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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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Pencarian Terjemahan Hadits Shahih Muslim menggunakan Metode BM-25 Bagus Abdan Aziz Fahriansyah; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hadits is second reference to moslem people. Furthermore function of hadits is explanation from alquran . but many common people difficult to find some topic of hadits because its amount very much and not everyone can memorize it. Actually paper book or electronic book can't give feedback which relevant first. In computer science there's subject that study about extracting words become a new information that we need or to know similarity a phrase with certain document namely text mining. writer wants implement one of text mining technique namely BM-25 in searching of hadits shahih moeslem . and writer wants to know how effective BM-25 algorithm for handle problem that writer give. From research that using hadits shahih moeslem as its data. Writer wants testing with five different queries and made variate of value of k and b variable that can optimize the result of query dan k variable optimum at q1=0.75 ; q2=0.5 ; q3=0.75 ; q4=0.75 ; q5=0.5 and b variable optimum at q1=0.1.2 ; q2=1.2 ; q3=1.2; q4=1.2; q5=1.2 and precision @ k that gets average precision value p@10=70%,p@20=54%,p@30=42%.
Prediksi Penerimaan Bea Cukai Menggunakan Metode Support Vector Regression (Studi Kasus Di KPPBC Tipe Madya Pabean C Jember) Dinda Adilfi Wirahmi; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Customs has the responsibility as a collector of state revenue. Revenue has an important role in supporting infrastructure development. To manage revenue, prediction is needed to make a good APBN planning. To control revenue, predictions are needed as a prerequisite for good planning of the National Budget (APBN). Prediction is used as an action to optimize and control reception. However, revenue prediction are difficult to do because of the revenue influenced by external factors that difficult to predict. Therefore, logical and accountable agreements are needed to to revenue prediction. Predictions are used to prevent actual are lower than predetermined targets thereby increasing revenue that can be controlled because it has an impact on economic growth in Indonesia. The prediction method used is Support Vector Regression (SVR). This algorithm has a strong performance to recognize time series dataset patterns and provides good prediction results if the parameters are well determined because their performance is very dependent on the parameters within them. SVR implementation in this study uses RBF kernel with parameter variation values, namely sigma = 0.13, lambda = 3.29, cLR = 0.02, epsilon = 0.00001 and C = 10, iteration = 15000 and using 4 data features produce the best MAPE <20% so that it can be categorized that SVR is accurate in predicting customs revenue.
Klasifikasi Isu Suku, Antar Golongan, Ras, Agama (SARA) pada Twitter Berbahasa Indonesia menggunakan Metode Improved K-Nearest Neighbor (K-NN) Firhad Rinaldi Saputra; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social network that has one of the most active users today. With the openness of information users move to send texts or tweets about other users, the number of Twitter users makes a lot of tweets related to ethnic issues, between groups, races, religions (SARA). Twitter cannot access the content of tweets that contain Sara's Issues, research is needed to classify tweets to understand including categories of Sara's Issues or Not Sara's Problems. Classification The Sara issue starts in several ways, namely preprocessing which consists of several stages, namely cleaning, folding cases, tokenisation, filtering and stemming. Followed by the term weghting process, to the classification process using the Improved K-Nearest Neighbor method. Based on the implementation and testing carried out in the research on Sara's Issue Classification on Twitter Using K-NN Increase, get the best results based on Precision averages of 0.976422, Remember at 1, F-Measure of 0.987944444 and Accuracy of 96%. Where the number of documents used as training data are 320 documents and test data as many as 80 documents. Where the number of documents, comparison or balance of training data and the value of k-value used determine the good or not classification process of the document.
