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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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Prediksi Price Earning Ratio Saham Menggunakan Algoritme Kernel Extreme Learning Machine (Studi Kasus: PT TELKOM) Mentari Adiza Putri Nasution; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stock investment is one of the most populer investments nowadays. This kind of investment has the "high risk high return" characteristic which come up with a threat of loss for stock investors. There are lots of paper have been implemented related to the estimation of stock price movements, but researchers focus more on technical analysis rather than fundamental analysis which is no less essential. One of the populer methods with a fundamental approach is Price Earning Ratio (PER) method. Extreme Learning Machine is a proven method of forecasting stocks with high performance and relatively low learning speed, but this method has weaknesses in determining random weights and biases that can reduce its stability. Kernel Extreme Learning Machine offers the utilization of kernel functions that can provide high stability and performance, but with relatively low learning speed. The results of this paper provide the optimal Mean Absolute Precentage Error (MAPE) is 2.78021%, with 8 features, training and testing data ratio 90%: 10%, using the Polynomial kernel function with a value of parameter 1, and using a regularization coefficient (λ) 1000. Nested Cross Validation evaluation was also performed which provide the MAPE value is 6.385713%.
Analisis Sentimen Pariwisata di Kabupaten Malang dengan Menggunakan Metode BM25F, Neighbor Weighted K-Nearest Neighbor dan Seleksi Fitur Chi-Square Pratitha Vidya Sakta; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

City Branding "The Heart of East Java" is one of the efforts used by the Malang District Tourism and Culture Office to market the region in the context of increasing regional tourism. Ratings and reviews relating to tourism in Malang Regency are numerous in the internet. One site to give ratings and reviews is on TripAdvisor. This study tries to analyze reviews from the public about Malang Regency tourism through sentiment analysis and is classified into two classes, positive and negative. In this study, sentiment analysis is carried out in several stages or processes. The process consists of preprocessing data, word weighting which is implemented using the BM25F algorithm, Neighbor Weighted K-Nearest Neighbor for document classification and Chi-Square for feature selection. K-Fold Cross Validation was tested (with K = 5) on the parameter 𝑘 for the classification of NWKNN, bc, boost and k1 for BM25F. The test results show that the determination of stream weight values ​​on BM25F sufficiently influences the results of the NWKNN classification. While the best final results for F-Measure, Accuracy, Precision, and Recall are produced at k = 30, chi-square ratio = 40%, constant (bc) = 0.5, boost head = 2, boost body = 5 and k1 = 1.9 as the best value for each parameter
Analisis Sentimen Ulasan Produk Kecantikan Menggunakan Metode BM25 dan Improved K-Nearest Neighbor dengan Seleksi Fitur Chi-Square Dewi Syafira; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 11 (2020): November 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pengaruh produk kecantikan merupakan hal yang mulai diminati oleh kaum perempuan. Dengan kemudahan yang diberikan saat ini terdapat platform khusus berbasis web maupun mobile phone yaitu Female Daily. Female daily merupakan situs media informasi yang berisi konten produk kecantikan tentang perawatan wajah hingga tubuh untuk siap diberi ulasan oleh konsumen yang telah mencoba atau sedang menggunakan produknya. Data ulasan dapat digunakan sebagai acuan sebelum konsumen ingin mencoba produk kecantikan. Banyaknya ulasan mengakibatkan konsumen sulit untuk memilih produk yang di inginkan. Pada penelitian ini membantu konsumen untuk mengetahui data ulasan tesebut masuk kedalam sentimen positif atau sentimen negatif. Proses dalam Analisis Sentimen memerlukan metode BM25 yang digunakan sebagai pembobotan kata, Improved K-Nearest Neighbor sebagai penentuan dalam memilah sentimen dan Seleksi Fitur Chi-Square untuk mengurangi jumlah kata dalam klasifikasi pada teks. Pengujian dilakukan menggunakan 5-fold cross validation dengan hasil terbaik diperoleh pada nilai k= 15 menghasilkan rata-rata nilai sebesar presisi= 0,9, recall= 0,8, accuracy= 0,7806 dan f-measure= 0,8428 selanjutnya dari pengujian seleksi fitur Chi-Square berdasarkan persentase dengan parameter k=15 didapatkan hasil tertinggi pada persentase sebanyak 40% dan 50% dengan nilai presisi= 0,888, recall= 0,8, accuracy= 0,7818 dan f-measure= 0,842.
