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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia JAST : Jurnal Aplikasi Sains dan Teknologi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) DEDIKASI PKM Jurnal Informatika dan Multimedia Enrichment: Journal of Multidisciplinary Research and Development Majority Science Journal
Dian Eka Ratnawati
Brawijaya University, Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Sistem Rekomendasi Pemilihan Sekolah Menengah Atas Sederajat Kota Malang Menggunakan Metode AHP Electre dan Topsis Ibnu Aqli; Dian Eka Ratnawati; Mahendra Data
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 1 (2017): Januari 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pemilihan tempat pendidikan yang bagus dan sesuai dengan kemampuan anak merupakan hal yang harus dikombinasikan untuk menunjang kemampuan perkembangan seorang anak. Apalagi pada masa pemilihan sekolah setelah lulus jenjang Sekolah Menengah Pertama (SMP) merupakan suatu keputusan yang harus dilalukan sambil mempertimbangkan masa depan. Dalam memilih sekolah lanjutan banyak hal yang biasanya dipertimbangkan, seperti Nilai Ujian Nasional (NUN) yang di dapat oleh siswa, jarak antar rumah siswa dan sekolah, fasilitas sekolah, bahkan prestasi-prestasi sekolah yang dianggap bisa menunjang kemampuan siswanya. Dari permasalahan tersebut, maka dirancang sebuah sistem untuk memberikan rekomendasi sekolah menengah atas sederajat di Kota Malang. Penelitian ini menerapkan metode Analytical Hierarchy Process (AHP) - Elimination Et Choix Tranduisant La Realité (ELECTRE) - Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). AHP melakukan perhitungan pembobotan kriteria, ELECTRE melakukan klasifikasi alternatif “favourable”, dan TOPSIS melakukan perankingan terhadap alternatif sehingga muncul rekomendasi sekolah yang sesuai dengan kriteria pengguna. Untuk pengujian, dilakukan uji akurasi pada metode TOPSIS dengan membandingkan data rekomendasi yang dikeluarkan oleh sistem dengan data yang didapat dari pakar. Pengujian akurasi pada metode TOPSIS mendapatkan nilai akurasi sebesar 82,98%.
Prediksi Jumlah Pengangguran Terbuka di Indonesia menggunakan Metode Genetic-Based Backpropagation Dyva Pandhu Adwandha; Dian Eka Ratnawati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 4 (2017): April 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of open unemployment in Indonesia has increased and decreased every year. The factors that can make unemployment happens is the number of the labor force is not balanced to the available jobs. In addition, the weakening of labor absorption in some industrial sectors has also the cause of the increasing number of open unemployment in Indonesia. Predict the number of open unemployment, expected can help the government and related parties to take the appropriate policy to reduce the number of open unemployment in Indonesia. Genetic-based backpropagation is one of the methods that can be implemented to perform predictions. This method performs weight and biases optimization process as parameters in backpropagation training. In this research the result value of Average Forecast Error Rate (AFER) of backpropagation method is 4.715198444% and genetic-based backpropagation method is 3.877514478%. Based on the result value of AFER, genetic-based backpropagation method can be used to predict the number of open unemployment in Indonesia with a better accuracy.
Implementasi Gabungan Metode Bayesian dan Backpropagation untuk Peramalan Jumlah Pengangguran Terbuka di Indonesia Yure Firdaus Arifin; Dian Eka Ratnawati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 4 (2017): April 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the major problems that Indonesia has is unemployment. It happens because of the low number of the job vacancy meets the high number of the graduate and job seeker. The unbalanced number of the job vacancy to the graduate and job seeker causes an increasing in the unemployment rate in Indonesia. The high rate of unemployment will inevitably give a direct or indirect impact to the poverty, crime, and other social issues. However, according to the survey conducted by BPS, Indonesia's unemployment rate was even increasing from the year of 2014 to 2015. It would really help the Indonesian government to make planning, program, or policy related to the job vacancy by being able to predict the number of unemployment. In addition, the data result from the prediction could be used as the measurement of the success of government's previous programs. Backpropagation is one of the methods that used to predict. This research works on the optimation of weights initialization in Backpropagation using Bayesian method that is a modification of the Kalman filter. According to the test result done in this research, the lowest value of Average Forecasting Error Rate (AFER) is 2,1003%. From that result, the combination method between Backpropagation and Bayesian has better accuracy rate than Backpropagation method with random initialization that has 2,5793% lowest value of AFER, but need a lot of iterasion. The sistem of this research can predict some future of years immediately but the most optimal result is the first year while in subsequent years the result of the prediction is more inaccurate.
Identifikasi Penyakit Tanaman Jarak Pagar Menggunakan Metode Fuzzy K-Nearest Neighbor (FK-NN) Eva Agustina Ompusunggu; Dian Eka Ratnawati; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Jatropha (Jatropha curcas L., Euphorbiaceae) is a plant that has many uses that as a raw material medicines and vegetable oil (biodiesel). Jatropha is used to cure various diseases. Some people also make Jatropha as a main ingredient of their livelihoods. However, the quality of Jatropha decreased due to various diseases. Lack of knowledge about the disease of jatropha and do not know how to overcome it became one of the causes. As well as the unavailability of media for the public to know the diseases that attack. To know and make it easier to diagnose diseases that attack jatropha, a system needs to be made. To support this diagnosis used k-nearest neighbor and fuzzy method. The first step of this method is entering training data that contains symptoms. Then classification using the k-nearest neighbor and fuzzy. Then we get the result of this system which is the diagnosis of Jatropha's diseases from nine diseases that there are. Results of tests performed on this study, obtained the highest accuracy by 80%
Identifikasi Penyakit Diabetes Mellitus Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Silvia Ikmalia Fernanda; Dian Eka Ratnawati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 6 (2017): Juni 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes mellitus is one of the diseases that can cause death and one of the diseases heredity. Most people do not care about a healthy lifestyle. Health is very important in everyday life. The public is less aware of the problem of health care so that the rate of deaths worldwide has increased. The public salso did not understand the similarity of the symptoms of disease appear not treated quickly lead to disease. To overcome these problems invented a system for the identification of diabetes mellitus using the Modified K-Nearest Neighbor (MKNN). Modified K-Nearest Neighbor (MKNN) is one method of classification is based on the number of class occurrence on data mining. There are 15 symptoms and 2 types of diseases are used as parameters in development of the system. An output as the result produced by the system is diagnosis of the type of disease and how to control. Based on method, this research obtain 93,33% of good accuracy and error rate of 6,67%. The system using of method Modified K-Nearest Neighbor (MKNN) can be applied in society based on result.
Analisis Perbandingan Metode K-Means Dengan Improved Semi-Supervised K-Means Pada Data Indeks Pembangunan Manusia (IPM) Gusti Ngurah Wisnu Paramartha; Dian Eka Ratnawati; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

At this time with the growing amount of information, the concept of data mining getting known as an important tool in the management information. Refers to the concept of data mining, the most popular concept in data mining is a clustering technique. One well known clustering method is k-means traditional. But in its application, k-means method has some problems such as determining the value of K cluster and determining the initial cluster centers were done randomly making process was inconsistent and the results of the cluster becomes worse. Therefore, there is a method to overcome these problems are improved semi-supervised k-means clustering. With improved semi-supervised method that combines the supervised and unsupervised method, users only need to label a bit of data that has not been labeled, then the labeled data is used to find the optimal value of initial cluster center and K cluster that will optimizes the process and result of clustering process. On implementation, this research combine k-means algorithm and improved semi-supervised k-means to clustering human development index (HDI) data. HDI data chosen because it has the right characteristics for clustering such amounts of data and the data is divided into several clusters. On the testing improved semi-supervised k-means method giving out the average accuracy of 90.3%, better than k-means clustering that giving 73.7% accuracy. In the second testing, improved semi-supervised k-means method produces an average time for one convergent 1222.9959 seconds, better than k-means with 1504.75 seconds. The third testing, improved semi-supervised k-means generates an average number of iterations for one convergent more efficient than k-means with the number of iterations of 7.11 compared 9.72. Last, on the cluster quality testing using silhouette coefficient, improved semi-supervised k-means method giving average value 0.69880, better than the traditional k-means with an average value of 0.62734.
Optimasi Biaya Pemenuhan Asupan Gizi pada Makanan bagi Anak-Anak Menggunakan Metode Simpleks Dua Fase Pratomo Adinegoro; Rekyan Regasari Mardi Putri; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Children are part of a group of human age classification which are 0 - 12 years old. Fulfilling the nutrition requirement for the children is really important for their healthy life. One of the best way to fulfilling the nutrition requirement is by fulfilling the requirement of macronutrients. However, the ability of fulfilling the nutrition requirement is depending on how much they should spend their money on. Therefore, the optimization is needed to determine the best combination of food which can fulfil the nutrition requirement for the children and have a minimum cost. Simplex two-phase is one of optimization method from linear programming study. Simplex two-phase will minimize the cost to fulfil the nutrition requirement for the children. The outcome of this method calculation will be the quantity of food and the cost of it. The result show that the existence of feasible solution is determined by the selected foods and their cost which build the constraint function. Then, 0.1(or 1.5 gram) is the value of amount of food variable which has the minimum cost.
Optimasi Komposisi Makanan Bagi Penderita Hipertensi Menggunakan Metode Particle Swarm Optimization Ayulianita A. Boestari; Dian Eka Ratnawati; Titis Sari Kusuma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia ranks 2nd largest in Southeast Asia in the number of deaths caused by Hypertension. One way to treatment Hypertension disease is to control weight and reduce the amount of salt consumed. To solve the problem used Particle Swarm Optimization (PSO) method. Stages in the PSO algorithm are building the initial population, building initial velocity, fitness calculations, pbest and gbest determinations, velocity and position update. The representation of the particles used is the food index. The number of dimensions used is 14. The number of dimensions indicates the number of features consisting of breakfast, complementary food, lunch, complementary meals and dinner. Each features consisting of staple foods, sources of plant protein, sources of animal protein, vegetable and appendages. PSO parameters used in the test are: the number of iterations used is 130, the number of particles used is 100 and the value of wmin and wmax used are 0,4 and 0,5. Based on trials of 4 cases of patients, it can be stated that the system can produce food recommendations That can fulfill the nutritional adequacy of ± 10% within the specified tolerance limits
Implementasi Algoritma Genetika Pada Metode AHP dan SAW untuk Rekomendasi Varietas Unggul Tanaman Tebu (Studi Kasus: Pusat Penelitian Gula PTPN X Jengkol) Nilna Fadhila Ganies; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sugarcane varieties are one of the main factors that determine the yield of quality cane productivity. In this study used several criteria to determine the recommendation of superior varieties of sugar cane that is the number of stems, stem diameter, stem height, brix, nirs1, nirs2, nirs3, and nirs4. In order to get the recommendation of superior varieties of cane that is optimal, then in this research used genetic algorithm. The genetic algorithm will optimize the weight value on the AHP and SAW methods. In the process of genetic algorithm the representation of the chromosome used is real code, with a gene length of 28, which is adjusted by the number of varieties criteria. In the reproduction process, the crossover method used is the extended intermediate crossover, and the mutation method used is random mutation. For fitness value obtained from the calculation of accuracy on SAW. While the selection method used is elitism selection, by choosing the highest fitness value as much as the population size. From the test results obtained optimal parameters that the population size of 50 and many generations of 50, with the average fitness of 0.912 so that the obtained accuracy of 91.2%
Optimasi Komposisi Makanan untuk Penderita Hipertensi Menggunakan Algoritma Genetika dan Simulated Annealing Agustin Kartikasari; Dian Eka Ratnawati; Titis Sari Kusuma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypertension ranks third largest as a disease that causes early death (Depkes, 2006). One way to prevent and treat hypertension is to modify food intake. But for the layman, arranging the composition of everyday food is still considered difficult. The problem is then solved by a combination of genetic algorithm and simulated annealing. The combination of these two algorithms aims to improve the solutions generated by genetic algorithms and avoid the occurrence of early convergence. At this problem solving used one-cut crossover method, reciprocal exchange mutation, elitism selection, and neighborhood move on simulated annealing. Based on the parameters test, the best parameter values ​​are population size of 1000, the number of generations is 200, the combination value of cr and mr is 0.6 and 0.4, the final temperature (Tn) is 0.2, and the cooling rate of 0.9. While based on system testing conducted can be seen that the combination of both algorithms able to solve this problem because the resulting nutritional content is within the limit of tolerance given by nutritionists is ± 10%
Co-Authors Abdurrahman Airlangga, Aria Abhiram, Muhammad Tegar Achmad Arwan Achmad Ridok Achmad, Riza Putra Adhitya, I Made Yoga Adrian Firmansah, Dani Afif Ridhwan Afrida Djulya Ika Pratiwi Agus Wahyu Widodo Agustin Kartikasari Ahmad Afif Supianto Akbar, Rozaq Aldy Satria Alfa Fadlilah Alifah, Syafira Almira Syawli, Almira Alvian Akmal Nabhan Amonito, Kurnia Ana Mariyam Puspitasari Anak Agung Bagus Arisetiawan Anam, Syaiful Ardhiansyah, Muhammad Hanif Arief Andy Soebroto Arif Pratama Asmoro, Priandhita Sukowidyanti Asroru Maula Romadlon Audia Refanda Permatasari Ayu Dwi Lestari, Cynthia Ayulianita A. Boestari Azizul Hanifah Hadi Bayu Rahayudi Bayu Satriawan, Eka Bayu Septyo Adi Bella Krisanda Easterita Bening Herwijayanti Berton, Freddy Toranggi Buce Trias Hanggara Buce Trias Hanggara Buchori Anantya Firdaus Budi Darma Setiawan Cahyo Gusti Indrayanto Candra Dewi Dany Primanita Kartikasari Darma Setiawan, Budi Darmawan, Riski Davia Werdiastu Denny Manuel Yeremia Sinurat Deny Tisna Amijaya, Fidia Devi Nazhifa Nur Husnina Dewi Yanti Liliana Dhiva Mustikananda Dimas Diandra Audiansyah Dimas Fachrurrozi Azam diniyah, zubaidah Diva, Zahra Djoko Pramono Dwi Ari Suryaningrum Dwi Febry Indarwati Dwi Purwono, Prayoga Dwija Wisnu Brata Dyva Pandhu Adwandha Dzulkarnain, Tsania Dzulkarnain, Tsania - Easterita, Bella Krisanda Edgar Maulana Thoriq Edy Santoso Elfa Fatimah Ema Agasta Entra Betlin Ladauw Eva Agustina Ompusunggu Fadhil, Muhammad Farrasseka Fadila, Putri Nur Faiz Anggiananta Winantoro Fanka Angelina Larasati Fathin Al Ghifari Fatthul Iman Fauzan Dwi Kurniawan, Fauzan Dwi Fauzidan Iqbal Ghiffari Figgy Rosaliana Firdaus, Muhammad Fariz Fitra