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All Journal Jurnal Ilmu Pendidikan CommIT (Communication & Information Technology) Journal of Food and Life Sciences Journal of Innovation and Applied Technology Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer AGRIVITA, Journal of Agricultural Science Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JINOP (Jurnal Inovasi Pembelajaran) Premiere Educandum Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Pendas : Jurnah Ilmiah Pendidikan Dasar Journal of Education Technology Jurnal Ilmiah Sekolah Dasar Istawa: Jurnal Pendidikan Islam UPEJ Unnes Physics Education Journal AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Research and Educational Research Evaluation Pendagogia: Jurnal Pendidikan Dasar Cendikia Pendidikan Advances In Social Humanities Research JURNAL ILMIAH EKONOMI, MANAJEMEN, BISNIS DAN AKUNTANSI Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research
Candra Dewi
Fakultas Ilmu Komputer, Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Optimasi Fuzzy Time Series Untuk Prediksi Jumlah Produksi SAGA Leather Fashion Menggunakan Metode Algoritme Genetika Aditya Chandra Nurhakim; Budi Darma Setiawan; Candra Dewi
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

Leather wallets are an item that has a lot of interest nowadays, but because the process of makingproducts handmade and the price of raw materials is increasingly expensive, so making wallets andother leather-based goods has a higher price value, which makes the producers have decreasedorders. Therefore, an effort is needed to optimize the remaining raw materials used by predicting thenumber of items to be produced in the following month. The prediction method used to solve theproblem in predicting the amount of production is fuzzy time series, which is then optimized using thegenetic algorithm method. From the results of these studies, it is expected that the predicted resultscan help producers in optimize the remaining raw materials in the production process. In this study,the best individual from genetic algorithm on wallet products produce a RMSE (Root Mean SquareError) value of 5.24611658 with an accuracy rate of 96.44%, where the RMSE value is smaller thanthe test without optimization which results in a RMSE value of 7.20507068 with an accuracy of95.06%.
Prediksi Rating Otomatis Berdasarkan Review Restoran pada Aplikasi Zomato dengan menggunakan Extreme Learning Machine (ELM) Diajeng Tania Ananda Paramitha; Imam Cholisoddin; Candra Dewi
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 culture, technology advancement are growing better than we ever discovered before. One of the apps we use to search for information about restaurant in Jakarta are known as Zomato. Zomato is an application that provides various information about a restaurant from it facility, price, review, and rating. Users of The Zomato App can input various information that people haven't aware of about the restaurant into the app. Besides of inputting information into the app, Users of The Zomato App can also input a review and rating of a specific restaurant. The data review is used as an information about the restaurant for the potential customer from The Zomato App but sometimes the data review doesn't yet include a restaurant rating. This lack of misinformation will surely make the restaurant owner to occure some difficulties such as improving the restaurant services status for future outcomes. This research helps to classifying the review into the rating. Test protocol of this research are using a prediction with Extreme Learning Machine (ELM) Methods as it core. The prediction process however are build from a several steps such as pre-processing, word weighting with TF-IDF, and Extreme Learning Machine (ELM) Method calculations. Test result of The ELM parameter provides accuracy result 80,01% with k=10 amount hidden neuron 25 Interval weights -0.5 until 0,5 using function activation Sigmoid biner. We have come to conclusion were ELM method could positively solve the prediction problem exquisitely.
Implementasi TOPSIS Pada Sistem Rekomendasi Kafe di Kota Malang Berbasis Lokasi Wiandono Saputro; Ratih Kartika Dewi; Candra Dewi
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 Malang, there are at least 400 cafes and restaurants that college students can visit. A large number of cafes available creates its own problems for college students while looking for a gathering place for refreshing or doing a final assignment, which is choosing which cafe to visit. There are several factors that determine the choice of a cafe such as rating, distance, price, and facilities. To solve this problem, we need a system that can provide cafe recommendations to its users based on the location and the criteria of the cafe they are looking for, also provides information such as prices, menus, coffee beans, brewing methods and directions to the cafe. The cafe recommendation system developed using the TOPSIS. The TOPSIS method is used to generate alternative of cafe based on the concept that the best alternative is not only the the alternative with the shortest distance from the positive ideal solution but also with the farthest distance from the negative ideal solution. Alternative data is stored using the Firebase Realtime Database database service, and directions are given using third-party applications, namely Google Maps. From the testing the system obtained 100% valid on functional testing, 100% valid in the validation of algorithm that compares the system output with manual calculation, and the absence of ranking reversal on rank consistency testing which is done by adding new alternatives, and testing usability based on usefulness, ease of use, ease of learning, and satisfaction which give results in a percentage of 80.55%.
