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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research 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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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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Abstract

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.
Implementasi Algoritma Fuzzy K-Nearest Neighbor (FK-NN) Untuk Penentuan Kualitas Mutu Air: Implementation Of The Fuzzy K-Nearest Neighbor (FKN-NN) Algorithm For Determining Water Quality Yose Parman Putra Sinamo; Sutrisno Sutrisno; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Water has an important role in maintaining life. Without water, humans cannot carry out their daily activities, because water is an inseparable part of their daily activities. Water that is good for humans is water that has met the requirements as suitable water for daily use. For this reason, there have been many studies on water classification in determining water quality. However, in determining the results obtained, the accuracy is less than satisfactory and can be improved again. In determining the classification of water, the K-Nearest Neighbor (FK-NN) fuzzy method is used. Some of the attributes or parameters that will be used in this research are the degree of acidity (pH), TDS, NO2, NO3, hardness, chloride, manganese. There are several tests carried out including testing the k value, the distribution of data ratios, and the distribution of data classes. From this test, the accuracy value is 95.52%, with the data ratio level consisting of 70% training data and 30% test data, with a K value of 10.At the level of data class distribution, 80% accuracy is obtained with the distribution of test data classes using 20 data with class 0 is 10 and class 1 is 10. From the accuracy obtained, it is concluded that the accuracy value obtained is much greater than the accuracy of previous research which is only around 78.70% and 85.70% in the Support Vector Machine and Naive Bayes methods..
Analisis Sentimen pada Ulasan Aplikasi Mobile JKN Menggunakan Metode Maximum Entropy dan Seleksi Fitur Gini Index Text Muhammad Mauludin Rohman; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobile JKN application is a form of BPJS Kesehatan's commitment in providing services and ease of access for BPJS Kesehatan users. BPJS Kesehatan in organizing the health insurance program since 2014, can be assessed how the people of Indonesia make use of health insurance implementation facilities through the JKN Mobile application based on user reviews of the application. Sentiment analysis needs to be done to analyze reviews provided by app user. This study used the Maximum Entropy classification method coupled with the Gini Index Text for feature selection. Sentiment analysis consists of data collection process, text preprocessing, word weighting with raw tf, followed by feature selection using Gini Index Text, then classification using Maximum Entropy with features obtained from the previous feature selection. The results of this study are that the best accuracy value is obtained when using the number of features or threshold of 80%, with a value of evaluation as an accuracy of 85,36%, a precision of 92,18%, a recall of 75,59%, and f-measure of 82,85%.
Analisis Emosional Pelajar terhadap Pembelajaran Daring Dengan Menggunakan Latent Semantic Indexing (LSI) dan N-Gram Afif Musyayyidin; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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In the industrial era 4.0 a lot affects human activities, especially among students. Technology applied in the world of education is online learning. Online learning is a learning method implemented in communication media both asynchronously both text and video. In order for this learning to be more effective and much better in the future, they provide a place to provide input or feedback in the form of criticism and suggestions on social media such as YouTube, Twitter and Facebook. To find out whether online learning is getting more effective, a student emotional analysis is carried out on online learning. In this study, the Latent Semantic Indexing (LSI) method was used in classifying the emotional of students and added the N-Gram method in word selection. The process in this emotional analysis includes data collection, text preprocessing which is useful in producing clean data, N-gram, weighting using the term weighting method, Single Value Decomposition (SVD), Latent Semantic Indexing, Vector Support Machine (VSM) which results in a classification process. . The data used in this study are primary data sourced from social media such as Youtube, Twitter and Facebook. The best results occur when the N-Gram is a combination or combination. From the 5 Fold, it was obtained an average accuracy of 77%, precision 76%, recall 78% and f-measure 77%.
