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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jupiter Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Bulletin of Electrical Engineering and Informatics Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Sistem dan Informatika INFORMAL: Informatics Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Inspiration Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Development Research INTEGER: Journal of Information Technology Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Journal of Education Technology Building of Informatics, Technology and Science Specta Journal of Technology Dinasti International Journal of Education Management and Social Science Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Sewagati Bulletin of Social Informatics Theory and Application
Darlis Herumurti
Departemen Informatika Institut Teknologi Sepuluh Nopember
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Remote Control Komunikasi Robot Berbasis Pergerakan Tangan Pada Smartphone Menggunakan Metode Logika Fuzzy Musri, Tengku; Herumurti, Darlis; Munif, Abdul
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 7, No 1 (2017): Jurnal Inspiration Volume 7 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v7i1.2436

Abstract

Robot mobile diklasifikasikan menjadi dua, yaitu menurut lingkungan tempat robot tersebut bekerja dan alat yang digunakan untuk bergerak. Media-media yang digunakan dalam pengendalian robot ada berbagai macam yaitu berupa remote control, computer, joystick ataupun memanfaatkan sensor accelerometer yang ada pada smartphone sebagai pengendali gerak robot. Sensor ini juga dapat mengukur kemiringan suatu benda dikarenakan memiliki 3 sumbu yaitu X, Y, dan Z. yang dapat digunakan untuk mengendalikan sebuah robot. Dengan cara memiringkan smartphone kearah depan, belakang, kesamping kanan dan kesamping kiri. Pada metode pengendalian ini masih terdapat kelemahan yaitu kecepatan robot masih konstan ketika digerakan sehingga kemungkinan robot menabrak halangan yang ada didepan bisa saja terjadi. Pada penelitian ini mengusulkan sebuah pengontrolan mobile robot melalui gerakan tangan memanfaatkan sensor accelerometer yang terdapat pada smartphone melalui media komunikasi bluetooth dan menggunakan logika fuzzy sebagai pengatur kecepatan secara adaptif terhadap halangan yang dilalui oleh robot sehingga kecepatan motor bisa berubah berdasarkan informasi jarak yang diterima dari sensor ultrasonic yang terpasang pada robot.Dalam skenario ujicoba, digunakan beberapa jarak dan kecepatan dengan tujuan untuk mengetahui performa gerakan robot berhenti ketika menemukan halangan. Berdasarkan pengujian pada metode usulan, diperoleh hasil nilai terbaik diambil dari nilai error terkecil yaitu pada jarak 50 cm dengan kecepatan 200 pwm yaitu 2.9%. dari hasil ujian terbukti bahwa metode yang di usulkan sangat efektif diterapkan untuk mengontrol robot mobile.
First Person and Third Person Perspective in Virtual Reality: Analysis of Cybersickness Symptoms I Gde Agung Sri Sidhimantra; Darlis Herumurti
Journal of Development Research Vol. 5 No. 1 (2021): Volume 5, Number 1, May 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v5i1.130

Abstract

Advances in technology makes it easier to gain access to the virtual world. This has led to more and more application and games being targeted towards the virtual world. But with the growing popularity of the virtual world, cybersickness has grown in popularity as well. This study aims to evaluate the factors affecting cybersickness in the Virtual Reality (VR) environment. There are few factors causing the effect of cybersickness in VR like duration, field of view, speed, habituation, and susceptibility of said user. Those factors affect differently in first person perspective(1pp) and third person perspective(3pp). To measure the cybersickness, a Virtual Reality Questionnaire (VRSQ) measurement index is utilized. The experiment was conducted with the following settings. The participants consisted of 20 males and 4 females who never used VR before. They performed task using short games. It consisted in total of 4 tasks (2 types of game (action and adventure) x 2 perspective (1pp and 3pp) = 4 tasks). The Latin Square design was used to minimize the effect of order. Then, a questionnaire was conducted after each treatment. Paired Dependent T-Tests was performed to check if there are differences in oculomotor, disorientation and VRSQ total score. There was a significant difference in 1pp and 3pp in both games. It is recommended to use third person perspective to reduce the cybersickness in VR environment.
KLASIFIKASI SECARA EFISIEN PADA DATABASE MULTI RELASI DENGAN ALGORITMA CROSSMINE Sarwosri Sarwosri; Darlis Herumurti; Indri Sulistyowati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 6, No 1: April 2008
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v6i1.545

