parameter. Furthermore, research was also carried out by looking at the epoch. This study was conducted to monitor the accuracy and accuracy of the validation. The model made in this study has a higher accuracy value when predicting the letters v, and n. The result of the accuracy value of this study is 82.1%]]>
<![CDATA[Automatic image slice marking propagation on segmentation of dental CBCT ]]>
<![CDATA[Agus Zainal Arifin]]>;
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Baskoro Nugroho]]>;
<![CDATA[Arif Mudi Priyatno]]>;
<![CDATA[Rarasmaya Indraswari]]>;
<![CDATA[Eha Renwi Astuti]]>;
<![CDATA[Dini Adni Navastara]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v17i6.13220
<![CDATA[Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.]]>
<![CDATA[MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV ]]>
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Chastine Fatichah]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 13, No 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v13i2.808
<![CDATA[The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods.]]>
<![CDATA[Penandaan Otomatis Tempat Parkir Menggunakan YOLO Untuk Mendeteksi Ketersediaan Tempat Parkir Mobil Pada Video CCTV ]]>
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Chastine Fatichah]]>
<![CDATA[ BRILIANT: Jurnal Riset dan Konseptual Vol 5, No 1 (2020): Volume 5 Nomor 1, Februari 2020 ]]>
Publisher : <![CDATA[Universitas Nahdlatul Ulama Blitar ]]>
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DOI: 10.28926/briliant.v5i1.434
<![CDATA[Sulitnya menemukan tempat parkir terutama saat jam sibuk adalah masalah yang umum dialami oleh pengemudi. Banyak penelitian untuk mendeteksi ketersediaan tempat parkir memanfaatkan CCTV. Namun, penelitian tersebut memiliki beberapa masalah seperti mendeteksi tempat parkir dilakukan secara manual menjadi tidak efisien ketika diterapkan pada tempat parkir yang berbeda. Oleh karena itu, penelitian ini menggunakan YOLO V3 untuk mendeteksi secara otomatis tempat parkir pada data CCTV kemudian diklasifikasikan terisi atau tidak. Hasil terbaik penandaan menggunakan YOLO V3 yaitu saat kondisi cuaca mendung dengan nilai akurasi rata-rata 94,49%.]]>
<![CDATA[DETEKSI EKSPRESI WAJAH MANUSIA MENGGUNAKAN CONVOLUTION NEURAL NETWORK PADA CITRA PEMBELAJARAN DARING ]]>
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Timotius]]>;
<![CDATA[David Christian Kartamihardja]]>;
<![CDATA[Timotius Leonardo Lianoto]]>
<![CDATA[ JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer Vol 12 No 3 (2021): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer ]]>
Publisher : <![CDATA[LPPM Sekolah Tinggi Teknologi Pagar Alam ]]>
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<![CDATA[During the pandemic, many activities that were originally carried out face-to-face were turned into virtual face-to-face due to reducing the spread of the virus. In conducting face-to-face activities, video conferencing applications are widely used for meetings and conducting activities such as learning. To support learning, sometimes teachers or lecturers find it difficult to observe whether the participants understand or not. In this study, used a dataset from KDEF which has seven classes. To find out the expressions of the participants, a model was made using the Convolution Neural Network that can detect human facial expressions. This convolution neural network has a contribution where the model used is a YOLO-face which is pipelined with CNN for classifications that have Alexnet architecture and modifications of Alexnet. The workings of this model is that the image is processed into a YOLO-face then the results from the YOLO-face are used by CNN classification to be able to classify the facial expressions of the participants. Then the photos will be classified using a CNN modification of the Alexnet architecture. The accuracy is 0.94 during training, precision is 0.92 during training, and recall is 0.96 during training. In this study, the face was successfully detected and classified the expressions on the face. For further development, it is necessary to optimize and increase the accuracy of the model to be able to classify facial expressions properly.]]>
<![CDATA[Business Simulation Training Using Monsoonsim ]]>
<![CDATA[Yuwono Marta Dinata]]>;
<![CDATA[Christian Tanjono]]>;
<![CDATA[Rinabi Tanamal]]>;
<![CDATA[Evan Tanuwijaya]]>
<![CDATA[ ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2021): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat ]]>
