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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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Implementasi Algoritme K-Means Sebagai Metode Segmentasi Citra Dalam Identifikasi Penyakit Daun Jeruk Falih Gozi Febrinanto; Candra Dewi; Anang Tri Wiratno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the factors that causes poor quality of citrus crops is the disease which attacks the leaves. The development of information technology in digital image processing field allows to identify the citrus leaf disease automatically. This research identifies the citrus leaf disease includes Downy Mildew, Cendawan Jelaga, and CVPD (Citrus Vein Phloem Degeneration). The identification process of citrus leaf disease begins with resizing to equalize image size and rescaling to adjust the image brightness. Next, converting RGB to L*a*b* color space. After converting the color space, the results of the conversion is used as an input to image segmentation using K-Means algorithm. There are two segmentation parts, namely leaf segmentation and disease segmentation. After segmentation process, the results of disease segmentation are classified by using K-Nearest Neighbor (K-NN) algorithm on the train data to knows the class of their diseases. Tests conducted on this research are testing the value of Scale Factor, optimal cluster value, and optimal K value. Based on the three conducted tests, it recommends that the Scale Factor value is 1.1, the optimal cluster value on leaf segmentation is 2, the optimal cluster value on the segmentation of disease is 9, and the optimal K value is 4. The highest accuracy that obtained for disease identification in this research is 90.83%.
Prediksi Hasil Panen Benih Tanaman Kenaf Menggunakan Metode Support Vector Regression (SVR) Pada Balai Penelitian Tanaman Pemanis dan Serat (Balittas) Robih Dini; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kenaf (Hibiscus cannabinus L.) is a fiber plant that has many benefits. Kenaf is grown by seed so it is necessary to handle the seeds in order to ensure the quality of the seed is not decreased so as to increase the productivity of the kenaf. Balai PenelitianTanaman Pemanis dan Serat (Balittas) as the producer of the seeds has constraint to predict the yields of kenaf seed for the proper handling preparation of kenaf seeds. Therefore in this research proposed regression method using Support Vector Regression (SVR) by using Radial Basis Function (RBF). Hopefully this research can help Balittas to prepare the handling of the harvested of kenaf seeds properly. The research used 100 data about the characteristics of kenaf plants measured from the beginning of planting until the time of harvest. From the testing results that have been done, the result of prediction show the error value using Mean Absolute Percentage Error 3,5371% by using the best SVR parameters value which is cLR = 0,01, σ = 0,25, ε = 1 x 10-7, C = 0,5, λ = 0,6, and the number of iterations = 25000.
Prediksi Curah Hujan Menggunakan Metode Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series Yulia Trianandi; Wayan Firdaus Mahmudy; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Prediction of rainfall is needed in increasing the production of food crops in a region, one of them is Tengger area. Errors in predicting rainfall can cause errors when determining the planting period, and the right type of plant. In order to produce rainfall predictions with a slight error rate, this study uses the Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series method. The method has been proven to predict the closing price on the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAEIX) with fewer error rates than Chen Fuzzy Time Series methods. This study will produce rainfall predictions in the four districts of Tengger area, namely Puspo, Sumber, Tosari, and Tutur. From the results of testing 36 data in 2014, obtained the best Mean Square Error (MSE) value of 28.0470 in Tosari district.
Optimasi Jumlah Produksi Metal Roof Menggunakan Algoritme Genetika (Studi Kasus: PT. Comtech Metalindo Terpadu) Febri Ramadhani; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Manufacturing industry in Indonesia continues to increase, especially in the molding industry. PT. Comtech Metalindo Terpadu is one of molded goods industry company located in Pekanbaru City. The company is an industrial company that produces metal roof. The metal roof is printed using Prepainted Galvalum (PPGL) raw material or more commonly referred to as coil, the raw material is imported from other countries. The ordering of raw materials takes 2 months until the raw material arrives. There are 3 types of metal roof products sold are spandek, zigzag and zigzag charcoal. All three items have the composition of raw materials, as well as providing benefits that are different. Setting the right amount of production is the thing that must be taken into account by the owner of the company in order to obtain optimal benefits. Based on these problems to get the right amount of production on the use of the remaining raw materials, it is necessary to optimize the number of metal roof production based on the existing demand and the remaining stock of raw materials. Optimization is used to regulate the amount of existing production so that the remaining raw materials can be used optimally and provide optimal benefits as well. Genetic Algorithms are used to optimize the 3 genes that represent each product. The value of the gene represents the original value of the existing query with the integer type. In the reproduction, the crossover method that used is the extended intermediate crossover. Whereas the mutation is performed by reviving the gene values of a randomly selected chromosome. For the selection process used elitism selection to screen the best individual and used random injection method to prevent early convergence. Based on testing of parameters that have been done with 5 times each parameter is got the best population size 90, the combination of cr = 0.1 and mr = 0.9, and total of best generation equal to 225 with average fitness value 7.12126.
Implementasi Algoritma Genetika untuk Optimasi LVQ pada Penentuan Kelayakan Kredit (Studi Kasus: Bank X) Aghata Agung Dwi Kusuma Wibowo; Candra Dewi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In determining debtor credit, if there is an error in the debtor's analysis, it will cause problems such as bad credit in the future. So, it needs more accurate selection in the analysis of debtors who deserve credit. A more rigorous and consistent analysis takes longer due to the large amount of analytical data. To obtain a more accurate analysis and more efficient analysis time, it can be done by making a credit analysis system using the Learning Vector Quantization (LVQ) method to classify data and determine debits that are eligible for credit. To obtain accurate credit results, the use of the LVQ method depends on the weight. Analysis with LVQ method shows the accuracy value obtained is 79.37% by testing 63 test data. To obtain optimal accuracy values, the weights used in the LVQ method are optimized first with genetic algorithms. Optimal weight test results obtained a higher accuracy value of 93.65% for testing with popsize 20 parameters, Cr 0.9, Mr 0.1 and number of generation 10.
Perbandingan Jaringan Saraf Tiruan LVQ Dengan Backpropagation Dalam Deteksi Dini Penyakit Jantung Koroner Mohammad Setya Adi Fauzi; Bayu Rahayudi; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Coronary heart disease is one of the highest causes of death in the world. The World Heart Federation estimates the number of deaths from this disease in Southeast Asia to reach 1.8 million cases in 2014. In Indonesia in 2013 recorded 883,447 people diagnosed with coronary heart disease with the majority of patients aged 55-64 years and the death rate due to this disease is enough high, ie 45% of all deaths in Indonesia, so early detection of coronary heart disease is very important for the risk of this disease can be minimized. One of the popular machine learning techniques and fits in this case is the artificial neural network. Artificial neural networks are systems that are inspired by reasoning processes in human neural networks. In this study the authors compared the performance of artificial neural network LVQ method and Backpropagation method for early detection of coronary heart disease. The variables of coronary heart disease used in this study were gender, age, pulse, systolic blood pressure, cholesterol, blood sugar, triglycerides, chest pain, shortness of breath, and cough. From the results of this study showed that the Backpropagation method is better than the LVQ method with the comparison of the accuracy value of training of 95,99097% for Backpropagation compared to 66,89659% for LVQ and the accuracy value of testing of 68,76034% for Backpropagation compared to 54,30313% for LVQ.
Implementasi Metode Learning Vector Quantization Untuk Klasifikasi Penyakit Demam Nurhidayati Desiani; Lailil Muflikhah; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fever is an early symptom of various diseases that have been experienced by almost everyone. Some of the diseases include typhoid fever, malarial fever and dengue fever. These three diseases have similar early symptoms. Similar symptoms of each disease often cause difficulty in obtaining anamnese (temporary diagnosis) so that patients get the initial handling is less precise and further worsen the condition of the patient. To overcome this required a system that can facilitate in identifying the disease based on the symptoms felt by the patient. In this study using Learning Vector Quantization method which is a method of classification. The system works with the training and testing phases that will result in classes of typhoid fever classes, malarial fever and dengue fever. The parameters used are 15 parameters of symptoms of febrile illness. The best average accuracy result is 100% using comparison of test data and training data of 10:90, learning rate 0,1, learning rate reduction constant 0,1, minimum learning rate 10-5, and maximum number of iteration 10.
Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery) Noval Dini Maulana; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bread is one of the favorite foods of the Indonesian people, the proof is the increasing import of wheat flour. One of the bakery companies that is currently developing is Harum Bakery. Constraints that are often faced by Harum Bakery are customer demand forecasting systems that are still manual and seem to be guessing. The forecasting process give a big impact on the sales process. With the forecasting of bread sales, it is hoped that Harum Bakery can help bakeries in preparing raw materials and everything needed for bread making. Support Vector Regression (SVR) is one method that can be used in forecasting. The data used is data on sales of sweet bread, cake and white bread with time series data types and uses 4 features. In this study the SVR method used to predict the results of the sale resulted in an evaluation value of RMSE for sweet breads is 0.00176, bread cake is 0.00019, and large breads is 0.00010.
Optimasi Fuzzy Time Series Menggunakan Algoritme Particle Swarm Optimization Untuk Peramalan Produk Domestik Bruto (PDB) Indonesia Dloifur Rohman Alghifari; Bayu Rahayudi; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

As one of the input indicators for development programs. This Gross Domestic Product (GDP) forecasting is expected to provide information about economic growth and performance in Indonesia. Data sources of GDP usually come from survey results or from administrative records from various institutions. Sometimes the source data is incomplete or not available when calculating GDP values, it must be determined how to calculate the GDP value so that it can be used to estimate GDP forecasting using fuzzy time series. To improve forecasting accuracy, we use fuzzy time series optimization intervals using particle swarm optimization (PSO). Based on the parameters obtained with a dimension length of 40, many particles of 40, 450 for maximum iteration, the value of c1 and c2 is equal to 1.5 and for inertial weight of 0.3, the forecasting error rate generated using MAPE is 2.48% of the 10 test data. These results indicate good forecasting ability with a low error rate. The comparison of forecasting results for the proposed method is slightly better than the fuzzy time series method with the determination of the average interval based on MAPE 2.66%. But it is no better than the linear regression method with MAPE 1.52%
Diagnosis Tingkat Risiko Penyakit Stroke Menggunakan Metode K-Nearest Neighbor dan Naive Bayes Annisa Puspitawuri; Edy Santoso; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stroke is a disease that arises due to the dissolution of blood supply to the brain because of bursts in the blood vessels or there was a blockage of blood clots. The supply of oxygen and nutrients to the brain stops and can cause damage to the tissues in the brain. Stroke is number one leading cause of disability and number three cause of death after cancer and heart disease. Based on Riskesdas data, stroke prevalence in Indonesia in 2013 has increased when compared with Riskesdas data in 2007 with a value of 8.3%, increase up to 12.1% per 1,000 population. Therefore, we need an action to detect the level of risk of stroke to be immediately addressed in accordance with the level of risk. This research proposes an application of diagnosis of stroke risk level using K-Nearest Neighbor and Naive Bayes methods, because the data obtained using numerical and categorical attributes. K-Nearest Neighbor algorithm is used to process numerical data, and Naive Bayes algorithm is used to process categorical data. The results showed that the highest accuracy value obtained in the balanced class data was 96.67% with 45 training datasets, 30 testing datasets and value of K=15-22. Meanwhile, the training datasets that is not balanced shows the highest accuracy of 100% with the number of training datasets is 60, 30 testing datasets and the value of K=20-30.
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