Article Per Year (5 Year)
p-Index From 2021 - 2026
8.568
P-Index
This Author published in this journals
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

Published : 173 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 173 Documents
Search

 10   11   12   13   14 
Pemerolehan Informasi Artikel terkait Covid-19 dengan menggunakan Metode Vector Space Model dan Word2Vec untuk Query Expansion Franklid Gunawan; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

COVID-19 has shaken the world since the end of 2019. There are still many people who are not aware of the risk of COVID-19 despite the updated information. To prevent misinformation, trusted access is required. The amount of information provided in an information access is also not small in numbers. From those problems, a system is needed that can make it easier for people to find desired information in accessing the information provided. One system that suitable for the problem is COVID-19 articles information retrieval based on keywords provided by users. The method that can be used to build an article information retrieval is the Vector Space Model combined with Query Expansion using Word2Vec. The stages of article information retrieval system are pre-processing the dataset, word weighting, training the Word2Vec model, performing Query Expansion, calculating similarity between document and query, and sorting the document articles. The process will produce 10 news article documents related to COVID-19 that have similarities between its content and the keyword from user, the test results that get the best precision@10 and recall@10 is when the system uses 500 hidden neuron for Word2Vec training and 40 words added at the Query Expansion stage.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Klasifikasi Diagnosis Penyakit Diabetes Gestasional pada Ibu Hamil menggunakan Algoritme Neighbor Weighted K-Nearest Neighbor (NWKNN) Vinesia Yolanda; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Gestational diabetes is a state of high blood sugar levels that occur during pregnancy. The presence of this disease is common and usually occurs in the 24th to 28th week of pregnancy. However, the condition of this high blood sugar level cannot be underestimated because it can cause several complications that can harm both mother and baby. In addition, untreated gestational diabetes can also increase the risk of type 2 diabetes for both mother and baby in the future. The cause of the onset of gestational diabetes is not certain. However, gestational diabetes is a multifactorial disease which the presence can be caused by various factors that play a role in increasing the risk of this disease. Therefore, gestational diabetes becomes difficult to diagnose because doctors need to consider these factors, analyze them, and compare them with previous patients under similar conditions. Eventually, the diagnosis depends on the doctor's interpretation and is prone to human error. A solution that can be applied is by using a classification algorithm that can identify the presence of gestational diabetes. Pima Indians Diabetes Dataset is a dataset that is widely used in some research of diabetes prediction. This dataset has a characteristic of imbalanced data, so that Neighbor Weighted K-Nearest Neighbor (NWKNN) can be applied to the dataset. By deleting data containing missing value and testing the value of K and E of NWKNN, the best results for sensitivity was 0,8125, specificity was 0,8788, and F1 score was 0,7879 were achieved at K = 25 and E = 2. Meanwhile for k-fold cross-validation testing, the NWKNN algorithm was found to be better than K-Nearest Neighbor (KNN). The best results were obtained by 4-fold cross-validation test i.e. sensitivity was 0,6043, specificity was 0,8703, and F1 score was 0,6383.
Deteksi Konten Negatif di Twitter Menggunakan Support Vector Machine dan Pemisahan Hashtag dengan Algoritme Pipeline Hanson Siagian; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Social media is one of the most used media to get information in Indonesia. The high number of social media usage makes the risk of spreading negative content even greater. This was shown in 2018 the Ministry of Communication and Information received 547.506 complaints of negative content on social media where Twitter became the first most complained social media. The number of complaints creates problems if it has to be checked manually. Therefore, the authors propose research to build a negative content detector on Twitter documents. This research uses the Support Vector Machine method and Pipeline for hashtag segmentation. The process starts with preprocessing the data, then do hashtag segmentation with Pipeline, weighting using Term Frequency-Inverse Document Frequency, followed by classification using Support Vector Machine. In this research the test was carried out by K-Fold Cross Validation using 300 data divided into 10 fold. The test results with the highest accuracy were obtained at 0,8325 with learning rate = 0,0001, complexity = 0,001, lambda = 0,1, epsilon = 0,0001 and maximum iteration = 50.
