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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Optimasi Metode Extreme Learning Machine Dalam Penentuan Kualitas Air Sungai Menggunakan Algoritme Genetika Regina Anky Chandra; Edy Santoso; 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

Seiring dengan meningkatnya jumlah populasi manusia, sumber air bersih yang ada di bumi terus berkurang. Dampak yang diberikan akibat tercemarnya sumber air juga tidak dapat diremehkan. Beberapa dampaknya antara lain adalah menurunnya kadar oksigen yang ada di bumi dikarenakan tumbuhan tidak dapat berfotosintesis dengan baik, mengganggu kesuburan tanah, mematikan hewan-hewan yang hidup di dalam air dan masih banyak dampak lainnya. Salah satu sumber air di muka bumi ini berasal dari sungai. Untuk menjaga kualitas air agar tetap pada kondisi alamiahnya, perlu dilakukan pengukuran dan analisis terhadap air sungai tentang status mutu airnya. Pada penelitian ini digunakan 7 parameter pengukuran kualitas air sungai yang kemudian akan diklasifikasikan menjadi 3 kelas berbeda. Kelas klasifikasi dibagi menjadi tercemar ringan, tercemar sedang, dan tercemar berat. Metode yang digunakan untuk pengukuran dan analisis pada penelitian ini adalah metode Extreme Learning Machine (ELM) dan Algoritme Genetika. Dalam penelitian ini, bobot awal yang digunakan pada proses training dan testing ELM akan dioptimasi menggunakan Algoritma Genetika. Data training dan data testing yang digunakan, ditentukan oleh 5 fold yang telah dibentuk dari data awal yang berjumlah 150 data. Data tiap fold akan diuji menjadi data testing secara bergantian. Berdasarkan hasil pengujian dari penelitian yang telah dilakukan, penelitian ini mampu meraih tingkat akurasi sebesar 88.0002%.
Pengelompokan Dokumen Petisi Online Di Situs Change.Org Menggunakan Algoritme Hierarchical Clustering UPGMA Irwin Deriyan Ferdiansyah; Sigit Adinugroho; Mochammad Ali Fauzi
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

Change.org is a website that is often used by people, which means for online delivering petitions and social campaignings. Campaign through social media had been proven that can make a change. The flow information of online petitions documents is updated daily in large numbers. It makes documents clustering being very important. Documents clustering is a process of grouping documents which have same topic. It aims to devide documents by its similarly, so the process of searching will be easier. This study uses hierarchical clustering UPGMA or unweighted pair-group method by arithmetic averages with adding feature reduction using latent semantic indexing method, that is the result of splitting singular value decomposition matrix. The result of this study conclude that latent semantic indexing method can solved the problem in high-dimensional data. The data conducted by 100 petitions. The result of performance testing which used cophenetic correlation coefficient obtained cophenetic value of 0.75959 at LSI matrix rank of 10 % and silhouette coefficient of 0.36862 with number of clusters as many as 2 clusters.
Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal Ratna Ayu Wijayanti; Muhammad Tanzil Furqon; 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

Kidney failure is a condition that the kidneys can not function properly. Worldwide cases of kidney failure are on the rise every year is chronic renal failure. In Indonesia the disease sufferers of chronic kidney failure are categorized as very high. According to data from the penetri (Union of Netrologi Indonesia) was estimated at 70 thousand kidney failure chronic disease sufferers. To help knowing the status of kidney function someone, we made an intelligent system using support vector machine (SVM) algorithm for classification of risk of kidney failure and using one-againts-all strategy. The flow of research those are using correlation analysis to look at the relationships between features, with normalization for data values are at the same interval, the calculation kernel RBF, do the training process with sequential training, then use one-againts-all for the process ofclassification. This study The final test result of this research obtained the average value of accuracy is 83,998% and the highest accuracy is 98,33% using the ratio of data 80%: 20%, with the parameter value of λ (lambda) = 1, γ (gamma) = 0,0001 , σ for kernel RBF = 2, C (Complexity) = 0,0001 and the number of iterations =100. Based on these results it can be concluded that the SVM algorithm and strategy one-againts-all can be used for classification of risk of kidney failure.
Implementasi Algoritme Average Time Based Fuzzy Time Series Untuk Peramalan Tingkat Inflasi Berdasarkan Kelompok Pengeluaran Mohammad Angga Prasetya Askin; Imam Cholissodin; 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

Inflation is a condition in which the sale price of goods or services experienced a general increase or decrease in economic activity. This affects the people of the country so that the effect is enormous. But in determining the rate of inflation is still experiencing difficulties in predicting inflation. Therefore, this study aims to determine / predict the rate of inflation by expenditure category by the Average Time Based Fuzzy Time Series method. This study uses scenarios based on consecutive monthly data, consecutive years, and the mean divisor of the difference. Inflation expense category data obtained from Indonesia Central Bureau of Statistics (BPS) and predicted results obtained is the average value of RMSE 0.486 in data month 15, the average value of RMSE 0.335 in the data year 3, and the last average RMSE 0.314 in the value of divisor 1.9 for consecutive month data categories and the mean RMSE 0.336 in the divisor value 2 for the consecutive year data categories.
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.
Perbandingan Algoritme K-Means Dengan Algoritme Fuzzy C Means (FCM) Dalam Clustering Moda Transportasi Berbasis GPS Rahman Syarif; Muhammad Tanzil Furqon; 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

