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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 Dokumen Abstrak Skripsi Berdasarkan Fokus Penelitian di Bidang Komputasi Cerdas Menggunakan BM25 dan K-Nearest Neighbor Deri Hendra Binawan; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the process that can be implemented in text mining is categorizing text documents. Problems that related the categorizing text documents are found in universities, especially in the reading room of the Faculty of Computer Science, Universitas Brawijaya (FILKOM UB). There is no process for categorizing thesis documents automatically is one of the problem. The thesis documents categorization in FILKOM UB's reading room is still not organized according to the focus of the existing research. The categorization is completed using the BM25 and K-Nearest Neighbor methods. The process was done is pre-processing text document, calculate the BM25 score of each document, then classify them using the K-Nearest Neighbor algorithm. The testing process in this research uses 10 k-fold. Each test used 31 testing documents and 300 training documents. The average results obtained in each test produced the best results at the value of k=11 with a f-measure value is 0.9092, recall is 0.9087, and precision is 0.9265. The greater the value of k cause the classification process runs less optimally because it produces a smaller f-measure value.
Sistem Temu Kembali Informasi Pasal-Pasal KUHP Menggunakan Metode Cosine Similarity dan Pembobotan Inverse Book Frequency Billy Sabilal; Mochammad Ali Fauzi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia law based on the Kitab Undang-undang Hukum Pidana (KUHP) is a matter that must be obeyed as a people or related in their field, such as the police, judges or people associated with the trial. Kitab Undang-undang Hukum Pidana contains 569 pasal in the book that will be very inefficient and practical if you have to take it and also if you want to find related articles that have to open pages one by one manually. Based on these conditions the application developed an application using the cosine similarity method and weighting inverse book frequency. Cosine similarity method is used to calculate the similarity or proximity of article documents to queries. Weighting inverse book frequency weighting terms that consider the distribution of book collections. The value of each term is assumed to have a proportion opposite to the number of books containing that term. The performance of the system is indicated by the results of testing on each variation of 10 queries by dividing 3 1-word queries, 3 2-word queries, 3 3-word queries and 1 4-word queries tested, with performance precision values of 0.5273, recall 1, f.measure 0.6063 while the best precision@k results in the third rank of 0.6498.
Optimasi Kebutuhan Gizi Untuk Ibu Hamil Dengan Menggunakan Hybrid Algoritma Genetika dan Simulated Annealing Binti Robiyatul Musanah; Wayan Firdaus Mahmudy; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nutritional needs are very necessary for pregnant women. Good nutrition is balanced nutrition and according to needs. If the needs of pregnant women are not suitable can experience Chronic Energy Deficiency with these problems, a system is needed to fulfill the nutrition of pregnant women who can provide solutions in the form of food ingredients in accordance with the method of Hybrid Genetic Algorithms and Annealing Simulation. In the process of solving this problem at Simulated Annealing or Genetic Algorithm. This hybrid process is in a crossover process using a single intersection, mutation insertion for the mutation process and the use of elits in the selection and simulation of Annealing. The previous test results obtained parameters with the Hybrid GA and SA method obtaining the best parameter value which is equal to 100 (population size) fitness which obtained an average of 0.06268, the value of 105 (generation) obtained by average fitness0. 06823, i 4 with a fitness value of 0.6, the average is 0.06792, and the average temperature with a value of 1 and apha is 0.5 obtained by the best fitness with an average of 0.06800 along with 0.08876. The hybridization suitability of GA and SA reached 0.08804 higher than the average GA suitability value of 0.05519 and SA suitability which was 0.04382 with a specified time of 1 minute and the requirements generated from the system were insufficient for the nutritional needs of the pregnant women.
Klasifikasi Rating Berdasarkan Komentar Tempat Wisata Di Media Sosial Dengan Menggunakan Metode Fuzzy K-Nearest Neighbor Nanda Ajeng Kartini; Fitra Abdurrachman Bachtiar; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

At present, with the ease of access to information, many tourist sites use rating features to help facilitate information. Rating is used as an indicator to support quality and popularity. Users only give an overall assessment of each comment and do not provide an assessment in accordance with the aspects discussed, making it difficult for comment readers to analyze the superior aspects of the comment. From this problem, in this study a rating classification system will be made on tourist attractions using the Fuzzy K-Nearest Neighbor (FKNN) method. FKNN method is one of the development methods of the KNN method, the difference is that there is a membership class to determine the classification class. In addition, this study uses a Lexicon Based dictionary to determine feature extraction. The results of the tests in this study showed the highest accuracy of K=20 values of 60% while the accuracy of precision and recall values reached 40% and 40% respectively. In testing the K-Fold Cross Validation with 5 fold it produces an average of 51.4%.
Temu Kembali Informasi Lintas Bahasa untuk Dokumen Berita Berbahasa Indonesia-Inggris Menggunakan Metode BM25 Putri Rahma Iriani; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

