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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 Penyakit Skizofrenia dan Episode Depresi Pada Gangguan Kejiwaan Dengan Menggunakan Metode Support Vector Machine (SVM) Silvia Aprilla; Muhammad Tanzil Furqon; 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

Psychiatric disorders are disorders of the human brain that is not normal or different from people in general. There are many types of psychiatric disorders. Schizophrenia and Depression are a type of psychiatric disorders suffered by many people. There are also types of Schizophrenia and Depression, one type of disease in each is Schizophrenia Hebephrenic and Psychotic Depression. According to data in the soul hospital of Dr. Radjiman Wediodiningrat Lawang, both of these diseases are included in the top 10 diagnoses of outpatient and outpatient illnesses in 2017 which reached over 22.000 people. Due to a large number of patients affected by the disease, soul hospital needed a system that can classify Schizophrenia Hebephrenic and Psychotic Depression Disease. Classification is the manufacture of a model that used to make a group for an object with the same characteristics into a determined class. To classify the disease used support vector machine (SVM) algorithm with the polynomial of degree 2 kernel. The data used are 200 data taken from soul hospital of Dr. Radjiman Wediodiningrat Lawang. This data consists of 80% data training and 20% data testing. The test method used is K-fold cross-validation. Based on the results of testing SVM parameters obtained the highest average accuracy is 79% with the value of γ = 0,00001, λ = 0,1, C = 0,01, max iteration = 150, and ɛ = 1.10-10.
Analisis Sentimen Film pada Twitter Berbahasa Indonesia Menggunakan Ensemble Features dan Naive Bayes Rosy Indah Permatasari; Mochammad Ali Fauzi; Putra Pandu Adikara; Eka Dewi Lukmana 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

Sentiment analysis or opinion mining is one of the latest research topics in the field of information processing. It aims to know whether the polarity of a text-shaped data (document, sentence, paragraph) will lead to positive, negative, or neutral trait. This research used document text about Indonesian movie review which was obtained from Twitter. The method used in this research was Naive Bayes using Ensemble Features as a renewal feature beside Bag of Words Features. There are several types of Ensemble Features which are Twitter specific features, textual features, part of speech features, and lexicon based features. 500 data were used in this research, which were later divided into two types of data with the comparison of 70% for training data and 30% for testing data. The result of system accuracy obtained from sentiment analysis with Naive Bayes and Ensemble Features methods is 61.33%, 0.6369 precision, 0.5467 recall, and 0.5814 f-measure. The result of system accuracy using Ensemble Features and Bag of Words Features is 89.33%, 0.9041 precision, 0.88 recall, and 0.8922 f-measure.
Klasifikasi Berita pada Twitter Menggunakan Metode Naive Bayes dan Query Expansion Hipernim-Hiponim Fakhruddin Farid Irfani; Mochammad Ali Fauzi; Yuita Arum Sari
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

The large number of posted tweets resulted in scattered tweets and appearing on the Twitter homepage very diverse and not classified by categories such as health, sports, technology, economics, tourism and so on. The absence of categorization causes the user difficulty to read or retrieve information related to certain desired categories. Solution that can be done is by the method of text classification, which in the process of classification is able to classify automatically against some categories on unstructured text with natural language. In this research will be done classification process using Naive Bayes method with additional query expansion to add term in initial document. The addition of term aims to optimize the classification process because the tweet is a short text that can lead to ambiguity of classification classi. The additions made are hyponym and hypernym from original documents extracted from WordNet. Accuracy calculation method used is k-fold that aims to test the robustness of system. The accuracy obtained was 72% for the classification without query expansion, 65.75% for hyponym and hypernym addition, 66.3% for hyponym addition, and 67.5% for hypernym addition. It can be concluded that the addition of queries made less effective to improve the accuracy of the classification process.
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dengan Ensemble Feature dan Seleksi Fitur Pearson Correlation Coefficient Fachrul Rozy Saputra Rangkuti; Mochammad Ali Fauzi; Yuita Arum Sari; Eka Dewi Lukmana Sari
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

