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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information System,Graphics, Hospitality and Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal of Robotics and Control (JRC) Jurnal Teknika Teknika Insyst : Journal of Intelligent System and Computation JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer International Journal of Robotics and Control Systems J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Inovasi Teknologi dan Edukasi Teknik
Yosi Kristian
Institut Sains Dan Teknologi Terpadu Surabaya
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Detection of Militia Object in Libya by Using YOLO Transfer Learning Yosi Kristian; Hatem Alsadeg Ali Salim; Endang Setyati
Jurnal Teknologi dan Manajemen Informatika Vol 6, No 1 (2020): Juni 2020
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v6i1.4025

Abstract

Humans can recognize and classify shapes, names, and provide responses to object that are received by visually quickly and accurately. More importantly, it is expected that the system created is able to help provide response in all tasks and time, for example when driving, walking in the crowd even when patrolling as a member of the military on dangerous terrain.This has become a problem in the system used on the battlefield. In the proposed system, the object detection model must be able to sort out the objects of armed humans (militia) with unarmed human objects. To overcome the problem the author uses the YOLO transfer learning algorithm which currently has the third version. It is stated that YOLOv3 has very extreme speed and accuracy. In mean Average Precision (mAP) obtained by 0.5 IOU, YOLOv3 is equivalent to 4x faster than Focal Loss. Moreover, YOLOv3 also offers optimal speed and accuracy simply by changing the size of the model, without the need for retraining. DOI: https://doi.org/10.26905/jtmi.v6i1.4025
Estimasi Arah Tatapan Mata Menggunakan Ensemble Convolutional Neural Network William Sugiarto; Yosi Kristian; Eka Rahayu Setyaningsih
Teknika Vol 7 No 2 (2018): November 2018
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v7i2.126

Abstract

Studi arah tatapan mata adalah salah satu masalah dalam bidang computer vision. Pengetahuan akan arah tatapan mata dapat memberikan informasi berharga yang dapat dimanfaatkan untuk berbagai macam keperluan dalam bidang lainnya, khususnya dalam bidang interaksi manusia dengan komputer. Dalam paper ini nantinya akan meneliti arah tatapan mata menggunakan Ensemble Convolutional Neural Network dengan menggunakan dataset CAVE (Columbia Gaze Dataset). Convolutional Neural Netwok (CNN) merupakan sebuah bidang keilmuan dalam bidang machine learning yang berkembang cukup pesat khususnya untuk mengklasifikasi citra. Nantinya, paper ini akan menganalisa dan membandingkan hasil F1 score dan weighted kappa (w-kappa) score serta error dari klasifikasi dengan menggunakan 3, 9, dan 21 kelas. Dengan sama-sama menggunakan kanal RGB sebagai gambar input, maka dapat dibandingkan dan disimpulkan bahwa dengan menggunakan metode Ensemble Convolutional Neural Network dengan koefisien 1 untuk mata kiri, 1 untuk mata kanan, dan 3 untuk kedua mata untuk klasifikasi dengan 3 dan 9 kelas, serta dengan koefisien 1 untuk mata kiri, 1 untuk mata kanan, dan 5 untuk kedua mata untuk klasifikasi dengan 21 kelas dapat menghasilkan hasil F1 score dan w-kappa yang lebih baik, serta tingkat error yang lebih rendah daripada menggunakan koefisien dengan nilai lainnya.
Detection of Banana and Its Ripeness Using Residual Neural Network Erwin Dhaniswara; Yosi Kristian; Esther Irawati Setiawan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.4844