Implementasi Extreme Learning Machine dan Fast Independent Component Analysis untuk Klasifikasi Aritmia Berdasarkan Rekaman Elektrokardiogram Aditya Septadaya; Candra Dewi; Bayu Rahayudi
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

The type of arrhythmia can indicate the location of the disorder and its causes. The way to identify the arrhythmia is to use an electrocardiogram (ECG) strip. Machine learning can be used as an approach to assist identification of arrhythmias through an ECG. Extreme Learning Machine (ELM) is one single-hidden layer feedforward neural networks (SLFNs) that can be used for the classification of arrhythmias in order to assist medical diagnosis. To optimize ELM performance, Fast Independent Component Analysis (FastICA) algorithm is used for preprocessing and extracting ECG signals. In this study, several parameter tests were conducted to determine the impact on the performance of the classification model. ECG data obtained from the arrhythmia database managed by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH). Each data is a 3 seconds ECG snippet with total of 210 data divided into 6 arrhythmia classes and normal rhythms. The results showed that the classification model was able to achieve perfect performance with accuracy, precision-recall, and F-1 score of 100% at the training stage. However, the classification model was experiencing overfitting at the testing stage with the mean of matthew correlation coefficient is approximately 0. Overfitting occured because the feature representation is too complex and not proportional to the amount of available data. This resulted in poor performance in the ELM-FastICA test for data that was not yet recognized.
Analisis Sentimen pada Ulasan "Lazada" Berbahasa Indonesia Menggunakan BM25 dan K-Nearest Neighbor (K-NN) dengan Perbaikan Kata Menggunakan Jaro Winkler Distance Desy Wulandari; Indriati Indriati; Candra Dewi
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

Online shopping is one way that is currently in great demand by the public, especially in Indonesia. By shopping online, especially at Lazada stores, consumers don't need to spend a lot of time and energy. Because of the ease of technology that can now be used in shopping online. But to find out the quality of a product, consumers will see reviews of items that have been sold. Therefore with the number of consumers who write a lot of data collected so that a way is needed to be able to sort out positive or negative sentiments by doing word repairs because of the many word writing errors that we often encounter on a review. So it needs word repairs so that consumers can understand more clearly the contents of a review. In this study the researchers made the system using the Jaro Winkler Distance method which was used to improve the word and then performed scoring calculations with BM25, as well as the classification with K-Nearest Neighbor (KNN). Based on the test results get the best accuracy value of 89% with the value of F-Measure 88% in the second k-fold test with a value of k = 11. So the use of word normalization on training data and improvement of words in the test data can increase the results of sufficient accuracy better than without using word repairs and without normalizing training data.
Klasifikasi Penempatan Siswa di Sekolah Menengah Atas menggunakan Metode Extreme Learning Machine Akmal Subakti Wicaksana; Budi Darma Setiawan; Candra Dewi
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

Placement of majors is one of the actions that can support the ability and motivation of student learning. Placement of the department itself has several factors that are used as considerations to determine the majors according to the abilities and interests of students. Factors used in determining majors between student psychological test results, report card grades for junior high school, BK evaluation, student interest, and parents' interests. Efficient it is not efficient. In the process of validation, the results of department placement are needed for 1 semester by looking at student learning outcomes data. In the placement of majors a system is needed that can help in classifying students' majors quickly and accurately, can be adjusted to the completion time of majors and errors in the arrangement of majors that are not in accordance with the interests and abilities of students. In this study using the Extreme Learning Machine method in grouping majors in high school students. In applying the ELM method the classification results obtained with the best average value of 98% using 14 features. To transfer data, practice and test the data used is 80:20, and the weight value is changed [-1,1], the value is biased with a range of [0,1], and use the activate sigmoid function.
Rancang Bangun Sistem Rekomendasi Tempat Kuliner Khas Malang Berbasis Lokasi Iqbal Santoso Putra; Ratih Kartika Dewi; Candra Dewi
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

Malang is a place in Indonesia that has a variety of unique culinary. This causing the appearance of places that sell unique culinary and only took place at Malang. However, none of the systems nowadays provide to give the recommendation of unique culinary places that using other criteria aside from using the nearest range. Therefore, we initiating to build a system that gives a recommendation of Malang unique culinary places using more criteria based on location. The system is designed for using TOPSIS to deciding which place will become the best recommendation for the user using four criteria. They are : the nearest range, the cheapest price, the highest rating, and the oldest age. The system will be implemented in the Android application using RESTful web service as data exchange. The test result over the system shows that the system has been implemented using functionals that corresponding with the functionals from the system requirement. TOPSIS algorithm that applied in the system has been stated as valid based on the result of the manual calculation has the same result as the result from the by-system calculation. The test result also stated that the system has been succeeded to approach an A grade with an average score of 81.00. Therefore the system is acceptable for the user.
Perbandingan Kualitas Hasil Klaster Algoritme K-Means dan Isodata pada Data Komposisi Bahan Makanan Reza Wahyu Wardani; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Health is the most important part of human beings. One way that can be done to maintain health to regulate diet. Setting diet can be done by calculating the amount of daily nutrient content that enters body. Problems related to nutrition in community are malnutrition or condition where body doesn't get enough nutrition according to daily needs. This happens because most people dont understand how to regulate and classify food according to portion nutrients body. Fulfillment daily nutrition can be done if food has been in group based on nutritional similarity. Food grouping algorithm is needed so that people can find out enough daily nutritional alternatives based on potential commodities in Indonesia. Data used in study amounted 250 data sourced from Indonesian Ministry of Health regarding composition of food ingredients. Purpose of this study is examine which method is best by comparing K-Means and Isodata clustering algorithms based on the quality of clusters produced. Cluster quality measurements using Silhoutte Coefficient method. Based on tests conducted, Silhoutte Coefficient K-Means algorithm is 0.996762 and Isodata algorithm is 0.996910. Both these methods have small difference value but Isodata algorithm has greater Silhouette Coefficient value than K-Means algorithm in clustering Food Composition Data.
Klasifikasi Status Gunung Berapi dengan Metode Learning Vector Quantization (LVQ) Chelsa Farah Virkhansa; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

There are 129 active volcanoes and 500 inactive volcanoes located in Indonesia. Residents who live around areas that are susceptible to volcanic eruptions are quite numerous, which is as much as 10% of Indonesia's population. From the number of volcanoes that are still active there are only 69 mountains monitored, so there are still many active volcanoes which are not well monitored, which is around 40%. So that information on the status of the volcano is needed as quickly and accurately as possible to reduce the impact caused by the volcanic eruption. In this research, volcanic status classification will be carried out using the Learning Vector Quantization method. this study uses data totaling 110 data. The data was obtained from the website of the Volcanology Center and Geological Disaster Mitigation (PVMBG). From the tests that have been done, the highest accuracy is 88% when using the learning rate 0.1, the learning rate deduction is 0.1 and the minimum learning rate is 0.01.
Pengelompokan Wilayah berdasarkan Penyandang Masalah Kesejahteraan Sosial (PMKS) dengan Optimasi Algoritme K-Means menggunakan Self Organizing Map (SOM) Iskarimah Hidayatin; Sigit Adinugroho; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Persons with Social Welfare Problems are people, families, groups or someone who cannot function socially because there is a spiritual, physical or social difficulty. Grouping regions based on PMKS is very important to do to provide an overview of PMKS problems with the policy objectives taken right on target. Self Organizing Map (SOM) algorithm for determining the number of clusters and initial centroids while the K-Means algorithm for determining the cluster end result. The research flow, that is, the data is normalized, then the SOM process then to K-Means, then testing and analysis are carried out. SOM parameter testing using silhouette coefficient obtained the best parameter is the learning rate value of 0.2, beta at 0.8, r (neighboring) of 0, the number of clusters by 2, and epoch by 50. K-Means algorithm optimization using SOM is better than algorithm K-Means based on the silhouette coefficient value. The silhouette coefficient value in SOM is 0.21882702 while K-Means has a value of 0.201911102. Analysis of the results obtained K-Means algorithm optimization with SOM is cluster 1 with a total of 26 districts / cities by having similarities in the variable Social Problem Migrant Workers, Families with social psychological problems, and children who are victims of violence / who are treated wrongly have an average value high average and cluster 0 with the number of 12 districts / cities with similarities in variables other than cluster 1.
