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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal Community Empowerment
Putra Pandu Adikara
Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Seleksi Fitur Alternative Accuracy2 pada Analisis Sentimen Mengenai Kebijakan Pembatasan Sosial Berskala Besar dengan K-Nearest Neighbor Restu Amara; Yuita Arum Sari; Putra Pandu Adikara
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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Pembatasan Sosial Berskala Besar or PSBB is one of Indonesian Goverment new policies to surpress the spread of COVID-19 pandemic. This policy generates lots of public opinion about pros and cons, and it became the most discussed topic in social media such as Twitter. From this public opinion, we can get the information about the act of PSBB wich can be classify as it either positive opinion or negative opinion. Sentiment analysis is used to extract information from data text, to get to know whats the point behind every opinion. Excessive data size has become the main problem about text classification, there's a step called feature selection, this step is used to eliminate the unnecessery words in data. In this research, we aim to know the effect of Alternative Accuracy2 feature selection that used with classification method like K-Nearest Neighbor (KNN) on classification result. We used data text with total about 300 public opinion and used K-Fold K-Fold Cross Validation as validation process. The average evaluation results of 5-fold for the use of the Allternative Accuracy2 feature selection, which is equal to 0,7367 for the accuracy value with 0,7667 for precision, 0,7277 for recall, and the f-measure is 0,7453 with a k value in KNN k = 47, while K-Nearest Neighbor without using feature selection resulted in 0,7167 for accuracy, 0,7467 for precision, 0,7049 for recall, and 0,7249 for f-measure. Based on these results, it can be concluded that the use of Alternative Accuracy2 feature selection can increase the evaluation value because the resulting features can clarify the characteristics of each document.
Perbandingan Pembobotan Term Frequency-Inverse Document Frequency dan Term Frequency-Relevance Frequency terhadap Fitur N-Gram pada Analisis Sentimen Randy Ramadhan; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Sentiment analysis is a method used to extract sentiments in sentences based on their content. Sentiment analysis is a method in text mining that uses a text preprocessing process after which there is a process, namely word weighting. Term Frequency-Inverse Document Frequency (TF-IDF) is the most popular word-weighting method from the unsupervised term weighting category reported which is not suitable for grouping texts. Term Frequency-Relevance Frequency (TF-RF) is a method of combining TF and RF with the aim of getting better performance, this method focuses on all documents that contain terms or do not contain terms. Twitter is a place for people to express their thoughts about the pandemic they are experiencing. Reviews about employees being sent home on Twitter need to be classified into positive, negative, and neutral reviews, which are useful for companies and government consideration to make decisions in PSBB policies. There are several stages of this research, namely preprocessing for document processing, and using unigram and bigram features as well as word weighting using the TF-IDF and TF-RF methods in classification using the K-Nearest Neighbor classification method. The data used were 246 training data and 90 test data. The best results from the evaluation comparisons obtained are using TF.RF word weighting with the unigram feature in the KNN classification with a value of K = 3, namely accuracy of 0.677, precision of 0.526, recall of 0.654, and f-measure of 0.583. Bigram value does not have a big effect in this study because the best f-measure value is obtained Bigram with a value of 0.591, and the best unigram value is with a value of 0.583.
Klasifikasi Pertanyaan COVID-19 Bahasa Indonesia menggunakan Naive Bayes Glenn Jonathan Satria; Putra Pandu Adikara; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Question Answering (QA) is a system that provide answer from question given from user. In QA there is one task called question analysis. Question analysis act as type chooser from query user input. Question analysis can be found with classification. This research using Naive Bayes as classification method. Furthermore, several process used from natural language processing such as question feature extraction and preprocessing contain data cleaning, stemming, stopword removal, and tokenization. Next phase is to build a classification model from training data which contain 16 question categories. Based on test result with 2 scenarios with preprocessing and without preprocessing, we obtained accuracy value of 0,58364 with preprocessing. We also obtained accuracy value of 0,65060 without preprocessing. Application of preprocessing in question classification have a negative impact because it changed the given question context.
Deteksi Iklan pada Twit menggunakan Metode Naive Bayes Thariq Muhammad Firdausy; Putra Pandu Adikara; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Twitter is one of the social media whose users are increasing every day. With the ease of spreading information through Twitter, some people use Twitter as a place to promote their wares by writing advertisements. These ads for some Twitter users tend to be annoying, because they are irrelevant to the information they want to read. These ads can be classified so that they can separate advertising tweets from non-advertising tweets. The ad tweet classification process is carried out using the Naive Bayes method. In the classification process, tweet data is collected to be used as training data and test data, then preprocessing is carried out on the tweet data which will then be weighted using the term frequency method. The features used in the classification with Naive Bayes are the bag-of-word feature, the textual feature is time and link, and the numeral feature is money and phone number. The classification results are obtained from the comparison of the posterior results obtained from each class. The performance level of the results obtained by the Naive Bayes method using the bag-of-word feature has a precision value 0,96, recall 1, f-measure 0,98, and accuracy 0,98.
