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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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Prediksi Rating Novel Baru Berdasarkan Sinopsis Menggunakan Genre Based Collaborative Filtering dan Text Similarity Rhevitta Widyaning Palupi; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The novel is a story that has a long, imaginary plot. Based on the editor's choice on the Amazon.com website, 50 of the 100 best-selling books are novels. This shows that public interest in the novel is quite high as one type of reading. But when you want to choose a novel that you want to read, readers sometimes feel confused to know the quality of the novel. One reference in looking at the quality of a product is rating. The Goodreads site is one site that allows amateur reviewers to write reviews and ratings to help readers choose relevant books. But sometimes Goodreads users don't give ratings to a book so followers from that user want to know the rating given by the user in the book. This study uses the Genre Based Collaborative Filtering method as a calculation of rating predictions and Text Similarity to determine the value of similarity between documents with each other. The data used in this study were 31 users and 90 synopsis as training data and 35 synopsis as test data. System accuracy obtained from the classification results by using the similarity value on text similarity of 45,714286% and MAE value of 0,27742857 so that it can be concluded that the method of genre based collaborative filtering and text similarity can be used to make rating predictions.
Ekstraksi Fitur RGB Color Channel dan Simple Morphological Shape Descriptors dari Citra Makanan untuk Pencarian Resep Makanan Barbara Sonya Hutagaol; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nowadays food is no longer just a basic necessity, but food has been used as an entertainment. As can be seen on social media, there a lots of photos of foods that attract our attention, thus force us to cook and made the food. To make food, a food recipe is needed. In general, food recipes can be found in magazines, television, newspaper, and websites. The recipe is searched by the name of the dish. The limitation of knowledge obout food's name, makes it difficult to find the recipes. By seeing this problem, we can use Content Based Image Retrieval (CBIR) to make the image as the query. Searching by using an image we need digital image processing to obtain the features of the image. The used features are red, green, and blue (RGB) color channel as the color feature, simple morphological shape descriptors as the shape feature, and k-NN as the classification method. The result of this research give the best n value n=5 where mean average precision (MAP) is 94,1892% on the combination of color and shape feature. The use of color and shape feature commonly obtain the best result on the combination of the both feature at n=10, n=15, n=20, dan n=25. The conclusion is when the higher value of n give the worst result of MAP and the use the combination of color and shape features can provide the best results compared using of one feature.
Pencarian Resep Makanan Berdasarkan Citra Makanan Menggunakan Simple Morphological Shape Descriptors, Cie L*A*B* Color Moment Dan Local Binary Pattern Yosua Dwi Amerta; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A recipe is a guide that contains the ingredients, steps, and how to serve a food. Recipe searches are generally limited to using the title or name of a food, and to overcome these limitations image search is needed. Image based search requires extraction of image features, and there will be 3 feature extraction methods to be used. Extracting color features is done using the CIE L * a * b * Color Moment method, which will take the features of the mean, standard deviation, and skewness. In the shape feature, Simple Morphological Shape Descriptors (SMSD) is used to get 4 feature, aspect ratio, length, width, and diameter features. The third feature, which is texture extracted using the Local Binary Pattern method. Based on the results of these methods, it can be seen that the search uses CIE L * a * b * Color Moment gets the MAP value of 0.70. The SMSD method gets the smallest MAP with a value of 0.46. LBP gets the same value with the combined method which is 0.52. So it can be concluded that LBP has major effects to the results of the combined method.
Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naive Bayes Classifier Karunia Ayuningsih; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is a basic need that must be fulfilled in human life. Eating habits can lead to good and bad habits. Bad eating habits can cause various diseases. Komunikasi, informasi, dan edukasi (KIE) can provide education on eating habits. Food has a variety of types, it is necessary to recognize the type of food to make it easier to identify good types of food. The purpose of this study is to be able to provide education to recognize the types of food. The process begins with image identification using pre-processing to separate between food objects and background. On top of that, using the Hue Saturation Value (HSV) color extraction feature consists of the feature Mean, the Standard Deviation, and the Skewness. Then is the use of the Local Binary Pattern (LBP) texture feature extraction produce feature extraction uses gray scales in the histogram. The results of feature extraction from each image are then carried out using the Naive Bayes Classifier classification. Based on the test results, the use of only the HSV method produces a 65% accuracy value. Meanwhile, the use the LBP method, get a 60% accuracy value. In addition, the results of tests that have been carried out using the HSV method produce an accuracy of 65% and the LBP method produces an accuracy of 60%.
Analisis Sentimen Impor Beras 2018 Pada Twitter Menggunakan Metode Support Vector Machine dan Pembobotan Jumlah Retweet Renaza Afidianti Nandini; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Social media Twitter is one of the largest real time databases and is very useful for knowing people's perceptions in Indonesia. The issue of rice import polemic on Twitter tweets is an important thing to study as text processing. This study discusses sentiment analysis on 2018 rice import Twitter using the Support Vector Machine (SVM) method and Weighting the Number of Retweets. The use of the weighting feature of the number of retweets uses a comparison of certain constants (α and β) 11 times to obtain the results of positive and negative class analysis. The data used in this study were 318 data consisting of two types of data namely training data and test data with a ratio of 70% training data and 30% test data. From the results of accuracy testing using the Support Vector Machine method without weighting the number of retweets by 50.00%, precision by 49.46%, recall by 97.87%, and f-measure by 65.71%. Accuracy testing results using the Support Vector Machine method with a weighting of retweet amount of 50.00%, precision of 49.46%, recall of 01.00% and f-measure of 65.73%. It can be concluded that the use of the weighting feature of the number of retweets can provide optimal results and is able to classify sentiment analysis.
