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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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Analisis Sentimen Kebijakan New Normal Dengan Menggunakan Automated Lexicon Senti-N-Gram Rifki Akbar Siregar; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Untuk dipublikasikan ke JTIIK
Klasifikasi Dokumen Berita Menggunakan Feature Hashing Dan Jaringan Saraf Tiruan Guedho Augnifico Mahardika; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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News is a report about events that are important to be a public consumption or a report about something that is simply of interest to someone. News document are usually categorized as channel with a purpose of organizing the many news documents. With the development of technology, the large number of news has made it difficult to organize news documents manually. This study is to make a classification model to organize news document automatically. The classification model used in this study is Artificial Neural Network (ANN) model with an extraction feature of Feature Hashing. The dataset used in this study has 50,926 with the training data of 80% dataset and test data of 20%. The best model that is made in this study has the accuracy of 0.789 using 1.69% feature from the entire bag-of-word feature or 2,500 feature from 159,154 feature and artificial neural network with 50 neurons in its hidden layer. The easiest classes that can be classified by the model are “Sport” (Olahraga), “Politic” (Politik), “Techno” (Tekno) with f1 measure successively 0.96 0.87 and 0.84. Classes that are the hardest to be classified are “Lifestyle” (Gaya Hidup), “Tourism” (Pariwisata) and “Education” (Pendidikan) with f1 measure successively 0.65, 0.7 and 0.71. Furthermore, feature length from the result of feature extraction and the amount of neuron in the hidden layer of the ANN have an effect on the result of model's accuracy with a logarithmic relationship. Furthermore, n-gram feature also has an effect with the best accuracy can be achieved using uni-gram while hashing method doesn't have a significant effect on model's accuracy.
Sistem Pakar Diagnosis Penyakit Gagal Jantung Kongestif, Penyakit Paru Obstruktif Kronik, Dan Asma Berdasarkan Gejala Utama Sesak Kronik Menggunakan Kombinasi Metode K-Nearest Neighbor Dan Certainty Factor Jeffrey Junior Tedjasulaksana; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Health is very important for everyone's life and if it is not cured immediately, it can interfere with activities so it can cause death. According to several studies, one of the diseases that is often experienced by everyone is a disease with symptoms of shortness of breath or difficulty breathing. Chronic shortness is most often caused by heart diseases such as congestive heart failure or respiratory disease, asthma and chronic obstructive pulmonary disease (COPD). Several studies reported that the compatibility between a diagnosis by a general practitioner in primary health care and a final diagnosis by a specialist is only less than 50%. So in this study an expert system was made to diagnose congestive heart disease (CHF), chronic obstructive pulmonary disease (COPD), and asthma using a combination of the K-Nearest Neighbor method to classify diseases with the Certainty Factor method to determine the level of confidence from the previous classification results using 20 symptoms. The data used is patient data at Jumpandang Baru Public Health Center in Makassar City with a total of 100 data. The best accuracy results in testing variations in the K value are 100% when the K value is 3 and the results of testing the comparison of accuracy when using a combination of the K-Nearest Neighbor - Certainty Factor method and when only using the K-Nearest Neighbor method it produces the same accuracy value.
Analisis Sentimen pada Ulasan Aplikasi Mobile JKN Menggunakan Metode Maximum Entropy dan Seleksi Fitur Gini Index Text Muhammad Mauludin Rohman; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Mobile JKN application is a form of BPJS Kesehatan's commitment in providing services and ease of access for BPJS Kesehatan users. BPJS Kesehatan in organizing the health insurance program since 2014, can be assessed how the people of Indonesia make use of health insurance implementation facilities through the JKN Mobile application based on user reviews of the application. Sentiment analysis needs to be done to analyze reviews provided by app user. This study used the Maximum Entropy classification method coupled with the Gini Index Text for feature selection. Sentiment analysis consists of data collection process, text preprocessing, word weighting with raw tf, followed by feature selection using Gini Index Text, then classification using Maximum Entropy with features obtained from the previous feature selection. The results of this study are that the best accuracy value is obtained when using the number of features or threshold of 80%, with a value of evaluation as an accuracy of 85,36%, a precision of 92,18%, a recall of 75,59%, and f-measure of 82,85%.
Analisis Sentimen Tanggapan Masyarakat Aplikasi Tiktok Menggunakan Metode Naive Bayes dan Categorial Propotional Difference (CPD) Junda Alfiah Zulqornain; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The TikTok application is a social media that is currently often discussed among the public. Starting from the young to the old using this TikTok application. The TikTok application is an application that can share videos, but usually videos that have vulgar elements can be seen by minors because the TikTok application does not start from age. Therefore the authors conducted a sentiment analysis on the TikTok application reviews to help parents in selecting applications for their children. From these problems the authors conducted a sentiment analysis using the Naive Bayes method and the Categorial Propotional Difference. Research testing using 5-Cross Validation with a variation of the terms used. The maximum results obtained using the term 100% are used for testing with an accuracy of 0,729947, a precission value of 0,746854, a recall value of 0,926118, and an f-measure value of 0,824511.
