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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal Community Empowerment
Putra Pandu Adikara
Universitas Brawijaya
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Optimasi Parameter Support Vector Regression Dengan Algoritme Genetika Untuk Prediksi Harga Emas Muthia Azzahra; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Gold is one of the precious metals that many people interested as commodity to invest because of its resistance to inflation. Fluctuations can occur so extreme that affect the value of gold. Therefore, prospect of gold value in the future is quite important for the investors. One of prediction methods is Support Vector Regression (SVR), but the sensitivity of SVR parameters could influence the prediction result, therefore Genetics Algorithm (GA) can be applied, this method is flexible enough to be hybridized. This study discuss about the optimization of SVR parameters using GA to predict gold prices. Based on the testing result, the best mean absolute percentage error (MAPE) is 0.2407% with SVR loop 50, GA's generation 95, population size 70, crossover rate 0.01, mutation rate 0.99, elitism percentange 80%, range of 1x10-7-1x10-4, range of 0.01-5, range of 1x10-7-1x10-4, range of 1x10-5-1x10-4, and range of 1x10-3-0.1.
Analisis Sentimen Dengan Query Expansion Pada Review Aplikasi M-Banking Menggunakan Metode Fuzzy K-Nearest Neighbor (Fuzzy k-NN) Nanda Cahyo Wirawan; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this digital era, bussiness grow significantly by using digital application. Banking is one field of business that utilizes the current technological advances very well. Mobile banking is one of the most popular digital banking products, because it is not as complicated as SMS banking or internet banking. In order to face the strict banking business, every company applying feedback from their customers. Now customers can use the review feature that provided by apps store. There's a lot of reviews that received every day, and it takes some time to knowing what kind of review is that. Systems with machine learning are expected to save time to sort out textual data that containing polarity. The system's machine learning in this study was made using fuzzy k-nearest neighbor (fuzzy k-NN) method. The fuzzy k-NN method is a combined method between fuzzy logic and the k-Nearest Neighbor algorithm. The weighting method for processing textual data into numerical data that can be computed is using TF-IDF method with Cosine similarity to calculate the distance between data. The output of this system is the classified data review. Based on the results of the tests, this system produces the best F-Measure is 0.9273 and the worst is 0.8349.
Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Algoritme Genetika Untuk Identifikasi Jenis Attention Deficit Hyperactivity Disorder Pada Anak Raissa Arniantya; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.
Prediksi Tren Kurs Dollar Dari Berita Finansial Amerika Serikat Berbahasa Indonesia Menggunakan Support Vector Machine Ade Kurniawan; Putra Pandu Adikara; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

United States Dollar (USD) is the most used currency for international transaction, its daily circulation is bigger than the other currency in the world. America's financial data not only give an impact in America itself but also directly effecting the other country. The main focus of this research is to predict USD's trend from America's Financial News in Bahasa Indonesia Using Support Vector Machine Algorithm. Kernel that used in this research is polynomial degree d, the best data ratio is 80% for training data and 20% for testing data. The output generated into 2 class to weaken USD price (Down) and on the other hand to strengthen USD price (Up) to rival's currency. The best parameter combination that give best average accuracy are using under DF threshold = 15%, upper DF threshold = 85%, λ=0.1, CLR=0.01, C=1, epsilon=0.00001, maximum iteration=100 and generated average accuracy=76.66%, sensitivity=80% and specificity=73.33%.
