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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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Query Expansion Pada Line Today Menggunakan Algoritme Ide-Dec-Hi dan Ide-Regular Nana Nofiana; 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

LINE TODAY is an online news site that provides up to date news articles and comes from trusted news sources. To make it easier to find news, a search engine is needed so news is quick to get. But in the search process, sometimes the queries entered are not in accordance with the results of news articles obtained. So it is necessary to do query expansion to help get more specific news articles, query expansion will expand previously ambiguous queries to be more structured. Query expansion begins with preprocessing stages, followed by weighting TF.IDF, as well as cosine similarity to the calculation of the idea-dec-hi and ide-regular method. Based on the implementation and testing carried out on the Query Expansion research on LINE TODAY, Using Ide-Dec-Hi and ide-regular Algorithm by utilizing 200 training data and 25 queries that are in accordance with the document, then the results for idea-dec-hi method that is the precision value is 0.6622, recall is 0.2314, f-measure is 0.2987, and the accuracy is 0.9506. Where as for ide-regular method results in precision values of 0.6635, recall of 0.0146, f-measure of 0.0279, and accuracy of 0.9488. Accuracy values generated using the ide-dec-hi method increase up to 0.18% compared to the ide-regular method.
Klasifikasi Teks Bahasa Indonesia Pada Dokumen Pengaduan SAMBAT Online Menggunakan Metode Naive Bayes dan Kombinasi Seleksi Fitur Hilmy Khairi Idris; Mochammad Ali Fauzi; Indriati Indriati
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

SAMBAT Online is a form of e-Government realization in Malang City. SAMBAT Online or The Integrated Online Community Application System is a platform of a website provided by the Malang City Government to receive complaints, criticisms, suggestions, or questions to the government. Each incoming report will be grouped manually by the SAMBAT Online system manager. Grouping is based on The Regional Work Unit (SKPD) which is handled manually. Therefore, a classification system was built to save time in the process of grouping reports to SKPD using the Naive Bayes method and the Combination of Feature Selection between Chi-Square and Information Gain. In the tests conducted, the system succeeded in providing better accuracy results when using feature selection than without using feature selection with an accuracy value of 83.33%. Furthermore, when a feature selection combination is performed, the results of the accuracy obtained are the same as the results without a combination of 83.33%. So, the combination of selection has not been able to provide better results.
Klasifikasi Ujaran Kebencian pada Twitter Menggunakan Metode Naive Bayes Berbasis N-Gram Dengan Seleksi Fitur Information Gain Muhammad Hakiem; Mochammad Ali Fauzi; Indriati Indriati
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

Hate speech is one of the topics that often discussed in information technology. Hate speech has been usually used by the people that don't like or hate with someone or a group. People stated their hate speech with post it in social media. One of the most used social media to spread the hate speech is Twitter. Hate speech identification is needed to decrease the spread of hate speech. The method used in this research is Naive Bayes based on N-gram and feature selection Information Gain. N-gram features that used in this research are Unigram, Bigram, and combination unigram-bigram. 250 data are used in this research with hate speech label and 250 data with non hate speech label and have 80% proportion for data training and 20% for data testing. The best accuracy results in this research come from Unigram feature and without feature selection Information Gain. The best accuracy result is 84%, precision value 92%, recall value 79,31%, and f-measure value 85,18%. Based on the results obtained it can be concluded that to classify hate speech in Twitter using Naive Bayes has the best result with Unigram feature and without using feature selection Information Gain.
Optimasi Peramalan Metode Backpropagation Menggunakan Algoritme Genetika pada Jumlah Penumpang Kereta Api di Indonesia Mohammad Birky Auliya Akbar; Indriati Indriati; Ahmad Afif Supianto
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