Analisis Sentimen Masyarakat Terhadap Mass Rapid Transit Jakarta Menggunakan Metode Naive Bayes Dengan Normalisasi Kata Tania Malik Iryana; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Since the Jakarta mass rapid transit (MRT) was operated, there have been various opinions from the public regarding facilities and services of the Jakarta MRT which have been conveyed through social media such as Twitter, Instagram, TikTok, and YouTube. In writing opinions on social media, errors are often found such as slang words, abbreviated words, and typographical errors which can complicate the classification process. Based on that thing, this research will use word normalization with a dictionary of slang words and abbreviations and also use word normalization with Peter Norvig. Opinions that have been conveyed by the public must be analyzed properly in order to produce useful information for PT MRT Jakarta. In conducting sentiment analysis, a classification method is needed and the classification method to be used is the Naive Bayes method. Testing in this research used 5-fold and for each fold 200 training data and 50 test data were used. Based on the test results, it can be concluded that the classification is better to use word normalization because the existence of word normalization can equate two words that have the same meaning so that it can increase the weight of the words. The 5-fold average evaluation results of the Naive Bayes classification with word normalization using a dictionary of slang words and abbreviations and also word normalization using Peter Norvig yielded 0.903 for precision, 0.944 for recall, 0.922 for f-measure, and 0.903 for accuracy.
Analisis Emosional Pelajar terhadap Pembelajaran Daring Dengan Menggunakan Latent Semantic Indexing (LSI) dan N-Gram Afif Musyayyidin; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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In the industrial era 4.0 a lot affects human activities, especially among students. Technology applied in the world of education is online learning. Online learning is a learning method implemented in communication media both asynchronously both text and video. In order for this learning to be more effective and much better in the future, they provide a place to provide input or feedback in the form of criticism and suggestions on social media such as YouTube, Twitter and Facebook. To find out whether online learning is getting more effective, a student emotional analysis is carried out on online learning. In this study, the Latent Semantic Indexing (LSI) method was used in classifying the emotional of students and added the N-Gram method in word selection. The process in this emotional analysis includes data collection, text preprocessing which is useful in producing clean data, N-gram, weighting using the term weighting method, Single Value Decomposition (SVD), Latent Semantic Indexing, Vector Support Machine (VSM) which results in a classification process. . The data used in this study are primary data sourced from social media such as Youtube, Twitter and Facebook. The best results occur when the N-Gram is a combination or combination. From the 5 Fold, it was obtained an average accuracy of 77%, precision 76%, recall 78% and f-measure 77%.
Analisis Sentimen Ulasan Pengunjung Simpang Lima Gumul Kediri menggunakan Metode BM25 dan Neighbor-Weighted K-Nearest Neighbor Inosensius Karelo Hesay; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Simpang Lima Gumul (SLG) is a monument that has become an iconic building as well as a tourist destination in Kediri. Visitors who come can provide reviews on Google Review SLG to help the manager know the advantages and disadvantages of existing infrastructure. However, the SLG manager does not have a system that can automatically classify positive and negative reviews. This problem can be solved by using a sentiment analysis system. The sentiment analysis system used in this study uses Neighbor-Weighted K-Nearest Neighbor (NWKNN) and BM25 methods. The stages of this system include preprocessing process, weighting TF-IDF, ranking using BM25, and classification process using NWKNN. The number of data used is 1000 data, with the division of 800 training data and 200 test data. The test was carried out using 5-fold cross validation to test the k and exponential values in the NWKNN method and the k1 and b values ​​in the BM25 method. Based on the tests carried out on each tested parameter, it was found that the best value for the parameter value k1 = 1.2, b = 0.5, k = 20, and exponent = 2. The combination of these parameter values ​​produces an average value precision of 0.9509, recall of 0.9589, accuracy of 0.93, and f-measure of 0.9548.
Peramalan Debit Inflow Waduk Gajah Mungkur menggunakan Metode Extreme Learning Machine Yudha Irwan Syahputra; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The Gajah Mungkur Reservoir is one in all the most largest dams or reservoirs in Java which is categorized as a multipurpose reservoir. With various benefits, it is necessary to forecast the inflow discharge to avoid excess or shortage of water in the reservoir as well as errors in water disposal. A common mistake is the release of water which can cause flooding in areas lower than the reservoir. Discharge forecasting can also be used to plan water allocations such as power generation and irrigation. Changes in inflow discharge that occur are always fluctuating, so from these problems forecasting inflow discharge is needed to overcome the large amount of water discharge that comes out. The data used is inflow discharge from January 2009 to December 2019 and the method used is Extreme Learning Machine (ELM) because it has fast learning speed and good generalization. The test results obtained are the optimal number of features as many as 7, the optimal number of hidden neurons as many as 9, with the percentage of training data 80% and 20% of the test data producing RMSE 28.7303, MAD 21.8002 with a runtime of 0.0272s. With an RMSE value that is far from zero, the error rate obtained is high and bad. And also with a runtime that is in seconds or less than seconds, this research also confirms that ELM has advantages in fast learning speed.
Klasifikasi Ulasan Palsu menggunakan Borderline Over-sampling (BOS) dan Support Vector Machine (SVM) Aisyah Awalina; Fitra Abdurrachman Bachtiar; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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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