Klasifikasi Penyimpangan Tumbuh Kembang Anak Menggunakan Metode Extreme Learning Machine (ELM) Makrina Christy Ariestyani; Putra Pandu Adikara; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Growth and development of children at an early age affect the child's personal ability in the future. Every child is unique, so growth and growth are not the same. Slow growth and development are often considered normal. Deviation of late child growth is known to result in long-term and difficult to repair. Based on these problems, this research was conducted by using the Extreme Learning Machine (ELM) method for the classification of child growth deviations. ELM method consists of training process as system learning and testing to obtain the result of classification. The parameters test are test of ratio of training data and test data, testing the influence of number of hidden neurons over time, and comparative test of activation function. Accuracy calculation is done by using confusion matrix to know the accuracy of system work in each class. The result of parameter test shows that the ratio of training data and test data with ratio 70:30, the number of hidden neurons as many as 10 units, and the binary activation function is the parameter with the best accuracy value. The comparison of the result of the classification of child growth deviation with the help of psychologist shows that the system produces poor accuracy. This can be due to the small and unbalanced data used for the research.
Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Support Vector Machine (SVM) Daisy Kurniawaty; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Insanity is the most common disease. One of insanity is schizophrenia. The process of diagnosis of schizophrenia is difficult, because there is no specific characteristic of behavior or appearance for the sufferer, some sufferer can behave and look like normal people and expensive treatment. It will make the patient's condition worse. To resolve this issue, this can be done with schizophrenia classification using support vector machine (SVM) algorithm. In this study there are 75 data that is divided into two types of schizophrenia, that is paranoid and simplex. The method in this study using support vector machine algorithm, wich to the category of good classification method, provides a statistical approach in pattern recognition, and is a linear method, but SVM provides kernel trick, which can solve problems related to non-linear classification. The result obtained using SVM 100% accuracy using ratio data 90%:10%, gamma = 0,00001, lambda = 3, C = 0,01, kernel polynomial of degree, maximum iteration is 1000.
Voting Based Extreme Learning Machine dalam Klasifikasi Computer Network Intrusion Detection Sindy Erika Br Ginting; Agus Wahyu Widodo; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Intrusion Detection System (IDS) is useful software or system to detect intrusion on computer networks. It works by utilizing artificial intelligence to identify anomalies or signatures from the activity on computer networks. To refine more the IDS, it requires the development of intrusion classification algorithms with high accuracy. Voting based Extreme Learning Machine (ELM) is a new scheme algorithm which updates the Extreme Learning Machine (ELM) in improving ELM classification performance and is known more reliable for many data. In this study, the performance of the V-ELM has been evaluated on the Knowledge Discovery and Data Mining (KDD) Cup 99 dataset to support IDS development. This study showed that V-ELM was produced bad performance when using some data from KDD Cup 99. It was using 1000 training data and 250 testing data from KDD Cup 99 datasets. The data was divided into 3 variants are 40 classes, 5 classes, and 2 classes attack. The parameters which tested are the values of hidden neurons (L), independent training (K), and sensitivity of each intrusion class. This study found that the best accuracy result on independent training (K) was 3 and 100 hidden neurons in 2 attack class data with an accuracy of 72%. The lowest accuracy was obtained on hidden neurons was 100 and independent training (K) was 11 in 40 attack classes with an accuracy of 12%. This result showed that good classification capability in 2 classes and bad classification capability in 40 classes.
Penyelesaian Multiple Travelling Salesman Problem (M-TSP) Dengan Menggunakan Algoritme Genetika: Studi Kasus Pendistribusian Barang Di Kantor Pos Lumajang Anang Hanafi; Randy Cahya Wihandika; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Online buying and selling currently more and more in demand by all societies, the provider of application companies that bring together sellers and buyers also do not stop releasing promotions on every occasion. The increasing of online trading market it caused the rise of goods delivery process. The freight forwarding company also strives to provide the best service in delivery process. The companyy also need to minimize the cost to be made during the shipping process. In the post office company, especially in Lumajang had 4 sales and 16 delivery destinations, the problem is called Multiple Traveling Salesman Problem (M-TSP). This research discussed issues for optimized the distance, weight, and volume of goods to be delivered from the starting point to some point of destination. From the research that had been done in this case by using genetic algorithms optimal parameters were obtained with a population size of 200, the maximum generation of 500, with a combination of crossover rate 0.4 and mutation rate 0.6 and also uses elitism selection methods and the result of fitness was 0,05288.