News is an information about someone's needs to find out what is happening. Efforts to get relevant news from a variety of languages ​​and documents are not easy to obtain. News documents usually written in foreign language. This becomes difficult because not all users understand foreign language, while the news needed in the collection of foreign language. Users can read one by one to get news as it needed, but this process is inefficient and will take a long time. A cross-language automatic news search system is needed to solve this problem, where users only enter requests with the native language and the system will recover documents in other languages. This problem can solve by creating a system to obtain automatic news without language barriers. This system will builds using the BM25 method which has been proven to be able to improve documents that are relevant to the ranking. The free parameters used are k1 = 2.5 and b = 8.0. Weighting is done by comparing IDF BM25 and IDF modification which results in the highest value of 0.95 with k = 5 in testing of precision@k.
Klasifikasi Dokumen pada Laporan Kepolisian dengan Menggunakan Metode BM25 dan Improved K-Nearest Neighbor (IKNN) Ardhimas Ilham Bagus Pranata; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The National Police of the Republic of Indonesia is one of the law enforcers in the Unitary State of the Republic of Indonesia. One of the tasks of the Indonesian Police is to provide services to the community. Accusation of crime is one form of service to the community offered by the Police. Crime can happen to anyone no matter an employee, a student or others. The stage after the report of a crime is received by the police is the issuance of investigation. However, within one month the police had difficulty classifying every police report that have been accepted especially Polres Kota Malang. Therefore a system for helping the police to classified a accusation of crime into three cases are persecution, stealing, and fraud is needed. Process in this study is by doing a pre-processing text which the next stage is counting the weight of tf, df, and idf and continue to classification. In this study classification do by using BM25 and Improved K-Nearest Neighbor Methods (IKNN). The results of the k-fold cross validation test, the highest average value of precision=0,953373, recall=0,931382, f-measure=0,938122 and accuracy=0,956795 at the value of k = 15.
Deteksi Plagiarisme pada Artikel Berita Berbahasa Indonesia menggunakan BM25 Dina Dahniawati; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the cases that had tarnished the world of journalism was the plagiarism that had been carried out by a journalist related to the news articles he wrote. Plagiarism was not given strict observation, so that the reuse of all news articles could be carried out freely in the past. But as time goes by, news agencies are no longer able to ignore the case of plagiarism, so detection of plagiarism is very important to implement. The method used to detect plagiarism in this study is BM25. The process of calculating plagiarism using BM25 begins with text preprocessing, searches for term frequency value, inverse document frequency, weighting using BM25, then calculating the percentage of plagiarism. Testing is done by changing the threshold value by 75%, 50%, and 25%. Then the results of plagiarism calculations using BM25 will be compared with the results of cosine similarity. The average results from BM25 are closer to the threshold with a difference of 6.12%, 9.77%, and 10.01%. These results prove that BM25 works better than cosine similarity which has a difference of 14.25%, 26.43% and 32.36% of the threshold. The average value of precision from BM25 for each threshold is 0.87, 0.80, and 0.63.
Relevance Feedback Pada Sistem Temu Kembali Informasi Dokumen E-Book Berbahasa Indonesia Menggunakan Metode BM25 Tasya Agiyola; Indriati Indriati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The use of e-books on the development of digital technology makes it easy for users to get more practical information than having to use printed books. The number of e-books spread on the internet is very numerous and varied, therefore a system for retrieving information on e-book documents in Indonesian is needed. To improve the relevance of the results of the returned documents, relevance feedback techniques can be applied. Relevance feedback is a technique where users can provide feedback on previous document search results. Sorting the number of documents returned based on queries is calculated using the BM25 method. This study aims to determine the results of the application and the results of testing of relevance feedback on the retrieval system of Indonesian e-book document information using the BM25 method. Based on the test results, the AVP value after relevance feedback has decreased. In testing based on K values, the AVP value before RF is 0.592, after RF(20) is 0,558, and after RF(50) is 0.573. While in testing based on the expansion terms, the AVP value before RF is 0.593, after RF(20) is 0,587 and after RF(50) is 0.570.
Analisis Sentimen pada Ulasan "Lazada" Berbahasa Indonesia Menggunakan BM25 dan K-Nearest Neighbor (K-NN) dengan Perbaikan Kata Menggunakan Jaro Winkler Distance Desy Wulandari; Indriati Indriati; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Online shopping is one way that is currently in great demand by the public, especially in Indonesia. By shopping online, especially at Lazada stores, consumers don't need to spend a lot of time and energy. Because of the ease of technology that can now be used in shopping online. But to find out the quality of a product, consumers will see reviews of items that have been sold. Therefore with the number of consumers who write a lot of data collected so that a way is needed to be able to sort out positive or negative sentiments by doing word repairs because of the many word writing errors that we often encounter on a review. So it needs word repairs so that consumers can understand more clearly the contents of a review. In this study the researchers made the system using the Jaro Winkler Distance method which was used to improve the word and then performed scoring calculations with BM25, as well as the classification with K-Nearest Neighbor (KNN). Based on the test results get the best accuracy value of 89% with the value of F-Measure 88% in the second k-fold test with a value of k = 11. So the use of word normalization on training data and improvement of words in the test data can increase the results of sufficient accuracy better than without using word repairs and without normalizing training data.
Klasifikasi Emosi Lirik Lagu menggunakan Improved K-Nearest Neighbor dengan Seleksi Fitur dan BM25 Febrina Sarito Sinaga; Indriati Indriati; Bayu Rahayudi
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

Emotions is a person's reaction or feeling into a situation. Emotion is temporary that can occurred by a stimulus because of some people around and the environment. One of example an environment that can trigger someone's emotion is from the song being listened to. Song lyrics are the parts that can build emotions. Choosing the right words for lyrics are very important because it will create the right emotion. In this case the emotional classification of song lyricis will be done classifying process using several methods are Improved K-Nearest Neighbor, BM25 and feature selection. The proses of classification have seome stages, which is the stage of pre-processing documents, stages of calculation the BM25 score and sorting document, and the classification stage with using the algorithm is Improved K-Nearest Neighbor. The testing for classifications was done uses 6 times K-fold and use the confusion matrix. This research is the amount of training data used by 100 documents, and testing data used by 20 testing documents. In the all the tests have done obtained the best average results when the value K = 55 with a result of f-measure is 0.6693, recall is 0.6582, and precision is 0.7427.
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