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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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Klasifikasi Film Berdasarkan Sinopsis dengan Menggunakan Improved K-Nearest Neighbor (K-NN) Nurul Muslimah; Indriati Indriati; Randy Cahya Wihandika
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

Movie is audio visual communication media, which imply the message the movie creator wants to convey. Movie has several genres namely romantic, horror, thriller, comedy, fantasy and so on. Not a few movie connoisseurs are still confused about the differences in these genres. This resulted in many movie lovers who were difficult to distinguish the genre of movie so that the message in the movie could not be fully conveyed to the audience of the movie. Therefore, the classification of movies based on the synopsis of the movie can be one of the solutions to the problem. Classification in the movie synopsis will help in grouping movies with the appropriate genre. The genre genre classification process based on the synopsis begins with preprocessing, then weighting the term to classification with the Improved K-NN method. Based on the implementation and testing conducted in the movie classification research based on the synopsis using Improved K-NN which uses 250 documents as training data and 50 documents as the test data the best results are precision = 1, recall = 0.88, f-measure = 0.936170213, and an accuracy rate of 88%. As well as comparison with K-NN, it was proven that classification using the Improved K-NN method was better than the K-NN method.
Analisis Sentimen Konten Radikal Di Media Sosial Twitter Menggunakan Metode Support Vector Machine (SVM) Ferdi Alvianda; Indriati Indriati; Putra Pandu Adikara
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

Lately, there are many terrorist threats by the radicals in Indonesia. Radicals keep growing by numbers each day as they share their radical beliefs to other people. These radical beliefs can be shared through social media, such as Twitter. Therefore, a research regarding that problem is conducted. Documents of Twitter that contain radical tweets are classified to two categories, positive radical content and negative radical content. The method used for this research is Support Vector Machine (SVM) with Polynomial Degree Kernel. The highest accuracy rate achieved from this research is 70% with the parameter value of λ is 0,1, constant value of γ is 0,1, maximum iteration of 5 with training data sets of 80 documents (60 negative documents and 20 positive documents) as training data sets and 20 documents (15 negative documents and 5 positive documents) as testing data sets.
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.
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%.
Identifikasi Jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Rizky Nur Ariyanti; Indriati Indriati; Randy Cahya Wihandika
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

Growth and development in early childhood certainly affects how a child is when reaching adulthood both in terms of mental, physical, and intelectual. In the development phase of course not all children experience normal development, there may be a developmental disorder. One developmental disorder that is often experienced in early childhood is ADHD (Attention Deficit Hyperactivity Disorder). For ADHD itself there are three types, among others Inattention, Impulsive, and Hyperactivity. In this research will be identification type of ADHD based on symptoms that appear by using method of classification of Modified K-Nearest Neighbor (MKNN). MKNN method is one method of development of the KNN method, which distinguishes the MKNN there is a validity process and also weight voting of each type to be classified. In this study will be done type identification consisting of 4 types include Inattention, Impulsive, Hyperactivity, and Not ADHD. The results of this study indicate that MKNN method can identify ADHD type well when the data used is 80 data with 20 test data, K = 3 with 90% accuracy. In this study also proves that MKNN method tends to be lower accuracy than KNN method, it is caused by low validity value which will affect weight voting and also accuracy.
Algoritma Genetika Untuk Optimasi Fuzzy Time Series Dalam Memprediksi Debit Air (Studi Kasus: PDAM Indramayu) Mohamad Alfi Fauzan; Budi Darma Setiawan; 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

