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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research 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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Klasifikasi Penyimpangan Tumbuh Kembang pada Anak Menggunakan Metode Neighbor Weighted K-Nearest Neighbor (NWKNN) Afrizal Rivaldi; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Humans during life must experienced a phase of growth and development. This growth and development phase is very influential on the quality of child growth. The critical period of growth and development occurs in the first years of a child's life. At an early age, the process of growing physical, mental, and psychological development is very fast so that requires more attention from parents. In the development phase may occur disorders where the process of growth and development of children obstructed or unnatural. Development disorders are often encountered autism, ADHD, and Down syndrome. This study will classify development disorders based on symptoms that appear using Neighbor's Nearest K-Neighbor (NWKNN). The NWKNN method is the development of the KNN method, which is weighted on each class to be classified. In this research will be classify various types of development disorderds that include autism, ADHD, Down syndrome and normal. The results of this study indicate that the NWKNN method can classify well by using 80 training data and 20 test data, K = 10, and E = 4 with 95% up to accuracy. This study also proved NWKNN method which has 3% average of accuracy better than KNN method in doing classification of growth and development of child.
Prediksi Penjualan Mi Menggunakan Metode Extreme Learning Machine (ELM) di Kober Mie Setan Cabang Soekarno Hatta Ayustina Giusti; Agus Wahyu Widodo; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kober Mie Satan Soekarno Hatta branch is a company engaged in the field of food. The number of consumer demand of restaurant Kober Mie Setan Soekarno Hatta branch that is erratic every time affect the remaining raw materials. Raw materials that are stored for too long are not good for consumption. When demand is low and the raw materials provided are high, then the rest of the raw materials from the day's sales will be discarded. In order for raw materials are not wasted, then the sales prediction required by Kober Mie Setan Sukarno Hatta branch. With these sales predictions the restaurant can prioritize the expenditure of certain menu ingredients that have a high interest so that the remaining raw materials can be reduced. This research applies method of artificial neural network (JST) that is Extreme Learning Machine (ELM) to predict the sales of noodles in Kober Mie Setan restaurant of Soekarno Hatta branch. The prediction process of noodles sales in Kober Mie Setan is normalization of data, training process, testing process, data denormalization, and error value calculation using Mean Square Error (MSE). ELM method has advantages in learning speed and small error rate. Based on the tests conducted to determine the differences in the use of data features in this study resulted in the smallest error rate of 0.0171 using the features of historical data and features of residual sales data.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Bali Menggunakan Support Vector Regression dengan Algoritma Genetika Listiya Surtiningsih; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The tourism sector becomes one of the pillars in the Indonesian economy. As Bali has been contributing for more than 40 percent of international tourist arrivals in Indonesia. Predicting tourism demand are very important for the government and industry, as predicting the basis for effective policy planning. Support Vector Regression (SVR) is prediction method that has the ability to handle large-scale data in the training phase and it can to recognize patterns of time series data. The predicted result will be good if the value of the important parameters of the SVR can be determined correctly by optimization. One of optimization methods is Genetic Algorithm (GA). GA will be optimizing parameter of SVR to get the right value of SVR parameter to getting better predictions. The test shows the value of MAPE obtained is 2,513% with best parameters those are range of lamda 1 - 10, range of complexity 1 - 100, range of epsilon 0,00001 - 0,001, range of gamma 0,00001 - 0,001, range of sigma 0,01 - 3,5, Iteration of SVR 1250, generation of GA 90, population 70, crossover rate 0,6, mutation rate 0,4, features 2 and prediction period 1 month. Based on the test results, GA-SVR method on the data of foreign tourist arrivals to Bali is appropriate for short-term prediction.
Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking Shima Fanissa; Mochammad Ali Fauzi; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tourism is one of effort to promote a city. Malang currently has a branding city called "Beautiful Malang". Indonesian choose Malang tourism as a destination and review it on the website, one of them is TripAdvisor. Thus this research tried to analyze the reviews from the public about the tourism of Malang City through sentiment analysis and classified into two classes, that is positive and negative. In this research the method used is Naive Bayes with Query Expansion Ranking feature selection to reduce the number of features in the classification process. The process of sentiment analysis consists of preprocessing, feature selection with Query Expansion Ranking method, and classification with Naive Bayes. This research is testing the accuracy by using the variation of feature selection ratio, the result of 75% feature selection has the best accuracy of 86.6%.
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.
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 Anggi Gustiningsih Hapsani 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 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 Lukman Hakim 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 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 Siti Mutrofin 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