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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
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Klasifikasi Penyakit Chronic Kidney Disease (CKD) Dengan Menggunakan Metode Extreme Learning Machine (ELM) Ivan Fadilla; Putra Pandu Adikara; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kidneys are important organs that are focussed on maintaining blood composition by preventing accumulation of waste and controlling fluid balance in the body. Chronic Kidney Disease (CKD) is one of the diseases of the kidneys caused by infection in the kidney and also the blockage caused by kidney stones. In this case medical personnel and experts are still not maximized in classifying CKD disease, the authors apply the method of Extreme Learning Machine (ELM) on the problem of classification of CKD disease. ELM is one method of artificial neural network classification that has a fast learning speed and based on previous research has a good accuracy value compared with existing methods in artificial neural networks. In this research got comparison of data of train and optimal test data with ratio 70:30 and amount of hidden neuron counted 50 hidden neuron accuracy value equal to 96,7%. It can be concluded that the method of Extreme Learning Machine (ELM) is quite well implemented for the classification process of Chronic Kidney Disease (CKD) disease.
Penerapan Klasifikasi Tweets pada Berita Twitter Menggunakan Metode K-Nearest Neighbor dan Query Expansion Berbasis Distributional Semantic Galih Nuring Bagaskoro; Mochammad Ali Fauzi; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The use of short text based on digital to date is still growing and extending to various social media. Twitter has news features in tweets to represent information representing each type. Each categorization of this type is done to make it easier for users to use it. The purpose of the use of categories in this classification, to evaluate and improve the quality of social media in grouping categories of content of the content provided. Traditional classification is still used today, but the results are sometimes not maximal, it is necessary to expand the word to add words to the text in order to improve the accuracy. Word expansion is used with a semantic-based distributional euclidean distance technique to find the closest word from an external source to be a query to be added to the test data text. Using test data 105 and training data 400, the classification using K-Nearest Neighbor can obtain 90% results with nearest neighbor K=5. These results are similar to the results of tests conducted without using word expansion techniques. While the test is done by adding the expansion of words with threshold 0.5 and the nearest immediate value K-Nearest Neighbor K=5 obtained an accuracy of 92%.
Prediksi Indeks Harga Konsumen (IHK) Kelompok Perumahan, Air, Listrik, Gas Dan Bahan Bakar Menggunakan Metode Support Vector Regression Krishnanti Dewi; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the most commonly used indicators to measure the inflation rate is Consumer Price Index (CPI). Based on the consumer price index metadata published by Bank Indonesia in 2016, housing, water, electricity, gas and fuel group is the CPI group which has the highest proportion of living cost from other CPI groups, which is 25.37 %. In this research, CPI will be predicted by using Support Vector Regression (SVR) method. The stages of the SVR method include normalization of data, calculates Hessian matrix by using Radial Basis Function (RBF) kernel function, sequential learning process, calculate the regression function to get predicted results and evaluates predicted results with Mean Absolute Percentage Error (MAPE). The test results show the minimum MAPE value obtained by 4.271% with the parameter value σ = 50; λ = 1; cLR = 0.0005; ε = 0.0005; C = 1000; the number of training data is 36 for 12 testing data with 100 iterations. The average of predicted results obtained is 112.19605 with the average of the difference between the actual data and the predicted result is 1.52645.
Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa Menggunakan Extreme Learning Machine Dan Improved-Particle Swarm Optimization Bayu Andika Paripih; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Milk is source of protein which is contain all of easy digested and required nutrition. Milk production by dairy cows are low so Indonesian need of milk can't be fulfilled. PE goat can produce qualify milk cow and it also suitable to be cultivated at Indonesia so they can be alternative of milk source. Produced milk quality is affected by given feed. This research uses Extreme Learning Machine and Improved-Particle Swarm Optimization to search best feed composition so the goat can produce good milk. Parameter calibration for building model are hidden node = 9, population size 70, maximum iteration 40 with fitness value 0.973892. Parameter calibration for searching feed composition are population size = 90 and maximum iteration 20 with fitness value 38,51344218.
Named Entity Recognition (NER) Pada Dokumen Biologi Menggunakan Rule Based dan Naive Bayes Classifier Dayinta Warih Wulandari; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Named Entity Recognition (NER) is useful to help identify and detect entities of a word. The biomedical field has many literature so NER is highly demanded in this domain. Since biomedical has a large scale, research will only focus on biology cell documents. This research will use rule based and Naive Bayes Classifier for NER in biology cell documents. With 19 training documents which processed and annotated manually to search for Named Entity (NE) and obtain 1135 word training data. Test documents are denoted and tagged by tagger site then search for bigram and trigram. Furthermore, rule-based process, if in the rule based not found solution, it will continue on feature extraction process and NBC. Using 16 NE classes, 18 rules, and 7 features were tested with three scenarios: rule based testing, NBC, and a combination of both. The highest average precision, recall and f-measure with micro average on rule based is 0.85. With macro average the highest recall and f-measure obtained combination is 0.66 and 0.45, while the highest precision obtained rule based is 0.39.