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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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.
Temu Kembali Informasi terhadap Sinopsis Film menggunakan Metode BM25F Adinda Chilliya Basuki; Muhammad Tanzil Furqon; 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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Films were originally live images or moving photos forcing the viewer to see continuous motion between different objects quickly and successively. The world of cinema is currently increasingly being embellished with technological advances, so that public interest in watching films is also increasing. Before launching a new film, the film industry always provides a brief description first in the form of writing how the story of the film is called a synopsis. Searching a synopsis of a film that is done manually takes a long time because of the large amount of data available on social networks. This makes it difficult for users to find the most relevant synopsis to what they are looking for. The information retrieval system for the film synopsis is used to meet the needs of users in searching for information from a document. One of the document ranking methods that can be used for information retrieval is the BM25F method. Testing was performed on BM25F independent parameters. The best value in the precision@k and r-precision tests on 300 film synopsis documents against 6 queries using the free parameter BM25F, the best combination of boost values obtained in the boost variable test is in the title field = 5 and content = 1, the best bc value from bc variable testing is 0.75, and the best k1 value from the results of k1 variable testing is 1.2. The best average value for precision@k is 0.93 and for the best results in the r-precision test, the value is 1.
Analisis Sentimen Masyarakat terhadap Isu New Normal Scenario berdasarkan Opini dari Twitter menggunakan Algoritma Naive Bayes Classifier Muhammad Nurhuda Rusardi; Bayu Rahayudi; 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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The COVID-19 pandemic has spread to various countries around the world, the country of Indonesia has not escaped the ferocity of the COVID-19 pandemic, the rate of positive cases in Indonesia has experienced an increase. The government has taken various ways to suppress positive cases of COVID-19, one of which is the implementation of the New Normal. Twitter is a social media that is widely used by Indonesian people in many ways. Topics regarding the New Normal can appear on Twitter's trending topic feature because many Indonesians discuss the implementation of the New Normal in Indonesia, various opinions regarding the implementation of the New Normal scenario in Indonesia will be obtained using text mining techniques by using the Twitter API crawling, the texts data must go through data cleaning process to make easy classification process by using Naive Bayes Classifier method. At first, the data have been labelled to ease the sentiment analysis process. The last process is classification process by using Naive Bayes Classifier method. After the classification process, the predicted sentiments are evaluated using 5-fold cross validation and confusion matrix. This research yields average accuracy 0,86, precision, 0,86, recall 0,86, and f-measure 0,86.
Pengenalan Entitas Bernama pada Bahasa Madura menggunakan Algortima Viterbi dan Hidden Markov Model (HMM) Moh. Dafa Wardana; Putra Pandu Adikara; Randy Cahya Wihandika
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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Diterbitkan di JTIIK (Jurnal Teknologi Informasi dan Ilmu Komputer)
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
Klasifikasi Pengaruh Polusi Udara di Indonesia terhadap Kesehatan menggunakan Algoritme Kernel Modified K-Nearest Neighbor Fayza Sakina Maghfira Darmawan; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Air pollution has many problems, one of which is health problems. The effect of air pollution on health can have a mild, severe, and even death effect. The solution to this problem is to classify so that the amount of levels that cause air pollution gives the appropriate results of influence. The method used in this study is the Kernel Modified K-Nearest Neighbor (KMKNN) algorithm. KMKNN is a modified algorithm of KNN (MKNN) that uses kernel distance. The data used is 480 data with three features and twelve classes. In the research test, the data sharing methods used were K-Fold Cross Validation and Hold-Out. The test was performed using three different kernels. The best constant of the RBF kernel is 0,97 at constants 1 through 5. The best parameter degree of polynomial kernels is 0,97 at degrees 1 and 2. The results of the K-Fold test with each kernel provide an average accuracy at 0,87. The Hold-Out test results of each kernel provide the highest average accuracy of 70%:30% split at 0,972.
