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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Teknologi Informasi Cyberku

 1 
PEMISAHAN VOICE DAN UNVOICE MENGGUNAKAN TEKNIK OVERLAPING BLOCK, ZERO CROSSING RATE, DAN SHORT TIME ENERGY DALAM PENGENALAN SUARA Ade Yusupa; Abdul Syukur; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 2 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Dalam proses speech recognition, speech syntesis dan speech enhancement, signal suara yang diinputkan tidak dapat langsung dikenali atau diindentifikasi sebagai gelombang signal voice atau unvoice. Proses analisis ucapan dalam menetapkan voice dan unvoice biasanya dilakukan dengan ekstrasi dari speech signal atau signal suara. Dalam penelitian ini, kami melakukan dan membandingkan 3 akurasi metode yakni: penentuan manual dengan software Adobe Audition dibandingkan pada tool matlab dengan metode separation of voice and unvoice using non overlapping block, zero-crossing rate and energy of a speech signal, dan juga membandingkan dengan metode peneliti yaitu penggabungan teknik overlapping blocks, zero crossing rate dan short time energy dalam menentukan voice dan unvoice untuk memisahkan bagian unvoice dan voice ucapan dari sinyal suara. Dengan ada perbedaan pada overlapping block dan non-overlapping block. Kami mengevaluasi hasil dari semua metode tersebut bahwa teknik yang digunakan peneliti dengan overlapping blocks, zero crossing rate dan short time energy terbukti lebih efektif dalam pemisahan voice dan unvoice.
DETEKSI API MENGGUNAKAN BACKGROUND SUBSTRACTION DAN ARTIFICIAL NEURAL NETWORK UNTUK REAL TIME MONITORING Andi Kamaruddin; Vincent Suhartono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The most important initial step in the detection and localization of the fire is to detect fire quickly and reliably. Video-based surveillance is one of the most promising solutions for automatic fire detection with the ability to monitor a large area and ease of reading an alarm to the operator through the monitorSupervision, unfortunately, the main drawback of video-based fire monitoring system that uses optic is a false alarm caused by an Error detection (Error detection), for it is then in this study using the feature extraction GLCM (Gray level Coocurance Matrix) as input spectral classification of Neural network to detect fire, the approach can reduce the Average Error detection with Error detection rate Average is 7%
OPTIMASI PARTFICLE SWARM OPTIMIZATION (PSO) PADA ALGORITMA KLASIFIKASI NEURAL NETWORK (NN) DALAM PENENTUAN KELAYAKAN PEMBERIAN SERTIFIKASI GURU Kurnia Prayoga Wicaksono; M. Arif Soeleman; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

To improve the quality of national education, the government through the Ministry of Education. This is of course interesting for the community to be part of this program, many of whom choose to become teachers, though not from an education-based college. One of the factors that is the main attraction is the benefits that will be obtained for teachers who have passed the certification exam. Government through Master's law, the issuance of a regulated policy of preparedness can be the basis for establishing Master's eligibility as a professional, so that the profession is allowance. owever, conditions in the field, found some teachers who are not yet eligible to hold the certification, there are still many teachers under the standard Teacher Compotency Test. Therefore, built a system made using Artificial Neural Network optimized with Particle Swarm Otimation, to determine the feasibility of certification so that later this case does not happen again. In this study gives a general idea that certified teachers are not all worthy of the predicate. Artificial Neural Network is optimized with Particle Swarm Optimization algorithm, giving higher accuracy with 80.80% accuracy level compared with 79.65% neural network algorithm model.
KLASIFIKASI CITRA TELUR FERTIL DAN INFERTIL DENGAN ANALISIS TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX DAN SUPPORT VECTOR MACHINE Dewi Nurdiyah; Stefanus Santosa; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility testusually use egg candling. The purpose of observation is to choose eggs fertile (eggs containedembryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered intothe incubator for hatching eggs and infertile can be egg consumption. However, there are obstaclesin the process of sorting the eggs are less time efficient and inaccuracies of human vision todistinguish between fertile and infertile eggs. To overcome this problem, it can be used ComputerVision technology is having such a principle of human vision. It used to identify an object basedon certain characteristics, so that the object can be classified. The aim of this study to classifyimage fertile and infertile eggs with SVM (Support Vector Machine) algorithm based on inputfrom bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-occurrenceMatrix). Eggs image studied are 6 day old eggs. It is expected that the proposed method is anappropriate method for classification image fertile and infertile eggs.