Publisher : <![CDATA[LPPM Universitas Muhammadiyah Tasikmalaya ]]>
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DOI: 10.35568/abdimas.v4i2.1537
<![CDATA[Business simulation training has been conducted by the School of Information Technology, Information Systems Department, Ciputra University, using MonsoonSIM. The aim of this activity is to introduce the concept of Enterprise Resource Planning (ERP) to students through virtual business simulation game. This training was conducted online, due to to Covid-19 pandemic. By using MonsoonSIM software, students can manage a virtual company and learn the company's business processes. Learnings in this training were in the form of material module explanations, simulations, discussions, and evaluations. This program was attended by students from Junior High School and Senior High School. Participants not only play individually to learn from the game experience, but also in a team to compete to be the best virtual company that can dominate the market within a predetermined period of time. By participating in this training, students can manage the operational processes of a virtual company, understand existing resources, and can make a company planning strategy in order to survive and grow.]]>
<![CDATA[Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah-Rempah Indonesia ]]>
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Angelica Roseanne]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 1 (2021) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v21i1.1492
<![CDATA[Rempah-rempah merupakan salah satu kekayaan alam yang dimiliki oleh Indonesia. Rempah-rempah sendiri memiliki banyak manfaat untuk Kesehatan ataupun hal-hal lain. Dari banyaknya rempah yang berada di Indonesia, ternyata masyarakat Indonesia sendiri masih memiliki pengetahuan yang rendah akan rempah-rembah tersebut. Hal ini menyebabkan banyak orang bahkan petani mengalami kesusahan dalam mengenali jenis rempah terutama remaja. Membedakan rempah satu dengan yang lain merupakan tantangan yang banyak dihadapi oleh masyarakat. Oleh sebab itu, penelitian ini membuat sebuah model klasifikasi dengan menggunakan convolution neural network dengan arsitektur VGG 16 yang dimodifikasi. Arsitektur modifikasi VGG 16 memiliki 10-layer yang terdiri dari 7-layer convolution dan 3-layer fully connected. Untuk fase latih model modifikasi VGG 16 ini menggunakan dataset rempah yang disediakan oleh Kaggle. Validasi model yang digunakan adalah akurasi, loss, precision, dan recall untuk membandingkan model mana yang memiliki nilai yang terbaik. Untuk model modifikasi VGG 16 yang dibuat untuk melakukan klasifikasi, mendapatkan hasil evaluasi rata-rata akurasi sebesar 81%, nilai recall sebesar 76%, dan nilai precision sebesar 81% untuk fase training dan untuk fase validasi, akurasi sebesar 85%, nilai recall sebesar 80%, dan nilai precision sebesar 84%. Jadi dengan model modifikasi VGG 16 dapat disimpulkan bahwa model mampu memprediksi rempah-rempah lebih baik dari model Alexnet.]]>
<![CDATA[RECOGNITION OF HUMAN FACES IN VIDEO CONFERENCE APPLICATIONS USING THE CNN PIPELINE ]]>
<![CDATA[Evan Tanuwijaya]]>;
<![CDATA[Reinaldo Lewis Lordianto]]>;
<![CDATA[Reiner Anggriawan Jasin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.2.219
<![CDATA[The COVID-19 pandemic has forced daily face-to-face activities to be carried out online using video conferencing applications. To record participant participation in meetings using a video conference application, an online form application is used. However, participants sometimes do not see this and are often missed due to the large number of incoming chats. Therefore, the use of face detection for attendance using a combination of CNN to detect all the faces in a video conference using YOLO Face and CNN to recognize the owner of a face using Smaller VGG in a pipeline will make it easier to recognize participants who are present at the video conference. The results of the Smaller VGG training are obtained, namely the loss value of 0.059, the accuracy value is 0.995, the recall value is 0.994, the precision value is 0.996. Meanwhile, for the validation phase of the model, the loss value is 0.497, the accuracy value is 0.979, the recall value is 0.979 and the precision value is 0.981. In terms of training duration, the smaller VGG has a duration of 4 minutes and 16 seconds. The Smaller VGG model was combined with YOLO to create a CNN pipeline and was successful in recognizing the faces of video conference participants]]>