Co-Authors Adani, Rafi Malik Ade Kurniawan Adinda Chilliya Basuki Adinugroho, Sigit Adiyasa, Bhisma Adriansyah, Rachmat Afrizal Rivaldi Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo Ahmad Fauzi Ahsani Akhmad Sa'rony Al Farisi, Faiz Aulia Al Huda, Fais Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Alvandi Fadhil Sabily Amaliah, Ichlasuning Diah Amar Ikhbat Nurulrachman Ananda Fitri Niasita Anang Hanafi Andina Dyanti Putri Andre Rino Prasetyo Anggraheni, Hanna Shafira Ani Budi Astuti Annisa Alifia Annisa, Zahra Asma Arsya Monica Pravina Aulia Jasmin Safira Aulia Rahma Hidayat Avisena Abdillah Alwi Azhar, Naziha Baliyamalkan, Mohammad Nafi' Barbara Sonya Hutagaol Bayu Andika Paripih Bayu Rahayudi Bryan Pratama Jocom Budi Darma Budi Darma Setiawan Candra Dewi Candra Dewi Dahnial Syauqy Daisy Kurniawaty Danang Aditya Wicaksana Dayinta Warih Wulandari Deri Hendra Binawan Dhanika Jeihan Aguinta Dheby Tata Artha Dian Eka Ratnawati Dika Perdana Sinaga Dimas Fachrurrozi Azam Dwi Suci Ariska Yanti Dwi Wahyu Puji Lestari Dyva Pandhu Adwandha Edy Santosa Eka Dewi Lukmana Sari Elmira Faustina Achmal Evilia Nur Harsanti Faiz Aulia Al Farisi Farid Rahmat Hartono Fattah, Rafi Indra Fayza Sakina Maghfira Darmawan Febriarta, Renaldy Dwisma Ferdi Alvianda Ferly Gunawan Ferly Gunawan Firdaus, Agung Firmansyah, Ilham Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Nuring Bagaskoro George Alexander Suwito Gilang Widianto Aldiansyah Glenn Jonathan Satria Guedho Augnifico Mahardika Haekal, Firhan Imam Hanson Siagian Hendra Pratama Budianto Hernawan, Yurdha Fadhila Hibatullah, Farras Husain Husein Abdulbar Ichsan Achmad Fauzi Ika Oktaviandita Imam Cholisoddin Imam Cholissodin Imam Ghozali Imanuel Juventius Todo Gurning Indah Mutia Ayudita Indriati Indriati Indriati Indriya Dewi Onantya Ivan Fadilla Ivan Ivan Jesika Silviana Situmorang Jojor Jennifer BR Sianipar Jonathan Reynaldo Junda Alfiah Zulqornain Karina Widyawati Karunia Ayuningsih Katherine Ivana Ruslim Khalisma Frinta Krishnanti Dewi Laila Restu Setiya Wati Lailil Muflikhah Laksono Trisnantoro Lubis, Saiful Wardi Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Maghfiroh, Sofita Hidayatul Makrina Christy Ariestyani Marina Debora Rindengan Maya Novita Putri Riyanto Mayang Arinda Yudantiar Mayang Panca Rini Melati Ayuning Lestari Moch. Khabibul Karim Moh. Dafa Wardana Mohammad Fahmi Ilmi Mohammad Toriq Muh. Arif Rahman Muhammad Faiz Al-Hadiid Muhammad Fajriansyah Muhammad Iqbal Pratama Muhammad Nurhuda Rusardi Muhammad Rizaldi Muhammad Rizky Setiawan Muhammad Tanzil Furqon Muhammad Taufan Muthia Azzahra Nadhif Sanggara Fathullah Nadia Siburian Nanda Agung Putra Nanda Cahyo Wirawan Naufal Akbar Eginda Naziha Azhar Niluh Putu Vania Dyah Saraswati Novan Dimas Pratama Novanto Yudistira Nur Hijriani Ayuning Sari Nurul Hidayat Panjaitan, Mutiharis Dauber Panji Husni Padhila Pengkuh Aditya Prana Prais Sarah Kayaningtias Prakoso, Andriko Fajar Pretty Natalia Hutapea Putri Rahma Iriani Radita Noer Pratiwi Rahma Chairunnisa Raissa Arniantya Randy Cahya Wihandika Randy Cahya Wihandika Randy Ramadhan Ravindra Rahman, Azka Renata Rizki Rafi` Athallah Renaza Afidianti Nandini Restu Amara Rezky Dermawan Rhevitta Widyaning Palupi Ridho Agung Gumelar Riza Cahyani Rizal Maulana, Rizal Rizal Setya Perdana Rizal Setya Perdana Rosy Indah Permatasari Sagala, Revaldo Gemino Kantana Salsabila Insani Salsabila Rahma Yustihan San Sayidul Akdam Augusta Santoso, Nurudin Sigit Adinugroho Sigit Adinugroho Silaban, Gilbert Samuel Nicholas Silvia Ikmalia Fernanda Sindy Erika Br Ginting Sri Indrayani, Sri Sutrisno Sutrisno Tania Malik Iryana Taufan Nugraha Thariq Muhammad Firdausy Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Uke Rahma Hidayah Utaminingrum, Fitri Vergy Ayu Kusumadewi Vinesia Yolanda Vivin Vidia Nurdiansyah Wijanarko, Rizqi Yerry Anggoro Yohana Yunita Putri Yoseansi Mantharora Siahaan Yosua Dwi Amerta Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Yulia Kurniawati Yurdha Fadhila Hernawan Yure Firdaus Arifin Zahra Asma Annisa