Articles
<![CDATA[Physic Simulation Experiments using Augmented Reality ]]>
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Christyowidiasmoro Christyowidiasmoro]]>
<![CDATA[ IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013 ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j23546026.y2014i1.428
<![CDATA[Augmented Reality (AR) is a technology which render visual information such as three-dimensional (3-D) virtual objects and allow users to interact with virtual and real object at the same time. This technology can help student to learn intuitively and interactively through simulation which is equipped with AR. This paper discusses the physic simulation such as force, gravity, friction, spring, gears and chain through AR. The system that we built is deployed on mobile platform specifically Android based devices and based on marker-less AR. Instead of a black-and-white square marker, we use image corresponding the context to trigger and visualize the simulation. By simply touch the correspondent image, student can interact and alter the course of the physic simulation.]]>
<![CDATA[Membangun Sistem Text-to-Audiovisual Bahasa Indonesia Berdasarkan Database Suara Berbasis Suku Kata Untuk Mendukung Pembelajaran Pelafalan Bahasa Indonesia ]]>
<![CDATA[Arifin Arifin]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mochamad Hariadi]]>;
<![CDATA[Arry Maulana Syarif]]>
<![CDATA[ AITI Vol 15 No 1 (2018) ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Kristen Satya Wacana ]]>
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DOI: 10.24246/aiti.v15i1.14-26
<![CDATA[This paper aims to develop a system Text-to-Audio Visual Indonesian to support learning of Indonesian pronunciation based on speech database syllable-based. This system can visualize the pronunciation of the sentences Indonesian synchronized with speech signals. We conduct several research stages, namely forming the Indonesian viseme models, creating the speech database syllable-based, converting the text into syllables dan synchronizing. The synchronization process is a compilation the viseme models and the speech signal based on input text. This system was evaluated by involving 30 respondents who rate the system based on “lip-reading”. Each respondent provides an assessment of the 10 Indonesian sentences about the level of compatibility between the visualization of syllable and speech spoken based on text input. The MOS methode (Mean Opinion Score) is used to calculate the average ratings of respondents. MOS calculation results is 4.24, It shows that the level of conformity visualization syllable pronunciation and spoken voice is good.]]>
<![CDATA[Interaksi 3D Sensor Leap Motion untuk Menggenggam Benda Virtual ]]>
<![CDATA[Lukman Hakim]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Supeno Mardi Susiki Nugroho]]>
<![CDATA[ CYCLOTRON Vol 3, No 2 (2020): CYCLOTRON ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surabaya ]]>
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DOI: 10.30651/cl.v3i2.5674
<![CDATA[Abstrak - Penelitian ini membahas tentang interaksi 3D sensor Leap Motion untuk simulasi menggenggam Benda virtual Plastis. Sebuah interaksi 3D sensor Leap Motion yang digunakan sebagai simulasi untuk menggenggam benda virtual Plastis dengan menggunakan media objek telur virtual secara presisi dan akurasi yang tepat. Pada dasarnya menggenggam merupakan suatu kegiatan yang menerapkan kinerja motorik halus pada tangan untuk melakukan gerakan. Penggunaan sensor Leap Motion sebagai interaksi 3D dipakai untuk menggenggam objek maya dalam hal ini bentuk 3D telur virtual sebagai media praktiknya. Telur sendiri merupakan benda yang gampang distimulasi dan memiliki sifat texture yang halus untuk merespon segala bentuk gerakan pada genggaman tangan. Dalam penelitian Interaksi 3D Sensor Leap Motion untuk simulasi untuk menggenggam benda Virtual Plastis dengan menggunakan media objek telur virtual, ini di peruntukkan untuk mengetahui akurasi dan presisi dari pola gerakan tangan secara imersif. Pengembangan dari metode ini bertujuan untuk simulasi menggenggam benda atau objek maya dengan adanya interaksi pola gerakan tangan.Kata kunci: leapmotion, 3d, virtual reality, benda, telurAbstract - This study discusses about the 3D interaction of the Leap Motion sensor for the simulation of holding virtual plastic objects. A 3D interaction of the Leap Motion sensor that is used as a simulation to hold Plastis virtual objects by using virtual egg object media with precise and right accuracy. Basically, holding is an activity that applies fine motor performance on the hands to make movements. The use of the Leap Motion sensor as a 3D interaction is used to hold virtual objects in this case a 3D form of virtual eggs as practice media. Eggs are objects that are easily stimulated and have subtle texture to respond to all forms of movement in the hands. In the 3D interaction Leap Motion Sensors for virtual plastic objects holding simulation by using virtual egg object media, it is intended to find out the accuracy and precision of immersive hand movement patterns. The development of this method aims to simulate holding virtual objects or objects with the interaction of hand movement patterns.Keywords: leap motion, 3d, virtual reality, object, egg]]>
<![CDATA[Tactical Planning in Space Game using Goal-Oriented Action Planning ]]>
<![CDATA[Restuadi Studiawan]]>;
<![CDATA[Mochamad Hariadi]]>;