Microblogging has become the media information that is very popular among internet users. Therefore, the microblogging became a source of rich data for opinions and reviews especially on movie reviews. We proposed, sentiment analysis on movie review using ensemble features and Bag of Words and selection Features Pearson's Correlation to reduce the dimension of the feature and get the optimal feature combinations. Use the feature selection is done to improve the performance of the classification, reducing the dimension of the feature and get the optimal feature combinations. The process of classification using several models of Naive Bayes i.e. Bernoulli Naive Bayes for binary data , Gaussian Naive Bayes for continuous data and Multinomial Naive Bayes for numeric data. The results of this study indicate that by using the non-standard word on tweet evaluation results obtained accuracy 82%, precision 86%, recall 79.62% and f-measure 82.69% using Feature Selection 20%. Then after using manual standardization of word the evaluation results on the accuracy increased by 8% and then the accuracy becomes 90%, precision 92%, recall 88.46% and f-measure 90.19% using 85% feature selection. Based on these results it can be concluded that by using the standardization of word can improve the performance of classification and feature selection Pearson's provide optimal feature combinations and reducing the total number of dimensions feature.
Klasifikasi Berita Pada Twitter dengan Menggunakan Metode Naive Bayes dan Feature Expansion Berbasis Cosine Similarity Resti Febriana; Mochammad Ali Fauzi; Rizal Setya Perdana
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

Information has become indispensable in this modern era, especially with the existence of various social media that support information update. Twitter as one of the most active social media is used to update information belonging to short text or short stories that have some difficulty when done classification, such as ambiguous word, the word contained in the test data never appear in the data train and so on. This research was conducted to determine the effect of using feature expansion or addition of word on short text in the result of classification. Prior to classification, the first data to be tested is added to the list of pre-made words as an external source or dictionary with specified limits. This limitation aims to determine the minimum value of the most optimal limit in generating the highest accuracy in the classification process. In the process of making external sources cosine similarity process is done to find the closeness between words. The result of this research is accurate showing effect of expansion of feature expansion in classification result, 83% accuracy in classification without feature expansion and increased to 87% on feature expansion with threshold value 0.9.
Identifikasi Ujaran Kebencian Pada Facebook Dengan Metode Ensemble Feature Dan Support Vector Machine Aditya Kresna Bayu Arda Putra; Mochammad Ali Fauzi; Budi Darma Setiawan; Eti Setiawati
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

In the beginning, social media is used for socializing and interacting with other people. One of the most used social media for socializing is Facebook, with users amounting to over hundred million people around the world. Nowadays, on Facebook, its often found there's hate speech writing being shared at massive pace. Of course an assistance from language expert is a must for identifying hatespeech on Facebook because there's not yet an automatic system that can identify a hatespeech. The system in this research are made using Ensemble feature and Support Vector Machine. Ensemble feature is used for combining some of the feature extracted from each writing to ease the process of identifying a hatespeech. Support Vector Machine then used to identifying a hatespeech from a writing based on feature that are combined using ensemble feature. According to the result of testing, we acquired a 70% accuracy for the system so we can conclude that ensemble feature and support vector machine is good to use for identifying hatespeech on social media Facebook.
Peringkasan Multi-Dokumen Berbasis Clustering pada Sistem Temu Kembali Berita Online Menggunakan Metode K-Means Amalia Kusuma Akaresti; Mochammad Ali Fauzi; Fitra Abdurrachman Bachtiar
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

The growing number of online news sites resulted in an explosion of information and information redundancy occurred. On this issue it takes the search engine to make it easier for users to find information, but users still have to read it one by one, therefore it needs also a summary system. Therefore a summary system is required to facilitate Internet users avoid getting the same information from different sources. In this study, multi-document clustering based on online news retrieval system using K-Means method. The process of searching system using Cosine Similarity method and on the summary using K-Means Clustering method. The results show that the optimum results in the recall system are Recall 71%, Presicion 65.82%, F-Measure 66.35% and on Recall system of Recall 37.3%, Presicion 18%, F-Measure 19.2%.
Penentuan Rating Review Film Menggunakan Metode Multinomial Naive Bayes Classifier dengan Feature Selection Berbasis Chi-Square dan Galavotti-Sebastiani-Simi Coefficient Thio Marta Elisa Yuridis Butar Butar; Mochammad Ali Fauzi; Indriati Indriati
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

