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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 Tweets Pada Twitter Menggunakan Metode K-Nearest Neighbour (K-NN) Dengan Pembobotan TF-IDF Rakhman Halim Satrio; Mochammad Ali Fauzi; Indriati Indriati
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

Twitter is a microblog that is currently favored by many people and has turned out to be a very fast spreader of information at this time. Information released and circulates through this media is very free and has many variations, like news, opinions, questions, criticisms, comments either positive or negative. Classification is a rule in text mining that collects content based on the similarity of the script. With this classification allows a tweets on Twitter to be grouped into one based on the category. For example, football, basketball and chess content are grouped into sports categories. Prosedure of classification begins using preprocessing, then term weighting is done, then categorization consists of cosine similarity calculations. Preprocessing itself consists of several phases, that is document cleaning, tokenizing, stopword removal, and stemming. The word weighting method used in this thesis is Term Frequency - Inverse Document Frequency (TF-IDF) & using K-Nearest Neighbor (K-NN) for its classification method. The KNN method is a classification of a set of data based on data learning that has been previously classified. Accuracy testing of the classification of tweets on Twitter with step of K-Nearest Neighbor (K-NN) theorem resulted in accuracy where the total data amounted to 140, with descriptions of 100 training data and 40 testing data and the values of k entered were 1, 3, 5, and 7, each the result is when k = 1, the accuration is 75.0%; k = 3, accuration is 72.5%; k = 5, accuration is 62.5%; k = 7, accuration is 55.0%.
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dan Lexicon Based Features Arifin Kurniawan; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of information technology has resulted in many people writing their opinions on social media as in the KASKUS forum. KASKUS is an online forum site that provides a place to find information and share hobbies. One is called Movies forum which contains discussions about a movie that has been watched. Users writes their opinion about a film whether the film is good or bad. These opinions can be analyzed to determine how the user feedback about the film in order to produce useful output for the filmmaker by perform sentiment analysis to classify opinions into positive or negative classes. The process of sentiment analysis was performed using methods Naive Bayes for classification and Lexicon Based Features to weight the sentiment value of a word. The process starts from text preprocessing, term weighting, Naive Bayes training, and Naive Bayes testing with Lexicon Based Features weighting using Barasa's lexicon. Based on the results of tests performed, using Naive Bayes and Lexicon Features Based method, the values of accuracy, precision, recall, and f-measure were 0.8, 0.8, 0.8 and 0.8. While using the Naive Bayes method without Lexicon Based Features, the values of accuracy, precision, recall, and f-measure are 0.95, 1, 0.9 and 0.9474. So, the use of Naive Bayes and Lexicon Based Features methods still cannot provide better results.
Pencarian Terjemahan Hadits Shahih Muslim Menggunakan Metode Cosine Similarity Dengan Seleksi Fitur Term Frequency Achmad Burhannudin; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Muslims use the Hadith as their legal basis, so the hadith can also be a way of life for all humans, especially for Muslims. Even though the hadith books are very large and thick, one of them is the Saheeh Muslim Hadits. In this modern era, most people prefer something that is efficient and easy, and computer science has been able to extract books into data that is easier to process, making it easier for people to keep reading, learning hadith, and also can be used to solve problems. without having to carry a large book. In this case, the researcher wants to apply a computer science namely Text Mining, using the Cosine similarity method as the Algorithm in the search is supported by the selection of the Term frequency feature and the researcher wants to know how effective the method is in dealing with this search problem. After the research was conducted, the researcher got the maximum results from the specified query with a precision value of p @ 10 = 90%, p @ 20 = 54%, and p @ 30 = 42%.
Bray-Curtis Distance Untuk Pencarian Resep Kue Tradisional Berdasarkan Ketersediaan Bahan Makanan Febriana Ranta Lidya; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In modern times like today there are several problems, especially regarding the introduction of traditional food which is one of the cultural heritages. Most Indonesian people today prefer modern cuisine to traditional food, especially for general snacks such as cakes. The lack of introduction of traditional cakes to the community is currently the background of a bray-curtis distance study to search for traditional cake recipes based on the availability of food ingredients. In this study the data used are food recipes obtained from Twitter users using the number of likes and retweets and are processed using the Bray-Curtis Distance calculation, and also compare with Euclidean Distance, the influence of Like and Retweet are also seen to find out whether the likes and retweets can provide better traditional cake search results. The Bray-Curtis Distance and Euclidean Distance Calculation Results for MAP produce the MAP value for Bray-Curtis Distance on Top-1 is 0.76 while for Euclidean Distance on Top-1 gives 0.18 results. In this case it can be seen that Bray-Curtis Distance is superior compared to Euclidean Distance, this is because in Euclidean Distance occurs in words that have high dimensions so it cannot work properly, because Euclidean Distance will assume that there is a high degree of dependency between vectors coordinates on the vector.
Analisis Sentimen Kepuasan Pengguna Pada Ulasan Aplikasi Marketplace Menggunakan Metode BM25F dan Neighbor-Weighted K-Nearest Neighbor Sabrina Hanifah; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobile applications are increasing in use, because many applications are offering convenience for their users. Users who already use the application have the right to review their experiences during application usage. These reviews are useful for new users and application developers. But there are no features in an app store that can classify these reviews into positive or negative categories. These problems can be solved by an automatic process that can analyze the reviews according to positive and negative reviews. The method used for ranking documents is BM25F and as a classification method the Neighbor-Weighted K-Nearest Neighbor (NWKNN) method is used. Testing done using K-fold Cross Validation method to determine the best number of k and confusion matrix for testing each parameter of BM25F and NWKNN. Based on testing conducted on each parameter the BM25F and NWKNN methods can produce a percentage of f-measure and accuracy reaches 97% and 96%. This proves that the NWKNN method can classify the dataset with an unequal number of classes.