<![CDATA[Surya Sumpeno]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 2, No 1 (2018): April ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/j25796216.v2.i1.32
<![CDATA[Along with improvement of modern electronic games, necessity of an intelligent agent that easily build is needed. One of electronic games that need good intelligent agent is real-time tactics. In this game type, good action planning is necessary to provide best experience to the player. On this paper, we try to find out whether if Goal-Oriented Action Planning (GOAP) is robust enough to be used at tactical game. By using GOAP, tactic dynamism still can be provided with reasonable amount of runtime.Keywords: Artificial intelligence, Games, Goal-Oriented Action Planning, Planning, Unity3D]]>
<![CDATA[Clustering Data National Examinations based on Social Media Using K-Means Methods ]]>
<![CDATA[Chandra Eko Wahyudi Utomo]]>;
<![CDATA[Mochamad Hariadi]]>;
<![CDATA[Surya Sumpeno]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 4, No 2 (2020): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/j25796216.v4.i2.152
<![CDATA[The development of social media as a source of data is now increasingly interesting to study. The social media studied in this research is Twitter. Twitter as one of the top-ranked social media among social media accessed by the people of Indonesia. People's behavior can be learned by collecting and processing data, one of which is people's sentiments or opinions about national examinations in Indonesia. Twitter user behavior in the form of their comments about the national exam in Indonesia. This study aims to analyze the public sentiments of social media users about the National Examination in Indonesia. Data is retrieved by crawling data via the Twitter API. The data needs to be preprocessed first and feature extracted using TF-IDF. However, because the text data on Twitter is unstructured and very diverse data (variety), the grouping stage must be done first. Grouping technique using K-Means Clustering on Spark. Spark clustering techniques are used to overcome the grouping of data on very large and complex amounts of data. From the clustering process using Spark it was found that the grouping process resulted in 3 clusters where elbow detection was found in the third cluster of the number of clusters between 2 and 50. The results of clustering in the form of 3 large groups were further processed (with classification techniques) to get a positive or negative sentiment comparison of social media user comments about the national exam. Furthermore, these results become recommendations and new knowledge about community behavior regarding Social Media-based National Exams.Keywords: clustering, K-Means, national exam, sentiment analysis, social media.]]>
<![CDATA[Analisis Pendapat Masyarakat terhadap Berita Kesehatan Indonesia menggunakan Pemodelan Kalimat berbasis LSTM ]]>
<![CDATA[Esther Irawati Setiawan]]>;
<![CDATA[Adriel Ferdianto]]>;
<![CDATA[Joan Santoso]]>;
<![CDATA[Yosi Kristian]]>;
<![CDATA[Gunawan Gunawan]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mauridhi Hery Purnomo]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v9i1.115
<![CDATA[The uncertainty of health news content, which is spread on social media, raises the need for validation of the truth. One validation approach is to consider the opinion or attitudes of most people, which is called a stance on a topic, whether they support, oppose, or being neutral. This paper proposes a stance analysis model to classify the relationship between sentences so that it can recognize the correlation of the opinion of the writer in the headline of the problem claim. The proposed model uses several Long Short-Term Memory (LSTM), which represent the interrelationship of news for analysis of the relationship between a claim with other news. The formation of word representation vectors is carried out in conjunction with LSTM-based stance classification training. Sentence embedding is done to get the vector representation of sentences with LSTM. Each word in a sentence occupies one time-step in LSTM and the output of the last word is taken as a sentence representation. Based on the results of trials with the Indonesian health-related dataset that was built for this study, the proposed stance classification model was able to achieve an average F1-score value of 71%, with the supporting value 69%, opposing as much as 70%, and neutral 74%.]]>
<![CDATA[Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior ]]>
<![CDATA[Nova Rijati]]>;
<![CDATA[Diana Purwitasari]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mauridhi Hery Purnomo]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v9i1.118
<![CDATA[Indonesia government has launched a program to encourage youth entrepreneurship as a strategy to improve national economy. This paper proposes a method to find an entrepreneurial potential based on academic behavior features that are extracted from the Higher Education Database PDDikti. The proposed approach applies the Fuzzy Multi-Attribute Decision Making (FMADM) technique. Rules for extracting features of student academic behavior were following Theory of Planned Behavior (TPB) and resulting in 14 features. The FMADM model combines Fuzzy Simple Additive Weighting and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, which is called FSAW-TOPSIS. Friedman Test demonstrated that FSAW-TOPSIS gives more optimal solution with the highest Mean Rank of the potential entrepreneurial value of 2.96. Besides, through Hamming Distance Test, FSAW-TOPSIS results the best order with a 98% percentage and ranking of the smallest Squared Error of 0.3%, which makes the proposed model offered a better solution. It can be concluded that using TPB variables in PDDikti environment with FSAW-TOPSIS technique provides an optimal recommendation on student entrepreneurship potential, which can be used as a part of a decision-making system for higher education management.]]>