Abdurrachman Bachtiar Fitri Dwi Astuti Fitria Yesisca Fitria, Tharessa Ghani Fikri Baihaqi glenando Gusti Ngurah Wisnu Paramartha Hadi Wijoyo, Satrio Hamas, radityo Hana Chyntia Morama Hanggara, Buce Trias Hanifa Maulani Ramadhan Haris Haris, Haris Harris Imam Fathoni Hasibuan, Herida Hafni Hasibuan, Raka Ardiansyah Heru Nurwasito Hilal, Khaliffman Rahmat Hilmy Ramadhan, Achmad Zhafran Huda Minhajur Rosyidin I Dewa Gede Ngurah Bramasta Darmawan Ibnu Aqli Ibnu Aqli, Ibnu Ibrahim Kusuma Ilyas, Muhaimin Imam Cholissodin Imam Cholissodin Imam Cholissodin Immanuel Tri Putra Sihaloho Indriati ., Indriati Indriati Indriati Ismiarta Aknuranda Issa Arwani Issa Arwani Isti Marlisa Fitriani Izza, Aisyah Nurul Jesika Silviana Situmorang Jibril Averroes, Muhammad Juan Michel Hesekiel Kartika, Annisa Wuri Kelvin Anggatanata Kevin Renjiro Khairi Ubaidah Khoba, Ahmad Faiz Khofifatunnabilah, Khofifatunnabilah Kirana, Urdha Egha Krishna Febianda Kusuma, Salsabila Azzahra' Zulfa Lailil Muflikhah Leonardo, Ryan Luqman Rizky Dharmawan M. Ali Fauzi Madjid, Marchenda Fayza Maghfiroh, Sofita Hidayatul Mahendra Data Mahendra Data Mala Nurhidayati Maliha Athiya Rahmani Marji . Marji Marji Marji Marji Marji Marji Maulana Syahril Ramadhan Hardiono Michael Eggi Bastian Mochammad Iskandar Ardiyansyah Rochman Moh Fadel Asikin Muh. Arif Rahman MUHAJIR Muhammad Iqbal Mustofa Muhammad Kevin Sandryan Muhammad Reza Utama Pulungan Muhammad Tanzil Furqon Muhyidin Ubaiddillah Muslimah, Fakhriyyatum Muthia Maharani Nabilah Iftah Nella Naily Zakiyatil Ilahiyah Nanang Yudi Setiawan Nanang Yudi Setiawan Nanda Alifiya Santoso Putri Nanda Petty Wahyuningtyas Nilna Fadhila Ganies Norma Desitasari Novirra Dwi Asri Nugraha Perdana, Aditya Nugraheni, Miftakhul Fitria Nur Adli Ari Darmawand Nur Khilmiyatul Ilmiyah Nuraini Anitasari Nuralam, Inggang Perwangsa Nurul Hidayat Nyimas Ayu Widi Indriana Oceandra Audrey Pandu Adikara, Putra Pangestu Ari Wijaya Panjaitan, RE. Miracle Prahesti, Suherni Prakoso, Ricky Pratomo Adinegoro Priyono, Mochammad Fajri Rahmatullah Rendra Puji Indah Lestari Purnomo, Welly Putra Pandu Adikara Putra, Alland Rifqy Putri, Nindy Alya Rachmad, Zikfikri Yulfiandi Raden Rizky Widdie Tigusti Rahma, Dzakiyyah Afifah Rahmah, Yusriyah Raisha, Serefika Raja Farhan Ramadha Pohan Rama Humam Syarokha Randy Cahya Wihandika Rani Metivianis Ratih Diah Puspitasari RE. Miracle Panjaitan Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati, Retno Indah Revi Anistia Masykuroh Rifqi Irfansyah, Nandana Rizal Setya Perdana Rizal Setya Perdana Robiata Tsania Salsabila Aditya Putri Rodiah Rodiah Ryan Leonardo Salsabillah, Dinar Fairus Saparila Worokinasih Saputro, Dimas Sarie, Riza Athaya Rania Satriawan, Eka Bayu Satrio Agung Wicaksono Satrio Hadi Wijoyo Sema Yuni Fraticasari Setiawan, Alexander Christo Setya Perdana, Rizal Setyowati, Andri Shafira Margaretta Sherly Witanto Sherryl Sugiono Sindarto Sigit Pangestu Silvia Ikmalia Fernanda Siregar, Fauziah Syifa R. Siti Fatimah Al Uswah Sobakhul Munir Siroj Sormin, Hartati Penta Angelina Sri Indrayani, Sri Suhhy Ramzini Sukmawati, A'inun Sutrisno Sutrisno Sutrisno, Sutrisno Syaiful Anam Syifa Namira Neztigaty Thifal Fadiyah Basar Titis Sari Kusuma Ulfa Lina Wulandari Utomo, Yoga Cahyo Vina Adelina Welly Purnomo Wibowo, Shinta Dewi Putri Widhy Hayuhardhika Nugraha Putra Wijanarko, Rizqi Winda Fitri Astiti Winurputra, Raihan Wiratama Paramasatya Yahya, Faiz Yolanda Nailil Ula Yudi Setiawan, Nanang Yuita Arum Sari Yunita Dwi Alfiyanti Yure Firdaus Arifin Zahra, Wardah