Analisis Sentimen Pemilihan Presiden 2019 pada Twitter menggunakan Metode Maximum Entropy Alvandi Fadhil Sabily; Putra Pandu Adikara; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this modern era, communication can be done through various media, one of which is through online media, namely Twitter. Twitter is one of the social media that functions to exchange information and also express an opinion on something. Twitter posts that discuss presidential elections are the objects used in this study. To find out whether a sentiment has a positive or negative value, a sentiment analysis is needed as in this study. To analyze a sentiment, a method that can classify sentiments is needed, Maximum Entropy is the method used in this study with the evaluation method used is Confusion Matrix which will then calculate the value of Macro and Micro averaging from the evaluation value produced. The evaluation results carried out in this study resulted in quite high Macro accuracy values ​​of 89.16% with precision and recall values ​​of 100% and 89.16% and also F-measure values ​​of 94.27%. Testing is done by testing 120 tweets and training data used as many as 300 tweets.
Prediksi Suku Bunga Acuan (BI 7-Day Repo Rate) Menggunakan Metode Extreme Learning Machine (ELM) Yohana Yunita Putri; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Reference interest rates or often referred to as BI 7-Day Repo Rate is a policy interest rate that describes the establishment or view of monetary policy whose determination is made by Bank Indonesia which is then notified to the public.. BI 7-Day Repo Rate has an influence on economic activities, such as investment, inflation and currency changes. Investors and market players in making economic decisions will refer to the fluctuation of interest rates set by the central bank. Therefore, the prediction of the benchmark interest rate (BI 7-Day Repo Rate) is important. The purpose of the BI 7-Day Repo Rate prediction is to facilitate and assist investors and market players to make estimates of the decisions to be taken according to the prediction of the benchmark interest rate. This study uses the Extreme Learning Machine (ELM) method to predict the reference interest rate (BI 7-Day Repo Rate). The process of the first ELM algorithm is to normalize, then initialize the input and bias weights, then continue to carry out the training process and proceed with the testing process, then do the normalization to obtain the actual value. Based on the Extreme Learning Machine (ELM) algorithm that has been conducted, it produces the best Mean Absolute Percentage Error (MAPE) of 1,1% and the fastest processing time is 0.125 seconds using 50 hidden neurons, sigmoid activation function and 96 data counts.
Temu Kembali Informasi Lintas Bahasa untuk Dokumen Berita Berbahasa Indonesia-Inggris Menggunakan Metode BM25 Putri Rahma Iriani; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

News is an information about someone's needs to find out what is happening. Efforts to get relevant news from a variety of languages ​​and documents are not easy to obtain. News documents usually written in foreign language. This becomes difficult because not all users understand foreign language, while the news needed in the collection of foreign language. Users can read one by one to get news as it needed, but this process is inefficient and will take a long time. A cross-language automatic news search system is needed to solve this problem, where users only enter requests with the native language and the system will recover documents in other languages. This problem can solve by creating a system to obtain automatic news without language barriers. This system will builds using the BM25 method which has been proven to be able to improve documents that are relevant to the ranking. The free parameters used are k1 = 2.5 and b = 8.0. Weighting is done by comparing IDF BM25 and IDF modification which results in the highest value of 0.95 with k = 5 in testing of precision@k.
Klasifikasi Hate Speech Berbahasa Indonesia di Twitter Menggunakan Naive Bayes dan Seleksi Fitur Information Gain dengan Normalisasi Kata Ivan Ivan; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hate speech is a form of expression that is done to eliminate hatred and commit acts of violence and oppose someone or a group of people for various reasons. The cases of hate speech are very often encountered on social media, one of which is on Twitter. The goal to be achieved is to create a system that can classify a tweet on Twitter into a class of hate speech (HS) or non hate speech (NONHS). The method used is Naive Bayes and Information Gain feature selection with word normalization. Word normalization is used to solve problems on Twitter such as the number of words abbreviated, the use of slang, misspellings, and the use of languages ​​that are not in accordance with existing standards.Word normalization comes from Indonesian Natural Language Processing REST API. The data used supports 250 data tweets of hate speech in Indonesian with a ratio of 80% for training data and 20% for testing data. The threshold used is 20%, 40%, 60%, 80%, and 90%. Threshold is a limit that is determined to store a collection of terms or a collection of words with the aim of selecting a word that has a high value ​​in the Information Gain feature selection. The best accuracy results obtained by using word normalization in the pre-processing stage and using Information Gain feature selection with an 80% threshold. The best accuracy result is 98%, precision result is 100%, recall result is 96.15%, and f-measure result is 98.03%. Based on the analysis of the results and testing obtained, it can be concluded when doing hate speech classifications in Indonesian on Twitter using Naive Bayes and Information Gain feature selection with word normalization can improve better accuracy of the results.
Rekomendasi Film Berdasarkan Sinopsis Menggunakan Metode Word2Vec Alimah Nur Laili; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of movie production have increased each year. This shows that the society interest in the film industry is getting higher. It's difficult to get the appropriate result of what desired by searching for data with certain parameters on the internet because of the large amount of data exists but there is limited adequate tools. The screening of the excess data can be done using recommendation process. There are several stages in movie recommendation process. Those are Pre-processing to process film synopsis documents, TF-IDF method to obtain the highest value as much as the amount determined based on the query result on the document. Word2vec as a method to get the query expansion from the top word result that taken from TF-IDF process and Cosine Similarity is used to get the similarity between document and query. The Word2Vec method plays role to find the proximity value between words to one another in order to get the words that will be added to the initial query. The training data are 150 movies title with English synopsis. The evaluation process took 30 data of movie title and synopsis from the training data based on the movies selected by the examiners. The highest Precision@k value is 0,47 and the highest Mean Average Precision (MAP) value is 0.709603374.
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