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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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Temu Kembali Informasi Lintas Bahasa Dokumen Berita Bahasa Indonesia-Inggris menggunakan Metode BM25F Lusiyana Adetia Isadi; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

News is a source of information that displayed to the general public about an event and presented in various languages. Usually, a website only allows user to search only in one language. This causes problems for users who want to find broader information more quickly in several languages ​​at once. These problems can be overcome by developing a cross language information retrieval system. The system can improve the time efficiency because it can return documents in two languages ​​by simply entering a query in one language only. One of the method that can be used to develop the system is BM25F method that can return relevant documents and handle structured documents. The news data structure used in training and testing is the title and the content part of the news. The data used in this study are 300 Indonesian news documents and 300 English news documents that will be used to test the boost value, the Indonesian queries, and the English queries. For the boost value testing, the highest precision@k value obtained when the title boost is 5 and the content boost is 1. This value will be used for query testing. Query testing is performed using precision@k and got the highest value of 0.98 when k=5 in the Indonesian queries test which returned Indonesian and English documents.
Klasifikasi Jurusan Siswa menggunakan K-Nearest Neighbor dan Optimasi dengan Algoritme Genetika (Studi Kasus: SMAN 1 Wringinanom Gresik) Vergy Ayu Kusumadewi; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Majors is the process of selecting and placing study programs that are suitable for students, this process will affect the future of students, both when they become students in high school and after graduating when continuing their studies in collage. Based on the results of interviews the problems that often occur there are some students who want to change majors in the middle of the semester, this is because of students cannot follow their lessons and feel left behind by their friends. Therefore, we need an intelligent system that can facilitate the school in grouping students into majors in accordance with the interests and talents of students. In this research the system was made by applying the K-NN method and genetic algorithm optimization. The type of validation used in this research utilizes 9-fold cross validation and hold-out validation. The number of datasets which originally consisted of 288 data will be divided into 9 sections and each sections will amount to 32 data. In general, the best fold number to use is 10, but the share of fold must also be adjusted to the amount of data used. The hold-out test is divided into 2 test scenarios, the first is testing uses the polynomial kernel formula, the RBF kernel and the linear kernel which are elaborated (substituted into the the elaborated distance formula) get a fitness value of 64.338% while the second is testing uses the polynomial kernel formula, kernel RBF and linear kernel which are not elaborated (without substituted into the elaborated distance formula) get a fitness value of 93.182%. The highest fitness value is generated in the 9-fold cross validation test which is 100%.
Klasifikasi Penyakit Tuberkulosis (TB) menggunakan Metode Extreme Learning Machine (ELM) Vivin Vidia Nurdiansyah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tuberculosis (TB) is the highest cause of death in the world. This disease attacks the respiratory system and included in infectious diseases. In 2019, Indonesia occupies the third highest position for the number of TB disease case namely as many as 842.000 cases. The increase in TB cases from year to year is due to people with insufficient information about the dangers as well as the treatment and prevention of this disease. Therefore, it is necessary to do the classification of tuberculosis for the community in order to determine the risk of developing TB disease based on symptoms experienced. From these problems, it is necessary to classify TB disease as an effort to increase public awareness of the TB disease. This study aims to obtain the result of the TB classification using the Extreme Learning Machine (ELM) method. Based on the result of testing and analysis using confusion matrix using TB data from Dinoyo Puskesmas in 2018-2019, the highest accuracy value is 99.33% with the number of hidden neurons 20, the percentage of training data and testing data is 70% : 30%, and uses the sigmoid biner function activation.
Klasifikasi Pertanyaan untuk Question Answering System Bahasa Indonesia menggunakan Support Vector Machine berdasarkan Taksonomi Li & Roth Muhammad Faiz Al-Hadiid; Putra Pandu Adikara; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Untuk dipublikasikan di 2020 5th International Conference on Sustainable Information Engineering and Technology (SIET 2020)
Klasifikasi Hoaks Kesehatan di Media Sosial menggunakan Support Vector Machine Aulia Rahma Hidayat; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Of the various types of communication tools available, social media is often used by the people of Indonesia, but as a communication tool that is often used, not everything that is found in social media is true. As part of the communication tool used by everyone it is not uncommon to find unclear sources or Hoaks. Hoaks about health are widely spread on Social Media and this can affect public awareness of the importance of health. Separating true and untrue health news needs to be done to avoid this. The separation process is carried out by classifying health news on Social Media with the Support Vector Machine method with Bag of Words and Lexicon Based Features. Total data in this study were 80 news from various social media. The data is then entered in the pre-processing process to get the word that shows a document, then proceed to the word weighting process using the TF-IDF calculation. The results of the word weighting process are included in the core process, namely the calculation of the Support Vector Machine method. Optimal parameter test results obtained gamma value (γ) = 0.001, lambda value (λ) = 1, epsilon value = 0.000001, degree value (d) = 2 and the maximum iteration value = 30. The results of system evaluation using both features get results which is good compared to using just one feature, showing the results of Accuracy of 1; Precision of 1; Recall of 1; F-measure of 1. Testing using K-fold Cross Validation was also carried out with a fold value of 10 and obtained an average value of Accuracy results of 0.6; Precision of 0.68; Recall of 0.47; F-measure of 0.48.
Pengenalan Jenis Kelamin dan Rentang Umur berdasarkan Suara menggunakan Metode Backpropagation Neural Network Avisena Abdillah Alwi; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Technology in the field of speech recognition is currently experiencing rapid progress. One technology that utilizes speech recognition is virtual assistants such as Google Assistant, Cortana, and Alexa. In order to improve the quality of communication between virtual assistants and humans, virtual assistants need to know who their communication opponents are. One way is by knowing the gender and age. Recognition of gender and age range based on voice is one part of speech recognition. Audio cannot be directly classified, therefore there is a need for feature or feature extraction, feature extraction that can be used include Mel-Spectogram, Mel Frequency Cepstral Coefficients (MFCC), and Chroma- Short-Time Fourier Transform. Artificial neural network architecture is able to classify it, one of the methods is Backpropagation. From the tests conducted by gender classification and age range with a dataset from Mozilla Common Voice, the accuracy is less good, that is 0.18357. From the test results it is necessary to do additional testing, namely testing the dataset. When testing the dataset for gender classification alone, the accuracy of classification with the Mozilla Common Voice dataset is 0.62504, while the accuracy of the classification with the dataset from Free ST American English Corpus gets 0.9349. From the tests conducted it was concluded that the use of the Mozilla Common Voice dataset was less recommended for gender and / or age recognition.
Analisis Sentimen Ulasan Kedai Kopi Menggunakan Metode Naive Bayes dengan Seleksi Fitur Algoritme Genetika Naziha Azhar; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Artikel dipublikasikan di JTIIK
Klasifikasi Jenis Kelamin Berdasarkan Suara Menggunakan Metode Learning Vector Quantization Allysa Apsarini Shafhah; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Human voices vary from person to person. Men usually have larger vocal folds than women so their voice tend to be lower. Today virtual assistant and voice-based chatbot are still unable to differentiate gender based on human voice whereas if the user's gender could be known we can use it to understand behaviours of a particular gender. Learning Vector Quantization (LVQ) version 1 is used in this research as a method to classify human voices with two classes which are male and female. Sound characteristics that used as features in this research are energy, zero crossing rate, entropy of energy, spectral centroid, spectral spread, spectral entropy, spectral flux, and spectral rolloff. Highest result are at 75,5% when using 10 as maximum epoch, 0.1 as learning rate, and Normalized Cross Correlation as similarity measurement. Accuracy when using Normalized Cross Correlation to measure similarity is at 75,5% thus making it higher compared to Euclidean distance and Manhattan distance which only get 74,4% accuracy both. This research also tested using K-fold Cross Validation with 5 folds and highest accuracy obtained when testing fourth fold at 75,6%. Therefore, this research also used Recursive Feature Elimination to determine impacts of sound features on accuracy resulting best feature is spectral entropy whilst worst features are zero crossing rate, spectral rolloff, and spectral centroid.
Penerapan Metode Fuzzy K-Nearest Neighbor pada Klasifikasi Penyakit Menular Seksual Pria Nadia Siburian; Imam Cholissodin; Putra Pandu Adikara
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