In the current era there are various kinds of movies, although the way of approach varies, all movies can be said to have one goal, namely to attract people's attention to the contents of the problem. From the contents of the movie there are many responses from the author and write them in a short review. With the review can help consumers to be more selective again in choosing a movie. And from the production side can be helped to measure how far the quality of the movies they produce. But from the production itself sometimes have difficulty in sorting and categorize the review, whether the movie is good quality, good enough, not good, and so forth. In this study the assessment of a moview based on the review given is Rating. So it takes a Rating prediction system to predict and determine the right Rating based on the reviews given by the users of a movie. To support the system built required methods to solve the problem, in this study researchers used the method of Multinomial Naive Bayes along Chi-Square and Galavotti-Sebastiani-Simi Coefficient. Multinomial Naive Bayes is a method for classification whereas Chi-Square and Galavotti-Sebastiani-Simi Coefficient is a feture selection to futher optimize the results of classification. From the test results, obtained the best accuracy level when the use features by 90%, and 100% with an accuracy of 36%. These results are the best results of the results with other features usage percentages. From these results CHI-GSS proven to make the selection of words that are considered relevant or irrelevant to do classification.
Analisis Performansi Algoritma Greedy Best First Search dan Dijkstra Pada Aplikasi Pencarian Jalur Pendonor Darah Terdekat Andro Subagio; Bayu Rahayudi; Mochamad Ali Fauzi
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

The Indonesian Red Cross (PMI) plays a very important role in providing the information needed by the community. One of the main information needed by the community is the blood supply. To obtain information related to the blood supply at PMI, there is a procedure that must be done in advance by the patient's family ie sending a form and blood sample to a blood bank at the hospital or to the laboratory of the PMI Transfusion and Blood Donor Unit (UTDD) in the City. However, the availability of blood stocks in the PMI UTDD is often not sufficient for blood needs and compulsory family families of patients who should look for blood donors that match the blood type of the patient. This certainly does not make it easy for the patient's family. In addition, families of patients who are in need of blood is also not easy to find blood donors who are located closest to the hospital. The shortest path search has several problems, but the main problem is finding the shortest route, of course, looking for the shortest possible route or path. However, for its implementation, this issue can be expanded more broadly among others to find the minimum cost, etc. In this research will be analyzed the performance of software to find information of the nearest blood donor with two different method, that is method of Greedy Best First Search and Dijkstra, besides analyzing the performance of the algorithm, this research also can know the percentage of similarity of output from searching the closest distance of both algortima , so it can know how many percent level of similarity of both algorithm. The result of the Dijkstra algorithm's order of growth value is better than the Greedy Best First Search method with a value of O (n ^ 2) and the similarity of the results of the two algorithms is 75%.
Query Expansion Pada Sistem Temu Kembali Informasi Berbahasa Indonesia Dengan Metode Pembobotan TF-IDF Dan Algoritme Cosine Similarity Berbasis Wordnet Mahdarani Dwi Laxmi; Indriati Indriati; Mochammad Ali Fauzi
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

Query Expansion is generally a technique for adding queries in information retrieval in relevance feedback techniques. The initial query will be added with several terms or words in the query to facilitate the process of information retrieval. Information Retrieval begins with the provision of several collections of documents to be used. Using text operations will be processed into an inverted index file. To find it, this research uses TF-IDF weighting method and wordNet based cosine similarity algorithm. By using wordNet, a query is added to correct a particular text so that it matches the concept of a particular sentence. In this research will be used synset in the form of a hyponym word relation to be added to the query. Based on the results of testing using precision @ 20 from 10 queries, the average precision value was 0.7. This means that the probability of the system can rediscover the relevant documents without using the query expansion is 70%. Based on the results of testing using precision @ 20 from 10 queries obtained an average precision value of 0.52. This means that the probability of the system can rediscover the relevant documents without using the query expansion is 52%.
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