Optimasi Susunan Bahan Makanan Bagi Anak Penderita Attention Deficit Hyperactivity Disorder (ADHD) Menggunakan Algoritme Genetika Muhammad Taufan; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Proper nutrition can reduce symptoms of Attention Deficit Hyperactivity Disorder (ADHD). However, children are easily bored when given the same food constantly. In addition, food prices are also considered by parents. In this study sought the optimal solution of the problem of food ingredient preparation using genetic algorithm. To solve this problem, we use an integer permutation representation with chromosome length of 12 genes per day. These genes are a representation of a diet consisting of carbohydrates, proteins, and fats. The crossover method used is one-cut point, while for mutation method using exchange mutation. For the selection stage, elitism selection method is used. From the results of tests that have been done, we obtain optimal parameters that is 50 generations with an average fitness value obtained 13,928. The final result obtained is the composition of the food ingredient in accordance with the number of days desired.
Peramalan Harga Saham Menggunakan Metode Extreme Learning Machine (ELM) Studi Kasus Saham Bank Mandiri Muhammad Iqbal Pratama; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stock investment is one of the most profitable type of investment. One of the biggest problem in stock investing is the difficultness to predict a stock price and it led to doubt whether to buy or sell a stock. Extreme Learning Machine is implemented to predict a stock price using Bank Mandiri's stock as a case study. This algorithm has some advantages such as fast training time and small error value. Extreme Learning Machine's processes involve normalizing Bank Mandiri daily stock data, generating input weight and bias weight, training the model, testing the model, denormalizing predicted value and evaluating the model using Mean Absolute Percentage Error (MAPE). The features used to predict Bank Mandiri's stock price are Open, High and Low price. The smallest MAPE value obtained from the testing phase is 1,012% using sigmoid activation function, four neurons in hidden layer and the data used is the last one year.
Analisis Sentimen Film pada Twitter Berbahasa Indonesia Menggunakan Ensemble Features dan Naive Bayes Rosy Indah Permatasari; Mochammad Ali Fauzi; Putra Pandu Adikara; Eka Dewi Lukmana Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis or opinion mining is one of the latest research topics in the field of information processing. It aims to know whether the polarity of a text-shaped data (document, sentence, paragraph) will lead to positive, negative, or neutral trait. This research used document text about Indonesian movie review which was obtained from Twitter. The method used in this research was Naive Bayes using Ensemble Features as a renewal feature beside Bag of Words Features. There are several types of Ensemble Features which are Twitter specific features, textual features, part of speech features, and lexicon based features. 500 data were used in this research, which were later divided into two types of data with the comparison of 70% for training data and 30% for testing data. The result of system accuracy obtained from sentiment analysis with Naive Bayes and Ensemble Features methods is 61.33%, 0.6369 precision, 0.5467 recall, and 0.5814 f-measure. The result of system accuracy using Ensemble Features and Bag of Words Features is 89.33%, 0.9041 precision, 0.88 recall, and 0.8922 f-measure.
Clustering Dokumen Skripsi Dengan Menggunakan Hierarchical Agglomerative Clustering Danang Aditya Wicaksana; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A minor thesis is a document of a scientific work compiled by a student at the level of stratum 1 which discusses a particular topic or field of research or development results that the student has undertaken in order to take the final examination to obtain a degree. In the Reading Room of the Faculty of Computer Science and the Central Library of Brawijaya University there is a problem that arises that there is no categorization of all minor thesis documents stored. Hierarchical Agglomerative Clustering (HAC) method is implemented for clustering minor thesis documents based on minor thesis title. HAC classifies iterative documents from the smallest cluster to the largest 1 cluster. Input data that is in the form of title of minor thesis document of Informatics Engineering Brawijaya University. The preprocessing stage is performed on the minor thesis title data to get the term feature. All the terms obtained are processed to get the weight of TF-IDF. The value of similarity between documents obtained from the value of cosine distance. The clustering process uses 3 distance options as the single linkage, complete linkage and average linkage parameters. The clustering results of each distance parameter are displayed on the label of each cluster generated and each cluster generated is evaluated using silhouette coefficient. From the test result on 100 minor thesis documents obtained the value of Silhouette Coefficient from single linkage is 0,10125, complete linkage is 0,155733 and average linkage is 0,160428. Average linkage is better in grouping documents than single linkage and complete linkage.
Analisis Sentimen Twitter Menggunakan Ensemble Feature dan Metode Extreme Learning Machine (ELM) (Studi Kasus: Samsung Indonesia) Alqis Rausanfita; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Business activity is very crucial and has a real impact on organizational growth and ROI (Return Of Investment) is to understand and respond appropriately sentiment from customers by conducting a sentiment analysis. The sentiment analysis can be a guide to evaluate a company's product, service, reputation, brand reputation, and the company can be a market leader supported by a very emotional customer condition so that disappointing products / services will lose the customer's commitment even customers will find it difficult to recover customer experience if a company does not care about customer sentiment. Based on the explanation, this research is done using ensemble feature and Extreme Learning Machine for Twitter sentiment analysis. The data used in this research is 72 tweets with the ratio of the amount of training and testing data 70:30 where the amount of data per class is balanced. Prior to the classification of data is done preprocessing, weighting ensemble feature, and weighting the word. The result of this research is get the best hidden neuron number as much as 5000, best activation function is sigmoid bipolar, and ensemble feature influence to accuracy result. Twitter sentiment analysis using ensemble feature and Extreme Learning Machine method in Samsung Indonesia case study did not get high accuracy. Accuracy in getting only amounted to 42.857 percent. The low accuracy caused by sparse data matrix resulting in overfitting which then resulted in low classification results.
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