Pencarian Resep Kue Tradisional berdasarkan Jumlah Likes dan Retweet menggunakan Metode Generalized Vector Space Model Berlian Bidari Ratna Sari B; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Traditional cakes is one diversity form of indonesia. Traditional baking requires a prescription as the basis of material needed to process. Housewives today don't know recipe and how to make traditional cakes who is now drowning by things junk food so they feel lazy to make traditional cakes. Based on the problems that have been described , carried out a search to be the recipe for food in the form of traditional cakes. To get that recipe desirable, the method of Generalized Vector Space Model is needed to determine relevance that recipe desirable. That recipe is in the training having the number of likes and retweet supporting selection a document relevant. Documents recipe used 100 data recipe with 10 types of tradisional cakes. After testing 25 documents, the best Mean Average Preecision was 0.583 using the Generalized Vector Space Model method with weighted likes and retweets. This proves that the search results approach the query entered by the user.
Klasifikasi Berita Olahraga Berbahasa Indonesia menggunakan Metode BM25 dan K-Nearest Neighbor Enggar Septrinas; Indriati Indriati; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Antara sumbar is a news portal aims to meet the public's right to get accurate information and complete information instantly. There are various kind of news presented, one of them is sports news. Rapid improvement on digital information requires prompt news dissemination. As result, web portal needs to be professionally managed. Antara Sumbar is a professionally managed web portal, however there are some shortfall, particularly on sport news categorization that causes difficulties for readers to find the news by its category. The problem can be addressed by BM25 and K-Nearest Neighbor methods. The steps to address the problem are preprocessing document news, calculate the BM25 score of each document news, and classification process using the K-Nearest Neighbor method. The testing process followed 7 k-fold method. Data used on test is 240 training documents and 40 testing documents. Performed test obtaines results in k's value as 20, with precision value = 0,921577, recall values = 0,914286, and f-measure values = 0,917917. The process of classification is affected by the number of document used and k value.
Klasifikasi Tweet Berbahasa Indonesia Berisi Ujaran Kebencian Menggunakan Metode Improved K-Nearest Neighbor dengan Pembobotan BM25F Nurdifa Febrianti; Indriati Indriati; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hate speech is a verbal hatred act that targets a group of people or parts of a particular community. In Indonesia, hate speech is increasingly found, especially on text-based social media such as Twitter. So that inspired the writing of this research, to identify hate speech on Twitter with the classification of tweets, especially those in Indonesian. The author chooses to use Improved K-Nearest Neighbor by using the BM25F term weighting, which is a weighting that considers the fields/streams in the document. So the tweet chosen as a training document and research test document, consists of 2 streams, the tweet and the hashtag. K-Fold Cross Validation testing (with K = 5) was performed on the parameter k for IKNN classification, bs, vs, and k1 for BM25F weighting, with 400 training documents and 100 test documents. The test results show that the determination of stream weight values ​​on BM25F sufficiently influences the results of the IKNN classification. Meanwhile the best final results for the F-Measure, Accuracy, Precision, and Recall of the average 5-Fold Cross Validation obtained were 79.77%, 68.80%, 68.80%, and 89.92% with k = 70, bs= 0,6, v1 = 2, v2= 5 and k1= 2 as the best value for each parameter.
Klasifikasi Emosi Lagu Berdasarkan Lirik pada Teks Berbahasa Indonesia Menggunakan K-Nearest Neighbor dengan Pembobotan WIDF Diajeng Ninda Armianti; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In a song-making, one of the main component which must be considered is lyric. Lyric in a song play a main part to deliver the emotion or meaning from the songwriter to the listener. Sometimes, the emotion means to delivered by the writer is misinterpreted by the listener. To avoid the misinterpreted song-lyric meaning manually, an automatic classification is needed. Classification is also needed to gain information about the emotion from the songs accurately. One of the method used is K-Nearest Neighbor. Before classifications process, there are several steps need to be done such as text pre-processing and weighting using WIDF method. 108 data used in this research with the ratio 1:5; in which, 18 data used for testing and 90 data used for training with the same amount of data each class. The result from 6 attempts of testing based on random K value shown the best average precision is 0,49 and the best recall is 0,53. Songs classification with WIDF weighting method shown a poor accuracy results for 66%. Ambiguity of the words and amount of data training cause the less optimal result.
Analisis Sentimen Review Produk Smartphone Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Metode K-Nearest Neighbor Dan Pembobotan Jumlah Likes Siti Robbana; Indriati Indriati; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is popular social media in great demand because it provides information needed by many internet users. Such information can be in the form of opinions, questions or review of a product's good or bad. Diverse smartphone product reviews make it difficult for companies to know people's interests and opinions on the smartphone product. To find a solution for this problem, sentiment analysis system is needed on tweets about smartphone products. This research conducted a sentiment analysis with the K-Nearest Neighbor (textual) method to carry out the classification process and add weighting features to the number of likes (non-textual). The result of combining intellectual and non-textual weighting with certain constants is α constans and β constans will produce a class of positive and negative sentiments. The data was used is taken from Twitter in the form 300 data tweets of smartphone product reviews. The test results of 210 training data and 90 test data with textual weighting obtained an accuracy of 91.01%, using only non-textual weighting of 68.53% and combining textual and non-textual weighting resulted an accuracy of 94.38%
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