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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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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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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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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.
Klasifikasi Risiko Human Papillomavirus menggunakan Metode Naive Bayes dan Seleksi Fitur Relief Indah Wahyuning Ati; Sigit Adinugroho; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 9 (2020): September 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Human Papillomavirus is a virus that causes various types of diseases such as warts, infertility, miscarriage, vaginosis, and others. However, HPV status in tumors is a factor that helps in surviving and developing to survive in getting better response to radiotherapy and tumor control compared to tumors without HPV. Factors used to understand the problem or not. HPV does not only depend on status, age, age, tumor differences, sex and treatment strategies. But, also age, less exposure to tobacco and alcohol, as well as factors related to tumors. Classification and feature selection will be carried out to study features with significant weights used for the classification of HPV use in tumors. Algorithm flow in this research is by selecting features using the relief method, then classification using the naive bayes method is to predict the probability of class classification used in nominal and numeric type datasets. In this study, the appropriate features were obtained, namely, N_Category, T_Category, Tumor_side, Smoking_status_at_diagnosis, Tumor_substite, AJCC_Stage, and Age_at_diagnosis features. The best accuracy value is 90.97% by testing the number of features using 5 times, for each fold 25 test data and 98 training data are used. Meanwhile, the accuracy of testing the balanced data is 85% using 20 balanced data with 4 test data and 16 training data.
Analisis Sentimen Ulasan Aplikasi Mobile menggunakan Algoritma Gabungan Naive Bayes dan C4.5 berbasis Normalisasi Kata Levenshtein Distance Arrofi Reza Satria; Sigit Adinugroho; Suprapto Suprapto
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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Google Play Store has become the largest digital market place with more than 10 million products in it. Application developers make the product review column available on the Google Play Store one of the ways to find out user satisfaction. But not all reviews on the app have an alignment between ratings and comments, there are ambiguous reviews, that are reviews marked by ratings and sentiment of comments are not the same. Machine Learning (ML) has been very useful in the field of sentiment analysis. One method that is reliable and easy to use is Naive Bayes. C4.5 method is also very popular in solving the decesion tree problem which will be used for the sentiment classification process. While the Levenshtein Disance method is used to compare two strings for the word normalization process. The method flow start with text preprocessing dataset with Levenstein Distance, then the dataset will be divided into two for the Naive Bayes and C4.5 classification process. The sentiment text and text review will be processed by the Naive Bayes method while the rating and sentiment text will be processed by C4.5.The test results using the 10-Fold evaluation method are 85.3%. While the sentiment classification without using Levenshtein Distance is 85.6%, the difference is 0,3%, making the Levenshtein Distance method not significantly affect the classification results. Other test results with the application of the Edit Distance 1, 2, 3 and 4 limits were 86.9%, 85.9%, 87.1% and 86.1%, respectively. Testing Naive Bayes algorithm without C4.5 in classifying review texts has an 85.3% same result with previous test. The results of this test illustrate the effectiveness of this program in the classification of mobile application
Prediksi Kinerja Akademik Siswa menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain Rizky Adinda Azizah; Fitra Abdurrachman Bachtiar; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Untuk dipublikasikan di Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Pengembangan Aplikasi Pemantauan Kinerja Guru untuk Peningkatan Kualitas Pembelajaran berbasis Web (Studi Kasus: SDN Mulyorejo 1 Malang) Muhammad Dio Reyhans; Imam Cholissodin; Sigit Adinugroho
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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Monitoring teacher performance is an activity to ensure the implementation of teacher duties. Monitoring the performance of teachers at SDN Mulyorejo 1 Malang is currently still being carried out by visiting the supervisor, which makes it difficult to monitor the file processing done by the teacher. The large number of teacher files makes it difficult to search for files when a file is needed. Based on these problems an application was made to facilitate supervision activities. This system is built on web-based by applying the waterfall model for its development. The web-based system was chosen because the resulting system can be accessed through various platforms. In the requirement analysis process, there were 5 actors, 113 functional requirements, and 1 non-functional requirement. The design is built using sequence diagrams, class diagrams, conceptual data models (CDM), physical data models (PDM), and pseudocode. The implementation process uses the PHP language with object-based infrastructure and data storage using MySQL. The last process is testing using white-box testing for unit testing, black-box testing to validate each need, and usability testing using the System Usability Scale (SUS) method which in this study obtained a value above 80,3 which means very good.
Prediksi Omzet Restoran Haltoy Corner menggunakan Metode Recurrent Extreme Learning Machine (RELM) Ridho Ghiffary Muhammad; Muhammad Tanzil Furqon; Sigit Adinugroho
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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Haltoy Corner Restaurant is a new restaurant in wonosobo city that is famous for its beautiful scenery. Currently, Haltoy Corner is still not able to do the management of the number of employees and the allocation of turnover well. This led to the need for a turnover prediction system for Haltoy Corner to help optimize the number of employees to be employed. Extreme Learning Machine (ELM) is one of the prediction methods that have good accuracy and relatively fast training time, but in ELM the sequence of data has no effect so it can affect the accuracy for dataset timeseries such as Haltoy Corner turnover data. ELM developed a method to overcome this with Recurrent Extreme Learning Machine (RELM), this method adds recurrent to ELM so that it is better for dataset timeseries. The flow to conduct this research starts from data normalization, data training, data testing, data denormalization and finally the calculation of evaluation value. Based on the results of tests conducted using Haltoy Corner turnover data, an error value with Mean Absolute Precentage Error (MAPE) was obtained at the most optimal of 31.677%, with the number of eight features, the number of hidden neurons three, the number of context neurons five, and the comparison of the number of training data with data testing of 90%:10%.
