Informasi Topik Kelas Penulisan Proposal
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Dosen KoordinatorAnggota Tim Penulisan ProposalTopik untuk Tugas AkhirMata Kuliah yang disarankan diambil untuk mendukung Topik TA
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ADRHFR; SRN; AULIA; SPO; NGS; MAN; RYJ; AJG; END; EMJ; YOAInternet of Things; Embedded System; Network and Computer SystemINTERNET OF THINGS; ANALISIS PERFORMANSI JARINGAN KOMPUTER; MIKROKONTROLER
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ADWUNW, TSA, SFY, SYM, DTO, DQUClassification SystemNLP, damin,
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AYUDawam, Indra LukmanaE-Learning Personalization, Learning AnalyticsProbStat, Data Mining, Web Engineering, AI
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DASNDN, SYP, IDLhttp://danakusumo.staff.telkomuniversity.ac.idUntuk topik Information Architecture harus sudah memgambil matkulpil tsb pada sem 2017/2018-2
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DAYDLW, PHNnumerical modelling and simulation, high performance computingSKUT, PDA
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DJNVRE, MKS, DWM, SWD, SYP, IDLHCI, DSSWajib: IMK, Probstat, APPL, IMPAL; Pilihan: Desain UX, SPPK
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DJNHCI (VRE, MKS, DWM); DSS (SWD, SYP, IDL); Software Testing (SWD, EKD);Requiremen Engineering (SWD, YNR, EKD)HCI (Analisis metode design UI/UX untuk aplikasi berbasis web/mobile; Requirements Elicitation Frameworks untuk design UI/UX pada aplikasi berbasis web/mobile), DSS (Personal Desicion Support System; Decision Support System in Healthcare Industry), Software Testing (Implementasi beragam tool/method untuk pengujian software) , Requirement Engineering(Implementasi beragam Tool/Method untuk Requirement Engineering)HCI (IMK, Probstat, APPL, IMPAL, Desain Interaksi); DSS (ProbStat, APPL, IMPAL, AI, SPPK); Software Testing (APPL, IMPAL, ProbStat, PMPL);Requirement Engineering (APPL, IMPAL, ProbStat, RE)
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DNH-Adaptive learning, personalised recommender (konteks: adaptive learning), ontology matching (konteks: adaptive learning), user modeling, social recommender.
**paper-paper acuan utama sudah disediakan
Lulus AI. Untuk topik-topik yang berkaitan dengan ontology, disarankan hadir pada topik-topik tertentu matakuliah Semantic Web (S2)
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DWSFAY, TBH, AHT, EMJ, AJG, CWW, SRN, SKHTopik Penulisan Proposal Ganjil 2017/2018 (Responses)
Context Awareness (Energy Efficiency - Mobile Device) (FAY); Context Awareness (Easy Secure System - Mobile Device) (TBH); Named Data Networking (Comparative Case Study, Analysis & Case Study) (AHT); Named Data Internet of Things (AHT); IoT (CoAP & MQTT Interoperability) (AHT); VANET, MANET (AHT); Overlay Network (EMJ); Security Network (EMJ); Voice in The Cloud (AWS Cloud Case) (AJG); Smart Helmet (See digitaltrends.com/outdoors/5-best-smaert-helmets/) (AJG); Networking for Big Data (AWS Cloud Case, Emulation) (AJG); Home Automation (AJG, END, CWW, SKH); Home Automation (Rent Room System) (AJG, CWW, END); Implementation of Learning Computation for Efficient WSN Data Process (SRN); Implementation of Bit-parallelism for Attacking Detection (SRN); Surveillance Embedded IP Camera with Image Correction (No Pre-Processing Needed) (CWW); Advanced Metering Infrastructure with Two Way Communication (See bemoss.org) (CWW, AJG)
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Embedded Computer, Jaringan Komputer Lanjut, NetCentric
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EARFSV, KNR, ADF, ADEImage segmentation, object recognition, image captioningSistem rekognisi, pengolahan citra digital, machine learning, pemrosesan bahasa alami (untuk topik image captioning)
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ERFAndrian Rahmatsyah, Sidik Prabowo, Noviab AnggisEmbedded Soft Robotics, IoT ( Filtering, Fusion, Statistical Analysis), Cyber Physical System ( Control & Automation for Smart Buildings)IoT, Embedded Robotics
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IBR11. Poor man's Deep Learning
2.facebook-wants-to-use-machine-learning-to-stop-hoaxes-and-fake-news/
3. Generative Adversarial Networks
4. Sequence Prediction dengan Deep Learning
5. Bayesian Machine (/Deep) Learning
6. Beyond Visual Recognition.
Membahas trend visual understanding seperti detection, segmentation, tracking. Tidak bisa dipungkiri area computer vision adalah bahasan yang salah satu area favorit di AI.
7. Interprerable machine learning / deep learning.
Saat ini semakin canggihnya model2 Deep Learning juga membuat sulit memahami hasil prediksinya. Supaya tidak jd "black box", interpretable ML(DL) jg diperlukan. Topik terkait ini cukup menarik perhatian. Bbrp tutorial terkait di NIPS, AAAI, dan CVPR sejak tahun lalu.
8. Complex valued neural network/ deep learning
9. Deep reinforcement learning
10. Evolutionary Deep Learning (gabungan algen+cnn)
11. Multiple kernel learning
12. Manifold PCA, manifold learning
13. argumentation mining, opiniomln mining
data mining, text mining, nlp
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IMD-graph partitioning,graph mining,big data miningData Mining, Algoritma dan Pemrograman
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JDNRian umbara, untari, andit, aniq atiqiData science dan deep learningMachine learning, probstat
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KMAKMAIndoor / outdoor spatial data processingSpatial DB
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KMM-Deskripsi lengkap lihat: https://goo.gl/zQtZna