PENENTUAN THRESHOLD MENGGUNAKAN ALGORITMA SELF ORGANIZING MAPS (SOM) UNTUK SEGMENTASI REGION KARAKTER PADA PLAT NOMOR KENDARAAN Apriyanto Alhamad; Vincent Suhartono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

At this time the era of information technology , the use of automation and intelligent systems are becomingmore and more widespread. Transfortasi Intelligent Systems has received so much attention that manysystems are being developed and applied throughout the world. In a vehicle license plate recognitionsystem consists of several stages , namely pre-processing , license plate detection, the segmentation ofcharacters and the vehicle license plate character recognition . In previous studies determining thethreshold value using Otsu local method by dividing two variants of black and white . In a study usinglocal methods otsu susceptible to interference in the picture . In the study proposes a method of self -organizing map in determining the threshold value . Then from the results of the threshold determinationfollowed by segmenting the characters using the function bouding Box . From the experimental results ofthreshold determination method of self - organizing maps can increase the threshold value of 0.5241 andthe experimental test of character segmentation as measured using the function Means Square Error of6.93E +03 pixels , smaller than the test results of the local segmentation character Otsu method
OPTIMASI MULTI SCALE RETINEX CITRA BAWAH AIR DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION Pulung Nurtantyo Andono; Putu Samuel Prihatmajaya; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 1 (2019): Jurnal Teknologi Informasi CyberKU Vol. 15, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Penelitian tentang perbaikan citra sudah sejak lama dilakukan dan hingga saat ini masih dilakukan penelitian tentang perbaikan citra. Beberapa penelitian yang telah dilakukan menghasilkan beberapa usulan metode untuk perbaikan citra. Penelitian di bidang computer vision untuk lingkungan bawah air menjadi tantangan dari beberapa peneliti untuk melakukan image restoration. Karena untuk citra bawah air sering banyak menghadapi permasalahan intensitas cahaya, pertikel-partikel yang banyak mengganggu pandangan, ditambah jika terjadi gelombang akan membuat kestabilan dalam menggambil citra terganggu sehingga bisa mengakibatkan noise yang besar jika dibandingkan pengambilan citra di darat. Dalam penelitian ini pembahasan yang dilakukan adalah perbaikan citra bawah air dengan menggunakan MSCR (Multi Scale Retinex) dengan mengoptimalkan pembobotan dari MSCR dengan menggunakan teknik PSO(Particle Swarm Optimization) sebagai teknik optimasinya, sehingga mendapatkan tingakatan error yang lebih rendah. Sehingga hasil MSE (Mean Square Error) yang di dapatkan oleh MSCR adalah sebesar 5218,4249 dan hasil yang didapatkan dengan menggunakan PSO adalah sebesar 4955,0757
PREDIKSI HARGA KEDELAI LOKAL DAN KEDELAI IMPOR DENGAN METODE SUPPORT VECTOR MACHINE BERBASIS FORWARD SELECTION Fatkhuroji Fatkhuroji; Stefanus Santosa; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 1 (2019): Jurnal Teknologi Informasi CyberKU Vol. 15, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Besarnya permintaan harga kedelai yang tinggi untuk kebutuhan makanan baik untuk olahan atau bahan jadi menjadikan harga kedelai sangat fluktuatif seiring impor kedelai yang terus meningkat. Pola harga kedelai yang sangat fluktuatif memicu gejolak ekomoni yang memicu terjadinya inflasi di salah satu daerah. Untuk mengatasi hal tersebut perlu adanya suatu prediksi harga kedelai agar pemerintah dapat mengantisipasinya. belum ada model prediksi terhadap harga komoditas kedelai baik lokal maupun impor, model prediksi yang ada saat ini tentang komoditi sembako. Penelitian ini mengusulkan model prediksi harga kedelai dengan menerapkan algoritma Support Vector Machine (SVM) dengan optimasi menggunakan Forward Selection. Untuk prediksi kedelai lokal dengan menggunakan parameter inputan data 4 (empat ) hari sebelumnya, K-fold=10, nilai C= 0,1 diperoleh nilai RMSE terkecil sebesar 154.025 +/- 114.993. Setelah dilakukan seleksi atribut menggunakan Forward Selection diperoleh nilai RMSE sebesar 79.749 +/- 16.051, terdapat peningkatan RMSE sebesar= 74.276. Untuk prediksi kedelai lokal dengan menggunakan parameter inputan data 5 (lima) hari sebelumnya, K-fold=15, nilai C= 0,1 diperoleh nilai RMSE terkecil sebesar 126.008 +/- 78.371, setelah dilakukan optimasi menggunakan Forward Selection diperoleh nilai RMSE sebesar 122.270 +/- 56.049, terdapat peningkatan RMSE sebesar= 3.738.