The train is a kind of massive land transport with a lot of users, base on the results presented by Statistics for Safety Index and service reached 4.09 from 5 in year 2014, also supported by the fact that exposed by the daily Tempo (www.bisnis.tempo.co) indicating that the train users from time to time inCreased. However, with the inCrease in the number of passengers on top of the train without any prediction will be bad for the train in Indonesia. For this need a method of predicting the results that can be answerable, using popular methods such as artificial neural network Backpropagation and optimizations to do in determining the initial weights (W) with Using numbered variables 800 for the population, 20 for a number of generations, the composition of the value of Mr = 0.3 and Cr = 0.7, with the main variant of the Backpropagation artificial neural network that consists of multiple iterations is 100 and a value of Alpha is 0.9, also with dataset on a monthly basis, start from January 2006 to June 2017 in timeseries form data, with 100 training data pattern as initial data and 10 pattern of test data of last data. So the result is the level of precision based on error value (MSE) results 0.065869861 from the results of the hybridization method backpropagation artificially neural networks using a genetic algorithm, while without using the hybridization error value is 0.072517977.
Analisis Sentimen Pada Ulasan Aplikasi BCA Mobile Menggunakan BM25 Dan Improved K-Nearest Neighbor Indriya Dewi Onantya; 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

Mobile banking application is one of the applications that can provide convenience in human activities. One of these applications is BCA Mobile. This application makes users easily to do financial activities without having to go to the relevant bank. This is an option that is very useful for users who have a busy life in their daily lives. From existing mobile applications, there are no features that can be used to group or filter positive and negative reviews. To find out reviews that are classified as positive or negative reviews, a sentiment analysis review is needed. The analysis process begins with pre-processing data, weighing words using the BM25 algorithm, and the process of classification using Improved K-Nearest Neighbor. The results obtained based on the result of 5-fold cross-validation and get the best k-value at 10, with the result of precision value are 0.946, recall value is 0.934, f-measure value is 0.939, and an accuracy is 0.942. These results get fluctuating measurement results because of the amount of k-value. However, it does not influence by the amount classes of data, because even though there are different amounts or proportions of data classes, the new k-value adjust to the amount of data based on the value of each class.
Implementasi Algoritme Modified K-Nearest Neighbor (MKNN) Untuk Mengidentifikasi Penyakit Gigi Dan Mulut Muhammad Reza Ravi; Indriati Indriati; Sigit Adinugroho
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

Teeth and mouth are the most important parts of the human body that must be maintained and cared for. But the problem of dental and oral diseases in Indonesia still needs attention. There are several types of dental and oral diseases. The most common diseases suffered by the people of Indonesia are cavities, dental caries and periodontitis. The causes of dental and oral disease are poor hygiene of the teeth, eating foods and drinks that contain high carbohydrates, smoking, consuming alcoholic beverages, brushing teeth incorrectly and also growing imperfect gums. It has symptoms, among others, the teeth become more sensitive, the emergence of an erratic pain, and often feel pain or pain when biting something. In this study, identification of the types of dental and oral diseases determined from symptoms experienced using the classification method Modified K-Nearest Neighbor (MKNN). The MKNN method is the development method of the NNC, there are differences from MKNN and KNN namely MKNN there is a process of calculating validity and Weight Voting. This study used 6 classes which included Pulpitis, Gingivitis, Dental Caries, Periodontitis, Deposits, and Pulp Necrosis. This study proves that in the training data as many as 70 and 30 test data and K = 60, the MKNN method can identify the types of dental and oral diseases by reaching 86.6%. This study also proves that the MKNN method tends to be more accurate compared to the KNN method where the MKNN method has an accuracy rate of 76.66% while the KNN is 43.33%. this is caused by the calculation of the validity value which will affect the Weight Voting and also the accuracy.
Pencarian Berita Berbahasa Indonesia Menggunakan Metode BM25 Khalisma Frinta; 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