Prediksi Volume Penggunaan Air Bulanan Kota Batu Menggunakan Metode Extreme Learning Machine (ELM) Muhammad Alif Fahrizal; Sigit Adinugroho; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Seiring bertambahnya penduduk, juga selalu beriringan dengan bertambahnya kebutuhan dalam menunjang kehidupan sehari-hari. Salah satu kebutuhan tersebut adalah air. Kota Batu, sebagai kota wisata dengan jumlah penduduk yang menetap selalu berubah-ubah yang menyebabkan volume air yang digunakan juga selalu berubah. Sehingga dari permasalahan tersebut dibutuhkan prediksi volume penggunaan air bulanan pada Kota Batu untuk menyelaraskan dengan volume air yang diproduksi. Dalam penelitian ini dilakukan beberapa proses untuk melakukan prediksi yaitu proses preprocessing pada data yang digunakan, dilanjutkan dengan perhitungan nilai prediksi menggunaan data sebelumnya pada model jaringan Extreme Learning Machine (ELM), dan terakhir dihitung nilai evaluasi hasil prediksi menggunakan Root Mean Squared Error (RMSE). Berdasarkan proses pengujian yang telah dilakukan pada model jaringan ELM, diperoleh rata-rata nilai evaluasi sebesar 16437,5 ketika digunakan 6 input neuron, 5 hidden neuron dan 80%:20% untuk pembagian data latih dan data uji. Dari nilai evaluasi tersebut dianggap belum cukup baik. Hal ini dikarenakan jumlah data yang digunakan dalam proses training pada jaringan ELM masih terlalu sedikit sehingga jaringan tersebut masih belum memahami pola data secara keseluruhan.
Analisis Sentimen Ulasan Pengunjung Simpang Lima Gumul Kediri menggunakan Metode BM25 dan Neighbor-Weighted K-Nearest Neighbor Inosensius Karelo Hesay; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Simpang Lima Gumul (SLG) is a monument that has become an iconic building as well as a tourist destination in Kediri. Visitors who come can provide reviews on Google Review SLG to help the manager know the advantages and disadvantages of existing infrastructure. However, the SLG manager does not have a system that can automatically classify positive and negative reviews. This problem can be solved by using a sentiment analysis system. The sentiment analysis system used in this study uses Neighbor-Weighted K-Nearest Neighbor (NWKNN) and BM25 methods. The stages of this system include preprocessing process, weighting TF-IDF, ranking using BM25, and classification process using NWKNN. The number of data used is 1000 data, with the division of 800 training data and 200 test data. The test was carried out using 5-fold cross validation to test the k and exponential values in the NWKNN method and the k1 and b values ​​in the BM25 method. Based on the tests carried out on each tested parameter, it was found that the best value for the parameter value k1 = 1.2, b = 0.5, k = 20, and exponent = 2. The combination of these parameter values ​​produces an average value precision of 0.9509, recall of 0.9589, accuracy of 0.93, and f-measure of 0.9548.
Penentuan Tata Letak Produk menggunakan Algoritma FP-Growth pada Toko ATK Muhammad Yudho Ardianto; Sigit Adinugroho; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Stationeries are one of the basic needs in a workspace such as the office and most predominantly in education such as schools. During the beginning of the school calendar, the stationery stores are usually overcrowded by buyers. However, in these times of pandemics people tend to save money by restricting themselves from buying things. As a result, sales tend to drop as fewer people are willing to spend money on goods. One of the ways to increase sales is to observe the buyer's transactions. All of the transaction data are usually kept as an archieve in the stores. On the other hand, the transaction data of the buyers have informations which can be extracted using data mining techniques, such as information about the association rule in the consumer purchases. By understanding the habitude of the consumers, stores are able to consider on the arrangement of their goods. The FP-Growth algorithm which is being used in the shopping cart system will be able to help in developing the marketing strategy as it would observe the associations between items. The FP-Growth algorithm has a sequence of data collection, frequency counter, transaction data rearrangement, tree formation, and frequent item search. From testing the minimum support of 5%, 8 association rules are produced on which 3 of them has a confidence rate above 5%. Subsequently, there are 34 association rules with lift values above 1. The higher of the minimum support and minimum confidence values, the fewer combinations of association rules will be generated.
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 Anggi Gustiningsih Hapsani 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 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 Lukman Hakim 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 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 Siti Mutrofin 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