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All Journal Journal of Environmental Engineering and Sustainable Technology JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Electrical Engineering and Computer Science Community Empowerment Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI)
Indriati Indriati
Fakultas Ilmu Komputer, Universita Brawijaya, Malang, Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Sistem Rekomendasi Dosen Pembimbing Berdasarkan Dokumen Judul Skripsi di Bidang Komputasi Cerdas Menggunakan Metode BM25 Anak Agung Bagus Arisetiawan; Indriati Indriati; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the text mining there is a process for information retrieval. Problems related to information retrieval are found in universities, especially in the Faculty of Computer Science, University of Brawijaya (FILKOM UB). The problem is the selection of the thesis supervisor for the FILKOM UB Informatics Engineering S1 study program in the interest of Smart Computing is still done manually. Determination of supervisors only relies on personal knowledge related to the specialization of lecturers needed to guide during the execution of the thesis. These problems can be solved through a recommendation system based on information retrieval using the BM25 method. The process carried out is document preprocessing, calculation of BM25 score in each document, and taking the highest BM25 scoring result as much as k. In this study three tests were carried out. Each test uses the same testing data of 20 documents. The average results of each test obtained the best recommendation results, namely at the value k=3, with a value of precision @k of 0.87. The higher the value of k used can affect the recommendation results to be less optimal because more and more irrelevant documents are counted.
Klasifikasi Tingkat Stres Berdasarkan Tweet pada Akun Twitter menggunakan Metode Improved k-Nearest Neighbor dan Seleksi Fitur Chi-square Mohammad Imron Maulana; Indriati Indriati; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Today, social media has become a new lifestyle for modern society. One of the most popular social media is Twitter. The character limitation when tweeting on Twitter makes the message conveyed by its users short, solid and clear. Thus, the complaints of users as outlined in a tweet can be analyzed using text classification which can then be used as a new method to determine the stress level of a person. In which up to this time, to determine the stress level is still done manually using a questionnaire system. The text classification used in this study is the Improved k-Nearest Neighbor method which is an improvisation of the k-Nearest Neighbor method which has a weakness in the use of k values for all classes. In addition, to improve the accuracy of the system and eliminate less relevant features, chi-square feature selection is used which can eliminate less relevant features without reducing the accuracy of the system. From 5 feature ratio tests, the best value is obtained at the feature ratio of 25% and k-value = 20 with an average precision value of 70%, the average recall is 67.2%, the average accuracy is 83.3%, and the average f-measure is 66.3%. From this research, it can be concluded that feature selection can increase the average precision, recall, accuracy, and f-measure.
Asosiasi Tempat Wisata Dengan Kata Kunci Di Malang Raya Dengan Metode Association Rule Mining Menggunakan Algoritme FP-Growth Fardan Ainul Yaqiin; Fitra Abdurrachman Bachtiar; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tourist attraction in Indonesia is growing rapidly. This growth made tourists hard to choose a destination. FP-Growth is an algorithm that uses frequent patterns in forming associations. FP-Growth can be used in the search for tourist attractions associations with its keywords, where these associations can later be used by tourists for reference in choosing destinations, also can be used by managers to improve their services. This study uses commentary data as a set of transactions as input for the formation of associations. In the resulting rule withdrawal, only suffixes of tourist attractions is taken so that the rule produced is the association of tourist attractions with its keywords. Testing is done to determine the effect of minimum support and minimum confidence on the number of rules formed and the average value of lift ratio. The conclusion of this study is the FP-Growth algorithm could be implemented in the formation of association of tourist attractions with keywords. The average lift ratio value of the association formed is 2.77, which means that the association is considered beneficial. In addition, the results of the associations obtained will later be used by managers of tourist attractions to improve their services.