<![CDATA[Penentuan Kemampuan Motorik Halus Anak dari Proses Menulis Hanacaraka Menggunakan Random Forest ]]>
<![CDATA[Nurul Zainal Fanani]]>;
<![CDATA[Adri Gabriel Sooai]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mauridhi Hery Purnomo]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v9i2.153
<![CDATA[The children's Fine Motor Skill Assessment (FMS) at the beginning of school age is essential to get information about children's school readiness. The process of measuring FMS has been carried out by observing children, both directly and from the results of sketches or children's writing. This observation process is very dependent on the observer's perception. This study aims to determine the children's FMS using Javanese script. This research develops a new method for determining children's FMS from the process of writing the Javanese script. The system was recording data directly when the child is writing the Javanese script. Retrieval of data recording from the writing process involved 14 students in 1st grade and 2nd grade from three elementary schools in Jember district. The process of recording data from each student produces a large enough raw data. Therefore, this study uses random forest classification method,because this method can carry out the classification process on large amounts of data by combining several decision trees. Other classification methods, including naïve Bayes and k-NN, were used as a comparison. The experiment results show that the random forest classification method is the bestwith an accuracy of 98.7%.]]>
<![CDATA[Klasifikasi Interaksi Kampanye di Media Sosial Menggunakan Naïve Bayes Kernel Estimator ]]>
<![CDATA[Aryo Nugroho]]>;
<![CDATA[Rumaisah Hidayatillah]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mauridhi Hery Purnomo]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 2: Mei 2019 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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<![CDATA[The development of technology also influences changes in campaign patterns. Campaign activities are part of the process of Election of Regional Heads. The aim of the campaign is to mobilize public participation, which is carried out directly or through social media. Social media becomes a channel for interaction between candidates and their supporters. Interactions that occur during the campaign period can be one indicator of the success of the closeness between voters and candidates. This study aims to get the pattern of campaign interactions that occur on Twitter social media channels. This interaction pattern is classified as a model in measuring the success of campaigns on social media. The research begins with obtaining data through the data retrieval process using the API feature provided by Twitter. Furthermore, pre-processing is carried out before data can be processed in an algorithmic method. This stage is done to improve data quality so as to improve accuracy. Naive Bayes Classifier was chosen because of a simple procedure, then Kernel Estimator (KE) was used to improve performance. The use of naive Bayes Kernel Estimator can improve model performance from 76.74% to 80.14%. Testing models with split percentage methods on several combinations get satisfactory results.]]>
<![CDATA[Analisis Kinerja LSTM dan GRU sebagai Model Generatif untuk Tari Remo ]]>
<![CDATA[Lukman Zaman]]>;
<![CDATA[Surya Sumpeno]]>;
<![CDATA[Mochamad Hariadi]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 2: Mei 2019 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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<![CDATA[Creating dance animations can be done manually or using a motion capture system. An intelligent system that able to generate a variety of dance movements should be helpful for this task. The recurrent neural network such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) could be trained as a generative model. This model is able to memorize the training data set and reiterate its memory as the output with arbitrary length. This ability makes the model feasible for generating dance animation. Remo is a dance that comprises several repeating basic moves. A generative model with Remo moves as training data set should make the animation creating process for this dance simpler. Because the generative model for this kind of problem involves a probabilistic function in form of Mixture Density Models (MDN), the random effects of that function also affect the model performance. This paper uses LSTM and GRU as generative models for Remo dance moves and tests their performance. SGD, Adagrad, and Adam are also used as optimization algorithms and drop-out is used as the regulator to find out how these algorithms affect the training process. The experiment results show that LSTM outperforms GRU in term of the number of successful training. The trained models are able to create unlimited dance moves animation. The quality of the animations is assessed by using visual and dynamic time warping (DTW) method. The DTW method shows that on average, GRU results have 116% greater variance than LSTM’s.]]>
