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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
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Penerapan Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Notasi Simplified Molecular Input Line System (SMILES) Suhhy Ramzini; Dian Eka Ratnawati; Syaiful Anam
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

Active compound is a substance (medicine) capable of providing kind effect when the human bodies are in bad shape. Active compound often used for preventing or curing a disease. Active compound takes an important role in medical world. Simplified Molecular Input Line System notation, in short SMILES notation is representation of compound (carbon bond) created by David Weininger in 1980. SMILES notation composed of ASCII (American Standard Code for Information Interchange) characters so that it can be stored in string variable and easily processed by the computer. Currently, there are numbers of compounds (SMILES notation) and it makes the classification for tested compound that can be made into a medicine (active compound) becomes necessary. The purpose of this research is to classify the active compound function utilizing SMILES notation with Learning Vector Quantization (LVQ) method by using 2 active compound function classes, one for metabolic disease, and another for cancer disease. There are 467 datasets with each 11 features. On testing process, the obtained value for learning rate is 0.1, decrement alpha is 0.3, minimum alpha is , and maximum epoch is 15 by using a percentage of 80% training data and 20% testing data which produce accuracy of 76.34%.
Implementasi Fuzzy K-Nearest Neighbor (FK-NN) Untuk Mengklasifikasi Fungsi Senyawa Berdasarkan Simplified Molecular Input Line Entry System (SMILES) Raden Rizky Widdie Tigusti; Dian Eka Ratnawati; Syaiful Anam
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

The active compound is a chemical compound that has many functions. One of the functions of the active compound is as a medicine. Active compounds have special characteristics that determine function as a drug. To obtain a characteristic value on the active compound SMILES notation are used as input system. SMILES notation is a modern chemical notation that can be stored on string variables to use for the process of computing. To obtain the characteristic on the compound the SMILES notation will be divided into 12 features consisting of B, C, N, O, P, S, F, Cl, Br, I, OH and the length from SMILES notation. The value of each feature is obtained from the preprocessing process against the SMILES notation made at the beginning of the classification process.In the process of classifying the function of active compounds, the Fuzzy K-Nearest Neighbor method are used because it can do process by using large amounts of data. The Fuzzy K-Nearest Neighbor method is a combination of two methods namely Fuzzy and K-Nearest Neighbor. An important step of the classification process using the Fuzzy K-Nearest Neighbor is to calculate the distance from each test data to the train data or so-called by euclidean distance, pick value as much as k value and calculate the fuzzy. Tests in this study using the dataset as much as 631 and divided into 2 as the data train and test data. Each composition of data training and data testing are 80% (503 data) and 20% (128 data). The result of the accuracy is 71% with the value of k = 15, in other test by using k-fold cross validation the biggest accuracy is 77%.
Identifikasi Penyakit Mata Menggunakan Metode Learning Vector Quantization (LVQ) Entra Betlin Ladauw; Dian Eka Ratnawati; Ahmad Afif Supianto
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

Taking care and maintaining healthy eyes are very important for human, because eyes are one of the senses that help human to do daily activities. Eyes that give visual information to human, cannot be separated from the threat of many eyes diseases. The diseases can attack from small to big scale. Unfortunately, eyes diseases are usually considered not to have such potential to harm human, so eyes health often to be ignored by people in general. Therefore, in this paper a system to identify eyes diseases has been developed using Learning Vector Quantization (LVQ) method. This method can give classification to a pattern that represent specific class, which will move to a nearer position to corresponding class when the classification data point is true. In this research, there are 21 symptoms and 9 eyes diseases that processed in training and testing processes, where the data were divided into training data and testing data. In training process, LVQ method did some stages to get final weight. The weight will be used in testing process. Using LVQ method, obtained parameter values are α = 0.4, Dec α = 0.8, Min α = 0.00001, Max Epoch = 25, training data = 100 data (80%) and data test = 25 data (20%). From accuracy testing for this system, the result show 82.80% average accuracy and 92% highest accuracy, that means this system works fine. So, it can be concluded that LVQ method can be used for eyes diseases identification.
Optimasi Komposisi Pakan Ternak Ayam Petelur Menggunakan Algoritme Genetika Siti Fatimah Al Uswah; Budi Darma Setiawan; Dian Eka Ratnawati
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