Deteksi Emosi Pada Twitter Menggunakan Metode Naive Bayes Dan Kombinasi Fitur Fera Fanesya; Randy Cahya Wihandika; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Emotion shapes human behavior in general and very important in life. Detecting emotions provides an important role in various aspects because it can be applied in various fields such as decision-making, predicting human emotions conditions, providing a review product quality, tracking support for political problems, and recognizing depression disorders. Identifying emotions can use textual data that is text, text can be used to communicate and declare information. The social media that used to exchange information is Twitter. Twitter contains information about human attitude and human emotions. Therefore, emotional detection is needed to determine human emotions using Naive Bayes method and feature combinations. This research using several Naive Bayes classification models namely Bernoulli Naive Bayes for binary data types and Multinomial Naive Bayes for discrete data types. Feature Combination used in this research is as follows: linguistic features, orthographic features, and N-gram feature combinations. The best accuracy result obtained a value of 0.555 that is in testing N-gram feature combinations. While the combination of features including linguistic features, orthographic features, and N-gram features produced an accuracy value of 0.5317 which means this value was better than testing with a single feature and lower than testing the N-gram feature combinations. This is due to the influence of linguistic features, orthographic features, and N-gram features. Based on these results it can be concluded that by using combination features can cover the weaknesses of each feature that can improve the performance of accuracy even though the increase is not too significant.
Analisis Sentimen Pada Ulasan Aplikasi Mobile Banking Menggunakan Metode Support Vector Machine dan Lexicon Based Features Katherine Ivana Ruslim; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis is a very popular field of research in text mining. The basic idea of ​​sentiment analysis is finding the polarity of the document and classifying it into positive or negative. The text documents used in the research are reviews on the Google Play Store regarding the mobile banking application. Support Vector Machine is a method used and added Lexicon Based Features as additional feature besides using the Bag of Words. The research data is 500 data by dividing 90% training data and 10% test data. The system evaluation results obtained with a combination of Bag of Words and Lexicon Based Features are higher than the results of system evaluations that only use the Bag of Words and systems that only use Lexicon Based Features. The evaluation results obtained by the combination of the two features with testing using 10 fold cross validation are accuracy = 0,846, recall = 0,846, precision = 0,864, and f-measure = 0,855 with the Support Vector Machine parameter value used is the best parameter value of sigma kernel RBF = 3, lambda = 0,1, gamma = 0,001, complexity = 0,1, epsilon = 0,001, and iteration = 50.
Analisis Sentimen Review Shopee Berbahasa Indonesia Menggunakan Improved K-Nearest Neighbor dan Jaro Winkler Distance Liana Shanty Wato Wele Keaan; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The recent development in technology has allowed technology to give ease the lives of the general public. One of the conveniences of the development is an online shopping system. Shopee is one of the available online shopping platforms. Shopee's application has provided convenience in terms of buying and selling one's products online thru a smartphone where all of those features can be accessed. In online shopping activities, the price of sold products is equally important to its quality where it is mostly shown thru reviews. Unfortunately, many consumers have difficulty in understanding certain reviews from other consumers rooting from the usage of non-standard language. Therefore, this research focuses on sentiment analysis research of reviews in the form of text which will be divided into two classes, which are positive and negative. The analysis process is started by preprocessing, word weighting thru TF-IDF, followed by normalization, and cosine similarity using the Improved K-Nearest Neighbor and Jaro Winkler Distance to repair words that are not in the standard language. Based on testing of the value of k which is acquired thru evaluation using 5-fold, the optimal value is k=10, after word repairs were done, were a value of 0,876 for accuracy, precision value are 0,810, a recall score of 0,942, and f-measure score of 0,882. Based on the testing results, the accuracy values were fluctuate which were affected by the value of k-values.
Analisis Sentimen Tentang Opini Performa Klub Sepak Bola Pada Dokumen Twitter Menggunakan Support Vector Machine Dengan Perbaikan Kata Tidak Baku Swandy Raja Manaek Pakpahan; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Football is one of the most popular sports in the world, including in Indonesia. A football club is very dependent on its supporters so that the satisfaction of supporters of a football club must be maintained. Supporters of football clubs themselves often provide arguments to a football club via Twitter media. Therefore, the authors propose research to build a sentiment analysis system for football club performance opinions on Twitter documents. This research uses the Support Vector Machine method and Levenshtein Distance for non-standard word correction. The process starts with preprocessing the data, then do word correction with Levenshtein Distance, weighting using Term Frequency-Inverse Document Frequency, followed by classification using Support Vector Machine. The test results with the highest accuracy were obtained at 83.25% with learning rate = 0,0001, complexity = 0,001, lambda = 0,1, epsilon = 0,0001 and maximum iteration = 50.