Analisis Sentimen Pemindahan Ibu Kota Indonesia Dengan Pembobotan Term BM25 Dan Klasifikasi Neighbor Weighted K-Nearest Neighbor Marinda Ika Dewi Sakariana; Indriati Indriati; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The relocation of Indonesia capital city is one of the policies that is being intensively discussed at this time. With the policy regarding the relocation of the capital city from Jakarta to Kalimantan, it will certainly cause various reactions or comments from the community that can be found on social media, Twitter. Types of reactions can be divided into positive and negative comments. To find out a comment has a positive or negative value, sentiment analysis is needed as in this study. In this study, there are several steps that must be done to get the final results. These stages are pre-processing data, term weighting and ranking with the BM25 algorithm, and classifying the final results of tweets with Neighbor Weighted K-Nearest Neighbor (NWKNN) algorithm. This study uses 480 training data and 120 test data divided into positive and negative sentiments. The highest accuracy value obtained was 93.33% with a precision value of 92.45%, a recall of 94.67% and an f-measure of 93.55% with a K value of 25, =1,2 and =0,65 also an E value of 4.
Klasifikasi Emosi pada Komentar YouTube Menggunakan Metode Modified K-Nearest Neighbor (MKNN) dengan BM25 dan Seleksi Fitur Chi-Square Candra Ardiansyah; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

YouTube is the world's largest online video social media that is used to display various videos created by users and companies in the field of media content. Every video contained in YouTube can be done by giving a text type comment in the comments column of the video that has been watched. The large number of comments causes the content creator (video maker) to spend enough time to understand every emotion in the existing comment. After consideration of the solution used to resolve the problem, the authors chose to use the Modified K-Nearest Neighbor (MKNN) classification method with BM25 and Chi-Square feature selection. The test used is 5-fold cross validation to find the best k value which is then used for testing the Chi-Square feature selection. In Chi-Square test the data used is the best fold data based on the highest f-measure value in the 5-fold cross validation test. The results obtained are the maximum accuracy, precision, recall, f-measure values ​​achieved when k is 30, 72,82%, 72,94%, 72,26%, and 72,59%. While the Chi-Square test on the 4th fold of data the best number of terms used is 40% and 50%, with the value of accuracy, precision, recall, f-measure is 80,56%, 80,37%, 81,61 % and 80,98%.
Analisis Sentimen Terhadap Ulasan Pengguna MRT Jakarta Menggunakan Information Gain dan Modified K-Nearest Neighbor Adella Ayu Paramitha; Indriati Indriati; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mass Rapid Transit (MRT) is one of rail based public transportation that operates in DKI Jakarta. This transportation is expected to be able to reduce traffic congestion because of private car and motorcycle usage. Improvement on service quality is one of the way to attract people to use public transportation. Service quality improvement can be done by extracting positive and negative feedbacks from users using sentiment analysis. Methods used in this research are Modified K-Nearest Neighbor (MKNN) for classification and Information Gain for feature selection. Comment data will be carried out in the stages of pre-processing, vectorize, feature selection, term weighting using TF-IDF, and classification process. Based on evaluation result, we obtained accuracy value of 0,86769 and f-measure value of 0,86265 with k=3 and threshold-25% as parameter.
Temu Kembali Informasi Lintas Bahasa Dokumen Berita Bahasa Indonesia-Inggris menggunakan Metode BM25F Lusiyana Adetia Isadi; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

News is a source of information that displayed to the general public about an event and presented in various languages. Usually, a website only allows user to search only in one language. This causes problems for users who want to find broader information more quickly in several languages ​​at once. These problems can be overcome by developing a cross language information retrieval system. The system can improve the time efficiency because it can return documents in two languages ​​by simply entering a query in one language only. One of the method that can be used to develop the system is BM25F method that can return relevant documents and handle structured documents. The news data structure used in training and testing is the title and the content part of the news. The data used in this study are 300 Indonesian news documents and 300 English news documents that will be used to test the boost value, the Indonesian queries, and the English queries. For the boost value testing, the highest precision@k value obtained when the title boost is 5 and the content boost is 1. This value will be used for query testing. Query testing is performed using precision@k and got the highest value of 0.98 when k=5 in the Indonesian queries test which returned Indonesian and English documents.