Raising laying hens are considered a promising opportunity in Indonesia because the demand for eggs in the country continues to increase in line with the increasing human lifestyle and need for animal protein. Based on data from the ministry of agriculture in 2017 there is an increase in chicken egg consumption during the year 1987-2017 of 3.57% per year with an average consumption of 6.63 kg / kap / th in 2017. On the other hand, raising laying hens is costly especially when it comes to livestock feed, which can cost farmers 60% -70% of production costs. One way to reduce the cost of purchasing feed is by optimizing the feed composition, with purpose of achieving an optimal feed composition that also meets the nutritional needs, all obtained with as minimal cost as possible. The optimization method used in this research is Genetic Algorithm with permutation representation, single-point crossover, reciprocal exchange mutation, and elitism selection. This study used 50 feed data material of laying chicken and its nutritional content. From the results of the tests, the population parameters obtained with the highest fitness value in the population of 500 and 800 with the average fitness value of 2.573591, the optimal generation of 100 generations with an average fitness value of 2.479726 and a combination of probability of crossover 0.5 and the probability of mutation 0.3 with the average fitness value 2.58459. The final result is the composition of laying chicken feed that meets the nutritional needs with minimal cost.
Pengelompokan Fungsi Aktif Senyawa Data SMILES (Simplified Molecular Input Line Entry System) Menggunakan Metode K-Means Dengan Inisialisasi Pusat Klaster Menggunakan Metode Heuristic O(N LogN) Sherly Witanto; Dian Eka Ratnawati; Syaiful Anam
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

Active compounds have function as a medicine that can prevent or cure diseases. Some of the active compounds have been known the function and some are still in the research stage. Currently in Indonesia there is still no program that capable to classifying chemical compounds as drugs for certain diseases. SMILES notation is the conversion of chemical compounds in the form of line notation. Notation SMILES able to provide convenience to the process of computerization on the classification of chemical compounds. The classification of the SMILES notation is carried out by taking the values ​​of the B, S, N, O, I, F, C, P, Cl, Br and OH atoms present in the compound. Before being processed, to get the value of the feature is done by dividing the process of each atom with the length of the compound. K-Means algorithm is the most widely used clustering method because it is easy and simple. The grouping of active function using K-Means method has weakness in random cluster initialization process, so that heuristic method o (n logn) is used to get the cluster initials with better value. Based on the software that has been made, the test is done using 512 of training data and test data as much as 128. Accuracy obtained from the test that is equal to 63% and testing using ¬K-Fold Cross Validation with 10 times the test produces an average accuracy of 52,58 %. Testing using K-Means with heuristic o (n logn) yielded better accuracy compared to conventional K-Means.
Penerapan Algoritme C4.5 untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Kode Simplified Molecular Input Line System (SMILES) Mochammad Iskandar Ardiyansyah Rochman; Dian Eka Ratnawati; Syaiful Anam
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

Compounds are things that are often found in this world, with a substance that is a collection of compounds (Educated, 2015). The compound itself is divided into active and inactive compounds. The compound has a function that may be utilized for some aspect if it has a function like a drug or a stimulating hormone work. notation of SMILES (Simplified Molecular Input Line System) by David Weininger in 1980. SMILES notation takes advantage of ASCII characters that are very easy to process by the computer. SMILES notation classification process will be very useful to know the function class of the compound. This study was conducted to classify the function of the compound utilizing the SMILES notation by applying the C4.5 algorithm while the object is 2 classes of compound function, including the class of cancer and metabolism. Features tested from research as many as 11 features. The results of the best tests when the discretization technique is performed using entropy based discretization techniques, dividing the SMILES notation values ​​on each feature attribute, and the use of practicable data as much as possible will result in an accuracy of 79.34%. While the accuracy of the cross validation test shows an accuracy of 70.18%.
Implementasi Algoritme K-Means Clustering Dan Naive Bayes Classifier Untuk Klasifikasi Diagnosa Penyakit Pada Kucing Puji Indah Lestari; Dian Eka Ratnawati; Lailil Muflikhah
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