Klasifikasi Komentar Body Shaming Beauty Vlogger Pada Youtube Menggunakan Metode BM25 dan K-Nearest Neighbor Pengkuh Aditya Prana; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beauty vlogger is a term for people who do vlog activities to discuss beauty issues and make up tutorials on YouTube. Beauty vloggers often get body shaming comments. In Indonesia, body shaming comments are a violation regulated in the Electronic Information and Transaction Act (UU ITE). Body shaming comment classification system can help to classify body shaming comments more efficient and faster. Body shaming comment classification system in this research uses the BM25 and K-Nearest Neighbor methods. Process in this research are pre-processing each data to look for words that are characteristic for each data, then calculate the term frequency based on the number of words contained in each data, then calculate the inverse document frequency, then calculate the BM25 score and sorting the data. The last step is to do the K-Nearest Neighbor classification. This study uses 600 data comments with 300 data on body shaming class, and 300 data on not body shaming class. The average of all k-fold cross validation tests obtained the highest value, namely precision = 0.87153019, recall = 0.86666667, f-measure = 0.86606885, and accuracy = 0.86666667 at value k = 3. The value of testing using balanced data is much better than testing using unbalanced data, with the highest average value of testing unbalanced data, namely precision = 0.84306693, recall = 0.775, f-measure = 0.7582337, and accuracy = 0.775.
Analisis Sentimen terhadap Ulasan Hotel menggunakan Boosting Weighted Extreme Learning Machine Riza Cahyani; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Along with the increasing competition in hotel business, every hotel tries to improve their quality for increasing their profits. Hotel can improve their quality by understanding hotel reviews that written on the internet. However, the variety of types of review made hotels difficult to analyze the type of sentiment on review. In addition, the distribution of sentiment types in the reviews was unbalanced. Therefore, analysis sentiment is carried out to determine the sentiment of hotel reviews easily. The method that used by researcher is Boosting Weighted ELM because this method can handle unbalanced class. Sentiment analysis determine by doing some pre-processing, term weighting, normalization, and classification. Testing process were carried out using k-fold cross validation with k is 5. Data that used were 500 data consisting 343 positive class and 157 negative class. Testing result shows that the model is produced with the highest f-measure value is 0,953. Optimal value of each parameter are C =16, L = 64 and weak learner = 256.
Klasifikasi Hoaks Berbahasa Inggris menggunakan Boosting Weighted Extreme Learning Machine Luthfi Mahendra; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rapid technological developments have caused hoax to be more easily disseminated through the internet, especially for politic related news. Although it looks trivial, hoax can cause various kinds of problems such as community riots and the blocking of social media sites. To overcome the problems that can be caused by hoaxes, this study attempts to create an automatic English language hoax classification system using the Weighted Boosting ELM algorithm. The algorithm was chosen because it has high accuracy results for various types of document classification problems and has good results even if the data used has an unbalanced number of classes, making it suitable for hoax classifications which are fewer than factual news. The research methodology is divided into several stages consisting of pre-processing, term weighting, normalization, training and algorithm evaluation. The data used are 180 articles consisting of 90 hoax and 90 factual news. Evaluation was carried out by measuring F1 values ​​(results of average harmonic precision and recall) using K-Fold cross validation, the highest results obtained were 0,787.
Co-Authors Abdul Azis Adjie Sumanjaya Abel Filemon Haganta Kaban Achmad Arwan Achmad Burhannudin Achmad Ridok Ade Wahyu Muntizar Adella Ayu Paramitha Adinugroho, Sigit Afif Musyayyidin Aghata Agung Dwi Kusuma Wibowo Agus Wahyu Widodo Ahmad Afif Supianto Ahmad Fauzan Rahman Ahmad Nur Royyan Aisyah Awalina Alaikal Fajri Nur Alfian Alfita Nuriza Alvin Naufal Wahid Anak Agung Bagus Arisetiawan Andhika Satria Pria Anugerah Andre Rino Prasetyo Anggara Priambodo Jhohansyah Anjelika Hutapea Annisa Selma Zakia Ardhimas Ilham Bagus Pranata Arief Andy Soebroto Arifin Kurniawan Arinda Ayu Puspitasari Arthur Julio Risa Ashshiddiqi Arya Perdana Avisena Abdillah Alwi Ayu Tifany Novarina Bagus Abdan Aziz Fahriansyah Bayu Rahayudi Benita Salsabila Berlian Bidari Ratna Sari B Beta Deniarrahman Hakim Billy Sabilal Binti Najibah Agus Ratri Binti Robiyatul Musanah Brian Andrianto Budi Darma Setiawan Candra Ardiansyah Candra Dewi Chandra Ayu Anindya Putri Choirul Anam Daneswara Jauhari Dea Zakia Nathania Deny Stevefanus Chandra Deri Hendra Binawan Desy Andriani Desy Wulandari Dewi Syafira Dhaifa Farah Zhafira Dhony Lastiko Widyastomo Diajeng Ninda Armianti Dian Eka Ratnawati Dina Dahniawati Dinda Adilfi Wirahmi Durrotul Fakhiroh Dwi Suci Ariska Yanti Dyah Ayu Wulandari Edo Ergi Prayogo Edy Santoso Eka Putri Nirwandani Enggar Septrinas Erma Rafliza Fajar Pradana Faradila Puspa Wardani Fardan Ainul Yaqiin Febriana Ranta Lidya Febrina Sarito Sinaga Fera Fanesya Ferdi Alvianda Feri Angga Saputra Firda Oktaviani Putri Firda Priatmayanti Firhad Rinaldi Saputra Fitra Abdurrachman Bachtiar Frans Agum Gumelar Galuh Fadillah Grandis Ghiffary Rizal Hamdhani Guedho Augnifico Mahardika Hilmy Khairi Idris I Made Budi Surya Darma Imam Cholissodin Indah Mutia Ayudita Indriya Dewi Onantya Inosensius Karelo Hesay Jeffrey Junior Tedjasulaksana Jeowandha Ria Wiyani Joda Pahlawan Romadhona Tanjung Junda Alfiah Zulqornain Katherine Ivana Ruslim Khaira Istiqara Khalisma Frinta Kornelius Putra Aditama Ksatria Bhuana Lailil Muflikhah Liana Shanty Wato Wele Keaan Liana Shinta Dewi Liana Shinta Dewi Linda Pratiwi Ludgerus Darell Perwara Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Mahdarani Dwi Laxmi Mahendra Okza Pradhana Mardji Mardji Marinda Ika Dewi Sakariana Marji Marji Mentari Adiza Putri Nasution Merry Gricelya Nababan Moch Bima Prakoso Mochamad Havid Albar Purnomo Mohamad Alfi Fauzan Mohammad Birky Auliya Akbar Mohammad Fahmi Ilmi Mohammad Imron Maulana Muhammad Abdurasyid Muhammad Fauzan Ziqroh Muhammad Hakiem Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Tanzil Furqon Muhammad Yudho Ardianto Nadya Oktavia Rahardiani Nana Nofiana Nanda Ajeng Kartini Nanda Cahyo Wirawan Ni Made Gita Dwi Purnamasari Ni Made Gita Dwi Purnamasari Nihru Nafi' Dzikrulloh Nirmala Fa'izah Saraswati Novanto Yudistira Novia Agusvina Nur Intan Savitri Bromastuty Nurdifa Febrianti Nurina Savanti Widya Gotami Nurudin Santoso Nurul Hidayat Nurul Muslimah Pengkuh Aditya Prana Prais Sarah Kayaningtias Pratitha Vidya Sakta Puteri Aulia Indrasti Putra Pandu Adikara Putri Rahma Iriani Putu Amelia Vennanda Widyaswari Putu Rama Bena Putra Rachmad Ridlo Baihaqi Rahma Chairunnisa Rahmat Arbi Wicaksono Rakhman Halim Satrio Randy Cahya Wihandika Ratih Karika Dewi Ratna Tri Utami Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rien Difitria Rifki Akbar Siregar Rilinka Rilinka Riska Dewi Nurfarida Riski Nova Saputra Riyant Fajar Riza Cahyani Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Haqmanullah Pambudi Rizky Nur Ariyanti Sabrina Hanifah Salsabila Rahma Yustihan Sigit Adinugroho Sinta Kusuma Wardani Siti Robbana Sutrisno Sutrisno Swandy Raja Manaek Pakpahan Tania Malik Iryana Tania Oka Sianturi Tasya Agiyola Thio Marta Elisa Yuridis Butar Butar Titus Christian Vera Rusmalawati Wayan Firdaus Mahmudy Yane Marita Febrianti Yobel Leonardo Tampubolon Yudha Ananda Kresna Yudha Irwan Syahputra Yudha Prasetya Anza Yuita Arum Sari Yulia Kurniawati Zahra Swastika Putri