Pemerolehan Informasi pada Alkitab Berbahasa Indonesia Menggunakan Metode BM25 dan Query Expansion Word2Vec Pretty Natalia Hutapea; Putra Pandu Adikara; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 5 (2021): Mei 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Indonesian state officially recognizes six religions, namely Islam, Protestantism, Catholicism, Hinduism, Buddhism, and Confucianism. In Protestantism, the Bible is used as its holy book. In carrying out their worship, Protestants must have a close relationship with God through reading and studying the Bible. A Bible book contains a lot of information, so a system is needed to access and manage that information quickly and accurately. The information retrieval system can be used to get results relevant to user needs through query input. Queries entered by the user sometimes do not return relevant results due to errors in the query, so a query expansion technique that is capable of expanding or modifying the query is required. The modeling used for query expansion is Word2Vec, while to obtain relevant document results, the document is ranked using the Best Match 25 (BM25) method. Tests conducted in this study compared the document output when using query expansion and without using query expansion. The test results of this study get the highest results when not using the query expansion with the Precision @ 10 and MAP values ​​of 0.86 and 0.93.
Sistem Rekomendasi Film Menggunakan Content Based Filtering Muhammad Fajriansyah; Putra Pandu Adikara; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth in the number of cinema audiences is increasing in line with the large number of films being produced. Various films with plot stories, genres, and film themes that are similar or different have enlivened the industrial market from overseas to domestic film. Of the many films produced, it makes potential viewers confused and difficult to find and determine what film to watch next so that they spend more time looking for films. Some people use the features provided on some sites to search for movies to decide which movie to watch. Everyone has different tastes and tends to choose to watch movies that are similar to the movies he likes. One way to get the right information about a film is a recommendation system. Each film has some information in the form of different genre films and synopsis films. In this study, to obtain the recommendation results using a content based filtering algorithm by looking for the similarity in weight of the terms in the bag of words result of pre-processing film synopsis and film title. The weighting is carried out using the TF-IDF method which has been normalized. Then the weighting results will go through the cosine similarity stage to look for similarities based on weights and end with filtering based on genre. Based on the results of tests carried out by involving three participants with a total number of films as many as 4000 film titles, the accuracy value is obtained using the mean average precision @K (MAP @ K) is 0.823254 for the single query type and 0.7500556 for the multiple seed query type. From these results, it is found that the single query type produces better recommendations than the multiple seed query type.
Rekomendasi Lagu Berdasarkan Lirik Lagu Menggunakan Metode N-gram dan Cosine Similarity Jesika Silviana Situmorang; Putra Pandu Adikara; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Listening to songs is one of the human activities that is often carried out by humans. Song is an art that has a pitch or sound that has elements of poetry sequences and a combination with one or several combinations of musical instruments. The lyrics in the song usually contain several verses that have their own meaning for the songwriter. The development of songs has now progressed and made music and song lovers increasingly like songs or music. This happens because of the smartphone that allows song enthusiasts to listen to songs online and offline. But the number of songs available makes music lovers have limitations in choosing songs in the music player. This problem requires an innovation that makes it easy to search for songs based on lyrics that suit the user (music lover). This problem can be solved in the form of an information acquisition system. Song recommendation model can automatically select songs based on lyrics, making it easier for users to search for the desired song. The research for this song recommendation model used the N-gram method (unigram bigram and trigram) and cosine similarity. Song lyrics will go through the preprocessing stage then Term Frequency - Inverse Document Frequency (TF-IDF) so that the words in the song lyrics are selected first. The system will issue 10 song recommendations. The results of the evaluation of the best song recommendations use Unigram with a Precision@10 value of 0.656 and a Mean Average Precison (MAP@K) value of 0.82914032.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.
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