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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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Prediksi Rating Pada Review Produk Kecantikan Menggunakan Metode Naive Bayes Dan Categorical Proportional Difference (CPD) Fathor Rosi; Mochammad Ali Fauzi; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beauty products at this time become a popular thing in various circles, especially among women. Almost all of them have beauty products and are included as a primary requirement to support their better performances. The existence of a product can not be separated from a comment or review of the consumer for the product. Of course with the review can help consumers to be more selective again in choosing a product. And from the production side can be helped to measure how far the quality of the products they produce. But from the production itself sometimes have difficulty in sorting and categorize the review, whether the product is good quality, good enough, not good, and so forth. In this study the assessment of a product 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 product. To support the system built required methods to solve the problem, in this study researchers used the method of Naive Bayes and Categorical Proportional Difference. Naive Bayes is a method for classification whereas Categorical Proportional Difference is a feature selection to further optimize the results of classification. From the test results, obtained the best accuracy level when the use of features by 50% with an accuracy of 87%. These results are the best results of the results with other feature usage ratios of 25%, 75% and 100%. From these results CPD proven to make the selection of words that are considered relevant or irrelevant to do classification.
Sistem Pendukung Keputusan Penentuan Calon Penerima Beasiswa BBP-PPA Menggunakan Metode AHP-PROMETHEE I Studi Kasus : FILKOM Universitas Brawijaya Nining Nahdiah Satriani; Imam Cholissodin; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Scholarship is financial assistance given to individuals for the purpose of sustainability education. Every year, Universitas Brawijaya offers scholarships to underprivileged students, one of them i.e. BBP-PPA scholarships. Scholarships should be given to appropriate candidates so that the objectives of the program can be achieved on target. Selection of scholarship awardee includes several criteria, such as parental income, parental expenses, GPA, and parental dependents. However, complaints often arise from other students when the awardee is not eligible to get a scholarship. Morevover the selection still done manually so that the process of determining the awardees tend to take a long time.Analytical Hierarchy Process-Preference Ranking For Organizatiom's Evaluation I (AHP-PROMETHEE I) is one of the methods that combine the method of AHP and PROMETHEE I. The results of the tests to determine the effect of the matrix comparison on the accuracy of the system. The results showed the accuracy of 73% for calculations using leaving flow, and 93% calculation using entering flow data from experts. Based on the accuracy can be said that the method of AHP-PROMETHEE I has a good performance in the determination of the candidates BBP-PPA scholarship.
Pengenalan Entitas Bernama untuk Identifikasi Transaksi Akuntansi Menggunakan Hidden Markov Model Rika Raudhotul Rizqiyah; Lailil Muflikhah; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Accounting is a task which has an important role in supporting economic continuity, due to the recording of any business process that occurred was done in accounting. However, the recording of financial transactions in accounting for identification into journal is still done manually, so that required classification and extraction of information contained in the accounting transaction text to make it easier. Named Entity Recognition (NER) is the first step needed to perform information extraction. To solve this problem, named entity recognition done for identification of accounting transaction. In this research used method of Hidden Markov Model (HMM), because HMM can resolve labeling task and and known robustly in performing named entity recognition. The main process in this named entity recognition is divided into modeling process using Hidden Markov Model and decoding process using Viterbi Algorithm. In this research will be recognize 12 entities namely DATE, TITLE, PER, TRANS, EXP_MON, TYP_COMP, FIRST_ORG, SECOND_ORG, EXP_DATE, NO_DATE, MONTH and YEAR. Overall entity recognition with addition Laplace Smoothing and Regular Expression techniques produce a value of average precision, recall and f-measure consecutive 81.75%, 87.88%, and 82.39%.
Klasifikasi Dokumen Sambat Online Menggunakan Metode K-Nearest Neighbor dan Features Selection Berbasis Categorical Proportional Difference Nur Hijriani Ayuning Sari; Mochammad Ali Fauzi; Putra Pandu Adikara
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

Sambat Online is a platform to facilitate the suggestions, criticisms, complaints or questions from public to the Government of Malang through provided websites or via short messages. Incoming complaints, will be categorized into various fields of SKPD. To make it easier to organize the text and increase the efficiency of the administrator in sorting out and define the field of SKPD, an intelligent systems that can classify documents according to its SKPD's field is needed. K-Nearest Neighbor (K-NN) is a method of classification that will be used to find similarities between documents. Feature selection method used in this research is Categorical Proportional Difference (CPD) to measure the degree of contribution of a word. Started from collecting the test documents and training documents, continue to the preprocessing stage and selection features, weighting, and then do the classification, and analysing the results of the classification system by value of accuracy, precision, recall, and F-Measure. The result is the most optimal performance is the use of k = 1 with featured as much as 100% of 91.84%, which shows better value compared to the featured selection due to the removal of the term with low CPD value.
Penjadwalan Dinas Pegawai Menggunakan Algoritma Evolution Strategies pada PT. Kereta Api Indonesia (KAI) DAOP 7 Stasiun Besar Kediri Winda Fitri Astiti; Dian Eka Ratnawati; 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