Penerapan Genetic Algorithm Untuk Optimasi Peningkatan Laba Persediaan Produksi Pakaian Bryan Pratama Jocom; Nurul Hidayat; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Clothes as one of human need, in which the demand of clothing from time to time may increase or decrease. With the uncertainty of market demand for clothing making clothing sales into a promising business if appropriate in meeting market needs. Distro is an individual business or a group engaged in the production, marketing, and sales of clothing especially for young people. The success of the distro is measured by the amount of profit from clothing sales. Production design is one such factor. The problem will become more complex when there are many types of clothing sold, many sizes, and little capital in a month. Genetic algorithm is a meta-heuristic method that can solve the problem of clothing production. Proper fitness formula formulation can provide a value of chromosomes in the genetic algorithm, so the genetic algorithm can work best to solve production planning problems. The correct genetic algorithm parameters can give maximum results. The test shows the best results at the iteration of 75, the number of chromosomes by 425, the crossover rate of 0.9, and the mutation rate of 0.1. This study provides that genetic algorithm can solve complexity problems of production planning.
Implementasi Metode Fuzzy K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Kedelai Pada Citra Daun Yerry Anggoro; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Protein is one of essential thing to the human body, there are many source of protein and one of it is a soy which is nabati protein source. besides corn and rice, soy is the main food commodities in Indonesia. However, domestic production of soybean has not been enough to fulfil the necessity. Soybean production at the level of actual farmers could still be enhanced through technological innovation, one of that is to detect plant disease of soybeans on the leaves by the method of Fuzzy K-Nearest Neighbor and the segmentation using the method of Otsu. The image processed with the method of Otsu to separate parts that are diseased with parts that are not diseased and then do a classification by the method of Fuzzy K-Nearest Neighbor to determine leaf rust disease, Downy Mildew, and bacterial pustule. There are four tests such as test comparison data training and test data with the highest accuracy in comparison with a total of 90:10 54 training data and test data of 6 100%, testing against the values of Threshold with T = 10 generates 83,33% accuracy, testing against the values of k = 5 generates 83,33%, accuracy and testing against the values m = 2 with accuracy of 83,33%.
Co-Authors Adani, Rafi Malik Ade Kurniawan Adinda Chilliya Basuki Adinugroho, Sigit Adiyasa, Bhisma Adriansyah, Rachmat Afrizal Rivaldi Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo Ahmad Fauzi Ahsani Akhmad Sa'rony Al Farisi, Faiz Aulia Al Huda, Fais Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Alvandi Fadhil Sabily Amaliah, Ichlasuning Diah Amar Ikhbat Nurulrachman Ananda Fitri Niasita Anang Hanafi Andina Dyanti Putri Andre Rino Prasetyo Anggraheni, Hanna Shafira Ani Budi Astuti Annisa Alifia Annisa, Zahra Asma Arsya Monica Pravina Aulia Jasmin Safira Aulia Rahma Hidayat Avisena Abdillah Alwi Azhar, Naziha Baliyamalkan, Mohammad