Abstract

Multi-relation classifications can be widely used in many disciplines, such as financial decision making, medical research, and geographical applications, and information stored in multiple relations needs to be used in decision making. Crossmine is an efficient and scalable approach for multi-relation classification. Crossmine algoritm has three step, first is find-rules, the rule has been gotten from find a rule process than remove all positif tuples satisfying rule while there are more than ten percent positif tuple left. The second is find a rule, this step has input from the result of find best predicate process, that is the complex predicate with most foilgain. If foilgain value is more than mingain, the predicate is added with rule, and max rule length less than six. Third is find best predicate, in this step we find the best predicate with definition, if the foilgain value more than the max gain value, the predicate will be saved and the bigger gain value will replace the last gain value for next comperative process. In other side, the accuracy is computed from each rule that produce in find rules process. The test for this application use the sum tuple of 200, 500, 1000, 5000 for measuring the level of accuracy from rule which is produced by crossmine algoritm.
Robot Motion Control Using the Emotiv EPOC EEG System Sandy Akbar Dewangga; Handayani Tjandrasa; Darlis Herumurti
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.633 KB) | DOI: 10.11591/eei.v7i2.678

Abstract

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
Robot Motion Control Using the Emotiv EPOC EEG System Sandy Akbar Dewangga; Handayani Tjandrasa; Darlis Herumurti
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.633 KB) | DOI: 10.11591/eei.v7i2.678

Abstract

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS Nursuci Putri Husain; Nursanti Novi Arisa; Putri Nur Rahayu; Agus Zainal Arifin; Darlis Herumurti
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (205.767 KB) | DOI: 10.21609/jiki.v10i1.428

Abstract

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.
FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES Ratri Enggar Pawening; Tio Darmawan; Rizqa Raaiqa Bintana; Agus Zainal Arifin; Darlis Herumurti
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.816 KB) | DOI: 10.21609/jiki.v9i2.384

Abstract

Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature transformation (UFT) methods and joint mutual information maximation (JMIM) methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM) algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.
EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST Yuna Sugianela; Qonita Luthfia Sutino; Darlis Herumurti
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.748 KB) | DOI: 10.21609/jiki.v11i1.549

Abstract

EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.
Deteksi Kendaraan Pada Citra Udara Beresolusi Sangat Tinggi di Area Perkotaan dengan Menggunakan Metode Ekstraksi Oriented FAST And Rotated BRIEF Diagnosa Fenomena; Darlis Herumurti; Joko Lianto Buliali
Jurnal Teknik ITS Vol 5, No 2 (2016)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (700.309 KB) | DOI: 10.12962/j23373539.v5i2.16825

Abstract

Data mengenai lalu lintas mempunyai peranan penting dalam perencanaan tata kota, seperti untuk perencanaan jalan dan perkiraan tingkat pencemaran udara yang disebabkan oleh lalu lintas kendaraan di kota. Berbagai upaya telah dilakukan untuk mendeteksi kepadatan lalu lintas, salah satunya deteksi kendaraan menggunakan CCTV. Namun, penggunaan CCTV hanya efektif untuk deteksi kendaraan pada ruas jalan yang relatif terbatas. Oleh karena itu, dibutuhkan suatu implementasi algoritma yang secara otomatis dapat mendeteksi kendaraan melalui citra udara. Terdapat beberapa metode yang dapat digunakan untuk mendeteksi objek, beberapa diantaranya adalah template matching, klasifikasi, dan feature matching. Pada tugas akhir ini, menggunakan metode Template Matching dengan menggunakan persamaan korelasi, Haar Cascade classification dan Feature Matching dengan menggunakan ekstraksi fitur ORB. Nilai recall dan precision tertinggi dihasilkan oleh metode Feature Matching dengan MSER dan ORB masing-masing bernilai 100% dan 75%.
Permainan Augmented Reality dalam Mendukung Pembelajaran Anak tentang Binatang pada Perangkat iOS Radhea Wicaksono Putra; Darlis Herumurti; Imam Kuswardayan
Jurnal Teknik ITS Vol 5, No 2 (2016)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.414 KB) | DOI: 10.12962/j23373539.v5i2.18607

