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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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Pengaruh Seleksi Fitur Information Gain pada K-Nearest Neighbor untuk Klasifikasi Tingkat Kelancaran Pembayaran Kredit Kendaraan Ulfah Mutmainnah; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Intermittent credit is one of the problems or risks that are often faced by some auto loan service providers. The problem stems from the debtor's behavior, namely not paying the installments on time. In determining the smoothness of credit payments depends on the analysis of debtor data, but analyzing for large amounts of data can take up more time. This study uses the Information Gain feature selection and the K-Nearest Neighbor algorithm to overcome the problem of effectiveness and determine the accuracy of the classification level of the smoothness of auto loan payments so as to determine the effect of feature selection. Information Gain feature selection which is used to reduce feature dimensions so that relevant features can be obtained. The selected features are then processed for classification using the K-Nearest Neighbor algorithm. Based on testing from this study, the highest accuracy obtained is 94.44% when testing with a balanced class distribution using the number of features 3 and the value of K = 4 while the lowest accuracy is obtained at 33.33% using the number of features 10 with a value of K = 5 when testing with uneven class distribution. Features that produce the highest accuracy are jobs, income and price on the road (OTR). The three features are features with the largest order of gain values and have a gain value of more than 0.1.
Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Persalinan Romlah Tantiati; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One reference to measure health services in an area is how medical care is handled by medical personnel. In this case the Maternal Mortality Rate (MMR) as well as in infants is the Infant Mortality Rate (IMR) considered as an important indicator in health care. Antenatal care services are carried out as an effort to prevent complications during pregnancy and expectant mothers by determining the actions that must be given to pregnant women from the examination results so that they are able to suppress the Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). LVQ network is a competitive training with each output connected in a certain class. In this study the authors implemented LVQ learning to classify normal childbirth into 2 classes, namely whether childbirth is normal or at risk. By using the data collected in the Nursing Care (ASKEP) data on the general physical examination of pregnant women which contains information on age, pelvic size, fetal position, measurement of blood pressure, hemoglobin cell level (HB), results of psychology testing for prospective mothers, Upper Arm Circumference (LILA), proteinurea and Fetal Weight Interpretation (TBJ). The results of LVQ testing for the classification of normal childbirth with learning rate parameters (α) = 0.1, reduction constants LR (c) = 0.1, minimum LR = 10-7 and maxEpoch / iterations maximum 24 times with a comparison of the amount of training data and test data (64:16) is an accuracy value of 93,78%.
Penentuan Model Lajur pada Self-Driving Car menggunakan Hough Transform dan Kuantisasi Warna K-Means Pupung Adi Prasetyo; Randy Cahya Wihandika; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Technology continues to develop to the phase where human daily activities can be carried out by artificial intelligence. Research on artificial intelligence keeps on competing to produce more advanced program to simplify things. Nowadays artifial intelligence tools can be found in various application, one of them is self-driving car. This topic is one of the most widely researched topic due to its various division of functionality. The most frequently discussed functionality is the defining of navigation lane model. The lane model which is the navigation direction of a self-driving car must be visually determined based on the road markings, which also the navigation direction directions of public vehicles. Therefore, this study will determine the lane model visually using image processing methods. By only using image processing, the resulting precision can reach an average value of 88.45% in various road conditions. Therefore, it can be concluded that the visual image processing can be used to determine the lane model in a self-driving car.
Klasifikasi Emosi Berdasarkan Ciri Wajah Menggunakan Convolutional Neural Network Achmad Yusuf; Randy Cahya Wihandika; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Apart from being a person's identity, the face is also a supporting tool in direct socializing. A person can convey emotions experienced using expressions raised by their face. Emotion is a feeling to encourage an individual or a response to a stimulus. In consumer research, consumer testing is a method used to predict product acceptance by consumers in a market. Even though it has gone through extensive consumer testing stages before entering the market, the failure rate of new food products is still high. This shows that traditional consumer testing methods are not able to predict market performance and product acceptance by consumers in the long run. To be able to know consumer behavior more deeply, the use of emotional measurement is widely used in consumer testing because emotions affect consumer behavior. In this case, the classification of emotions based on facial characteristics is considered suitable to help improve the quality of consumer testing. The method used in this study is the Convolutional Neural Network (CNN). The data used are data obtained from the Extended Cohn-Kanade Dataset (CK +) taken from 210 subjects with a total of 327 images used. Testing the study using K-fold Cross Validation with a k value of 4. The test results show a certain learning rate value can train architecture better than other learning rate values. The best accuracy results in this study amounted to 86.4% and an average accuracy of 80.7%.
Optimasi Penentuan Centroid pada Algoritme K-Means Menggunakan Algoritme Pillar (Studi Kasus: Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Timur) Alan Primandana; Sigit Adinugroho; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The k-means clustering method is a non-hierarchical grouping method that groups data into several centroid centers. The simplicity of the k-means method is widely used in various fields because it has several advantages, namely it is easy to implement and has a high level of accuracy of the size of the object so that this method is relatively more measurable and efficient. However, the initial k-means algorithm calculates using a C (centroid) value that randomly causes random results. Dependence on C (centroid) values ​​makes the accuracy of the k-means algorithm less than optimal. The results of k-means calculations are often obtained by experimenting several times and tend to produce different clusters. But in getting better results, it is difficult to determine the limits of an experiment. The random determination of cluster centers causes the k-means method has not been able to get the best grouping results. In this study, we describe an algorithm that is also used to optimize the selection of the initial center point in the k-means method, the pillar algorithm. This algorithm is an initial centroid determination by calculating the distance of metric accumulation between each data and all previous centroids. The choice of points is determined by data points that have a maximum distance. This research determines centroid using the Pillar algorithm and the results of the algorithm are used for the cluster's focal point on the k-means algorithm. In each cluster pillar algorithm is able to get the value of Sum of Squeared Error (SSE) better than random centroids as evidenced by the decreasing value of SSE.
Peramalan Hasil Panen Kelapa Sawit Menggunakan Metode Multifactors High Order Fuzzy Time Series yang Dioptimasi dengan K-Means Clustering (Studi Kasus: PT. Sandabi Indah Lestari Kota Bengkulu) Yessica Inggir Febiola; Imam Cholissodin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 12 (2019): Desember 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Based on the export volume data recorded by the Ministry of Agriculture from 2012 to 2016, palm oil (Elaeis guineensis Jacq) became one of the centres of Government and investor attention. In the oil palm plantation company, one of which is PT. Sandabi Indah Lestari Bengkulu occurs constraints on palm oil crops that do not match the target expected. This target is the sum of harvest and when it is used to harvest the oil palm. When crops do not match the target, it can cause budgets of production that do not match the planned. Therefore, the company requires forecasting the crops to minimize the constraints caused by the crops that are not suitable for the target. The Multifactor High Order Fuzzy Time series method that is optimized with K-means Clustering to determine which subintervals are used can help in forecasting the oil palm harvest. The plot of this method is the determination of the Universe of discourse, the determination of the number of clusters, the formation of subintervals with K-means Clustering, the formation of the fuzzy set, the fuzzification process, the formation of Fuzzy Logic Relationship (FLR), and the defuzzification process. This research uses several factors that lacte over the harvest of oil palm Lot month, land area, age of the plant, and the amount of oil palm trees. From the results of the tested the sum of clusters, the sum of orders, the sum of training data, and the optimal threshold in the successive are 8, 6, 107, and 6 with the best AFER value of 36,98%.
Pemanfaatan Fitur Warna dan Fitur Tekstur untuk Klasifikasi Jenis Penggunaan Lahan pada Citra Drone Deo Hernando; Agus Wahyu Widodo; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 2 (2020): Februari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Land use is a way to use land in carrying out certain objectives. Examples of land use types are forests, rice fields, housing, roads and rivers. However, the many transfers of land use functions, such as illegal logging of land used for housing development, require the use of land use planning. Given this issue, encouraging the writing of this research to assist land use planning by classification of land use types. The author uses two image features namely color features of 8 different color spaces (CMYK, HSV, HVC, Lab, RGB, YCbCr, YIQ, and YUV) and texture features using the Support Vector Machine classification method. The data used are 25 training data and 200 test data where the amount of data for each class is the same. The tests conducted are testing the color features with the highest accuracy, testing the texture features that affect accuracy, and the combination of color and texture features with the highest accuracy. The first test result is the color feature in the HSV color space has the highest accuracy of 98%. The second test result is the accuracy of texture features affected by image size, membership distance, and angle in the GLCM calculation. The image size of 900x900 with a membership distance of 1% and a combination of 4 corner features (0o, 45o, 90o, 135o) produces the highest accuracy of 96.5%. The third test result is a combination of color features in the CMYK, HSV, HVC, Lab, YCbCr, YIQ, and YUV spaces with the texture features of the second test results yielding the highest accuracy of 99.5%.
Analisis Sentimen Pemindahan Ibu Kota Indonesia Dengan Pembobotan Term BM25 Dan Klasifikasi Neighbor Weighted K-Nearest Neighbor Marinda Ika Dewi Sakariana; Indriati Indriati; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The relocation of Indonesia capital city is one of the policies that is being intensively discussed at this time. With the policy regarding the relocation of the capital city from Jakarta to Kalimantan, it will certainly cause various reactions or comments from the community that can be found on social media, Twitter. Types of reactions can be divided into positive and negative comments. To find out a comment has a positive or negative value, sentiment analysis is needed as in this study. In this study, there are several steps that must be done to get the final results. These stages are pre-processing data, term weighting and ranking with the BM25 algorithm, and classifying the final results of tweets with Neighbor Weighted K-Nearest Neighbor (NWKNN) algorithm. This study uses 480 training data and 120 test data divided into positive and negative sentiments. The highest accuracy value obtained was 93.33% with a precision value of 92.45%, a recall of 94.67% and an f-measure of 93.55% with a K value of 25, =1,2 and =0,65 also an E value of 4.
Pengembangan Aplikasi Reado: Aplikasi Pembelajaran Bahasa Inggris Bagi Anak Pada Kegiatan Sosial Komunitas Katalis Pendidikan dengan Metode Design Sprint Rachmalia Dewi; Agi Putra Kharisma; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is a country listed at the 61st position of 100 countries in the world based on an English proficiency index value of 50.06 that is below the average value of 53 and Indonesia is placed in the low category. This is because English learning is currently not going well. English learning can be done by using teaching strategy which is learning through educational games using electronic devices for learning words in English. In addition, statistical data show that the majority of the public is using an Android-based mobile application. Therefore in this research, the development of educational game for children through Android-based mobile devices is called Reado application. The development of this application was carried out by using the design sprint method that were tested on children from the social activities of the Katalis Pendidikan Community which consisted of 22 children. From the application of the design sprint method, based on the result of the black box testing that is 10 tests of the functional requirements have passed and have been verified, so it is stated that the Reado application has fulfill user's specific needs. In addition, based on the results of usability testing from grades 1 to 6, the results obtained that the value of the efficiency aspect has an average of 93.5% which is "good", the value of the effectiveness aspect is 0.06 which is "very good" and the value of the user satisfaction aspect 80.37 which is "good".
Prediksi Harga Emas Dengan Menggunakan Metode Average-Based Fuzzy Time Series Muhammad Riduan Indra Hariwijaya; Muhammad Tanzil Furqon; Candra Dewi
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

Gold is a type of precious metal that has economic value and is often used for investmentl. Demand for gold increases from year to year, because many people already know that gold can be used as a safe haven. Safe haven is ownership in the form of investment assets that have a risk in low level, so that it becomes a protector of assets. Behind the benefits of gold, many investors discourage their investment because they are afraid of being cheated and cannot predict the increase or decrease in gold prices. Therefore we need a prediction of gold prices for investors to avoid losses when they want to invest in gold. One prediction method is Average-Based Fuzzy Time Series with the advantage to determine the interval effectively, the interval formed implements Average-based length so that it can increase the accuracy of the resulting prediction. The Average-Based Fuzzy Time Series implements fuzzy logic principles for the process of making predictions, such as fuzzy set, degree of membership, fuzzification, and defuzzification. The data used are daily gold prices taken from the official website of Logam Mulia with 2700 data with a time span from January 2010 to December 2019. The best error value MAPE obtained in the study was 0.34216% and included in the very criteria good because it's under 10%. Based on research conducted, the Average-Based Fuzzy Time Series method is good for predicting gold prices.
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