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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal
Rizal Setya Perdana
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pemodelan Sistem Pakar Deteksi Dini Resiko Penularan HIV/AIDS Menggunakan Metode Dempster-Shafer Marine Putri Dewi Yuliana; Lailil Muflikhah; Rizal Setya Perdana
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

Human Immunodeficiency Virus (HIV) is a disease that attacks the body immune and until now still not found a drug that can cure it. People with HIV are often regarded as a disgrace that can cause psychological pressure on patients and families around him. It is this kind of thing which can then lead to the process of AIDS happening faster. Lack of information and ignorance of the spread of this virus resulted in increasing HIV patients. Fear will be regarded as a sufferer and do not want his privacy disturbed, most people are reluctant to ask this specialist about HIV. Therefore the need for a system that can be used as a support activity solving a problem. The knowledge to be represented in the expert system is full of uncertainty and disguise. One way to overcome the problem of uncertainty can be done by using Dempster Shafer method. The result obtained from 28 existing data has an accuracy of 78%. This indicate the system can be used in the expert system for early detection of HIV/AIDS.
Implementasi Metode Naive Bayes Dengan Perbaikan Missing Value Menggunakan Metode Nearest Neighbor Imputation Studi Kasus: Penyakit Malaria Di Kabupaten Malang Riyant Fajar; Rizal Setya Perdana; Indriati Indriati
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

Malaria is an infectious disease that is transmitted among humans by the bites of female Anopheles mosquit. There are four types of Plasmodium that are frequently found in the case of malarial infection in Indonesia: Plasmodium vivax (Tertiana), Plasmodium malariae (Quartana), Plasmodium falcifarum (Tropica), and Plasmodium ovale (Pernisiosia). Thus far, people are having difficulty in differentiating the symptoms found in malaria and in another common cold or influenza as the laymen rely only on general knowledge without any medical facts and reviews. As a result, the patient of malaria is often mistreated. The symptoms of malaria depend on the types of malaria itself. Classic symptoms of malaria suffered by non-immune patients (patients who live in non-endemic area) are paroxysmal (sudden acute fever) preceded by chills and oversweating. On the other hand, classic symptoms of malaria suffered by immune patients are headache, nausea and vomitting, diarrhea , as well as muscle pain. Malaria is a life-threatening disease that can lead into death if not treated in an immediate manner. On that account, a computer system that can accelerate the detection is needed to help in diagnosing whether or not the patient is infected. The said system was designed using Naive Bayes method and the improvement of missing value with the usage of nearest neighbor imputation method. The verdict of the system's accurateness from two testing scenarios has been acquired with the best accuracy point of 77.14% in the first testing scenario and 64.70% in the second testing scenario.
Klasifikasi Dokumen Twitter Untuk Mengetahui Karakter Calon Karyawan Menggunakan Algoritme K-Nearest Neighbor (KNN) Yessivha Imanuela Claudy; Rizal Setya Perdana; Mochammad Ali Fauzi
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

Text mining is the process of mining the text for taking important meaning in it to be able to do the classification. In this study, conducted to know the classification of the characters prospective employees based on the tweets from a company. Tweet that comes from prospective employees will in the process and after that produces characters as one reference in the placement of prospective employees. Then this Employee characters divided into four large groups according the concept of MBTI (Myers-Briggs Type Indicator). Artisan, Guardian, Rational, and Idealist. In addition Artisan, Guardian, Rational and Idealist have characteristics and indicators. After getting the Tweets prospective employees, the next stage will be made classification. This classification method using KNN algorithm. Where, there are 160 tweet data from prospective employees will be grouped MBTI (Myers-Briggs Type Indicator). The data obtained from the company in the form of a tweet from this prospective employees in order to generate the test results are good, then it is divided into two types by a ratio of 50% training data and 50% for the test data. By entering the value of K that is 4 as the value to test. Then get a system accuracy results retrieved from the classification of the characters prospective employees based on their tweets is 66%. These results are the results where there are 53 results of test data and test data results 27 is wrong in the process of testing
Implementasi Metode Backpropagation Neural Network Berbasis Lexicon Based Features dan Bag Of Words untuk Identifikasi Ujaran Kebencian pada Twitter Muhammad Mishbahul Munir; Mochamad Ali Fauzi; 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

Hate speech is a language that expresses a hatred of a group or individual who intends to insult or humiliate and the media can be found anywhere, one of them Twitter. Twitter is a social media that allows users to express feelings and opinions through tweets, including tweets that contain hate speech. Document or tweet data comes from previous research on hate speech. The method used in processing the document data is Backpropagation Neural Network with feature updates using Lexicon Based Features combined with Bag of Words. In this study using data as much as 500 data is divided into training data as much as 400 data and test data as much as 100 data. From the evaluation test results, when using Lexicon Based Features, the average value of f-measure is 0%, worse than using the Bag of Words with an average f-measure of 76.638%, while when Lexicon Based Features is combined with the Bag of Words got the best average score among the previous features with a f-measure of 78.081%. And the result Backpropagation Neural Network using Lexicon Based Features combined with Bag of Words is not better than Random Forest Decision Tree using n-gram from previous research.
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.
Peramalan Curah Hujan Menggunakan Metode Jaringan Saraf Tiruan Dengan Optimasi Algoritma Bee Colony I Putu Bagus Arya Pradnyana; Arief Andy Soebroto; 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

A high drop in rainfall can cause a disaster, so it is necessary to forecast to estimate how much rain will come. One of the main factors causing the flood disaster is the intensity of rainfall is very high, then the rainfall is still very interesting to continue to be studied. in this study made a system that can make forecasting using ANT method backpropagation with Artificial Bee Colony to determine the error rate of rainfall forecasting. In the performance test algorithm of Backpropagation-Bee Colony method showed that the average value of MSE showed the best result at iteration to 1000 that is MSE average of 0,0329. As for testing the accuracy of the 1000th iteration obtained an average MSE of 0.030 in December. The accuracy value in December amounted to 95%.
Algoritme Genetika Untuk Optimasi K-Means Clustering Dalam Pengelompokan Data Tsunami Dwi Anggraeni Kuntjoro; Budi Darma Setiawan; 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

Tsunami is one of the most deadly disaster causing damage and loss of life and wealth. It happens in a sudden and unpredictable. Lack of awareness often leads to a great damage and worsening the impact of tsunami itself. This research implements genetic algorithm optimization into K-Means method for classify tsunami data. By optimazing the initial cluster center it will used as an input on K-Means method. The method result more optimal preference than the conventional K-Means method since the central point is optimized by genetic algorithm. It was proved on this research where fitness value resulted from Silhouette Coefficient to observe how suitable data with cluster. Chromosome representation used here is real code to initialize centroid value. Extended intermediate crossover applied for crossover method. For mutation method, random mutation is run here. Also for selection method it uses elitism selection. Based on testing result, the most optimum parameter accomplished are 50 population, 70 generation, and Cr =0.9 and Mr =0.1 combination with fitness value around 0.995934
Penerapan Named Entity Recognition Untuk Mengenali Fitur Produk Pada E-commerce Menggunakan Rule Template Dan Hidden Markov Model M Yusron Syauqi Dirgantara; Mochammad Ali Fauzi; 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

Information technology with the Internet gives the impact of the development of electronic commerce or e-commerce that gained a lot of popularity. APJII data in 2016 states as many as 130.8 million Indonesians use the internet to offer goods and services. In e-commerce management there is customer service that is tasked to handle all of questions submitted by customers. Submission of information by customer service is usually through a call center or chat application. In thrust the ability of intelligent digital assistants chatbot is widely used to help the work of customer services. It takes an analysis of the customer's language on chatbot in order to be able to recognize what information is contained in the question, so it takes the classification and extracting of information in order to get important information needed by chatbot in answering questions from customers. Named Entity Recognition (NER) is part of the extraction of information assigned to the classification of text from a document or corpus categorized into classes such as person's name, location, month, date, time and so on. Automatic name extraction can be useful for addressing some issues such as translation engines, information retrieval, frequently asked questions and text summary. In this study NER is done using the method of Hidden Markov Model and Rule Template with 6 entities i.e. BRAND, TYPE, PRICE, SPEK, N_SPEK and N_TAG. Overall introduction of entities conducted in this study resulted in the accuracy value in the Rule Template of 97.20% and the accuracy value in the Hidden Markov Model of 92.23%.
Implementasi Metode Improved K-Means untuk Mengelompokkan Dokumen Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Muhammad Abdurasyid; Indriati Indriati; 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

Journal of Information Technology and Computer Science Development (J-PTIIK) is a scientific journal in the field of computer that contains scientific writings of research results FILKOM Brawijaya University students that published periodically. J-PTIIK is a journal document that has journal topics that are in the field of information technology and computer science. At this time J-PTIIK is clustered by volume archive and published journal number. To facilitate the identification of journal topics contained in J-PTIIK, J-PTIIK documents can be clustered based on similarity of topics contained in J-PTIIK. J-PTIIK documents clustering is made using improved k-means method. The improved k-means method is the unsupervised clustering techniques with the initial centroid determination obtained by combining the optimization method of distance and density. Document pre-processing and formation of vector space model to perform term weighting is done first before clustering the J-PTIIK documents. Based on the evaluation results, J-PTIIK documents clustering obtained an optimal silhouette coefficient by 0.026574 at k = 19 and α = 0.50. Optimal purity test results obtained by 0.738197 at k = 23 and α = 0.50. The research result shows that the use of improved k-means method has better silhouette coefficient than k-means method, with average value of silhouette coefficient at improved k-means method by 0.016457654 and k-means method by 0.011820563.
Implementasi Fuzzy K-Nearest Neighbour (FK-NN) Untuk Pemilihan Keminatan Mahasiswa Teknik Informatika (Studi Kasus : Program Studi Teknik Informatika Fakultas Ilmu Komputer Universitas Brawijaya) Dhony Lastiko Widyastomo; Indriati Indriati; Rizal Setya Perdana
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

Concentration selection is one of few steps for a students to finish their studies. Informatics programee have 4 concentration consist of Artificial Inteligent(AI), Software Engineering (SE), Network and Game. Unfortunately because the limited and many internal problems from the students causing some problem for the concetration selection. To solve the problem of selection, a system who can give a classification is needed to give the solution. A classification for concentracion selection uses fuzzy k-nearest neighbor for its method. The method works with calculate the number of K value to Process the classification of 4 study concentration and resulting the recomendation class of concentration class based on the student data. Based on the research of study using 200 data of the students of Informatics engineering, from 2011 to 2013, the biggest accuracy was produced by K value=3 and have 87,5% accuracy. While the lowest precentages of accuracy was produced by K value=10 with the averages of 67,5% accuracy.
Co-Authors Agnes Rossi Trisna Lestari Arief Andy Soebroto Arina Indana Fahma Aryo Pinandito Bana Falakhi Budi Darma Setiawan Caesar Rio Anggina Toruan Claudio Fresta Suharno Claudio Fresta Suharno Dhony Lastiko Widyastomo Dian Eka Ratnawati Dinul Wikramaditya Syah Dwi Anggraeni Kuntjoro Dwi Rahayu Dwi Wahyu Puji Lestari Dyan Dyanmita Putri Edi Santoso Erma Rafliza Faisal Aji Prayoga Faiz Aulia Al Farisi Fakihatin Wafiyah Farid Syauqi Nirwan Fathor Rosi Fitra Abdurrachman Bachtiar Grady Davinsyah I Made Budi Surya Darma I Putu Bagus Arya Pradnyana Imam Cholissodin Imanuel Juventius Todo Gurning Indriati Indriati Irma Ramadanti Fitriyani Ivan Fadilla Kukuh Haryobismoko Lailil Muflikhah M Yusron Syauqi Dirgantara M. Ali Fauzi Makrina Christy Ariestyani Marine Putri Dewi Yuliana Muhammad Abdurasyid Muhammad Maulana Solihin Hidayatullah Muhammad Mishbahul Munir Muhammad Tanzil Furqon Muhammad Wafi Nana Nofiana Nirmala Fa'izah Saraswati Novanto Yudistira Nur Adli Ari Darmawand Nurul Hidayat Prananda Antinasari Putra Pandu Adikara Putu Amelia Vennanda Widyaswari Putu Rama Bena Putra Randy Cahya Wihandika Resti Febriana Riska Dewi Nurfarida Riyant Fajar Rizaldy Amsyar Umi Rofiqoh Wanda Athira Luqyana Wayan Firdaus Mahmudy Wijaya Kurniawan Winda Estu Nurjanah Yessivha Imanuela Claudy Yusuf Nurcahyo Zahra Asma Annisa