Rapid technological developments have resulted in increased use of the internet as a source of online information providers from around the world. Users use a search engine for searching information. These developments also make digital document storage increase. News is a source of information about an event or opinion that has important and interesting value to be widely publicized through the mass media. The unlimited reach of readers and the efficiency of time makes the various media reports turn to online media. Information retrieval aims to produce documents that are relevant to the needs of users of a collection of information automatically based on keywords in the queries given by users. The application of information retrieval is expected to facilitate information retrieval and obtain accurate results. BM25 is a system in the ranking process that is used to sort the results of a match (similarity) to all training documents based on the query. The BM25 method is categorized as the best method in the best match class. Tests are based on precision @k values ​​and r-precision values ​​for 12 queries. The best test results for precision @k values ​​when k=5, which is 0.83. While the best results for r-precision values ​​are 1.
Peringkasan Teks Otomatis Pada Artikel Berita Hiburan Berbahasa Indonesia Menggunakan Metode BM25 Desy Andriani; Indriati Indriati; Muhammad Tanzil Furqon
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 most often activitiy carried out by Indonesian internet users is reading news. More than 50% of Indonesian internet users use the internet to read news. However, problems will arise if the content of the article is a long text so that the reader needs time to read and understand the contents of the article. One way that users can still read and understand the contents of articles quickly is by reading the summary. Therefore we need an automatic text summarization system in entertainment news articles with the aim of emphasizing the main information and helping the reader get the main information from the text quickly and don't need to read the entire contents of the text or document. This study uses the BM25 method which is a method of weighting sentences that sort sentences based on terms that appear in each sentence in the document. BM25 is using tf idf weighting for word weighting and the relationship between terms and each sentence in the document is influenced by free parameters k1 and b. Based on the test results it was found that summarizing the text with the BM25 method obtained the best average precision result, recall and f-measure values ​​when the value of the compression rate used was 30%. Where the average values ​​of precision, recall, and f-measure are 0,730, 0,738 and 0,734.
Query Expansion Pada Sistem Temu Kembali Informasi Dokumen Jurnal Berbahasa Indonesia Menggunakan Metode BM25 Faradila Puspa Wardani; Indriati Indriati; Bayu Rahayudi
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

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) is a collection of scientific papers from the research Faculty of Computer Science's students, Universitas Brawijaya. In each journal there is an abstract section which is a summary of the entire contents of the scientific work document, which contains concepts, problem statements, approaches, and conclusions that are arranged. The number of journals contained in J-PTIIK is very large, therefore a document search system is needed to facilitate users in finding journal documents, a search system that implements an Information Retrieval (IR). To minimize the results of returning documents that are not relevant to the IR, query techniques can be applied, called query expansion techniques. Application of query expansion can be done by using BM25 method. BM25 is an IR that is used to sort the results of the relevance documents to the query that user want to search. This research was conducted by aiming to see the results of query expansion in information retrieval of Indonesian journal using BM25 method. The result, when adding words for queries as many as 4 words, the Precision @ K value increases by an average of 0.492.
Analisis Sentimen Tentang Kebijakan Ganjil Genap Kendaraan Bermotor di DKI Jakarta Pada Twitter Menggunakan BM25 dan K-Nearest Neighbor Dwi Suci Ariska Yanti; 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

Traffic congestion occurs in many places throughout Indonesia, especially in its capital region of Jakarta. Many strategies have been executed by the government of the capital region as a mean to solve the ongoing traffic congestion problem, one of them is the 'odd-even' policy. On the other note, the problem has inflicted a wide social media complains among Jakarta's residents. In this case, Twitter is considered as a relatively fast and effective social media platform to post opinions used by many Indonesians. Considering its large number of users and easy access to public's opinions, Twitter will have a lot of public's opinions' data which can be used as a material to evaluate the 'odd-even' policy in the capital region of Jakarta. Therefore a method which can separate sentiment from user is needed. It's to answer whether the sentiment is categorized as positive or negative class. In this study, the researcher used BM25 method and K-Nearest Neighbor (KNN) as classifiers. The best test results for f-measure values are 66,1% while the results of accuracy is 66,5%.
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