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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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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.
Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi (Studi Kasus: Universitas Brawijaya) Muhammad Sholeh Hudin; Mochammad Ali Fauzi; 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

Research or final assignment is a requirement of graduation students. Every year the research becomes increasing and allows the students to take the same or similar topics. Through this research developed an application to classify student thesis reports. The results of this grouping also indicate that the themes are varied and when the themes becomes non-varied. Student research reports or commonly called a thesis report can be grouped by theme, object or method of the research. The process of extracting this thesis is done by using text mining technology. Then the process of grouping thesis document can be done by using k-means clustering method on a set of thesis documents by taking abstract, keywords and table of contents as an important information that represents the content of the document. Then the document will be done preprocessing first by using text mining method. To process the preprocessing is divided into several parts, namely tokenisasi, filtering, stemming and term weighting. After the document passes through the preprocessing process, then the document can be grouped by using the method of k-means clustering. In this experiment, trials are conducted by entering the number of clusters that vary. From the results of the analysis by entering the different cluster values have obtained the optimal value by entering the number of with the resulting silhouette value 0,483695522.
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.
Sistem Pendukung Keputusan Pemilihan Skuad Utama Tim Bola Voli Menggunakan Metode AHP-TOPSIS Hangga Eka Febrianto; Muhammad Tanzil Furqon; 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

Exercise is one of the physical activities that a person does to maintain and mprove the quality of health. One of the most widely played sports is volleyball. Brawijaya University, which is one of the major universities in Malang, currently also has several volleyball teams organized by UB Volleyball Activity Unit (UABV-UB). In every year UABV-UB always receives new member registration for students who want to join. Seeing the development and the amount of nterest then this makes UABV-UB having difficulty in choosing the players. So in this to solve the problem is used method AHP-TOPSIS. The AHP method is used for weighting which consists of making matched pair matrices, calculating matrix normalization, computing consistency test and producing krteria weight. While Topsis consists of paired normalization process of alternative data, after calculating the weighted normalization value of AHP and paired normalization process TOPSIS. The weighted normalization value will be used to find the positive and negative deal solution value as well as the distance between positive and negative deal solutions. The value is used to calculate the preference value of each alternative. Then do a ranking against the preference value. The result of system accuracy obtained from the test result is 85.7%.
Analisis Sentimen Konten Radikal Melalui Dokumen Twitter Menggunakan Metode Backpropagation Brian Andrianto; Indriati Indriati; 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

Twitter is a social networking service where users can post and interact with messages, known as "tweets". Twitter is also used by some people to give their opinions on something but sometimes too excessive even sometimes found a tweet that smells radical. The radical actions that exist in social media are usually referred to as radical content. The radical content available on social media can certainly harm some parties. There are also certain parties who utilize radical content to achieve certain goals. Therefore, in this study try to analyze the Indonesian tweet that contains the word radical, including in the content of radical positive or negative radical. Tweet can be from twitter that contains public opinion that leads to radical content will be classified. Tweet can be called a document or data will first go through the preprocessing process. Then the document was broken into 6 types of words, including the nouns, verbs and adjectives where each type of word will be divided again into positive and negative. After the break will be calculated how many the number of types of words in each document so that it can be converted into numbers that can then be incorporated into the algorithm formula.
Klon Perilaku Menggunakan Jaringan Saraf Tiruan Konvolusional Dalam Game SuperTuxKart Arrizal Amin; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the important component of the video game is an artificial intelligence to make the game more competitive. Artificial intelligence used to decide action to reach goal in the game and challenge game player. In the process of developing artificial intelligence, developer needs to program an aritficial intelligence to make a decision for action for each states possible in the game. In this research, artifical neural network will be used as an artificial intelligence inside video game. Neural network will simplify process of developing artificial intelligence because developer does not have to program an algorithm to decide each action for each possible states in the game. Furthermore, neural network can learn or clone gamer's behavior while playing the game. In this research, SuperTuxKart will be used for an example to develop artificial intelligence inside video game. Artificial Intelligence with learning rate 0.0001, momentum 0.3 and epoch 100 reaches accuracy 86.72% for cloning game's behavior while playing video game. So this research concluded that neural network can be used as an artificial intelligence inside game.
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