Temu Kembali Informasi pada Berita Olahraga Berbahasa Indonesia dengan Seleksi Fitur Term Frequency dan Metode BM25 Rachmad Ridlo Baihaqi; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 11 (2020): November 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Coinciding with the progress of globalization in the modern era now, resulting in increased use of the World Wide Web (WWW) and the internet for sources that provide information online from various countries. A search engine is needed by internet users to search for information. Therefore, resulting in the number of documents stored in digital soared. The vast reach of readers and the short time resulted in the emergence of online media in broadcasting news. The information retrieval system is the function of obtaining information desired by the user or relevant through a query that has been filled out by the user. It is expected that the implementation of Information retrieval can be used in obtaining documents in accordance with user requirements. BM25 method is to calculate the value of similarity (similarity) and then do the ranking process of the similarity of the results of the query. Added to the term frequency feature selection when training data. The test is based on the precision @k value and the kappa measure value of 10 queries. The best value obtained on the precision @k test is when k = 5, with values ​​of 90% and 86%. For the value obtained from the kappa mesure test of 0.85.
Implementasi Naive Bayes Classifier untuk Klasifikasi Emosi Tweet Berbahasa Indonesia pada Spark Rizal Aditya Nugroho; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Emotion is a natural thing that every human being has because it is a response to an event. Because emotions are owned by every human being, classifying emotions has many benefits, for example, for identifying customer complaints. Emotions can be found in textual sources such as tweets. Tweet data on Twitter itself has a size that is growing every year and a system that classifies emotions on tweets is needed that can handle the growing data quickly and accurately. In this study the classification is carried out using the Naive Bayes Classifier algorithm and also the Spark framework with the process starting from preprocessing, training to find prior and likelihood values, ​​then testing to find posterior values ​​and performing classification, and finally calculating accuracy. The Spark framework itself is used to do work in parallel for faster computing time. Based on the test results from tweet data on June 1, 2018 to June 14, 2018, the accuracy of the Naive Bayes Classifier method for the classification of Indonesian tweets on Spark has the highest average value of 0,892 when the percentage is 90% training data and 10% test data. Then the highest average value is 0,880 when using smoothing. And finally, the highest average value is 0.888 when using constant priors. Comparison of execution times from using Spark and sequentially has a very large difference that it is almost 165 times faster on Spark. In Spark, the execution time takes an average of 0,525 seconds, while in the sequential method it takes 86,564 seconds on average.
Pengelompokan Terjemah Al-Quran Departemen Agama menggunakan Metode Fuzzy C-Means Mochamad Havid Albar Purnomo; Fitra Abdurrachman Bachtiar; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Quran covers a variety of sciences so that it serves as a guide to the lives of Muslims. In reading the Qur'an, Muslims must reading the interpretation of each verse in all chapter that has similar meaning, in order to fully define the Qur'an. To make this easier, the study used the Fuzzy C-Means clustering (FCM) and Vector Space Model (VSM) algorithm. The initial process that is text preprocessing to produce the terms so that it can be used in the next process is tf-idf with normalization. Then the clustering process is done using FCM and VSM processes for interpretation search based on queries in the group of verses that have already been formed. In the test, the best value in the FCM algorithm used silhouette coefficient of 0,005146488 for all parameters including the number of clusters which is 2, the smallest error 0.001, the weighting value 2, and the maximum iteration 3. Then for VSM testing using 6 queries tested with precision@k showed that k-rank 7 produced the best precision value of 0.8333. Based on these results, it can be concluded by using FCM and VSM can be used to look for interpretations of Qur'an verses that have similar meanings.
Analisis Sentimen Penghapusan Ujian Nasional pada Twitter menggunakan Document Frequency Difference dan Multinomial Naive Bayes Rilinka Rilinka; Indriati Indriati; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

On Twitter, there was one topic that being discussed, it was about the new 2020 curriculum, elimination of The National Examination, a policy from Minister of Education and Culture of Indonesia, Mr. Nadiem Makariem. Public opinions on Twitter are matters as references for evaluating that policy on improving services from Ministry of Education and Culture of Indonesia (KEMENDIKBUD). That was why this research was conducted by analyzing the sentiment of Twitter users' opinions through tweets that they have sent about that policy and classifying it into two classes, there were positive and negative classes. The analysis sentiment consisted pre-processing, Document Frequency Difference (DFD) features selection, and Multinomial Naive Bayes classifier. The test consisted the amount of training data and testing data, it showed the best average accuracy using 600 training data and 200 testing data, was 72%. Then, the DFD testing showed the best result at threshold equal to 0.5, was 73.13%.
Analisis Sentimen berbasis Aspek terhadap Data Ulasan Rumah Makan menggunakan Metode Support Vector Machine (SVM) Salsabila Rahma Yustihan; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Internet is a huge virtual space for people to share everything to others effectively including reviews. Reviews provided by someone on the internet have a big impact on other users and company. One of the most frequent reviews in internet is restaurant reviews. One restaurant review can contain several different aspects, to find out the aspects and sentiments contained in a review, an aspect-based sentiment analysis is needed. The data used in this study is restaurant review data obtained from SemEval-2016 Task 5 with 300 training data and 100 test data. To find out what aspects are contained in a review, opinion extraction is needed by doing POS tagging and extract the document into several opinions according to the basic grammar, then to classifying aspect and sentiment contained in a review, Support Vector Machine with the One-Against-All strategy is used in this research. The results of the evaluation using confusion matrix on aspect classification and sentiment classification produce precision of 0,94 and 0,86, recall of 0,6 and 0,98, accuracy of 0,88 and 0,86, and f-measure of 0,73 and 0,92.
Analisis Sentimen Pada Ulasan Pengguna Aplikasi Mandiri Online Menggunakan Metode Modified Term Frequency Scheme Dan Naive Bayes Eka Putri Nirwandani; Indriati Indriati; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Digital distribution service is a container for various applications that can be downloaded at any time. In addition to applications on Digital distribution service, there are also application reviews that contain comments from certain application users. The review contains a very large number of negative comments or positive comments. Due to the large number of reviews, the digital distribution service shares these reviews using ratings with inappropriate review content. To solve the problem of mismatch between the content of the review and the rating given by the user, a sentiment analysis is needed. This study uses the Naive Bayes method and the Modified Term Frequency Scheme. Naive Bayes method was chosen because it works well in document classification by estimating the required parameters. Used 1,500 data consisting of 627 positive reviews and 873 negative reviews. Preprocessing process is carried out, weighting using the Modified Term Frequency Scheme and document classification using the Naive Bayes method. In the 5-fold test, the average of the method used was accuracy 83%, recall 86%, precision 76%, f-measure 77,70% with the 3rd fold being the best fold with accuracy 85%, recall 84,50%, precision 81,34%, f-measure 82,88%.
Analisis Sentimen Aplikasi E-Goverment berdasarkan Ulasan Pengguna menggunakan Metode Maximum Entropy dan Seleksi Fitur Mutual Information Abel Filemon Haganta Kaban; Indriati Indriati; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobile JKN is an e-government application that is used by the government as an innovative use of information and communication technology in government administration. Mobile JKN is an application as part of BPJS Kesehatan's commitment in providing services and easy access for BPJS Kesehatan users. Indonesians can try using the Mobile JKN application and review it on application providers such as the Google Play Store and the App Store. Reviews as a way of expressing opinions, offer data sources in the form of user sentiments regarding the features and services available on the Mobile JKN application. Sentiment analysis research is conducted to analyze the sentiments contained in user reviews by classifying reviews as positive or negative. In this research, Maximum Entropy is used as a classification method with Mutual Information as a feature selection to reduce the number of features used in the classification of user reviews of the Mobile JKN application. In testing, better evaluation results are shown by the use of the Mutual Information feature selection in the classification using Maximum Entropy with an accuracy value obtained of 82.5% compared to without the use of feature selection which results in an accuracy of 79.5%.
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