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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Misykat: Jurnal Ilmu-ilmu Al-QurÆan, Hadist, Syariah dan Tarbiyah Systemic: Information System and Informatics Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) Kertha Widya : Jurnal Hukum
M. Ali Fauzi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Klasifikasi Tingkat Risiko Penyakit Stroke Menggunakan Metode GA-Fuzzy Tsukamoto Vina Adelina; Dian Eka Ratnawati; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stroke is clinical syndrome which usually comes sudden, quick, in a form of focal or global neurological deficits that happen within 24 hours or sometimes can cause a death. Stroke problems in Indonesia need a serious attention because of the number of death is high and always inCreasing. On of the necessary handling is detecting the symptoms of stroke in a form of SKD (Sistem Kewaspadaan Dini). Research found that to estimate the risk of stroke, it can use Fuzzy logic inference. From the 15 data test that has been done, the result gets 60% accuration. To optimize the result of membership degree function, it uses genetics algorithm in Fuzzy tsukamoto inference. Representation of chromosomes used is real code which every chromosome initialize the limitations in all Fuzzy variables. Crossover method using one cut point, random mutation used for mutation method and elitism selection used for election method. It is known that the result from optimization from the system accuration using Fuzzy tsukamoto-GA is 86.66% and the number of popsize which from the best parameter of the optimum result is 500, and the number of generations is 1000 as well as the combination Cr = 0,5 and Mr= 0,6. Keywords: stroke, genetics algorithm, Fuzzy tsukamoto, classification
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.
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.
Optimasi Fuzzy Inference System Tsukamoto Menggunakan Algoritme Genetika Untuk Mengetahui Lama Waktu Siram Pada Tanaman Strawberry Muhammad Khaerul Ardi; Budi Darma Setiawan; 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

Soil is a crusial component for plant growth. There are many parameters that used for soil examination, and one of its parameter is soil's dampness. Laboratorium Benih Balai Pengkajian Teknologi Pertanian Jawa Timur is one of the work units that has a duty to examine the soil for plant nursery purpose. However, due to the conventional tools that they used sometimes the examination result is not as accurate as they expected. Because of that problem the author did some research to make a smart computing system that can be implemented on a tool that can maintain the soil's dampness automatically. Fuzzy Inference System Tsukamoto is used to calculate how long does it take to water the plants by using two variable inputs; initial dampness and water volume. Genetic algorithm is used to get an optimal membership function by optimizing the boundaries of each membership function. The output of this research will display the optimal time to water the plants. From the examination result we got an error value for about 4,9570, but after optimization the number is reduced to 0,3790. With that result we can conclude that using Fuzzy Inference System Tsukamoto and optimized with genetic algorithm is able to calculate how much time that it takes to water the plants and still able to get a good outcome.
Penerapan Klasifikasi Tweets pada Berita Twitter Menggunakan Metode K-Nearest Neighbor dan Query Expansion Berbasis Distributional Semantic Galih Nuring Bagaskoro; Mochammad Ali Fauzi; Putra Pandu Adikara
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

The use of short text based on digital to date is still growing and extending to various social media. Twitter has news features in tweets to represent information representing each type. Each categorization of this type is done to make it easier for users to use it. The purpose of the use of categories in this classification, to evaluate and improve the quality of social media in grouping categories of content of the content provided. Traditional classification is still used today, but the results are sometimes not maximal, it is necessary to expand the word to add words to the text in order to improve the accuracy. Word expansion is used with a semantic-based distributional euclidean distance technique to find the closest word from an external source to be a query to be added to the test data text. Using test data 105 and training data 400, the classification using K-Nearest Neighbor can obtain 90% results with nearest neighbor K=5. These results are similar to the results of tests conducted without using word expansion techniques. While the test is done by adding the expansion of words with threshold 0.5 and the nearest immediate value K-Nearest Neighbor K=5 obtained an accuracy of 92%.
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%.
Prediksi Rating Otomatis pada Ulasan Produk Kecantikan dengan Metode Naive Bayes dan N-gram Irma Pujadayanti; Mochammad Ali Fauzi; Yuita Arum Sari
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 rise of beauty products also pound Indonesia especially imported products. This has triggered intense competition between local and foreign beauty products industry players. Therefore, the need for innovation in their products. The large number of review data in various online sources is useful as a review material for producers to innovate their products. For Consumer the data is useful as information before buying the product. The review data is often also has not been accompanied by a rating that makes manufacturers have difficulty in categorizing into a certain sentiment. In this study helps to accelerate the categorization of reviews into sentiment in the form of rating. The system built on this research uses the naive bayes classification method and the addition of n-gram method to pre-processing. The use of n-grams including unigram, bigram and combination of unigram and bigram aims to improve the classification results. On testing the best result system in full pre-processing scenario on all n-grams. Accuracy of 50%, 93%, 93% unigram while the accuracy of bigram is 39%, 87%, 83% and the highest accuracy is a combination of 49%, 97%, 96% with tolerance 0, tolerance 1 and sentiment reviews. The results showed that the use of n-grams was enough effective in solving the problems in the study.
Penerapan Analisis Sentimen untuk Menilai Suatu Produk pada Twitter Berbahasa Indonesia dengan Metode Naive Bayes Classifier dan Information Gain Ahmad Wildan Attabi'; Lailil Muflikhah; Mochammad Ali Fauzi
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

Twitter has a major role in the development of social, communication, psychological, marketing and political aspects. Posts Tweet comments or review indirectly will be a review of the assessment on a product. One of the most sought after products sectors today is beauty and skin care products. They look for products that they share with others, so they have a picture that affects their interest on the opinions of others who delivered via Twitter related results after using the product. Sentiment analysis can help in analyzing and classifying into positive and negative terms of twitter-related opinions about product trends and product quality in the public view. Opinions and comments related to Mustika Ratu's products are the subject of this study, citing the economic growth and the large number of users of Musitka Ratu who are companies in the field of beauty skin and beauty care. The Naive Bayes Classifier method is selected for implementation use, and has a fast performance in training, while the addition of Information is required for the feature selection process by reducing the presence of irrelevant words in the data used. The test is performed with 200 data (100 positive documents, and 100 negative documents) using the thresholds : 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, dan 0.10. The results obtained are adjusted for a difference of 4%, the highest average value if no Information Gain (threshold 0) is 70%, while using Information Gain (threshold 0.01) equal to 74%. This is influenced by several factors such as the amount of data and data that spread from data data and documents. The highest accuracy value is obtained at K1 (threshold 0,02), then K5, K6 (threshold 0.01), and K7 (threshold 0,02 and 0,08) with percentage 85%, while at k with threshold at the lowest point 50%.
Identifikasi Tweet Cyberbullying pada Aplikasi Twitter menggunakan Metode Support Vector Machine (SVM) dan Information Gain (IG) sebagai Seleksi Fitur Ni Made Gita Dwi Purnamasari; Mochammad Ali Fauzi; Indriati Indriati; Liana Shinta Dewi
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

Cyberbullying is one of the actions that violate the ITE Law where the crime is committed on social media applications such as Twitter. This action is difficult to detect if no one is reporting the tweet. Cyberbullying tweet identification aims to classify tweets that contain bullying. Classification is done using Support Vector Machine method where this method aims to find the dividing hyperplane between negative and positive class. This study is a text classification where more data is used, the more features are produced, therefore this research also uses Information Gain as feature selection to select features that are not relevant to the classification. The process of the system starts from text preprocessing with tokenizing, filtering, stemming and term weighting. Then perform the information gain feature selection by calculating the entropy value of each term. After that perform the classification process based on the terms that have been selected, and the output of the system is identification whether the tweet is bullying or not. The result of using SVM method is accuracy 75%, precision 70.27%, recall 86.66% and f-measure 77.61% on experiment iterMax value = 20, λ = 0.5, γ = 0.001, ε = 0.000001, and C = 1. The best threshold of information gain is 90%, with accuracy 76.66%, precision 72.22%, recall 86.66% and f-measure 78.78%
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.
Co-Authors Adi Sukarno Rachman Adinugroho, Sigit Aditya Kresna Bayu Arda Putra Agnes Rossi Trisna Lestari Agung Setiyoaji Agus Wahyu Widodo Agus Zainal Arifin Ahmad Galang Satria Ahmad Wildan Attabi' Akbar, Aldi Fandiya Alvandi Fadhil Sabily Amalia Kusuma Akaresti Andika Indra Kusuma Andro Subagio Anita Sumiati Annam Rosyadi Annisya Aprilia Prasanti Annisya Aprilia Prasanti Anny Yuniarti ari kusyanti Bayu Rahayudi Billy Sabilal Budi Darma Setiawan Budi Kurniawan Chusnah Puteri Damayanti Claudio Fresta Suharno Claudio Fresta Suharno Dahnial Syauqy Desfianti, Ruri Dhimas Anjar Prabowo Dian Eka Ratnawati Dimas Joko Haryanto Dwi Damara Kartikasari dwi taufik hidayat Edy Santoso Eka Dewi Lukmana Sari Elisa Julie Irianti Siahaan Eti Setiawati Fachrul Rozy Saputra Rangkuti Fakhruddin Farid Irfani Fathor Rosi Ferly Gunawan Ferly Gunawan Figgy Rosaliana Fitra Abdurrachman Bachtiar Galih Nuring Bagaskoro Gosario, Sony Hadiyan Hadiyan Hasbi Razzak Hidayat, Hasannudin Hilmy Khairi Idris Hurriyatul Fitriyah I Wayan Sudira Imam Cholissodin Imam Cholissodin Indriati Indriati Irma Pujadayanti Irwin Deriyan Ferdiansyah Ismiarta Aknuranda Isnan . Joda Pahlawan Romadhona Tanjung Komang Candra Brata Lailil Muflikhah Laksono Trisnantoro Liana Shinta Dewi Liana Shinta Dewi Lita Handayani Tampubolon M Yusron Syauqi Dirgantara M. Rizzo Irfan M. Rizzo Irfan Mahdarani Dwi Laxmi Mahendra Data Malahayati, Salsabila Nur Maulana, Muhammad Afif Moch. Yugas Ardiansyah Moh Fadel Asikin Moh Iqbal Yusron Muhammad Fhadli Muhammad Hakiem Muhammad Khaerul Ardi Muhammad Khatib Barokah Muhammad Mishbahul Munir Muhammad Sholeh Hudin Muhammad Tanzil Furqon Nanda Firizki Ananta Ni Made Gita Dwi Purnamasari Ni Made Gita Dwi Purnamasari Nining Nahdiah Satriani Nur Hijriani Ayuning Sari Nurul Dyah Mentari Nurul Dyah Mentari Nurul Hidayat Prananda Antinasari Primantara Hari Trisnawan Putra Pandu Adikara Qiindil, Audry Rachmad Indrianto Rahmat Yani Rakhman Halim Satrio Randy Cahya Wihandika Ratih Diah Puspitasari Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Resti Febriana Ria Ine Pristiyanti Rika Raudhotul Rizqiyah Rizal Maulana Rizal Maulana, Rizal Rizal Setya Perdana Ro'i Fahreza Nur Firmansyah Robertus Santoso Aji Putro Rodhiya, Hanif Robby Rosy Indah Permatasari Safier Yusuf Saiful Bahri Shandy, Ryo Shima Fanissa Silalahi, Gifo Armando Silvia Aprilla Sonny Christiano Gosaria Sudin, Mahmudin Suryani Agustin Sutrisno Sutrisno Thio Marta Elisa Yuridis Butar Butar Tibyani Tibyani Tibyani Tibyani Tri Afirianto Tri Afirianto Ulfa Lina Wulandari Umi Rofiqoh Ummah Karimah, Ummah Uswatun Hasanah Utaminingrum, Fitri Veronica Kristina Br Simamora Vina Adelina Wahyuni Lubis Widhi Yahya Wildan Aulia Rachman Winda Estu Nurjanah Winda Fitri Astiti Yessivha Imanuela Claudy Yuita Arum Sari Yuita Arum Sari Zafran, Muhammad Abyan Zubaidah Al Ubaidah Sakti