Pengelompokan Toko E-commerce Shopee berdasarkan Reputasi Toko menggunakan Metode Clustering K-Medoids Felicia Marvela Evanita; Imam Cholissodin; Sigit Adinugroho
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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The growth of internet encouraged the creation of e-commerce or electronic commerce. E-commerce with the most visitors in Indonesia is Shopee with more than 72 million visitors each month at the end of 2019. Although e-commerce has a lot of good impact, users are still faced with the risks from using e-commerce. Users must be more careful in choosing a store to trust in order to avoid these risks. Users are faced with many choices while looking for products and users must consider which store should they choose. Store clustering on Shopee e-commerce based on store reputation with K-Medoids clustering could solve this problem. The data that used in this study were taken from 100 store in Shopee e-commerce by web scraping. Steps that taken were preprocessing the data, normalization, finding the distance for each data, clustering with K-Medoids, and evaluate using Silhouette Coefficient. In this study, the number of cluster and data were tested. From these tests, it was found that the best Silhouette Coefficient average was 0,317681 while using 2 clusters and 100 data.
Pengembangan Sistem Manajemen Barang Inventaris SMKN 1 Pasuruan Berbasis Website Menggunakan Metode Rapid Application Development Muhammad Dzulhilmi Rifqi Bassya; Faizatul Amalia; Sigit Adinugroho
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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Inventory is one of the important things for institutions to support various kinds of needs. One of the institutions that cannot be separated from inventory is schools. There are so many items in school that it is necessary to have better inventory management to manage these items so that these items can be used properly. However, this can cause various problems if management is still conventional, data items can be lost, damaged, and mismatch data between one data and another. Therefore, an inventory management system was created so that can use make it easier to manage inventory. System is built in website platform using ReactJs and Redux, and programming language is javascript, html, and css. Database system using firebase. System development life cycle using RAD (Rapid Application Development), this methodology has been choosen because the design work process involves the user, so the results obtained are in accordance with the users hope. Functional testing in this research uses whitebox testing and blackbox testing. Non-functional testing uses compatibility testing and security testing. The results of functional testing using blackbox and whitebox testing are 100% valid and the results of non-functional testing which is security testing is 100% valid while the results of compatibility testing are only be run on certain versions of the browsers.
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

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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.
Co-Authors Afif Musyayyidin Afrizal Aminulloh Afrizal Rivaldi Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Muzanni Safi'i Alan Primandana Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Ananda Fitri Niasita Arifin Kurniawan Arrizal Amin Arrofi Reza Satria Aulia Rahma Hidayat Ayustina Giusti Bayu Rahayudi Brian Andrianto Budi Darma Setiawan Candra Dewi Cornelius Bagus Purnama Putra Dahnial Syauqy Danang Aditya Wicaksana Daris Hadyan Tisantri Dayinta Warih Wulandari Dese Narfa Firmansyah Dewan Rizky Bahari Dheby Tata Artha Diajeng Ninda Armianti Dwi Novi Setiawan Edy Santoso Eky Cahya Pratama Faizatul Amalia Felicia Marvela Evanita Fitra Abdurrachman Bachtiar Gessia Faradiksi Putri Gilang Pratama Hangga Eka Febrianto Hanson Siagian Humam Aziz Romdhoni Husein Abdulbar Ilham Firmansyah Imam Cholissodin Inas Hakimah Kurniasih Indah Wahyuning Ati Indriati Indriati Inosensius Karelo Hesay Irwin Deriyan Ferdiansyah Iskarimah Hidayatin Kenza Dwi Anggita Khairul Rizal Krishnanti Dewi Lailil Muflikhah Listiya Surtiningsih M. Ali Fauzi Mahendra Okza Pradhana Mayang Panca Rini Melati Ayuning Lestari Moch. Yugas Ardiansyah Mohammad Angga Prasetya Askin Muhammad Alif Fahrizal Muhammad Dio Reyhans Muhammad Dzulhilmi Rifqi Bassya Muhammad Iqbal Pratama Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Sholeh Hudin Muhammad Tanzil Furqon Muhammad Yudho Ardianto Muria Naharul Hudan Najihul Ulum Naziha Azhar Nendiana Putri Nurhana Rahmadani Putra Pandu Adhikara Putra Pandu Adikara Rahman Syarif Randy Cahya Wihandika Randy Cahya Wihandika Ratna Ayu Wijayanti Regina Anky Chandra Ridho Ghiffary Muhammad Rizal Maulana Rizky Adinda Azizah Salsabila Insani Salsabila Multazam Sarah Yuli Evangelista Simarmata Shima Fanissa Sukma Fardhia Anggraini Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Kurniawan Putra Tri Rahayuni Utaminingrum, Fitri Wahyu Rizki Ferdiansyah Yohana Yunita Putri Yose Parman Putra Sinamo Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari