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All Journal Jurnal POINTER Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Reformasi : Jurnal Ilmiah Ilmu Sosial dan Ilmu Politik Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Bulletin of Culinary Art and Hospitality
Edy Santoso
Fakultas Ilmu Komputer , Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Implementasi Algoritme Extreme Learning Machine (ELM) untuk Prediksi Beban Pemanasan dan Pendinginan Bangunan Alif Fachrony; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Energy conservation is a very important thing as the growth of the times and technology. Making energy-efficient buildings needs to be done by optimizing the use of tools for cooling and heating the building without affecting the health and comfort of the user of the building. Energy-efficient buildings can be achieved by calculate heating (HL) and cooling (CL) loads. HL and CL are the heat flow rates to be taken or added from the building to maintain relative air temperature and humidity of the building under desired conditions. The prediction of HL and CL will be used in calculating the power loads of heater or air conditioner. Currently HL and CL calculations still have constraints such as very complex calculations, time consuming because many disciplines are involved and it use very varied parameters. It needs learning machine to predict HL and CL easily, and quickly. The author uses the algorithm Extreme Machine Learning (ELM) to predict HL and CL. In the test analysis using ELM algorithm performed using binary sigmoid activation function, 3 input, 1 hidden neurons, 2 output targets and 130 dataset, the best Mean Absolute Error Percentage (MAPE) is 24.73% and it takes 0.0176 seconds to complete the process.
Rekomendasi Lokasi Pet Shop Di Kota Malang Menggunakan Metode Analytical Hierarchy Process (AHP) Simple Additive Weighting (SAW) Ghiffary Rizal Hamdhani; Edy Santoso; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nowadays raising animals like cats is like a lifestyle, sometimes many people think of the pet as family. Therefore pet owners always give the best care to the animals. This system uses one of the methods in the Decision Support System. Decision Support System is a computer-based system that can assist a person in improving its performance in decision making. By using one of the methods in the Decision Support System, it is expected to help solve problems that are in semi-structured areas such as the above problems. In this system will use the method of Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Analytical Hierarchy Process (AHP) is a method used to solve an unstructured complex situation into several components within hierarchical groups, by assigning subjective values and determining which variables have the highest priority to influence the outcomes in those situations . Simple Additive Weighting (SAW) is used for ranking.. Based on the results of the test can be analyzed that the AHP and SAW method is quite effective used in the recommendation process. The result of accuracy testing on custody service is 72,72% while accuracy testing of grooming service equal to 75%.
Optimasi Metode Extreme Learning Machine Dalam Penentuan Kualitas Air Sungai Menggunakan Algoritme Genetika Regina Anky Chandra; Edy Santoso; Sigit Adinugroho
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

Seiring dengan meningkatnya jumlah populasi manusia, sumber air bersih yang ada di bumi terus berkurang. Dampak yang diberikan akibat tercemarnya sumber air juga tidak dapat diremehkan. Beberapa dampaknya antara lain adalah menurunnya kadar oksigen yang ada di bumi dikarenakan tumbuhan tidak dapat berfotosintesis dengan baik, mengganggu kesuburan tanah, mematikan hewan-hewan yang hidup di dalam air dan masih banyak dampak lainnya. Salah satu sumber air di muka bumi ini berasal dari sungai. Untuk menjaga kualitas air agar tetap pada kondisi alamiahnya, perlu dilakukan pengukuran dan analisis terhadap air sungai tentang status mutu airnya. Pada penelitian ini digunakan 7 parameter pengukuran kualitas air sungai yang kemudian akan diklasifikasikan menjadi 3 kelas berbeda. Kelas klasifikasi dibagi menjadi tercemar ringan, tercemar sedang, dan tercemar berat. Metode yang digunakan untuk pengukuran dan analisis pada penelitian ini adalah metode Extreme Learning Machine (ELM) dan Algoritme Genetika. Dalam penelitian ini, bobot awal yang digunakan pada proses training dan testing ELM akan dioptimasi menggunakan Algoritma Genetika. Data training dan data testing yang digunakan, ditentukan oleh 5 fold yang telah dibentuk dari data awal yang berjumlah 150 data. Data tiap fold akan diuji menjadi data testing secara bergantian. Berdasarkan hasil pengujian dari penelitian yang telah dilakukan, penelitian ini mampu meraih tingkat akurasi sebesar 88.0002%.
Sistem Pakar Deteksi Dini Penyakit Stroke Menggunakan Metode Naive Bayes-Certainty Factor Renaldy Senna Hutama; Nurul Hidayat; Edy Santoso
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

Stroke became one of the diseases that have a high mortality rate in Indonesia and also in the world. It can be dangerous if it is not handled quickly because of a sudden disruption of blood that supplies the brain and if not handled quickly can cause permanent disability or death. In Indonesia, stroke disease in recent years has increased, but the government still has no solution in solving the disease problem. Though the process of handling stroke disease takes sufficient time in detection, if not handled quickly can lead to disability or even death. With the existence of these problems then required a system that can accelerate and simplify the detection of risk to reduce the number of people suffering from stroke. The method used in the detection process is Naive Bayes-Certainty Factor. The Naive Bayes method is used to look for an opportunity to appear from the risk of stroke. For certainty factor method is used to find the value of his belief. This system is built with android based with Java programming language. For the test is done by doing a comparison of detection results from experts with the results of detection conducted by the system where from 25 test data obtained obtained accuracy of 84%.
Prediksi Jumlah Kriminalitas Menggunakan Metode Extreme Learning Machine (Studi Kasus Di Kabupaten Probolinggo) Sema Nabillah Dewi; Imam Cholisoddin; Edy Santoso
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

The crime rate in Indonesia is highly increased. A lot of people want to become wealthy in a wrong way by commiting a crime. Criminality is an act that violates the rules of the law that can disturb the public. Every society has a risk of becoming a victim of crime. The greater the risk that the community has, the more unsafe their area is. However, the number of criminal acts cant't be ensured from time to time due to the uncertain number. This causes the police will having a trouble in resolving the criminal acts. A proper and accurate prediction can help minimizing criminal acts that will be happened. This research is intended to get predicted numbers of criminality using Extreme Learning Machine method (ELM). Based on the implementation and testing done by using crime data of Probolinggo District Police in 2012 until 2017, obtained the maximum network architecture that is the number of features as much as 7, the comparison of data ratio is 80%: 20%, and the number of neurons in the hidden layer as much as 7 and the binary sigmoid activation function. The low error value is calculated using the Mean Square Error (MSE) error rate and the result is 0.037662.
Peramalan Pemakaian Air Pada PLTGU Di Pembangkitan Listrik Jawa Bali Unit Gresik Menggunakan Extreme Learning Machine Dengan Optimasi Algoritme Genetika Heny Dwi Jayanti; Imam Cholissodin; Edy Santoso
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

Water is an absolute necessity every day that has an important role. One of the utilization of seawater used for the industrial sector is PLTGU in PT Pembangkitan Jawa Bali. This makes the electricity industry has treated the sea water into fresh water called desalination process. However, in the PLTGU process often experience problems in water treatment such as the occurrence of leaking pipe due to corrosion, the difference of water filling treatment, and the long-time desalination process resulted in unstable turbine performance. With some problems that arise, then needed a solution. In this study, researchers have proposed a water forecasting system using the method of extreme learning machine (ELM) with the optimization of Genetic Algorithm. The genetic algorithm is used to optimize the input weight values obtained randomly on the ELM method. Meanwhile, to represent chromosomes using real code. At the reproduction stage using extended intermediate crossover method and random mutation method. The result of ELM test method and genetic algorithm resulted in average MAPE value of 0.428 with a parameter value of crossover rate (Cr) value 0.4 and mutation rate (Mr) equal to 0.6, popsize amount 200, number of generation 1000, and training data amount 80% of the entire dataset. From the results obtained MAPE, shows that the combined ELM method with genetic algorithm able to minimize the error value in forecasting compared with the ELM method.
Penentuan Penerimaan Beasiswa Menggunakan Metode Modified K-Nearest Neighbor Caesaredi Rama Raharya; Nurul Hidayat; Edy Santoso
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

In determining the acceptance of students scholarship, the officers often face problem selecting student who are eligible for a scholarship that caused by several factors such as the number of students who apply for a scholarship while the quota for students who get scholarships is relatively small, the number of parameters used as a reference in determining the students who are eligible for a scholarship and the officers who are given only a relatively short time in determining the awardee. Therefore implement a classification system is required to this issue for help facilitate the scholarship selection officer. This research was using Modified K-Nearest Neighbor method. Modified Method K-Nearest Neighbor is modified method from K-Nearest Neighbor consists of the process of calculating distance euclidean, calculation of validity value and weight voting calculation. The highest average accuracy results obtained based on the tests and normalization data that have been done is 87.2%.
Klasifikasi Risiko Gagal Ginjal Kronis Menggunakan Extreme Learning Machine Dimas Prenky Dicky Irawan; Imam Cholisoddin; Edy Santoso
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

Kidney is an organ in humans that have a very important role in the process of managing fluid and electrolyte needs. Chronic renal failure is a disease of kidney that occurs due to kidney infection and the existence of blockage due to kidney stones. To perform the classification of chronic renal failure medical personnel are still not maximally in handling it, to deal with this problem researchers use the Extreme Learning Machine to perform the classification of chronic renal failure. The Extreme Learning Machine is a classification algorithm in which this algorithm is part of a neural network that has a good learning speed and also according to existing research results in a good accuracy value when compared to using other algorithms. This study obtained a comparison of the value of training data as well as the optimal test data with a 70:30 ratio value, many hidden layer neurons of 10 and using the bipolar sigmoid activation function of these parameters resulted in an accuracy of 99.13%. From the results of accuracy obtained, indicating that the method of Extreme Learning Machine is good enough to be used for the process of classification of chronic renal failure.
Implementasi Metode Analytic Hierarchy Process untuk Penentuan Prioritas Kategori Berita (Studi Kasus: LYT Media) Khusnul Aidil Santosa; Edy Santoso; Satrio Hadi Wijoyo
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

An online media company in the field of entertainment and creative industries has many categories of articles that are presented to readers, this will lead to excess material articles to be prepared by the authors. Effectiveness in the event of a news must be for the company there is a fairly. The editor-in-chief is responsible for the articles presented in the media. Obviously the company already has a category of articles presented through the criteria of entertainment and creative industries. The choice of articles is expected to maximize the visitors who read the pages of the website, so the company will benefit from the traffic that much as well. Analytic Hierarchy Process is one of the decision making methods that can provide suggestions to make category decisions from the required criteria. Hierarchical Process Hierarchy Process is a hierarchy with input of human assemblies. The Analytic Hierarchy Process procession is the use of pairwise comparison matrices to compare the relative weights between criteria and alternatives. A criterion will be compared with other criteria in terms of importance to the goal. In this research have accuration result 65,625%. The result of the process Analytic Hierarchy Process can be an efficient and effective solution in the decision categories of articles to be presented.
Optimasi Travelling Salesman Problem With Time Windows Pada Sistem Rekomendasi Tujuan Wisata Di Kota Batu Dengan Metode Evolution Strategies Dicky Manda Putra Sidharta; Nurul Hidayat; Edy Santoso
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

Tourism has an important contribution in the Indonesian economy. City of Batu is one of the place that have a great tourism potential. Having a cool characteristic of the weather becomes one of the more values for city of Batu. Batu has various tourist destinations, both natural and artificial tourism. Many choices of tourist destinations is a good thing for tourists. On the other side, because of the many choices of tourist destinations, can cause tourists difficult to divide travel time in accordance with expectations. The problem is often experienced by tourists is when they want to visit a tourism place, but did not have time to visit the other one. Tourists need to have an overview of the travel route in order to get an effective tour schedule. Looking for the best travel route with minimum cost, but it can visit some tourist places optimally. Looking for the best tour route considering time variables can be called Traveling Salesman Problem With Time Windows (TSP-TW). TSP-TW can be solved by using a method in the concept of Evolutionary Algorithm, Evolution Strategies (ES). Based on the results of tests that have been done using the following parameters are the number of population size is 90, the number of offspring is 7µ, and the number of generation is 6, that generate the highest fitness value of 0,0011223345. The final result obtained in this research is an optimal tourist route in the city of Batu.
Co-Authors Abdul Juli Andi Gani Achmad Arwan Achmad Ridok Adam Hendra Brata Adhi Mulhaq Adhie Indi Arsyanto Adhipramana Raihan Yuthadi Adinugroho, Sigit Aditya Sudarmadi Agung Dwi Budiarto Agus Prayogi Ahmad Faizal Akbar Imani Yudhaputra Akhsana Zufar Masyhuda Alif Fachrony Alif Prasetyo Aji Andriko Hedi Prasetyo Annam Rosyadi Annisa Puspitawuri Arief Andy Soebroto Arinda Ayu Puspitasari Aulia Dinia Ayu Tifany Novarina Bagus Aryo Herlambang Bayu Rahayudi Bregaster Bregaster Brigitta Ayu Kusuma Wardhany Caesaredi Rama Raharya Candra Dewi Charisma Amadea Putri Dayu Aprellia Dwi Putri Dendry Zeta Maliha Denis Ahmad Ryfai Denny Sagita Rusdianto Dewan Rizky Bahari Dhatu Kertayuga Dian Eka R Dicky Manda Putra Sidharta Dimas Prenky Dicky Irawan Dino Keylas Dwi Tyas Fitriya Ningsih Dytha Suryani Edwar Budiman Ega Ajie Kurnianto Elkaf Fahrezi Soebianto Putra Elna Diaz Pradini Fahri Ariseno Faizatul Amalia Fajar Pradana Faldo Sabillah Shidqi Faris Dinar Wahyu Gunawan Faturrahman Muhammad Suryana Febri Fahrizal Freddy Ajax Pratama Galih Aulia Rahmadanu Genjah Amartha Gora Ghiffary Rizal Hamdhani Greviko Bayu Kristi Habib Putra Kusuma Negara Hafshah Durrotun Nasihah Heny Dwi Jayanti Herlina Devi Sirait Heru Nurwasito Heryadi Mochamad Ramdani Hinandy Nur Anisa Imam Cholisoddin Imam Cholissodin Indriati Indriati Irwan Andriyanto Ivan Agustinus Jauhar Bariq Rachmadi Jeffrey Simanjuntak Jodi Irjaya Kartika Jojor Yeanesy Sinaga Kenty Wantri A Khairinnisa Rifna Khrisna Indrawan Eka Putra Khusnul Aidil Santosa Komang Anggada Sugiarta Krisna Andryan Syahputra Effendi Lailatul Fitriah Lailil Muflikhah M. Ali Fauzi Made Bela Pramesthi Putri Marji Marji Maya Febrianita Meilinda Dwi Puspaningrum Meutya Choirunnisa Miga Palma Putri Mochamad Rafli Andriansyah Moh. Zulfiqar Naufal Maulana Mohammad Zahrul Muttaqin Muh Hamim Fajar Muh. Thanthowi Lathif Muhamad Danis Firmansyah Muhamad Fahrur Rozi Muhammad Alfian Nuris Shobah Muhammad Alimuddien Rasyid Muhammad Aminul Akbar Muhammad Ardhian Megatama Muhammad Atabik Usman Muhammad Aulia Rahman Muhammad Dimyathi Muhammad Fachry Noorchoolish Arif Muhammad Fauzan Ziqroh Muhammad Fuad Efendi Muhammad Miftah Dhiaulhaq Muhammad Shafaat Muhammad Tanzil Furqon Mukhlis Anshori Witanto Nendiana Putri Ninda Silvia Tri Cahyani Nonny Windarti Novanto Yudistira Novi Fadilla Ulfa Nurudin Santoso Nurul Hidaya Nurul Hidayat Paul Manason Sahala Simanjuntak Putra Aditya Primanda Ratih Kartika Dewi Regina Anky Chandra Renaldy Senna Hutama Reyhan Dzickrillah Laksmana Reynaldi Ricky Putra Utama Guinta Rezza Hary Dwi Satriya Rezza Pratama Rhayhana Putri Justitia Richard Emmanuel Johanes Riesma Rahman Nia Rio Cahyo Anggono Rizky Maulana Iqbal Rizky Ramadhan Rizqi Addin Arfiansyah Roma Akbar Iswara Salam Maulana Sari Narulita Hantari Satrio Hadi Wijoyo Sema Nabillah Dewi Shibron Arby Azizy Stefanus Bayu Waskito Supraptoa Supraptoa Surya Dermawan Sutrisno Sutrisno Syailendra Orthega Tara Dewanti Sukma Tibyani Tibyani Tjahjanulin Domai Tony Faqih Prayogi Tri Afirianto Tuahta Ramadhani Tubagus Agung Nugroho Vogue Nevarika Wa Ode May Zhara Averina Wahyu Dwiky Rahmadan Wayan Firdaus Mahmudy William Muris Parsaoran Nainggolan Wunsel Arto Negoro Yogi Pinanda Yudha Eka Permana Yuita Arum Sari Yulianus Wayan Yudistira Rudja Yunita Dwi Lestari Yuwilda Wilantikasari