Scheduling employee service of PT. Kereta Api Indonesia aims to plan the number of employees on duty in turns based on each service time. Employee service scheduling is designed to meet scheduling in accordance with established standard operating procedures. The problem of scheduling the employee service has a very high complexity because in scheduling many factors must be considered such as the hours of work and the number of employees required by the office on a daily basis. The data used in this study is employee data of 14 employees who will be scheduled service. The algorithm used in this research is Algorithm Evolution Strategies. In the process of Evoluiton Strategies Algorithm the representation of chromosomes used is a representation of permutations, with length of gene 98, adjusted for the number of employees and the number of days for scheduling the employee service. The process of reproduction, mutation process using insert mutation. Process valuate the fitness value obtained from the calculation of the number of penalty value of each individual while the selection process used is elitism selection. In this algorithm generate scheduling according to the rules based on the optimal parameter of population size of 80 and many generations of 70 with average fitness value of 0.2. The result of this system is scheduling for a seven-day official schedule that complies with standard operating procedures established.
Analisis Sentimen Kurikulum 2013 Pada Sosial Media Twitter Menggunakan Metode K-Nearest Neighbor dan Feature Selection Query Expansion Ranking Nurul Dyah Mentari; Mochammad Ali Fauzi; Lailil Muflikhah
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

Kurikulum 2013 has become a hot topic that is often discussed by society on Twitter. Twitter is one of the social media that used by a society to talk about a particular subject. This study attempted to analyze tweets about the Kurikulum 2013 by classifying whether it is a positive opinion or a negative opinion. Classification process is done by K-Nearest Neighbor method by using Query Expansion Ranking method for feature selection. There are 4 main processes in this analysis sentiment system that first is text pre-processing, term weighting (TF-IDF), feature selection, and classification. Based on the tests in this study proven that feature selection improve accuracy of systemresults. The best acuracy results of 96.36%was obtained when k = 1 and using a feature selection of 50% ratio. The test results by using selection feature of 50% ratio get higher accuracy than a system does not use the selection feature because some noise features that have been removed.
Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking Shima Fanissa; Mochammad Ali Fauzi; Sigit Adinugroho
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

Tourism is one of effort to promote a city. Malang currently has a branding city called "Beautiful Malang". Indonesian choose Malang tourism as a destination and review it on the website, one of them is TripAdvisor. Thus this research tried to analyze the reviews from the public about the tourism of Malang City through sentiment analysis and classified into two classes, that is positive and negative. In this research the method used is Naive Bayes with Query Expansion Ranking feature selection to reduce the number of features in the classification process. The process of sentiment analysis consists of preprocessing, feature selection with Query Expansion Ranking method, and classification with Naive Bayes. This research is testing the accuracy by using the variation of feature selection ratio, the result of 75% feature selection has the best accuracy of 86.6%.
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
Analisis Sentimen Review Barang Berbahasa Indonesia Dengan Metode Support Vector Machine Dan Query Expansion Dimas Joko Haryanto; Lailil Muflikhah; 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

Shopping an item in online store is a common activity happening to the community now. The rise of time makes someone chooses to shop online rather than having to travel to the store to get what they need. Reviews of each items in an online store can be useful to see how the buyer's previous feedback through a comment. The comments categorized as positive comments or negative comments. Therefore, to overcome the problem then used sentiment analysis reviews of items using Support Vector Machine and Query Expansion method. This research uses 400 data comments that is divided into two comment, that is positive and negative. The method used is Support Vector Macine polynomial kernel with degree two and Query Expansion. Query Expansion is used to expand a word that has synonyms that are not contained in the training data. The final test result yields an average of accuracy is 96,25% with parameter value of learning rate = 0,001, value of lambda = 0,1, value of complexity = 0,01 and maximum iteration is 50. Accuracy of Support Vector Machine and Query Expansion method is better than just using Support Vector Machine method which only gets 94,75% of accuracy.
Analisis Sentimen Kurikulum 2013 pada Twitter menggunakan Ensemble Feature dan Metode K-Nearest Neighbor M. Rizzo Irfan; Mochammad Ali Fauzi; Tibyani Tibyani
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

The 2013 curriculum is a new curriculum in the Indonesian education system that has been enacted by the government to replace the 2006 curriculum or the Education Unit Level Curriculum. The implementation of this curriculum in recent years has sparked controversy in Indonesian education, students who are demanded more actively, added lessons and other matters that lead to various opinions that develop in the community, especially on Twitter. An estimated 200 million Twitter users post 400 million tweets per day. In this research, sentiment analysis is conducted to find out the developing opinion which is divided into positive opinion or negative opinion. The features and methods used are the ensemble feature and the K-Nearest Neighbor (K-NN) classification method. Ensemble feature is a combined feature, in the form of statistical Bag of Words (BoW) and semantic features (twitter specific, textual features, PoS features, lexicon based features). Based on a series of tests, the combination of features has an impact in improving the accuracy of the K-Nearest Neighbor (K-NN) method for determining positive or negative opinions. Merging this feature can complement the weaknesses of each feature, so the final result of accuracy gained by combining both features reaches 96%. In contrast to using only features independently, the accuracy achieved only reaches 80% on Bag of Words (BoW) features and 82% on ensemble features without Bag of Words (BoW).
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