Abstract

Automatic fruit detection utilizing computer vision techniques has been carried out to help the agriculture and plantation industries. This study researches smart systems to detect bananas and ripeness classification utilizing residual neural networks. The method used to detect bananas is transfer learning from pretraned Model VGG-19. Whereas, in the bananas ripeness classification process, residual neural networks, which are trained from the start, are used. Sliding Windows is used to detect the position of bananas followed by Non-Max Suppression to summarize the results of several detected bananas. Previous studies were limited to the level of ripeness, but in this study, bananas are detected and followed by the level of bananas ripeness (raw, ripe, and overripe). This study’s data uses bananas which were mixed with other kinds of fruit. There two kinds of bananas detection architecture used in this study, VGG-19 and Restnet. After they were used to detect bananas, it was found that VGG-19 was more suitable. The results of this study are very satisfying as it is seen from the bananas detection testing percentage using VGG-19 architecture which shows 100% ripe bananas, 99 % raw bananas, and 100% overripe bananas.Keywords: Detection of banana, banana ripeness, Non-Max suppression, residual block.
Game Playing untuk Othello dengan Menggunakan Algoritma Negascout dan MTDF Gunawan Gunawan; Yosi Kristian; Hermawan Andika
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2009
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Game playing adalah salah satu sistem kecerdasan buatan (AI). Untuk permasalahan pada permainan yangberbasis pada giliran pemain, salah satu algoritma yang cukup membudaya adalah Negascout dan MTDF(Memory-enhanced Test Driver value f) yang diimplementasikan pada game tree algoritma minimax. Dengankonsep utama yang terletak pada semua kemungkinan yang bisa ditelusuri pada permainan. Dalam hal ini,permainan othello dapat menggunakan algoritma ini karena permainan dapat diimplementasikan dalam sebuahtree. Tree memiliki cabang-cabang yang terdiri dari node yang akan menyatakan nilai yang selanjutnya akandigunakan dalam menentukan langkah terbaik dari permainan. Nilai tersebut didapat dari proses evaluasiterhadap segala kemungkinan yang terjadi pada tiap perubahan keping dari papan permainan othello. Nilaievaluasi ini berkisar antara minus tak hingga sampai tak hingga. Beberapa cara yang dapat digunakan dalammengevaluasi nilai pada permainan othello ini, diantaranya adalah dengan mengevaluasi jumlah langkah yangdapat dilakukan oleh pemain pada tiap kali kesempatan, memperkecil kemungkinan dari keping pemain yangberbatasan dengan petak kosong dan penguasaan pada posisi-posisi pojok dari papan permainan.Kata Kunci: Game playing, minimax, Negascout, MTDF, othello
Klasifikasi Ketertarikan Anak PAUD Melalui Ekspresi Wajah Menggunakan Metode CNN Ajeng Restu Kusumastuti; Yosi Kristian; Endang Setyati
Jurnal Teknologi Informasi dan Terapan Vol 7 No 2 (2020)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v7i2.176

Abstract

The character of emotions in children is different from that of adults, where the characteristics of the emotions in childres include, (1) Briefly and ends suddenly, (2) Seems greater or stronger, (3) Temporary or superficial, (4) Frequent, (5) Can be known clearly from behaviour, and (6) Reaction reflects individuality. Emotions that are felt can be expressed through faces, this is continuous with how interested the child is tho the material presented in fornt of him. Measuring the level of interest in PAUD children in this study using CNN. In the process of training the level of interest in PAUD children, the accuracy value of the four models always increases from epoch 25 until 100 with the highest value being the Rajmehra architecture. But during the data testing process, the architecture in this study increased slightly and the highest peak reached an accuracy value of 81.66%. It is 3.33% better than the result obtained with the Rajmehra architecture. Emosi adalah perasaan atau afeksi yang timbul, ketika seseorang berada dalam suatu keadaan yang dianggap penting oleh individu tersebut. Karakteristik emosi pada anak berbeda dengan karakteristik yang terjadi pada orang dewasa, dimana karekteristik emosi pada anak itu antara lain, (1) Berlangsung singkat dan berakhir tiba-tiba, (2) Terlihat lebih hebat atau kuat, (3) Bersifat sementara atau dangkal, (4) Lebih sering terjadi, (5) Dapat diketahui dengan jelas dari tingkah lakunya, dan (6) Reaksi mencerminkan individualitas. Emosi yang dirasakan dapat diekspresikan melalui wajah, hal ini berkesinambungan dengan seberapa tertariknya anak terhadap tayangan materi yang disajikan dihadapannya. Pengukuran tingkat ketertarikan anak PAUD pada penelitian ini menggunakan CNN. Dalam proses training tingkat ketertarikan anak PAUD, nilai akurasi keempat model selalu mengalami peningkatan mulai dari epoch 25 hingga 100 dengan nilai tertinggi adalah arsitektur Rajmehra. Tetapi saat proses testing data, arsitektur pada penelitian ini mengalami peningkatan secara perlahan dan puncak tertinggi mencapai nilai akurasi 81,66%. Hal tersebut jauh lebih baik 3,33% dibandingkan hasil yang diperoleh dengan arsitektur Rajmehra.
Universal Face Recognition Using Multiple Deep Learning Agent and Lazy Learning Algorithm Kenny Vincent; Yosi Kristian
CommIT (Communication and Information Technology) Journal Vol. 15 No. 2 (2021): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v15i2.6688