Transportation has become a basic necessity for today's society. But often the need for transportation is not followed by information on the availability of transportation in a certain place. In this case, data from GPS can be used to group the available modes of transportation and provide information on the number of each mode of transportation scattered in a certain place and time. Algorithm used to group modes of transportation in this research is K-Means and Fuzzy C Means (FCM). These two algorithms then compared which one with the better result. The transportation mode grouping on the K-Means algorithm is obtained from the smallest distance of the transport mode data with the cluster center. Whereas in the FCM algorithm, grouping is obtained from the greatest degree values. After 10 times testing, obtained an average of K-Means accuracy of 58.46154 and 70.86538 for FCM algorithm. While for the silhouette Coefficient value obtained an average of 0.4582670 for K-Means and 0.440682 for FCM algorithm. From the results, it was concluded that the FCM algorithm is superior to K-Means.
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.
Prediksi Nilai Tukar Rupiah Terhadap Dolar Amerika Dengan Menggunakan Algoritme Genetika - Backpropagation Dwi Novi Setiawan; Candra Dewi; 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

The exchange rate is the value of the currency of a country which is expressed in the form of currency of another country. Exchange rate has an important role in international trade. To maintain the stability of the rupiah exchange rate, the government needs to enact the right policy. Therefore, a prediction algorithm that is able to recognize the pattern of exchange rate changes is needed. Backpropagation is one of method that is able to recognize patterns in time series data, while Genetic Algorithm is one of the capable method to exploring wider solutions for Backpropagation. In the Genetic Algorithm, the weight of Backpropagation is represented in real-code. Implementation of Genetic Algorithm - Backpropagation has initialization phase of population, crossover, mutation, individual training using Backpropagation, evaluation, and selection. The most optimum parameters for Genetic Algorithm - Backpropagation are in 90th generation, 20 population size, 0.1 crossover rate, 0.9 mutation rate, number of neurons in hidden layer 13, learning rate 1 and number of iteration of Backpropagation training were 500. The results of the tests that have been done got the best MAPE value of 1.575318 and the average MAPE of 1.741747. The algorithm is also capable of performing the best validation with MAPE of 1,0004917 and the average MAPE of 1.077603.
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.
Pemilihan Alternatif Simplisia Nabati Untuk Indikasi Gangguan Kesehatan Menggunakan Metode Analytical Network Process (ANP) dan Simple Additive Weighting (SAW) Gessia Faradiksi Putri; Lailil Muflikhah; 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

Medicinal plants are one of natural resources that is rarely known by Indonesian. Medicinal plants, known as simplicia, have various health benefits. What people know about simplicia is still very low, so they tend to choose modern medicines which costs much more than simplicia. In fact, simplicia is also safe and can save cost. But, because there are so many alternatives, eventually people become confused to choose which one is the most suitable for them. The parameters used to choose simplicia alternatives are the price, taste, availability of materials and nutritious substances. These parameters are used as references for choosing simplicia alternatives. This research uses Analytical Network Process (ANP) and Simple Additive Weighting (SAW) methods which are used for weighting and ranking. The ranking result obtained by ANP and SAW methods has accuracy on fever 40%, diarrhea 50% and cough 40%. The low tendency accuracy is caused by different usage of criteria weighting between target data and outcomes as well as the influence of innerdepence between criteria.
Co-Authors Afif Musyayyidin Afrizal Aminulloh Afrizal Rivaldi Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Muzanni Safi'i Alan Primandana Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Ananda Fitri Niasita Arifin Kurniawan Arrizal Amin Arrofi Reza Satria Aulia Rahma Hidayat Ayustina Giusti Bayu Rahayudi Brian Andrianto Budi Darma Setiawan Candra Dewi Cornelius Bagus Purnama Putra Dahnial Syauqy Danang Aditya Wicaksana Daris Hadyan Tisantri Dayinta Warih Wulandari Dese Narfa Firmansyah Dewan Rizky Bahari Dheby Tata Artha Diajeng Ninda Armianti Dwi Novi Setiawan Edy Santoso Eky Cahya Pratama Faizatul Amalia Felicia Marvela Evanita Fitra Abdurrachman Bachtiar Gessia Faradiksi Putri Gilang Pratama Hangga Eka Febrianto Hanson Siagian Humam Aziz Romdhoni Husein Abdulbar Ilham Firmansyah Imam Cholissodin Inas Hakimah Kurniasih Indah Wahyuning Ati Indriati Indriati Inosensius Karelo Hesay Irwin Deriyan Ferdiansyah Iskarimah Hidayatin Kenza Dwi Anggita Khairul Rizal Krishnanti Dewi Lailil Muflikhah Listiya Surtiningsih M. Ali Fauzi Mahendra Okza Pradhana Mayang Panca Rini Melati Ayuning Lestari Moch. Yugas Ardiansyah Mohammad Angga Prasetya Askin Muhammad Alif Fahrizal Muhammad Dio Reyhans Muhammad Dzulhilmi Rifqi Bassya Muhammad Iqbal Pratama Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Sholeh Hudin Muhammad Tanzil Furqon Muhammad Yudho Ardianto Muria Naharul Hudan Najihul Ulum Naziha Azhar Nendiana Putri Nurhana Rahmadani Putra Pandu Adhikara Putra Pandu Adikara Rahman Syarif Randy Cahya Wihandika Randy Cahya Wihandika Ratna Ayu Wijayanti Regina Anky Chandra Ridho Ghiffary Muhammad Rizal Maulana Rizky Adinda Azizah Salsabila Insani Salsabila Multazam Sarah Yuli Evangelista Simarmata Shima Fanissa Sukma Fardhia Anggraini Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Kurniawan Putra Tri Rahayuni Utaminingrum, Fitri Wahyu Rizki Ferdiansyah Yohana Yunita Putri Yose Parman Putra Sinamo Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari