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Sutrisno Sutrisno
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Aplikasi Penentuan Lokasi untuk Usaha Lapangan Futsal di Kecamatan Bangil Menggunakan Metode Fuzzy Tsukamoto Muhammad Alfian Nuris Shobah; Edy Santoso; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The system is built with the aim to help predict the results of operations of futsal in Bangil. In futsal open businesses have competitor input, the number of residents, the facilities, the size of the field. And this system produces an output stratification business opportunities, namely the village suitable for open futsal field. These systems are processed using fuzzy logic reasoning method Tsukamoto. The purpose application made to function as expected that is able to help resolve problems that arise and are difficult to solve because of an uncertainty which is very thin difference. From the data obtained will be an Tsukamoto fuzzy calculation that produces an output to be able to predict the circumstances that will occur from the input-input affecting. From the test results the decision-making system of determining the right location to open futsal in Kecematan Bangil with Tsukamoto method to 7 futsal some courts have precisely select locations, but the majority is still not right.
Penyusunan Bahan Makanan Keluarga Penderita Penyakit Hiperkolesterolemia Menggunakan Algoritme Genetika Sabrina Nurfadilla; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

To improve the quality of human resource generation required good nutritional needs for everybody. Each member of family needs a nutrition in the same kind but amount of the nutrition is different, that is influenced by age, body shape, gender, physical condition, genetic heredity, and life style. Information of data analysis from Riskesdas which is held in 2007 and 2013 revealed that Indonesian citizen Having problems with quality food consumption of between 80 to 90 percent tend to be less fruit and/or vegetable consumption and approximately 40.7% consume risky foods containing excess fat, cholesterol and fried more than equal to one time per day. From these data nutritional needs with food processing which is consumed by Indonesian citizen still lack. Therefore, the preparation of family food ingredients of patients with hypercholesterolemia using genetic algorithms needed for food ingredients that are consumed are variety and able to meet the nutritional needs with minimal cost. Genetic algorithm is a stochastic optimization technique because using random value. Genetic algorithms are capable of providing complex and wide-ranging problem solutions objectively. In crossover process using extended intermediate method and mutation process using random mutation method. The best solution results obtained when the population size of 100, crossover rate of 0.8, mutation rate of 0.2, permutation limit value of 115 and generation of 65.
Penerapan Parallel Genetic Algorithm untuk Optimasi Penyusunan Bahan Makanan Keluarga Penderita Hiperkolesterolemia Anandita Azharunisa Sasmito; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cholesterol is a fatty substance that is essential for the sustainability of body functions. However, if cholesterol is above normal levels, the body can not eliminate it and will accumulate in the arteries that can cause heart attacks or strokes. A person with hypercholesterolaemia should maintain a diet and nutritional intake. The composition of food consumed should be in accordance with the needs. Unfortunately, few Indonesians realize the importance to pay attention to the diet and nutritional content of the food consumed each day. The algorithm to be used in this research is parallel genetic algorithm (PGA) where the algorithm is the result of modification of the genetic algorithm. In the PGA population will be divided into several sub-populations that run in parallel. In this study PGA still uses the concept of multi-population and migration but will only run on a single processor. In the application of parallel genetic algorithm for this research resulted the highest fitness solution using method parameter with popsize number of 65, sub population of 5, using generation 60, crossover rate with value 0,4 and mutation rate equal to 0,6, Permutation with value 145.
Optimasi K-Means Untuk Clustering Dosen Berdasarkan Kinerja Akademik Menggunakan Algoritme Genetika Paralel Endah Utik Wahyuningtyas; Rekyan Regasari Mardi Putri; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The main task of a lecturer is to produce quality human resources and solve problems that exist in the wider community through research, dedication, and so forth. Competence owned by a lecturer determines the quality of the implementation of the college tridharma. So it is necessary to evaluate the academic performance of lecturers conducted periodically by the quality assurance team. Evaluation of academic performance of lecturers aims to maintain the quality of institutions, facilitate the decision-making, and provide appropriate treatment for improving the quality of lecturers. Each lecturer can have different competencies with each other. Therefore, there needs to be a grouping of data related to the academic performance of lecturers optimally. In this research, a lecturer clustering system will be built based on academic performance using k-means clustering method. Given that the method has the disadvantage of often getting different clusters because the initialization of the centroid is done randomly, therefore there is a need for centroid optimization on the k-means algorithm. The parallel genetic algorithm can be used to optimize the cluster center on the k-means algorithm. The result of clustering shows that cluster center optimization using parallel genetic algorithm get better result than only k-means method.
Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak Indri Monika Parapat; Muhammad Tanzil Furqon; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Growth and development of children at an early age affect the child's personal ability in the future. Every child is unique, so growth and growth are different. Deviation of late child growth is known to result in long-term and difficult to repair. . Based on these problems, this research was conducted by using the sUPPmethod for the classification of child growth deviations. ELM method consists of training process as system learning and testing to obtain the result of classification. The parameters test are test of lambda, complexity, and maximal iteration. There are 90 data used in this research, which is divided into 3 classes. Classes in this study represent three types of diseases in growth and development are Down Syndrome, Autisme, dan Attention Deficit Hyperactivity Disorder (ADHD). Basically SVM algorithm is a method of linier classification, so there is kernel is used to overcome nonlinier data. The final result of this study produced the highest average accuracy on this research is 73,78% λ = 0,1, C = 0,1, itermax = 10 and also using polynomial kernel. The comparison of the result of the classification of child growth deviation with the help of psychologist shows that the system produces poor accuracy. This can be due to the small and unbalanced data used for the research.
Optimasi SVR dengan Ant Colony Optimization untuk Prediksi Tingkat Produksi Susu Segar (Studi Kasus pada Koperasi Susu SAE Pujon, Malang) Karuniawan Susanto; Rekyan Regasari Mardi Putri; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Milk is a food of livestock that has a complete and balanced nutrition where its protein nutrition is higher than vegetable protein. The consumption of milk and its products play a role in improving the quality of human resources in Indonesia that is still low. Therefore, the role of milk manufacture industry in Indonesia is very important in terms of providing and sufficient nutrition needs of the people. One of the milk manufacture industry is dairy cooperatives of SAE Pujon, Malang. In order to be able to play a role well, the production rates of fresh milk in dairy cooperatives of SAE Pujon is important things that need to be optimized. Improper production rates will result in losses, such as loss in the form of material or loss of consumers. Based on these problems, it takes support vector regression method optimized with ant colony optimization that is implemented into a system. Optimization is done to determine the most optimal SVR parameter. The optimized SVR parameter are (sigma), C (complexity), (epsilon), cLR (learning rate constants) and (lambda). Range of ACO parameter values to obtain optimal SVR parameter value is q0 = 0,5-1, α = 0,01-0,04, = 0,01-0,04, ρ = 0,001-0,004, δ = 0,001-0.004. The milk production rates forecasting in dairy cooperatives of SAE Pujon from January until December 2016 by using SVR-ACO resulted MAPE value of 3,30425%.
Sistem Pakar Diagnosis Penyakit Sapi Ternak Potong Menggunakan Metode Naive Bayes - Certainty Factor Dhimas Tungga Satya; Nurul Hidayat; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The low ability of domestic farms to fulfill necessity of beef and cow's milk is caused by many things. One of them is a disease. Such as anthrax, snore of cow disease, brucellosis and diseases caused by parasitic gastrointestinal worms which are the most agent of decreasing levels of beef and dairy production by breeders. Currently expert resources in diagnosing cattle diseases are limited, it takes an expert system that can replace the role of experts in diagnosing diseases in cattle. In this study to implement the expert system diagnosis of cattle disease using method Naive Bayes - Certainty Factor. Using the Naive Bayes - Certainty Factor method results in accurate and accurate diagnostic accuracy, since the output produced by the system has a level of accuracy of 92% and the system created has a user satisfaction level of 3.19411.
Optimasi Kandungan Gizi Dan Biaya Bahan Pangan Pada Makanan Sehat Untuk Penderita Kolesterol Tinggi (Dyslipidemia) Menggunakan Algoritma Evolution Strategies Rayindita Siwie Mazayantri; Randy Cahya Wihandika; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nowadays there are changes that occurring in our community, especially in technology department, that causes changes in lifestyle which results in imbalanced nutrition. Something like this could lead the body to catch symptoms of dyslipidemia, which is a disease wherein the level of blood lipids is high and hence higher possibilities of causing many more dangerous diseases. To reduce the level of cholesterol, it is highly recommended to examine the food contents and ingredients. For some people, it may not be easy to manage what ingredients they should consume due to lack of knowledge in that aspect, so to solve the problem we could apply Evaluation Strategies method, which has initialization process of chromosome representation as real-vector. In this study, the ES cycle that we apply is (µ+λ) that only requires mutation without recombination. The next process is fitness calculation, and evaluation, and then do the selection process. From the testing of parameters, we can conclude that the system can yield the most optimized results when the size of population is set to 120, offspring is set to 160, and generation is set to 40. A solution that the system can come up with compared to the experts' recommendation shows that the solution this system gave is more optimal, with 2.2825 as a fitness value, higher than the fitness that we get from experts' recommendation which only 0.4003, so the system can provide recommendations for cheap ingredients while reckoning the needs for nutrients intake of the patients.
Implementasi Jaringan Saraf Tiruan Backpropagation untuk Memprediksi Jumlah Penduduk Miskin di Indonesia dengan Optimasi Algoritme Genetika Arthur Julio Risa Ashshiddiqi; Indriati Indriati; Sutrisno Sutrisno
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

Poverty is a common issues encountered by every country, and Indonesia is one of them. The escalation of the poor occurred almost every year. According to Indonesia Statistic Bureau (Badan Pusat Statistik) using population indicator based on their monthly expense below the line of poverty can be categorized as poor people. The increasing amount of the poor can trigger criminality, that is because those individuals will do anything to make ends meet. By predicting the amount of the poor, hopefully the government or any related institution can help decrease poverty and unemployment rate in Indonesia. Artificial neural network backpropagation is one of the method that can be used to make predictions. Weight and bias in backpropagation's training optimized using genetic algorithm to obtain more optimal results. In this artificial neural network backpropagation research method that the weight training optimized using genetic algorithm generate 8.744579% AFER points.
Sistem Pendukung Keputusan Penentuan Prioritas Pemeliharaan Jalan Menggunakan Metode PROMETHEE II (Studi Kasus: Dinas Pekerjaan Umum Dan Penataan Ruang Kabupaten Ponorogo) Mahardhika Hendra Bagaskara; Muhammad Tanzil Furqon; Sutrisno Sutrisno
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

Road infrastructure has an important role in supporting the economic, social and cultural sectors. The government as the road operator must prioritize the maintenance of the road periodically in accordance with the minimum service standards set. The strategic target of Directorate General of Highways one of them is the stability of the road that reaches 70%. Due to the number of broken roads, the number of complaints from the public, and the limited budget, the government must have priority on which roads should receive maintenance. The selection of road segments implemented by Ponorogo District Office of PUPR can be done by applying PROMETHEE II method to consider several alternatives and get the best alternative ranking based on the aspect of steady road conditions, unsteady road conditions, LHR, access and inter-district liaison. The method of Promethee II performs calculations with several stages: weighting, multicriteria preference index calculation for 4 types of preference ie, usual, quasi, linear, and the level and calculation of leaving flow, entering flow, and netflow. Based on the test, the highest accuracy on the use of the usual and quasi type of preference is 55.56% and the lowest accuracy on the use of linear preferences type is 45.83%. The degree of accuracy in testing is influenced by the weights used for each of the criteria and the type of preferences used in the calculation process.
Co-Authors Abas Saritua Gultom Achmad Dwi Noviyanto Adinugroho, Sigit Aditya Negara Aditya Sudarmadi Agi Putra Kharisma Agus Prayogi Ahmad Galang Satria Anandita Azharunisa Sasmito Andi Amaliyah Maryama Arthur Julio Risa Ashshiddiqi Axel Iskandar Budi Darma Setiawan Candra Dewi Chalid Ahmad Aulia Chindy Putri Beauty Cindy Inka Sari Danastri Ramya Mehaninda Deby Chintya Dewi Syafira Dhavin Putra Alamsyah Dhimas Tungga Satya Dina Dahniawati Dita Sundarningsih Dyah Ayu Wahyuning Dewi Edy Santoso Endah Utik Wahyuningtyas Enny Trisnawati Fajar Pradana Faraz Dhia Alkadri Febriyani Riyanda Filan Maula Andini Firhad Rinaldi Saputra Fran's Dwi Saputra Atmanagara Galih Aulia Rahmadanu Heru Budiyanto Ian Lord Perdana Imam Cholissodin Imam Farouqi Faisal Inas Nabila Indri Monika Parapat Indriati Indriati Jeowandha Ria Wiyani Jodi Irjaya Kartika Karuniawan Susanto Kukuh Wicaksono Wahyuditomo M. Ali Fauzi Mahardhika Hendra Bagaskara Marji Marji Miracle Fachrunnisa Almas Mochamad Ali Fahmi Mochamad Rafli Andriansyah Mohamad Yusuf Arrahman Muhammad Abdan Mulia Muhammad Alfian Nuris Shobah Muhammad Hafidzullah Muhammad Tanzil Furqon Nanda Firizki Ananta Nurul Hidayat Putra Pandu Adikara Putri Indhira Utami Paudi Rachmad Faqih Santoso Rachmad Ridlo Baihaqi Rahmatsyah Rahmatsyah Rakhmadina Noviyanti Randy Cahya Wihandika Ratih Kartika Dewi Rayindita Siwie Mazayantri Rekyan Regasari Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati Rezza Hary Dwi Satriya Rich Juniadi Domitri Simamora Riski Adam Elimade Rizal Maulana Sabrina Nurfadilla Safira Dyah Karina Siti Utami Fhylayli Supraptoa Supraptoa Thariq Muhammad Firdausy Tibyani Tibyani Tri Halomoan Simanjuntak Tunggul Prastyo Sriatmoko Wayan Firdaus Mahmudy Widya Amala Sholikhah Yose Parman Putra Sinamo Yuita Arum Sari