Abstract

Kebun binatang adalah tempat untuk menampung beragam jenis binatang yang dapat kita lihat langsung dan juga tempat wisata yang tepat untuk keluarga, apalagi bersama anak-anak. Mereka dapat belajar banyak tentang binatang. Namun waktu bisa jadi kendala bagi orang tua karena tidak sempat membawa anak mereka pergi kesana, sehingga belum ada kesempatan bagi anak-anak untuk mengenal lebih tentang binatang. Augmented reality merupakan salah satu solusi untuk mengatasi masalah tersebut. Pembelajaran tentang binatang akan ditampilkan dalam bentuk realitas augmentasi. Lingkungan, binatang, objek, dan keadaan yang mendukung proses pembelajaran akan diubah ke dalam bentuk 3 dimensi yang menyerupai aslinya. Tugas akhir ini bertujuan untuk membuat sebuah permainan Augmented reality pembelajaran anak-anak tentang binatang. Pengujian dilakukan dengan bantuan marker sebagai penempatan objek tiga dimensi dan suara yang berupa pertanyaan. Pengguna menyelesaikan beberapa skenario pembelajaran seperti menghitung, mencari, dan memilih binatang dengan menggunakan iPad. Pengukuran hasil uji dibantu dengan kuesioner untuk menilai tingkat kepuasan dalam proses pembelajaran.
Co-Authors Abdi, Musta'inul Abdillah, Rifqi ABDUL MUNIF Afrizal Laksita Akbar Agus Zainal Arifin Agus Zainal Arifin Ahmad Ridwan Fauzi Alfan, Muhammad Bahauddin Andhik Ampuh Yunanto Anny Yuniarti Ardha Putra Santika Ardhana Praharsana Bilqis Amaliah Buliali, Joko Lianto Chastine Fatichah Deny Prasetia Hermawan, Deny Prasetia Devira Wiena Pramintya Dhian Satria Yudha Kartika Diagnosa Fenomena Dian Sani Dian Sani, Dian Dwi Syamsuifin Alham Eha Renwi Astuti Esa Prakasa Fabroyir, Hadziq Fitrah Humaira Fitrah Maharani Humaira Franky Setiawan Daldiri Giri Wiriapradja Hadziq Fabroyir Handayani Tjandrasa Herdianto Naufal Farras Hidayat, Fajrul Hidayati, Shintami Chusnul Humaira, Fitrah Humaira, Fitrah Maharani Humaira, Fitrah Maharani I Gde Agung Sri Sidhimantra I Guna Adi Socrates I Made Satria Bimantara I Made Widiartha I Made Widiartha I Wayan Supriana Imaduddin Al Fikri, Imaduddin Al Imam Kuswardayan Imam Kuswardayan Indri Sulistyowati Ishardan Ishardan Izza Mabruroh Januar Adi Putra Khairy, Muhammad Shulhan Mabruroh, Izza Maulana, Hendra Mohammad Sonhaji Akbar Muhammad Shulhan Khairy Nafis, Ari Mahardika Ahmad Nanik Suciati Nanik Suciati Nur Nafi’iyah Nursanti Novi Arisa Nursuci Putri Husain Pangestu Widodo, Pangestu Pratama, Moch Deny Putra, Ramadhan Hardani Putri Nur Rahayu Qonita Luthfia Sutino Radhea Wicaksono Putra Ratri Enggar Pawening Revindasari, Fony Ria Andriana Ridho Rahman Hariadi Rizqa Raaiqa Bintana Rohman Dijaya Saiful Bahri Musa Saiful Bahri Musa Sandy Akbar Dewangga Sarwosri Sarwosri Satria, Vinza Hedi Siska Arifiani Siti Rochimah Supria Supria Supria Supria, Supria Supria, Supria Suriawan, Matthew Vieri Suwanto Afiadi Tegar Palyus Fiqar Tegar Palyus Fiqar Tengku Musri Tio Darmawan Widarsono, Kukuh Wijayanti Nurul Khotimah Yanuar Risah Prayogi Yosi Kristian Yuhana, Umi Laili Yuna Sugianela Zulhaydar Fairozal Akbar