1. Implementasi face recognition, voice recognition, dan metode machine learning untuk pengembangan Robot NOVA (network optimized and voice activated) di Bandung Techno Park, Telkom University

2. NLP, text mining, machine learning untuk pengolahan teks Islam untuk pembangunan sistem Q&A tentang hukum Islam.

3. Social network dan sentiment analysis dari corpora media online dan Twitter.
Machine Learning (Pembelajaran Mesin), NLP (Pengolahan Bahasa Alami), Text Mining
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MABKMM, DYA, AFH, Dr. Engkos Kosasih (UIN Bdg), Dr. Totok Suhardiyanto (UI), Dr. Dhomas Hatta Fudholi (UII).1) Survei (implementasi, analisis dan perbandingan) metoda-metoda kesamaan dan keterkaitan semantik antar teks.
2) Pelabelan/anotasi kebahasaan untuk korpus Al Qur'an.
3) Implementasi kesamaan dan keterkaitan semantik antar teks pada Al-Qur'an.
4) Pembangunan WordNet Bahasa Indonesia.

Info lanjut link http://tiny.cc/arifbijaksana
Penambangan Teks dan Pemrosesan Bahasa Alami.
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MHDSri Suryani, Yuliant SibaroniKetersebaran Rute Angkutan UmumMatematika Diskrit
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PHNDAY, DLW, FTY, ZKA, TSA, NIQ, Dr. Esa Prakarsa (LIPI), Prof. S. R. Pudjaprasetya (ITB)HPC, Modeling and Simulation, Computer Vision (LIPI), Numerical Analysis in Modeling (ITB)Sains Komputasi Untuk Teknik (mhs IK), Pemodelan dan Simulasi (mhs IF)
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PMNEMJ, MAN, NGS, YOA, RYJCybersecurity, Identity Management (Smartcard, Biometric, etc), IoT Securitykamsis, crypto, ml
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RMBNIQ, FSV, KNRCek link berikut untuk detil topik: https://drive.google.com/drive/u/1/folders/1mcHHgoxiPdfAOo7KMll4SJYHjPzAdZi1?ogsrc=32 Probstat, Pengolahan citra digital, Sistem rekognisi.
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SMDSyahrul Mubarok, Erwid Jadid, Adji Gautama Putrada, Anditya Arifianto, Putu Hari Gunawan, Didit Aditya, Febryanti Sthevanie, Danang TriantoroInternet of Things - IOT (Smart Campus, Smart City, Smart Home), Network & Cyber Security (Secure Protocol, IDS, IPS), Biomedical Engineering based on IOTAdvanced Network, Topik khusus Telematika 1 (IOT for healthcare)
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SSIYLS, MHDModeling dan Simulasi TransportasiMatematika Diskrit, Probstat, AI, Pemodelan dan Simulasi
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SUOKNR, FSV, ADFSpeech Processing, Big Data Mining, Swam Intelligence, Smart City, Chatbot, Image Processing, Video Processing, Computer Vision, Deep LearningMachine Learning, Recognition System, Convolution Neural Networks, Data Mining, Digital Image Processing
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UNWFSV, RSM1. Expression recognition and analysis in image or video (using face detection+eye detection+mouth detection and make a new datasate colaboration with Multimedia Laboratory )
2. People counting in image or video (make a new data set colaboration with Multimedia Laboratory)
3. Face recognition (using face detection and make a new data set with Multimedia Laboratory)
4. Speaker identification (make a new data set colaboration with Multimedia Laboratory)
5. Biomedic classification based on image, signal, or gene expression
6. Recomender sistem
Machine Learning, Soft Computing, pengolahan citra digital, sistem pengenalan (sesuaikan dengan topik yang diambil)
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VIRDNS,NIQ,TSA,DTOSmart Agriculture, Agriculture Data mining/Soft Computing , Meteorological Data Mining, Prediksi penyebaran penyakitData Mining, Soft Computing, Machine Learning
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YLSSri suryani, Danang TriantoroText mining, meliputi analisis sentiment, klasifikasi teks, ekstraksi informasi, summarisasi, dsb.Machine learning, artificial intelegent, data mining, programing
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YRMMZIFormal reasoning & verification of software/system; computational logic; logic programming; formal modelling for social/political phenomenaMatkul prasyarat: MK Wajib: Logika Matematika A, Matematika Diskrit A, serta Teori Bahasa dan Automata (semuanya lulus dengan nilai minimum B); DAA (if necessary); MK Metode Formal harus sudah diambil atau sedang diambil secara konkuren
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ZKAYZR, EBS, YLS, PHNRecommender system di pariwisata, Updating basis pengetahuan berbasis ontologyArtificial Intelligence
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