At this time cat has become a popular pet community. This is because there are many benefits that exist from cats, such as an entertainer, and now developed countries many cats contested in the show cat. Treatment for cats is mainly because some of the cat's disease can spread to humans. The limitations of dentists in diagnosing diseases with a pattern of having the same symptoms as some diseases, are important in making a diagnosis. Therefore there need a system that can diagnose diseases that can be accessed by the cat owners and be dealt immediately. In this study can use K-Means Naive Bayes (KMNB) method for diagnosis in cats. The KMNB approach is formed by the incorporation of clustering and classification techniques. In the beginning Clustering on K-Means was used to group the same data. Further classification of data by category using Naive Bayes method. The data that have errors in the first stage are then organized by the second category. Identify data with the same character or data that shows similar characteristics from the start. Based on the results of tests that have been done by comparing the results of grouping on conventional K-Means proves that KMNB can produce the highest average of 90% while conventional K-Means has the highest average of 71, 379%.
Implementasi Algoritme Genetika dan Analytical Hierarchy Process untuk Penerimaan Siswa Baru pada Sekolah Menengah Kejuruan Mala Nurhidayati; Dian Eka Ratnawati; Yuita Arum Sari
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

Vocational high schools aim to produce qualified graduates in their expertise and skills in each majors. In the process of getting qualified graduates, it takes an initial step, namely selecting new students. The selection process is carried out by considering the value of the Indonesian National Examination, the value of the English National Examination, the value of the National Mathematics Examination, and the National Examination Science score. The methods used to solving this problem are Analytical Hierarchy Process (AHP) and Genetic Algorithms. Individuals in the Genetic Algorithm method are 6 genes. In the initial individuals formed the reproduction process namely crossover and mutation. The last process in this reasearch is the calculation of fitness value. This process resolved by calculating the results of accuracy. The results of the fitness value obtained the largest to the smallest fitness value. From research conducted the output is passing or not passing from prospective students in a predetermined majors. The average accuracy of the combination of the AHP method and the genetic algorithm is 86.85% and the accuracy is obtained using the AHP method is only 76.52%. Based on these results it can be concluded that the merger of AHP methods and genetic algorithms can solve the problem of new student admission at vocational high schools.
Optimasi Komposisi Bahan Makanan Atlet Olahraga Menembak dengan Menggunakan Metode Evolution Strategies (ES) Nuraini Anitasari; Dian Eka Ratnawati; Titis Sari Kusuma
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

The main energy source in humans to be able to grow and develop to be more optimal obtained from the food consumed. Based on differences in each food composition that has been consumed consisting of carbohydrate content, fat content, protein content, energy and so forth then it is needed an optimal diet for each individual. The role of an optimal diet is very important, especially for athletes in order to perform. Shooting sports is one sports that requires endurance aspect for athletes. In addition to regular exercise, so athletes should also be able to manage the portion and food consumption in order to success. In this research, Evolution Strategies algorithm is implemented to optimize the composition of food for shooting athletes with total food ingredients that are used 125 foods. Then reproduction process in this research using mutation method and selection process using elitism selection. Based on the results of the algorithm parameter test, the best population size was obtained by 30 individuals, the size of best offspring as 7μ, the best generation number of 60 generations and with the best fitness average 0,004904. Meanwhile, based on the trial of case studies conducted 4 times, it is known that the system is able to produce the result of food composition with minimal prices and it is also known that according to the indicator of nutritionist in assessing food consumption, carbohydrate and fat is categorized as sufficient and protein is categorized as good with an average carbohydrate is 90,1%, fat is 98,7% and protein is 105,4%.
Implementasi Gabungan Metode K-Means Learning Vector Quantization (LVQ) Untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Data SMILES Nur Khilmiyatul Ilmiyah; Dian Eka Ratnawati; Syaiful Anam
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

The active compound is a chemical compound that has many functions. While the inactive compound, doesn't have much function only as additional substances. Active compounds can be divided into two therapeutic functions as alternative medicine, and Pharmacology function to control drug containing the active compounds in it. In order to get functions in the active compounds used notation SMILES. SMILES notation is a representation of the active compounds with modern chemical notation, so that the computer can read the elements of the compound. Of the many SMILES notations at this time, all the SMILES notations cannot be used as medicine because they are still in the testing phase. SMILES notation that has been tested could be used as medicine. Therefore, this research will be built a fixed classification model that takes into account all the data. Based on the test results, the K-Means method of combined Learning Vector Quantization (LVQ) generate value accuracy of 72.22%, K-means conventional 52.65%, while Learning Vector Quantization (LVQ) owns 67.96%. The results show that the combined K-Means method of Learning Vector Quantization (LVQ) have better results than conventional K-means and Learning Vector Quantization (LVQ).
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