Abstract

Mainstream face recognition systems have a problem regarding the disparity of recognizing faces from different races and ethnic backgrounds. This problem is caused by the imbalances in the proportion of racial representations found in mainstream datasets. Hence, the research proposes using a multi-agent system to overcome this problem. The system employs several face recognition agents according to the number of races that are necessary to make data encodings for the classification process. The first step in implementing this system is to develop a race classifier. The number of races is arbitrary or determined differently in a caseby-case scenario. The race classifier determines which face recognition agent will try to recognize the face in the query. Each face recognition agent is trained using a different dataset according to their assigned race, so they have different parts in the system. The research utilizes lazy learning algorithms as the final classifier to accommodate a system with the constant data flow of the database. The experiment divides the data into three racial groups, which are black, Asian, and white. The experiment concludes that dividing face recognition tasks based on racial groups into several face recognition models has better performance than a single model with the same dataset with the same imbalances in racial representation. The multiple agent system achieves 85% on the Face Recognition Rate (FRR), while the single pipeline model achieves only 80.83% using the same dataset.
Pencarian Rute Line Follower Mobile Robot Pada Maze Dengan Metode Q Learning Samsul Arifin; Arya Tandy Hermawan; Yosi Kristian
Jurnal Otomasi Kontrol dan Instrumentasi Vol 8 No 1 (2016): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2016.8.1.5

Abstract

Dalampenelitianinirobotlinefollowerakandigunakan sebagaiagen.Robotagenbertugasmencarirutedalam suatuenvironmentberupamaze,tanpaadabimbingan langsungdarimanusia.Robotdiberikan  algoritmaQ Learningyangmerupakansalahsatumetodedalam domainReinforcementLearning.Tujuandaripenelitian iniadalahrobotharusbisamenemukanrutedariinitial state menujugoalstate.AlgoritmaQLearningberperan untukmenyimpanstate-statejalurmazeyangtelah dilaluidalammatrikQ.Setiapkalirobotmencapai salahsatudaristatepertigaan,belokkiri,belokkanan ataulurusmakarobotakanmemilihaksiyang mungkin.Haliniakandilakukanberulangsampainilai pasanganstateaksipadamatriksQmencapai  nilai yangoptimal.Darihasilujicobadidapatkandatasebanyak34state yangtelahdipelajariolehrobot.Prosestrainingtelah mampumeningkatkanpengetahuanrobotsehingga bisamenemukanrutedariinisialstatemenujukegoal state.Dari30kalipercobaantingkatkeberhasilan robotuntukmenemukanstateadalah23kali.Ini berartibahwatingkaterrorantara24%.Kata Kunci: Robot Line Follower, Reinforcement Learning, Maze, Q Learning
Klasifikasi Ketertarikan Belajar Anak PAUD Melalui Video Ekspresi Wajah Dan Gestur Menggunakan Convolutional Neural Network Ajeng Restu Kusumastuti; Yosi Kristian; Endang Setyati
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 10, No 2 (2021): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v10i2.1146

Abstract

Abstract—The Covid-19 pandemic has transformed the offline education system into online. Therefore, in order to maximize the learning process, teachers were forced to adapt by having presentations that attract student's attention, including kindergarten teachers. This is a major problem considering the attention rate of children at early age is very diverse combined with their limited communication skill. Thus, there is a need to identify and classify student's learning interest through facial expressions and gestures during the online session. Through this research, student's learning interest were classified into several classes, validated by the teacher. There are three classes: Interested, Moderately Interested, and Not Interested. Trials to get the classification of student's learning interest by teacher validation, carried out by training and testing the cut area of the center of the face (eyes, mouth, face) to get facial expression recognition, supported by the gesture area as gesture recognition. This research has scenarios of four cut areas and two cut areas that were applied to the interest class that utilizes the weight of transfer learning architectures such as VGG16, ResNet50, and Xception. The results of the learning interest classification test obtained a minimum validation percentage of 70%. The result obtained through scenarios of three learning interest classes four cut areas using VGG16 was 75%, while for two cut areas using ResNet50 was 71%. These results proved that the methods of this research can be used to determine the duration and theme of online kindergarten classes.
SVM UNTUK SENTIMENT ANALYSIS CALON KEPALA DAERAH BERDASAR DATA KOMENTAR VIDEO DEBAT PILKADA DI YOUTUBE Muhammad Harris Syafa'at; Eka Rahayu Setyaningsih; Yosi Kristian
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 15 No 2 (2021): November 2021
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v15i1.1539