Prediksi Pendapatan Penjualan Obat Menggunakan Metode Backpropagation Neural Network dengan Algoritma Genetika Sebagai Seleksi Fitur Nur Azise; Pulung Nurtantio Andono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The hospital is one of the means of health services for the community, in which there are multiple units, one of which was the installation of a pharmaceutical is a source of revenue for the hospitals contributed by 40 – 60%. Each month the sale of drugs on pharmaceutical erratic installation (fluctuating) and have an impact on earning spharma ceutical installations specifically and at hospitals in General, i.e. against the determination of the lead in policy development and the development of hospitals inthe future. Therefore the forecast or prediction about the drug's sales revenue is urgently needed. Forecasting technique commonly used is the technique of forecastingwith Artificial neural Network method or so-called Artificial neural Network which hasthe best accuracy with the value error. However, the method of Artificial neural Network has a number of shortcomings, so it takes an optimization method, one of themwith Genetic Algorithm optimization methods. In this study using data on drug sales revenue installation hospital Elizabeth Situbondo. In the process of training and testing data in this study using the method of Backpropagation Neural Network and Genetic Algorithm to feature selection. On this panelitian proves that the method of Backpropagation Neural Network with genetic algorithm as a selection of the best RMSE value generating features of 0115. While the test results with the method of Backpropagation Neural Network without Genetic Algorithms as a value generating features selection RMSE 0152.
PENGENALAN VARIETAS MANGGA BERDASARKAN BENTUK DAN TEKSTUR DAUN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK Fathorazi Nur Fajri; Purwanto Purwanto; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Pada saat ini mangga Indonesia sangat diminati oleh orang asing terlebih untuk mangga kualitas unggul seperti mangga manalagi dan gadung. Akan tetapi tak jarang masyarakat tidak mengerti atau keliru mengenali varietas mangga yang mereka tanam. Selama ini identifikasi atau pengenalan varietas mangga dilakukan dengan menggunakan mata. Hal ini pun dibutuh keahlian atau pakar dalam membedakan varietas mangga tersebut. Akan tetapi orang yang ahli mempunyai keterbatasan, tidak semua varietas mangga dapat dikenali atau diidentifikasi. Terdapat beberapa usulan model yang telah dilakukan untuk mengindentifikasi mangga dengan citra digital akan tetapi akurasi yang dihasilkan masih kurang yaitu di bawah 80 %. Selain itu masing masing peneliti hanya menggunakan satu fitur citra yaitu fitur tekstur. Penelitian ini mengunakan dataset sebanyak 300 citra daun mangga, 150 citra daun mangga varietas manalagi dan 150 citra daun gadung. Metode yang digunakan pada penelitian ini yaitu Backpropagation Neural Network (BPNN) dengan menggunakan fitur bentuk dan tekstur daun mangga. Model BPNN yang paling optimal pada penelitian ini yaitu menggunakan hidden layer = 19, learning rate = 0.9, momentum = 0.9 dan epoch = 100 dengan hasil root mean squar error (RMSE) = 0.0018. Kemudian hasil dari pengujian menggunakan citra daun mangga menghasilkan tingkat akurasi 96 %.
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Azzahra, Tarissa Aura Bastiaans, Jessica Carmelita Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Darmawan, Aditya Aqil Dewi Nurdiyah Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Fikri Diva Sambasri Firmansyah, Muhammad Ilham Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Hasan Asari Haydar, Muhammad Rifqi Fajrul Hendri Ramdan Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Kristhina Evandari Kurnia Prayoga Wicaksono Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Megantara, Rama Aria Mira Nabila Moch Arief Soeleman Mochamad Arief Soeleman Mochamad Hariadi Moh. Arief Soeleman Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muljono, - Muslih Muslih Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Prabowo, D.P. Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ratmana, Danny Oka Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Sri Winarno Stefanus Santosa Sulistyowati, Tinuk Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan