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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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Analisis Sentimen Pada Ulasan Aplikasi Mobile Menggunakan Naive Bayes dan Normalisasi Kata Berbasis Levenshtein Distance (Studi Kasus Aplikasi BCA Mobile) Ferly Gunawan; Mochammad Ali Fauzi; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of mobile application encourages the creation of many applications with a variety of uses to fulfill user needs. Each application allows users to post a review about the application. The aim of the review is to evaluate and improve the quality of future products. For that purpose, analysis sentiment can be used to classify the review into positive or negative sentiment. Application reviews usually have spelling errors which makes them difficult to understand. The word that have spelling error needs to be normalized so it can be transformed into standard word. Hence, words normalization is needed to solve spelling error problem. This research used word normalization based on Levenshtein distance. Based on testing, the highest accuracy is found in ratio of 70% training data and 30% testing data. The highest accuracy of this research using edit value <=2 is 100%, the second highest of edit value is obtained at edit value <=1 with accuracy of 96,4%, while edit value with the lowest accuracy is obtained at edit value <=4 and <=5 with accuracy of 66,6%. The result of using Naive Bayes-Levenshtein Distance has accuracy value of 96,9% compared to Naive Bayes without the Levenshtein Distance with accuracy value of 94,4%.
Klasifikasi Berita Twitter Menggunakan Metode Improved Naive Bayes Budi Kurniawan; Mochammad Ali Fauzi; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is one of the most widely used social media today. Besides being used as a social media, Twitter is also used to read news. Every year Twitter users have increased, so that information is also increasing. Increased information causes users who want to look for a certain information to experience difficulties. To solve the problem, news categorization is required. This study use Improved Naive Bayes method to categorize tweets by news contents. In Improved Naive Bayes posterior value will be calculated after the word is done by weighting using Bernoulli representation or by 1 and 0. This study use eight categories of news in Indonesia, which are: economy, entertainment, sports, technology, health, food, automotive, and travel. Based on the results of tests that have been done this study obtain precision value of 0.962961, recall 0.789164 and f-measure of 0.862973.
Peringkasan Teks Otomatis Pada Artikel Berita Kesehatan Menggunakan K-Nearest Neighbor Berbasis Fitur Statistik Rachmad Indrianto; Mochammad Ali Fauzi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Now days, information about healthy has been widely scattered and very easily obtained through the online website. But, within largest information that contain in the text of article make the reader can't understand about contents of the text. So, we need a system that can summarize a text to make easy the reader in understanding the contents of the text. Automatic text summary using k-nearest neighbor based on statistical features can be solution about the problem. Statistical features such as position of a sentence in a paragraph, overall sentence position, numerical data, inverted commas, the length of the sentence and keyword has important influence become parameter in summarization. From testing of statistical features that have been done by using k = 3, this method get result the best value of precision, recall and f -measure on feature set 9 with values 0.75, 0.71 and 0.72. From the test can concluded that the features that have a significant influence on the rise and fall of precision and recall values are position of a sentence in paragraph and sentence overall position. And then, from the test of k variation on the best feature set, we get maximum feature set value when k = 1 with the average value of precision, recall and f-measure of 0.89, 0.74 and 0.81.
Optimasi Biaya Bahan Menu Makanan bagi Penderita Penyakit Jantung dengan Menggunakan Metode Evolution Strategies Veronica Kristina Br Simamora; Imam Cholissodin; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

People who suffered heart disease should get serious handling. Not only taking medicines regularly, maintaining diet and nutritional intake for the body is also important. The price of the food ingridients which tend to be unstable makes it difficult for patients to consume foods in meeting their nutritional needs. This research used evolution strategies algorithm to optimize the cost of food ingidients for people with heart disease. Evolution strategies algorithm consists stages of initialization population with the real-vectors chromosome representation, reproduction method using intermediate recombination, and mutation, evaluation, and selection with method called elitism. The parameters were tested by the number of population testing, number of offspring testing, number of recombination testing, and generation testing. The greater the number of populations, number of offsprings, and generations does not guarantee produce more optimal results. The greater the number of population, the number of offspring, and many generations will bring up the various chromosomes, so the chances of this algorithm produce more optimal results will be even greater. This result can happen because the basic concepts of evolution strategies algorithms that use random values in the calculation process. Number of recombination testing indicates that the fewer parent's chromosomes involved in recombination will result a varied number of offspring's chromosomes. The more varied the result of offspring's chromosomes then the chances to achieve optimal results are greater. From the parameter tests results, this research obtained that the system can meet the nutritional needs of patients using the initial 105 population, 430 offspring produced, involving 2 parents on recombination, and 400 generations. Comparison of recommended food system recommendations with expert recommendations shows that the system has provided more optimal results compared to expert recommendations. This proved that the system delivers recommendations with cheaper prices and foods that varies.