sexually transmitted diseases is one of the dangerous diseases that spreads every year, especially in the city of Malang. One of these educational cities has a growing human population each year so that it can be a trigger for the spread of the sexually transmitted diseases, especially for people who have sex. Based on information from the Malang Health Service, most people are exposed to sexually transmitted diseases without being aware of the symptoms that arise in them. Compared with women, more men who have a sexually transmitted infection. Sexually transmitted diseases in men such as Syphilis, HIV, Gonorrhea, Herpes and Warts have symptoms that have similarities in each disease so it is difficult to distinguish. To find out and reduce errors in predicting a disease, the Fuzzy k-Nearest Neighbor method is used in this study to help classify sexually transmitted diseases. The classification process consists of three processes are the fuzzy initialization process. The kNN algorithm process and the kNN fuzzy algorithm process. In the research test used the influence of K value testing, K-Fold Cross Validation test using 60 data divided into 10-fold and obtained the highest accuracy results of 91.67% with K = 5 then inter-class performance testing using confusion matrix to determine Precision and Recall values ​​on 30 test data.
Penerapan Metode Modified K-Nearest Neighbor pada Klasifikasi Penyakit Menular Seksual Pria Yoseansi Mantharora Siahaan; Imam Cholissodin; Putra Pandu Adikara
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

Sexually transmitted diseasse are a type of disease that spreads quite quickly. According to a World Health Organization (WHO) report, cases of infection that spread through sexual contact can be found every day with sexually active populations, namely adults and adolescents, especially men. The similarity in symptoms for each disease and patients are generally less familiar with the initial symptoms so they cannot provide early help. By developing the Modified K-Nearest Neighbor algorithm and using the asymmetric binary distance, the test result obtained on the effect of the K values of 100% in the 9th test. The K-Fold = 10 gets 91,67% results by using K = 9. And the Precision value = 1for Gonorrhea and HIV classes, and Recall value = 1 for the Warts and HIV classes.
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