Nafi' Barbara Sonya Hutagaol Bayu Andika Paripih Bayu Rahayudi Bryan Pratama Jocom Budi Darma Budi Darma Setiawan Candra Dewi Candra Dewi Dahnial Syauqy Daisy Kurniawaty Danang Aditya Wicaksana Dayinta Warih Wulandari Deri Hendra Binawan Dhanika Jeihan Aguinta Dheby Tata Artha Dian Eka Ratnawati Dika Perdana Sinaga Dimas Fachrurrozi Azam Dwi Suci Ariska Yanti Dwi Wahyu Puji Lestari Dyva Pandhu Adwandha Edy Santosa Eka Dewi Lukmana Sari Elmira Faustina Achmal Evilia Nur Harsanti Faiz Aulia Al Farisi Farid Rahmat Hartono Fattah, Rafi Indra Fayza Sakina Maghfira Darmawan Febriarta, Renaldy Dwisma Ferdi Alvianda Ferly Gunawan Ferly Gunawan Firdaus, Agung Firmansyah, Ilham Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Nuring Bagaskoro George Alexander Suwito Gilang Widianto Aldiansyah Glenn Jonathan Satria Guedho Augnifico Mahardika Haekal, Firhan Imam Hanson Siagian Hendra Pratama Budianto Hernawan, Yurdha Fadhila Hibatullah, Farras Husain Husein Abdulbar Ichsan Achmad Fauzi Ika Oktaviandita Imam Cholisoddin Imam Cholissodin Imam Ghozali Imanuel Juventius Todo Gurning Indah Mutia Ayudita Indriati Indriati Indriati Indriya Dewi Onantya Ivan Fadilla Ivan Ivan Jesika Silviana Situmorang Jojor Jennifer BR Sianipar Jonathan Reynaldo Junda Alfiah Zulqornain Karina Widyawati Karunia Ayuningsih Katherine Ivana Ruslim Khalisma Frinta Krishnanti Dewi Laila Restu Setiya Wati Lailil Muflikhah Laksono Trisnantoro Lubis, Saiful Wardi Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Maghfiroh, Sofita Hidayatul Makrina Christy Ariestyani Marina Debora Rindengan Maya Novita Putri Riyanto Mayang Arinda Yudantiar Mayang Panca Rini Melati Ayuning Lestari Moch. Khabibul Karim Moh. Dafa Wardana Mohammad Fahmi Ilmi Mohammad Toriq Muh. Arif Rahman Muhammad Faiz Al-Hadiid Muhammad Fajriansyah Muhammad Iqbal Pratama Muhammad Nurhuda Rusardi Muhammad Rizaldi Muhammad Rizky Setiawan Muhammad Tanzil Furqon Muhammad Taufan Muthia Azzahra Nadhif Sanggara Fathullah Nadia Siburian Nanda Agung Putra Nanda Cahyo Wirawan Naufal Akbar Eginda Naziha Azhar Niluh Putu Vania Dyah Saraswati Novan Dimas Pratama Novanto Yudistira Nur Hijriani Ayuning Sari Nurul Hidayat Panjaitan, Mutiharis Dauber Panji Husni Padhila Pengkuh Aditya Prana Prais Sarah Kayaningtias Prakoso, Andriko Fajar Pretty Natalia Hutapea Putri Rahma Iriani Radita Noer Pratiwi Rahma Chairunnisa Raissa Arniantya Randy Cahya Wihandika Randy Cahya Wihandika Randy Ramadhan Ravindra Rahman, Azka Renata Rizki Rafi` Athallah Renaza Afidianti Nandini Restu Amara Rezky Dermawan Rhevitta Widyaning Palupi Ridho Agung Gumelar Riza Cahyani Rizal Maulana, Rizal Rizal Setya Perdana Rizal Setya Perdana Rosy Indah Permatasari Sagala, Revaldo Gemino Kantana Salsabila Insani Salsabila Rahma Yustihan San Sayidul Akdam Augusta Santoso, Nurudin Sigit Adinugroho Sigit Adinugroho Silaban, Gilbert Samuel Nicholas Silvia Ikmalia Fernanda Sindy Erika Br Ginting Sri Indrayani, Sri Sutrisno Sutrisno Tania Malik Iryana Taufan Nugraha Thariq Muhammad Firdausy Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Uke Rahma Hidayah Utaminingrum, Fitri Vergy Ayu Kusumadewi Vinesia Yolanda Vivin Vidia Nurdiansyah Wijanarko, Rizqi Yerry Anggoro Yohana Yunita Putri Yoseansi Mantharora Siahaan Yosua Dwi Amerta Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Yulia Kurniawati Yurdha Fadhila Hernawan Yure Firdaus Arifin Zahra Asma Annisa