Penerapan Support Vector Regression dan Particle Swarm Optimization untuk Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Daerah Istimewa Yogyakarta Rien Difitria; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The tourism sector is a contributor to national income, foreign exchange and a large provider of employment for Indonesia. With the increase in the number of foreign tourist arrivals and the value of foreign exchange tourism can strengthen the Rupiah exchange rate against the US dollar. Yogyakarta region still contributes a small foreign exchange tourism sector which is only 1.2% of all regions in Indonesia. There was an increase in visitors in 2011 which touched 508,476 visitors where in the previous year it only reached 368,906 visitors. Increasing the number of visitors accompanied by facilities and infrastructure that is inadequate or inadequate to the expectations of tourists can result in a decrease in visitor interest in the future and can threaten the economic sector of the people of Yogyakarta. Prediction of the number of tourist arrivals to the Special Region of Yogyakarta is very necessary to know the range of the number of visits in the future, so that tourism actors can prepare operations better, optimize facilities and infrastructure, and develop better marketing strategies. Prediction in this study uses the Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) methods. Prediction results from this study produce the best range of SVR parameters from Complexity (C) = 100-500, Sigma (s) = 5-20, Lamda (l) = 1-5, Epsilon (e) = 0,0001-0.1 , cLR = 0.001-0.1 iteration SVR = 500, Particles = 30, PSO iteration = 50, number of features = 3 and number of prediction periods of 1 month by producing the smallest mean Absolute Percentage Error (MAPE) value of 1.088%. The MAPE value produced in this study is less than 10% so this prediction is able to predict the number of foreign tourist visits to Yogyakarta Special Region very well.
Prediksi Permintaan Keripik Buah dengan Metode Jaringan Syaraf Tiruan Backpropagation (Studi Kasus: CV. Arjuna 999) Benita Salsabila; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The production of fruits in Indonesia tends to increase on a yearly basis. During harvest season, so much of those fruits would be left unsold or left to rot even though the sales value would also be lower than usual. Thus, a way to preserve the shelf-life of those fruits are needed so that they would not lose their value as quickly. One way to preserve fruits is to process them into dried fruit snacks, which is the expertise of CV. Arjuna 999 located in Batu, East Java. However, the process of turning real fruits into dried fruit snacks takes a while, which is why a strategy plan is needed to anticipate rising demands and the time it takes to make dried fruit snacks. The prediction uses an artificial neural network method, backpropagation. The dataset used contains of monthly dried fruit snacks demands of CV. Arjuna 999 starting from 2017 until 2019, with 80% of overall data used as training data while the other 20% is used as testing data. The result is a MAPE score of 4.429% which was derived from a combination of parameter values such as 10 (9 + 1 bias) hidden neurons, a learning rate value of 0.8 and a maximum iteration of 900.
Analisis Sentimen Penggunaan Tol Trans Jawa Periode Mudik Lebaran 2019 dengan Metode K-Nearest Neighbor dan Seleksi Fitur Information Gain Ahmad Fauzan Rahman; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the facilities provided by the government to speed up travel time is the toll road. Toll roads can also be referred to as highways that are connected from one city to another for four or more wheeled vehicles. In December 2018, the Trans Java Toll Road officially connected the two major cities in Indonesia, Jakarta and Surabaya. The new Trans Java Toll Road can be used in the period of eid 2019 will generate various opinions from its users. Many people in Indonesia use social media as one of the media to express their opinions. Thus this study tries to analyze public opinion on social media to be classified into two classes, namely positive and negative classes. The method used in this research is K-Nearest Neighbor with Information Gain Feature Selection. The classification process consists of preprocessing text including data cleansing, case folding, stop word removal, stemming and tokenization, term wighting with tf-idf, feature selection using Information Gain and classification using K-Nearest Neighbor. Tests in this study using confusion matrix produce accuracy of 85%, precision of 85%, recall of 100%, and f-measure of 91.89%.
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&#039; Dzikrulloh Nirmala Fa&#039;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