The availability of water in the country of Indonesia reaches 694 billion m3 per year, where the amount is a potential that can be utilized but only about 23% is utilized. With the increasing number of people needing clean water but low water debit distribution, the concept of forecasting or prediction is needed as one of the inputs in making decisions to increase the flow of water to be distributed. To solve these problems in this study fuzzy time series methods are optimized with genetic algorithms in predicting the distribution of water discharge. Genetic algorithm is used to optimize sub intervals in fuzzy time series. Based on the results of the test, the accuracy of the prediction results obtained using the Average Forecasting Error Rate (AFER) method obtained the percentage error rate of 15.33% which included in the good qualifications.
Implementasi Algoritma Genetika untuk Optimasi LVQ pada Penentuan Kelayakan Kredit (Studi Kasus: Bank X) Aghata Agung Dwi Kusuma Wibowo; Candra Dewi; 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 determining debtor credit, if there is an error in the debtor's analysis, it will cause problems such as bad credit in the future. So, it needs more accurate selection in the analysis of debtors who deserve credit. A more rigorous and consistent analysis takes longer due to the large amount of analytical data. To obtain a more accurate analysis and more efficient analysis time, it can be done by making a credit analysis system using the Learning Vector Quantization (LVQ) method to classify data and determine debits that are eligible for credit. To obtain accurate credit results, the use of the LVQ method depends on the weight. Analysis with LVQ method shows the accuracy value obtained is 79.37% by testing 63 test data. To obtain optimal accuracy values, the weights used in the LVQ method are optimized first with genetic algorithms. Optimal weight test results obtained a higher accuracy value of 93.65% for testing with popsize 20 parameters, Cr 0.9, Mr 0.1 and number of generation 10.
Klasifikasi Kemacetan Lalu Lintas di Kota Malang Pada Sosial Media Twitter Menggunakan Metode Improved K-Nearest Neighbor Riska Dewi Nurfarida; Indriati Indriati; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social media network that has many users that can be used for communication media. And from Twitter you can also get various forms of information including negative and positive opinions and various other types of information. One of the information that can be obtained from Twitter is information about traffic conditions. Malang City community uses Twitter social media as one of the media to get information about traffic conditions. Through the @PuspitaFM account, the people of Malang City share information about the state of traffic around them. From the @PuspitaFM account, every day I will share tweets about traffic conditions in Malang City either by tweeting directly or tweets from followers that will be retweeted by the @PuspitaFM account. Of all the tweets that exist, sometimes there is confusion that occurs in the categorization of traffic jams or not jammed in the tweet. Therefore, the classification of tweets is jammed or not jammed as a solution to the problem. There are several processes carried out in this study, namely starting from prepocessing text which is divided into cleansing, case folding, tokenisation, filtering and stemming processes. The process will continue with the term weighting or weighting process, followed by normalization, cosine similiarity and classification processes with the Improved K-NN method. The results obtained from this study are recall value of 0.42857, precision value of 0.71428, f-measure value of 0.53571 and the best accuracy of 65.33%. The training data used is 600 tweet documents, and 150 test data tweet documents.
Klasifikasi Kemacetan Lalu Lintas Kota Malang Melalui Media Twitter Menggunakan Metode Neighbor Weighted K-Nearest Neighbor (NW-KNN) Putu Amelia Vennanda Widyaswari; Indriati Indriati; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social media that is still widely used today. Like other social media Twitter is useful for making friends, exxchanging messages, and information about various things such as entertainment, economy, politics, and so forth. Twitter is also useful for finding information about the state of traffic on a road by accessing traffic accounts on Twitter. However, tweets are often found with ambigous words about the condition of the road. So tweets needs to classified to make it easy for road users. Classification begins with doing preprocessing stages on training and test documents, then proceeding with weighting TF-IDF until the classification stage using the NW-KNN (Neighbor Weighted K-Nearest Neighbor) method. Based on the implementation and testing carried out on the study of Malang City Traffic Congestion Classification Through Media Twitter Using Neighbor Weighted K-Nearest Neighbor (NW-KNN) method which uses 600 training data and 150 test data, obtained results of 0.7336507 for the average precision, 0.2210526 for recall, 0.3002686 for f-measure, and accuracy obtained at 0.665.
Rekomendasi Resep Masakan Berdasarkan Ketersediaan Bahan Masakan Menggunakan Metode N-Gram dan Cosine Similarity Ratna Tri Utami; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cooking recipes are the guidelines of a housewife in making a dish. Many recipes that there are easy for housewives to cook. But the reality, there are still a lot of housewife who doesn't understood the compatibility between the composition of cooking materials with dishes to be made. So it takes innovation to facilitate the search for a recipe in accordance with the composition of the available ingredients. It can be included in a form of information retrieval system. N-gram and cosine similarity methods can be used to match the available ingredients with the recommended recipes. Excess cosine similarity method didnt affect by the short length of a text document, because it just calculated only the term value of each document. The N-gram method consists of 3 types of processes: unigram, bigram, trigram which are serves for word processing. In this research, a model for recommendation of relevant recipes using N-gram method and cosine similarity was developed. The tests performed were the measurement of similarity and threshold determination. The results obtained that the system succeeded in calculating the similarity with the value of cosine 0.9. The greater of the value so it closer to the recommendation of the recipe in accordance with the query. From the third results of the best N-gram process is unigram with a threshold value is greater than or equal to 90% and a recall value of 1 and precision 0,2. It can be concluded that unigram is the best N-gram method process to recommend the recipes based on the ingredient.
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