Co-Authors Abdul Fatih Achmad Yusuf Adam Sulthoni Akbar Adinugroho, Sigit Aditya Chandra Nurhakim Aditya Septadaya Adiyasa, Bhisma Afrialdy, Firman Aghata Agung Dwi Kusuma Wibowo Agi Putra Kharisma Agus Wahyu Widodo Ahmad Afif Supianto Ahmad Afif Supianto Ahmada Bastomi Wijaya Akmal Subakti Wicaksana Alan Primandana Almasyhur, Muhammad Bin Djafar Amalia Luhung Amita Tri Prasasti, Pinkan Anang Tri Wiratno Andhika Satria Pria Anugerah Anggita Mahardika Ani Budi Astuti Ani Rusilowati Anim Rofi'ah Annisa Puspitawuri Annisa Salamah Rahmadhani Arbawa, Yoke Kusuma Aria Bayu Elfajar Arief Andy Soebroto Arjunani, Rusmalistia Intan Ayuri Alfarianti Azhari, Muhammad Rizqi Azizul Hanifah Hadi Barik Kresna Amijaya Bayu Rahayudi Brillian Aristyo Rahadian Budi Astuti Budi Darma Setiawan Chelsa Farah Virkhansa Daneswara Jauhari Daneswara Jauhari, Daneswara Dany Primanita Kartikasari Dennes Nur Dwi Iriantoro Deo Hernando Desy Wulandari Dewanti, Amalya Trisuci Diajeng Tania Ananda Paramitha Dian Eka Ratnawati Dloifur Rohman Alghifari Dwi Fitriani Dwi Novi Setiawan Dwi, Endah Dyang Falila Pramesti Edo Ergi Prayogo Edy Santoso Edy Santoso Erik Aditia Ismaya Eriq Muh. Adams Jonemaro Falih Gozi Febrinanto Faris Febrianto Febri Ramadhani Fenori, Muhammad Dajuma Feri Angga Saputra Fianti Fianti, Fianti Fitri Anggarsari Fitriana, Rosita Nur Fitriani , Dwi Fitriani, Delvi Guntur Syafiqi Adidarmawan Himawan, Alfian Iftinan, Salsa Nabila Ikhwanul Kiram, Muh Zaqi Ilham Harazki Imam Cholisoddin Imam Cholissodin Imam Cholissodin Indah Lestari, Indah Indah Wahyuning Ati Indah, Yuliana Indra Eka Mandriana Indriati Indriati Indriati Indriati Indriati, Indriati - Iqbal Santoso Putra Iskarimah Hidayatin JANAH, NURUL Jumadi Jumadi Khairiyyah Nur Aisyah Kharisma, Agi Krisyanto, Edy Kurnianingtyas, Diva Kurniawan, I Gede Jayadi Kusumawardani, Septyana Dwi Lailil Muflikah Lailil Muflikhah Maharani Tri Hastuti Mardji Mardji Marinda Ika Dewi Sakariana Marinda, Vira Marwa Mudrikatussalamah Maulan, Erika Maulana Putra Pambudi Maulida, Farida Mochammad Tanzil Furqon Mohammad Nuh Mohammad Setya Adi Fauzi Muh Arif Rahman Muhammad Ihsan Diputra Muhammad Misbachul Asrori Muhammad Noor Taufiq Muhammad Prabu Sutomo Muhammad Riduan Indra Hariwijaya Muhammad Tanzil Furqon Muhja Mufidah Afaf Amirah Muhyidin Ubaiddillah Mukh. Mart Hans Luber Nabila Arief Nadia Artha Dewi Naily Zakiyatil Ilahiyah Naniek Kusumawati Nazzun Hanif Ahsani Nirzha Maulidya Ashar Nooriza Fariha Rumagutawan Noval Dini Maulana Novanto Yudistira Nur Hidayat Nur Sa'diyah Nurhidayati Desiani Nurul Faridah, Nurul Nurul Hidayat Nuryatman, Pamelia Nuzula, Nila Firdauzi Pande Made Rai Raditya Phutpitasari, Rosa Devi Pupung Adi Prasetyo Putra Pandu Adikara Putri Aprilia Putu Gede Pakusadewa Rachmalia Dewi Rahma Juwita Sany Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Reiza Adi Cahya Reza Wahyu Wardani Rifan, Mohamad Rina Christanti, Rina Rizal Setya Perdana Rizal, Moch. Khabibur Robih Dini Rohmah, Yushinta Lailatul Rohmanurmeta, Fauzatul Ma’rufah Rokky Septian Suhartanto Romlah Tantiati Rosyita, Elyana Santoso, Allegra Santoso, Andri Saputra, Rendi Ramadani Saputro, Rinaldi Eko Saputro Sekar Dwi Ardianti Selle, Nurfatima Selvi Marcellia Setya Perdana, Rizal Sigit Pangestu Siti Nurjanah Siti Nurlaela Sundari, Suci Sunyoto Eko Nugroho, Sunyoto Eko Susenohaji, Susenohaji Sutrisno . Syarif, Adnan Tirana Noor Fatyanosa, Tirana Noor Ulfah Mutmainnah Veni, Silvia Wahyu, Dwi Wayan Firdaus Mahmudy Werdha Wilubertha Himawati, Werdha Wilubertha Wiandono Saputro Wilis Biro Syamhuri Wiratama Paramasatya Yasin, Patbessani Septani Firman Yessica Inggir Febiola Yosua Christopher Sitanggang Yudha Eka Permana Yudistira, Indrajati Yuita Arum Sari Yulia Trianandi Yulian Ekananta Yusi Tyroni Mursityo Zulhan, Galang