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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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Ekstraksi Topik Dokumen Berita Menggunakan Term-Cluster Weighting dan Clustering Large Application (CLARA) Rizal Maulana; Sigit Adinugroho; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growth of technology makes it easy to get informations and a kind of informations is often used is news media. As technology growth, news can be spread through news portals in form of web-bases such as Kompas, Detik, Tempo, and many others. Users of information technology sometimes don't have time to read news all the time and sometime can't get the news that they need. One of many solution to solve the problem is to do clustering news documents and after that topic extraction is used to get get important topics from the news cluster. In this research using Clustering Large Application (CLARA) for the clustering algorithm because CLARA is an optimization of k-medoid which is better than k-means from various aspects and on topic extraction uses term-cluster weighting to calculate term weights in the cluster. The proses of this research is used text preprocessing documents so it become structured data, after that Singular Value Decomposition (SVD) used to decomose features. Then CLARA is used to clustering documents and for topic extraction is using term frequency-inverse cluster frequency (TF-ICF). Data in this research is secondary data that obtained from Kaggle website which is an English language news documents. The result of silhoette sore from using 226 documents and 2 clusters is 0,005. As for accuracy topic extraction is 1 with taken number topic from 1 to 10.
Optimasi Penentuan Centroid pada Algoritme K-Means Menggunakan Algoritme Pillar (Studi Kasus: Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Timur) Alan Primandana; Sigit Adinugroho; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The k-means clustering method is a non-hierarchical grouping method that groups data into several centroid centers. The simplicity of the k-means method is widely used in various fields because it has several advantages, namely it is easy to implement and has a high level of accuracy of the size of the object so that this method is relatively more measurable and efficient. However, the initial k-means algorithm calculates using a C (centroid) value that randomly causes random results. Dependence on C (centroid) values ​​makes the accuracy of the k-means algorithm less than optimal. The results of k-means calculations are often obtained by experimenting several times and tend to produce different clusters. But in getting better results, it is difficult to determine the limits of an experiment. The random determination of cluster centers causes the k-means method has not been able to get the best grouping results. In this study, we describe an algorithm that is also used to optimize the selection of the initial center point in the k-means method, the pillar algorithm. This algorithm is an initial centroid determination by calculating the distance of metric accumulation between each data and all previous centroids. The choice of points is determined by data points that have a maximum distance. This research determines centroid using the Pillar algorithm and the results of the algorithm are used for the cluster's focal point on the k-means algorithm. In each cluster pillar algorithm is able to get the value of Sum of Squeared Error (SSE) better than random centroids as evidenced by the decreasing value of SSE.
Penggunaan Fungsi Aktivasi Linier dan Logarithmic Normalization pada Metode Backpropagation untuk Peramalan Luas Kebakaran Hutan Gilang Pratama; Sigit Adinugroho; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 12 (2019): Desember 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Forest fires is a disaster that often occurs in various countries in the world, especially those with many forest areas. In June 2017, Portugal hit by a forest fire with a loss of more than 565 million US Dollars. In this case, meteorological data can affect several fire indices and can be used area forecasting for extra protection to prevent excessive losses and preservation of natural resources. This paper uses the backpropagation method, which begins with the calculation of preprocessing data (min max normalization, and logarithmic normalization). The weight normalization calculation use Nguyen Widrow method, the calculation of the feed forward process to determine the output at each iteration index, the error value is calculated at the iteration index and the weight is corrected using the backpropagation process. Furthermore, the output value is normalized to return the data to the initial range. The test results are calculated using Mean Square Error (MSE) on each parameter test. Test parameters get the best learning rate value that is 0.1 with the results of MSE 6743,716, 3 hidden neurons with MSE 6745,456, 10 epoch with MSE results 6740,684, and the 10% ratio of test 90% ratio of training data which produces MSE 1881,604.
Prediksi Pertumbuhan Penduduk di Kota Malang menggunakan Metode Extreme Learning Machine (ELM) Inas Hakimah Kurniasih; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 2 (2020): Februari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dinas Kependudukan dan Pencatatan Sipil (Dispendukcapil) in Malang City is tasked with provide public services in terms of civil registration such as making electronic Kartu Tanda Penduduk (e-KTP) and birth certificates. Dispendukcapil prepared this by planning for the target needs and predicting the population in the next 5 years, but the error value is unknown. This research helps predict with a small error value using Extreme Learning Machine (ELM) method and calculates the error value using Mean Absolute Percentage Error (MAPE). Based on the results of testing implementation and analysis, using data from 2009 to 2019 obtained MAPE error value of 0.498% and runtime 1.166 seconds with the use of 3 input neurons, 5 hidden neurons, binary sigmoid, as well as 50 training data and 66 testing data. Then, in implementation of testing using data from 2012 to 2019 obtained MAPE error value of 0.117% and runtime 1.227 seconds with the use of 3 input neurons, 6 hidden neurons, binary sigmoid, as well as 70 training data and 4 testing data.
Implementasi Metode Extreme Learning Machine pada Klasifikasi Jenis Penyakit Hepatitis berdasarkan Faktor Gejala Salsabila Multazam; Imam Cholissodin; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Virus infection becomes very serious problem in medical world. Currently there are many viruses in Indonesia, including Human Immunodeficiency Virus (HIV), Novel Coronavirus (COVID-19), Dengue Virus (DENV), and Hepatitis A&B (HVA & HVB). It was recorded that in 2014 Hepaitits sufferers increased every year for the population aged above 15 years that is Hepatitis A (19.3%) and Hepatitis B (21.8%). To highly pay attention to the disease is very curcial considering Hepatitis sufferers often do not know already got infected by hepatitis. In this thesis the researchers is classifying Hepatitis types based on their symptoms using ELM method.The data being used is primary one gotten from the documents of patients infected by Hepatitis. There are 100 data with 20 features and 2 classes, namely Hepatitis A and Hepatitis B. This research was conducted in several stages from data normalization, followed by training process of the obtained data and then finally to verify the tested data input as well as data from the training process result. Based on the test results, the best ratio between train data and test data is 80: 20. This study uses several parameters to get optimal results including using 7 Hidden Neurons and the activation function used by Sigmoid Binary. By using these parameters obtained an average accuracy of 80.00%. It can be concluded that the use of the Extreme Learning Machine method can solve classification problems quite well.
Deteksi Perundungan Siber (Cyberbullying) pada Instagram Menggunakan Metode Naive Bayes Classifier dengan Lexicon Based Features Salsabila Insani; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Untuk dipublikasikan di 5th International Conference on Sustainable Information Engineering and Technology (SIET)
Prediksi Luas Serangan Hama pada Tanaman Padi Menggunakan Metode Extreme Learning Machine (ELM) dan Particle Swarm Optimization (PSO) Cornelius Bagus Purnama Putra; Randy Cahya Wihandika; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Indonesia is one of the countries with the largest population in the world. The majority of the population consuming rice as the staple food, rice becomes an important commodity. Recent global warming has resulted in extreme climate change, so that it can affect crop productivity and the intensity of OPT (Plant Pests) attack on rice plants. In meeting the increasing need for rice, it is necessary to prevent pest attack so that widespread prediction of pest attack area is needed in order to know earlier about upcoming pest attack. This study used hybrid algorithm Extreme Learning Machine and Particle Swarm Optimization with used data on pest attacks and climatology of Sidoarjo Regency from January 2009 to December 2018. Based on the research, the optimal parameters obtained are the ratio of training data 80% and testing data 20%, activation function of TanH, total population of 40, combination acceleration coefficient of 1 & 2, inertia weight limit of 0,4 & 0,9, hidden neuron of 5, and a maximum iteration of 100. Based on these parameters, the average value of the Mean Absolute Percentage Error (MAPE) is 25.143% which is included in the MAPE category of quite good, which is within the range of 20% -50%.
Klasifikasi Hoaks Kesehatan di Media Sosial menggunakan Support Vector Machine Aulia Rahma Hidayat; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Of the various types of communication tools available, social media is often used by the people of Indonesia, but as a communication tool that is often used, not everything that is found in social media is true. As part of the communication tool used by everyone it is not uncommon to find unclear sources or Hoaks. Hoaks about health are widely spread on Social Media and this can affect public awareness of the importance of health. Separating true and untrue health news needs to be done to avoid this. The separation process is carried out by classifying health news on Social Media with the Support Vector Machine method with Bag of Words and Lexicon Based Features. Total data in this study were 80 news from various social media. The data is then entered in the pre-processing process to get the word that shows a document, then proceed to the word weighting process using the TF-IDF calculation. The results of the word weighting process are included in the core process, namely the calculation of the Support Vector Machine method. Optimal parameter test results obtained gamma value (γ) = 0.001, lambda value (λ) = 1, epsilon value = 0.000001, degree value (d) = 2 and the maximum iteration value = 30. The results of system evaluation using both features get results which is good compared to using just one feature, showing the results of Accuracy of 1; Precision of 1; Recall of 1; F-measure of 1. Testing using K-fold Cross Validation was also carried out with a fold value of 10 and obtained an average value of Accuracy results of 0.6; Precision of 0.68; Recall of 0.47; F-measure of 0.48.
Analisis Sentimen Masyarakat terhadap Uji Coba LRT Jakarta Menggunakan Improved K-Nearest Neighbor dan Information Gain Mahendra Okza Pradhana; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Current transportation development system has giving easiness for society to moving from place to another places. One of quite new public transportation is Light Rail Transit (LRT). LRT or light railroad have an opportunity to held public trial access for free just by registering yourself in LRT Jakarta website. For improving and maximize LRT Jakarta services, they have social media account where people may give feedback and assessment. One of way that could be done is by sentiment analysis to find out whether the society likes the services provided by LRT Jakarta. This study is using the Improved KNN as a classification method to determine people sentiment coupled with Information Gain to select features used during the classification process. The process of sentiment analysis includes data collection, text preprocessing that produces clean data, then weighting the terms with tf idf followed by feature selection using Information Gain. The next step is classification with Improved KNN using the features of the previous selection. The data used are primary data sourced from three social media namely Youtube, Twitter and Facebook. The results of this study are the best f-measure obtained when k = 11 using a 100% threshold or the whole term used that is equal to 85.51% with an average computational time calculated from 5-fold of 0.4647 minutes.
Klasifikasi Kelas Kata (Part-of-Speech Tagging) Untuk Bahasa Madura Menggunakan Algoritme Viterbi Ilham Firmansyah; Putra Pandu Adhikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Artikel dipublikasikan di JTIIK
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