Sistem Rekomendasi Bahan Makanan Bagi Penderita Penyakit Jantung Menggunakan Algoritma Genetika Elisa Julie Irianti Siahaan; Imam Cholissodin; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Lack of public awareness in regulating the consumption of food based on nutrition can cause several diseases including heart disease. Heart disease is caused from blockage of cholesterol and fat in the coronary artery. It is very important for people with heart disease to regulate food intake in order to reduce the blockage. Managing the food for the heart diet is difficult because heart diet is different from the other diets, because the amount of protein and fat is reduced. Genetic algorithms can solve the problem of managing food by computation process. The data that are used in this research are diet food ingredients data that consist of 8 kinds of food ingredients, carbohydrate, animal protein, vegetable protein, vegetable, fruit, milk, sugar and oil. In converting food into chromosome, chromosome real code representation is used. The crossover method that is used is extended intermediate crossover, the mutation method that is used is random mutation and the selection method is elitism selection. From the results of the testing, the optimal parameter scores of the genetic algorithm are the population number of 280 with the average fitness score of 103.7, Cr and Mr scores are 0.5 and 0.5 with the average fitness score of 103.3 and for the generations score is 100 with average fitness score of 111.2. Output of the system is food ingredients recommendation with 5 times a day meal time, which consists of breakfast, snack, lunch, snack and dinner with number of days based on user choice.
Peringkasan Teks Ekstraktif Kepustakaan Ilmu Komputer Bahasa Indonesia Menggunakan Metode Normalized Google Distance dan K-means Dhimas Anjar Prabowo; Mochammad Ali Fauzi; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The yearly rapid increase of digital data surface a problem for a person to be able to read every information that was served. One example of its data was a textual data document, which could be in a form of research document. This problem urges for a solution that is a technique to present all of the information in a clear and concise form, and one of its solution is a text summarization technique. This research proposed a text summarization technique using Normalized Google Distance (NGD) and K-means as its extractive algorithm, with a textual data that is a research document based on computer science studies in an Indonesian language as its research object. NGD will be used as an algorithm to derive sentences that was related to its document's title, and K-means will be used as an algorithm to obtain important sentences by its several topics that occurs in the document. The experiment result showed that this research possess an average best of precision, recall, and relative utility measures scores by 0.27, 0.43, and 0.45 respectively. In the other hand, the experiment result also showed that this research possess an average of kappa measure score by 0.41 or moderate.
Analisis Sentimen Tentang Opini Pilkada DKI 2017 Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes dan Pembobotan Emoji Agnes Rossi Trisna Lestari; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis is a part of text mining, the main focus is to analysis text documents. Sometimes text documents contain non-textual elements, e.g. emojis. Emoji is an Unicode graphic Symbol representation using pictures to express a person's feelings. The algorithm used in this research is Naive Bayes with renewal in addition of non-textual weighting (emoticon). The results of normalised textual and non-textual weightings with Min-Max method will be combined with certain constant values that resulting in both positive and negative sentiments. Data taken from Twitter about 2017 DKI Jakarta elections as much as 900 data tweet. From the accuracy test results, 68,52% were obtained for textual weighting conditions, 74,81% for non-actual weighting, and 73,57% for merging conditions 0,5 for textual and 0,5 for non-textual. From the result of the examination non-textual weighting effect, can be conclude that the non-textual weighting had an effect on the accuracy and classification, with the best multiplier constants when α = 0,4 and β = 0,6 to α = 0,1 and β = 0,9.
Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features Umi Rofiqoh; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis is a part of research from Text Mining which is usefull to classify text documents contained opinion based on sentiment. Text document that is used in research comes from Twitter from people's opinion about cellular telecommunication service provider. The used method is Support Vector Machine with using Lexicon Based Features as its feature renewal instead of using TF-IDF features. The used data in this research is 300 data which divided into two types of data with ratio 70% for training data and 30% for testing data. The result of system accuracy that is obtained from sentiment analysis using Support Vector Machine and Lexicon Based Features method is 79% using degree value 2, constant learning rate value 0.0001, and maximum iteration is 50 times. While sentiment analysis system without using Lexicon Based Features is resulting accuracy at 84% with the same parameter values.
Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku Prananda Antinasari; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid growth of social media does not make Twitter left by its users. Twitter is one of the social media that allows user to interact each other, share information, or even to express feelings and opinions, including in expressing film opinions. Comments or Tweets about movies that exist on Twitter can be used as an evaluation in watching movies and increasing film production. To figure it out, sentiment analysis can be used to classify into negative or positive sentiments. In Tweets contain many languages ​​used in the form of non-standard languages ​​such as slang, word-outs, and misspellings. Therefore it takes special handling on Twitter comments. In this research used non-standard word dictionary and Levenshtein Distance normalization to improve non-standard word to standard word by classification Naive Bayes. Based on the result of the test, the highest accuracy, precision, recall, and f-measure value are 98.33%, 96.77%, 100%, and 98.36%.
Penentuan Lokasi Pasang Baru Wifi.id Corner Menggunakan Metode AHP dan Algoritma Genetika (Studi Kasus : PT. Telkom Witel Kediri) Figgy Rosaliana; Dian Eka Ratnawati; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Wifi.id corner is a location provided by Telkom Corporation to access @wifi.id network. Determining new location of wifi.id corner needs consideration and right decision. Several criterias are used to this study such as Fiber Optic network availability, level of crowdedness, location type and density level of wifi.id corner location. Analytical Hyrarchy Process (AHP) method and Genetic Algorithm are applied to solve those problems. Genetic Algorithm will optimize the weight of AHP process. This algorithm uses real code chromosome representation with length of 6 genes, each gene represent element of weighted matrix value. The Reproduction uses crossover intermediate and random mutation. In the evaluation, AHP process determine feasibility of location then will be calculated its fitness by using accuracy formula. Selection method uses elllism s election. From the result of study, optimal parameter obtained in population size of 80, number of generation 85, combination of cr 0.4 and mr 0.2 with average fitness value 0.700 or accuracy of 70%. Location feasibility will be shown to users as the final result of the system.
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&#039; 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&#039;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