Abstract

YouTube is a social media that is widely used by people to share videos that contain various types of content. Unregistered users can view videos, while registered users can upload videos and provide an unlimited number of comments. Mostly, videos on YouTube are music clips (video clips), movie trailers, educational videos, review videos, discussion videos, and debate or dialogue. Users’ comments and opinions on YouTube can be used as an indicator to see their inclination to a particular regional head candidate; therefore, comments can be a source of data on public opinion and sentiment in a social study. Inside the candidate team for regional head elections, sentiment analysis is used as a rationale for determining policies and campaign tactics to increase the popularity of their candidate and to test whether the candidate is well accepted in the public eye. Support Vector Machine (SVM) is a sentiment analysis model. SVM belongs to the algorithm group with the supervised technique. The three groups of the categorization used in SVM will look for the maximum value of the hyperplane which divides the test room into separate classes. SVM is a computational algorithm that requires a large operation because it includes discretization, normalization, and repeated product point operations. It is expected that Support Vector Machine (SVM) can automatically process comment data on the debate video of regional head candidates posted on YouTube, and then continually classify sentiment analysis of people’s comments on the regional head candidates. Additionally, this study is significant to become a further reference for those interested in developing SVM.
Sistem Drone Cerdas Yang Dilengkapi Face Detection dan Face Recognition Untuk Pembuatan Sinematik Video Yunan Kholilul Fatah; Yosi Kristian; Devi Dwi Purwanto
Journal of Information System,Graphics, Hospitality and Technology Vol. 4 No. 01 (2022): Journal of Information System, Graphics, Hospitality and Technology
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37823/insight.v4i01.192

Abstract

Pembuatan konten video melalui drone pada umumnya membutuhkan seorang professional untuk mengendalikan drone tersebut agar mampu menghasilkan pergerakan drone dengan hasil video yang diinginkan. Video sinematik adalah video yang mempunyai alur cerita atau dapat menyampaikan sebuah cerita pada film ataupun video pendek. Pengarahan alur cerita tersebut membutuhkan juga seorang film director yang mengarahkan adegan yang akan ditampilkan didalam video. Penelitian ini mengusulkan untuk membuat autonous drone agar mampu bergerak dan menangkap video sesuai dengan arahan film director. Penelitian ini menggunakan face detection dan face recognition dengan algorithma Local Binary Pattern Histogram (LBPH) dan memanfaatkan Rule base system sebagai system cerdas yang terdapat pada system agar drone mampu mengikuti wajah yang dikenali sesuai pergerakan subject yang telah di rencanakan oleh film director. Setiap pergerakan drone memiliki catatan terbang yang terdapat pada system drone berupa Inertial Measurement Unit (IMU) sehingga system mampu memberikan grafik 3 dimensi setelah drone sudah tidak berada di udara. Skenario yang diusulkan dalam penelitian ini membuktikan bahwa drone mampu bergerak sesuai ekspektasi penulis dan film director. Selain itu survey berupa kuesioner untuk responden umum juga membuktikan bahwa drone sudah mengikuti salah satu konsep cinematography seperti jarak sudut pandang, object yang menarik (shape saliency) dan area pergerakan kamera.
Co-Authors Adriel Ferdianto Ajeng Restu Kusumastuti Aji Prasetya Wibawa Aji Wibawa Albertus Josef Andika Andy Hermawan Anggay Luri Pramana Anik Nur Handayani Arya Tandy Hermawan Ayyub Bayu Anggara Putra Chastine Fatichah Citra Mahaputri Damayanti, Elok Darlis Herumurti Devi Dwi Purwanto Dhaniswara, Erwin Edwin Pramana Effendy Hadi Sutanto Eha Renwi Astuti Eka Rahayu Setyaningsih Elkana Lewi Santoso Endang Setyati Endang Setyati Endang Setyati Esa Prakasa Esther Irawati Setiawan Evan Kusuma Susanto Fitra Hidayah Gunawan Gunawan Gunawan Gunawan Gunawan Hariadi Yutanto Hartono, Nickolas Hatem Alsadeg Ali Salim Hendrawan Armanto Hendrawan, William Hartanto Hermawan Andika Heru Wahyu Herwanto I Ketut Eddy Purnama Joan Santoso Kenny Vincent Kevin Setiono Kms Yusuf Effendi Kurniawan S, Putu Widiarsa Lukman Zaman Luthfi Alwi M. Najamudin Ridha Masrur Anwar Mauridhi Hery Purnomo Maysas Yafi' Urrochman Muhammad Harris Syafa'at Nur Nafi'iyah Nur Nafi’iyah Putra, Ramadhan Hardani Retno Wardhani Riko Gunawan Sandhopi Shandy Darmawan Stephen Ekaputra Limantoro Suriani Alamgunawan Surya Sumpeno Wahyu Sakti Gunawan Irianto Wahyu Widodo Wikjatmiko, Zhulfi Bajra William Sugiarto YOGA DWI WAHYU NUGRAHA Yuliana Melita Pranoto Yulianto, Masrizal Eka Yunan Kholilul Fatah Zaeni, Ilham Ari Elbaith Zuhan, Miftahuz