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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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Pengelompokan Tweets mengenai Covid-19 dengan Metode BM25 dan K-Means Clustering Kornelius Putra Aditama; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The COVID-19 pandemic has hit Indonesia, many community activities are carried out at home. At that time, people often expressed their concerns through social media. One of the popular social media is Twitter which has a Tweets feature. Indonesian people who use Twitter use Tweets to write various opinions on the situation caused by COVID-19, be it government policies, vaccines, new variants of COVID-19 and so on. The diversity of these Tweets can be reflected in a section or group based on the context of the Tweets. The results of grouping Tweets can get opinions that are often expressed by the public about COVID-19. Where the results of the analysis can be used as reference material for the government in making policies during the COVID-19 pandemic. In this grouping using the BM25 method as a weighting and measuring Tweets. And K-Means Clustering where this algorithm is used. The results of the analysis and testing show that the number of terms must be reduced because the number of terms is a description of the many features used. a major feature that causes the BM25 method to be unable to distinguish the data. With the number of terms 20, parameters BM25 k1 = 1.2 and b = 0.5 and with a value of K = 3 will get the highest Silhouette Coefficient value, which is 0.3003
Analisis Sentimen Angket Kepuasan Pasien Puskesmas menggunakan Metode Improved K-Nearest Neighbor Prais Sarah Kayaningtias; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The questionnaire is a data collection media done by giving written questions. Some agencies this day still have not used digital media as a medium. The use of digital media technology for filling out questionnaires can ease employees' work in the process of sentiment analysis, where data is automatically stored in the database and generates sentiment analysis results filling out the questionnaire. Based on this problem, we need systems that can automatically perform sentiment classification to make it easier for employees to get the report results used as an evaluation of the Local Government Clinic. The classification executed through several stages starting from pre-processing which consists of cleaning, case folding, tokenization, filtering, and stemming. Then proceed with the calculation of term weighting and cosine similarity to the sentiment classification process using Improved K-Nearest Neighbor method. Based on the results of the implementation and testing of the system using k-fold cross-validation get an average Precision value of 0.7657, a Recall value of 0.8088, F-Measure value of 0.7847, and accuracy value of 0.7738. In testing the amount of training data, the best test results were obtained with an average Precision value of 0.8544, Recall value of 0.78, F-Measure value of 0.8151, and an accuracy value of 0.823. It takes accuracy in determining the documents used as training data on the system to produce optimal accuracy. Some words in the document still use non-standard words, causing the system to be wrong in classifying sentiments. It would be nice if the documents used training data were normalized first to obtain optimal results.
Information Retrieval Hadis Terjemah Bahasa Indonesia dengan Metode Vector Space Model dan Hybrid Fuzzy C-Means Algoritme Genetika Alaikal Fajri Nur Alfian; Fitra Abdurrachman Bachtiar; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Dipublikasikan di JTIIK (Jurnal Teknologi Informasi dan Ilmu Komputer)
Analisis Sentimen Masyarakat Indonesia tentang Vaksin Covid-19 di Twitter dengan menggunakan Metode K-Nearest Neighbors dan Seleksi Fitur Chi Square Ksatria Bhuana; Indriati Indriati; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The disease outbreak caused by the corona virus (2019-nCov) or commonly called COVID-19 is an outbreak of a disease that causes infection in the lungs. The topic of the COVID-19 vaccine has become a hot topic of discussion for the majority of Indonesian people. One of the biggest social media platforms, Twitter, has become a place for the aspirations of the Indonesian people to express their opinions regarding the COVID-19 vaccine. Therefore, a sentiment analysis system is needed to examine polarities of publics response and to facilitate the data analysis process. The data analyzed comes from the opinion of the Indonesian people on Twitter as many as 1482 tweets with the distribution of training data totaling 1185 and test data totaling 297. The data will then be grouped based on 3 sentiment classes there are negative sentiment class, neutral sentiment, and positive sentiment. Before starting sentiment analysis process, the data set used will be preprocessed including case folding, cleaning, tokenizing, filtering, and stemming. Furthermore, chi square feature selection is applied to eliminate unimportant features or terms, then proceed with TF-IDF weighting. After weighting the TF-IDF, then calculating the cosine similarity, and for the last stage, applying the KNN method approach to find the classification results. The results of the confusion matrix evaluation produce accuracy with a value of 88.5522%, precision with a value of 88.18%, recall with a value of 89.95%, and f-measure with a value of 89.05%.
Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naive Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based Abdul Azis Adjie Sumanjaya; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a very popular social media platform in the current millennial era. Twitter is widely used as a means to express opinions and criticisms on issues that are currently being discussed. At the beginning of July the government had made efforts to handle COVID-19 in Indonesia by establishing a policy Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat. Such a policy is very necessary considering the spread of the COVID-19 virus is still high, especially in big cities. But on the other hand, the limitation of activities as part of the policy has a very large impact on the community, especially with the addition of the extension of the policy which makes people bored because they find it difficult to carry out activities. For this reason, this research conducted a sentiment analysis to see the tendency of public sentiment during the implementation Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat policy in Indonesia using the Naive Bayes classification method and the addition of the Lexicon Based feature. The provision of the Lexicon Based feature aims to filter sentimental words, so that data processing becomes faster. Based on the test results obtained, through the division of cross validation with the confusion matrix test, the accuracy is 0.75, precision is 0.76, recall is 0.76, and f-measure is 0.75. The use of the stopword feature has an influence on the classification results, because the use of the stopword feature can eliminate some of the terms resulting from the Lexicon Based feature which causes a reduction in term variations so that the accuracy results obtained are lower than without using the stopword feature.
Analisis Sentimen terhadap Karyawan Dirumahkan pada Media Sosial Twitter menggunakan Fitur N-Gram dan Pembobotan Augmented TF - IDF Probability dengan K-Nearest Neighbour Rahma Chairunnisa; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis is one of method that is often used to determine the sentiment in a sentence based on its analysis. Sentiment analysis is one of the methods in text mining that uses the process of text preprocessing and then continued the process of word weighting. The case of corona virus or COVID-19 in Indonesia has reached 6 thousand more on Saturday afternoon (18/4/2020). A total of 11 regions in Indonesia apply PSBB with Jakarta as the first city to do it. Many companies finally do layoffs with the employees due to the pandemic corona virus. There are exposed Termination of Employment (PHK), laid off, working part, cutting salaries, and so on. There are some stages in this research, namely preprocessing for processing of documents, and use the features of the unigram and a bigram and by weighting words using the Augmented TF - IDF Probability and will be clarified by using the method of K-Nearest Neighbour. The Data are used as much as 250 training data and 100 test data. The best results obtained from this study with values of K = 3 to unigram, is accuracy worth 0.68, precision worth 0.415, recall worth 0.404, f-measure worth 0.406. And for the value of a bigram that accuracy is worth 0.776, precision is worth 0.591, recall is worth 0.408, f-measure is worth 0.437. The selection term unigram and a bigram is very influential on the results of this study. So that the visible results of each evaluation value that has been done have a considerable difference in value
Analisis Sentimen IMDB Movie Reviews menggunakan Metode Long Short-Term Memory dan FastText M. Aasya Aldin Islamy; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Current technological developments make it easier for humans to explore a lot of information using the internet such as review information or opinions about films. Public opinion about the film can be found on the IMDB website. By doing a sentiment analysis on public opinion about the film, we can conclude whether a film gets more positive or negative opinions. To perform this sentiment analysis, one of the deep learning methods is used, namely Long Short-Term Memory (LSTM) with FastText as a vector representation of words in the IMDB movie reviews dataset of 50,000 data. Performance using the Long Short-Term Memory and FastText methods produces an accuracy of 0.863; precision of 0.865; recalls of 0.861; and f1-score of 0.863. This LSTM and FastText method produces better performance than using LSTM alone with a difference of 0.053 on the f1-score value with details of accuracy reaching 0.808; precision reaches 0.804; recalls reached 0.816; f1-score reaches 0.810 for the LSTM method only.
Pencarian Dokumen Skripsi menggunakan BM25 dan Faceted Search berdasarkan Kata Kunci Abstrak (Studi Kasus: Universitas Muhammadiyah Sidoarjo) Mohammad Fahmi Ilmi; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A thesis is a graduation requirement for students to complete their studies. However, along with the much research that students have carried out, many universities are not ready to provide a forum that can make it easier for students to access the required thesis files, one of which is the Muhammadiyah University of Sidoarjo. To overcome this problem, the researchers developed a program that focuses on finding the thesis document with the help of Information Retrieval. Information Retrieval can help sort out which thesis data is appropriate and will group it into data groups given to seekers. As an improvement, this program is also given an additional feature in the form of category grouping, which is done using Faceted Search based on the keywords of each document. In this research, the BM25 method is used to prove whether this method can produce good accuracy in searching for student thesis data. The tests carried out on 25 queries resulted in the most considerable average value at the value of k=5, with a value of 0.928. This shows that the search results for the most relevant documents are collected in the top 5 documents.
Klasifikasi Stres berdasarkan Unggahan pada Media Sosial Twitter menggunakan Metode Support Vector Machine dan Seleksi Fitur Information Gain Jeowandha Ria Wiyani; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stress can happen to anyone and prolonged stress can cause mental health problems. However, many people continue to be unwilling to consult with mental health professionals about their concerns instead opting to complain on social media, such as Twitter. Many people use Twitter to vent their frustrations, making it possible to utilize text classification to determine someone's stress level from their tweets. In this work, the Support Vector Machine technique with Information Gain feature selection is used for text categorization. The data used in this study were 87 documents with details of 29 'Heavy' class documents, 29 'Medium' class documents, and 29 'Light' class documents. With a k value of 5, the test was run using the K-Fold Cross Validation method, and the distribution of training and test data was 80:20. The comparison of the results between the Support Vector Machine method alone with the combination of the Support Vector Machine and Information Gain methods produces the best accuracy on the Support Vector Machine method alone with an accuracy of 59.11%, precision of 29.99%, recall of 38.67%, and f-measure of 33.53%.
Klasifikasi Jenis Barang Bekas menggunakan Metode Naive Bayes dengan Seleksi Fitur Information Gain (Studi Kasus : Akun Instagram Jual Beli Barang Bekas @infobarkas_Jogja) Muhammad Fauzan Ziqroh; Indriati Indriati; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Instagram is one of the most popular social media for marketing. The rapid development of this social media function gave rise to a new term, known as Social Media Influencer. Social media influencers are advertising agencies that reach a certain number of Instagram account followers so that advertisers can place their ads on the account by paying a certain amount of fees. Instagram account of @infobarkas_jogja is one of the social media influencers that provides paid advertising services with advertising content about used goods in Yogyakarta City. However, the management of the account has several obstacles, one of which is the classification or grouping of the goods based on the types of category.The purpose of this research is to create a system that is able to classify types of goods based on their categories. This research uses the Naive Bayes Classifier method using the IG feature selection.The data for this study is in the form of text taken from the caption posts on the Instagram account @infobarkas_jogja with a total of 500 data.With a total of 400 training data and 100 testing data. The classes for the categories in this study were divided into 5 classes, namely property, vehicles, clothing, furniture, and gadgets. The threshold used is 10% ranging from 10% to 90% and produces the highest accuracy of 98% when the threshold is 10%, 40%, 80%, and also 90%. The highest accuracy is also obtained when carrying out classification without resorting to feature selection.
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