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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Jurnal Pendidikan Vokasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Natural A Jurnal POINTER Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Jurnal Teknik Otomotif Kajian Keilmuan dan Pengajaran Jurnal Preventia Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian, Pendidikan dan Teknologi Jurnal Graha Pengabdian Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Abdimas Indonesian Journal Prosiding Seminar Kesehatan Nasional Sexophone National Journal of Occupational Health and Safety
Marji Marji
Fakultas Ilmu Komputer, Universitas Brawijaya
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Optimasi Fuzzy Time Series Menggunakan Algoritma Particle Swarm Optimization Untuk Peramalan Jumlah Penduduk Di Kabupaten Probolinggo Cahyo Adi Prasojo; Budi Darma Setiawan; Marji Marji
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

Population growth occurs due to the increasing number of births. The impact of population growth is affecting human welfare, Both in the economic, health, social, politic and cultural fields. Therefore it is necessary to forecast the population, to know how fast the rate of population growth. One of the most commonly used forecasting methods is the Fuzzy Time Series (FTS). However, this method still has a deficiency that is on the determination of the value of the interval that is less precise. therefore it is necessary the optimization algorithm to find the optimal value of the interval. This study aims to perform population forecasting in Probolinggo District by using FTS method which will be optimized using Particle Swarm Optimization (PSO) algorithm. Optimization is performed to obtain optimal interval value on FTS and optimal parameter value on PSO. The parameters in the optimized PSO are (Inertial Weight), (velocity constant 1) and (velocity constant 2). The result of the test, that is got the best fitness , and value, is 0,559140, 0,535084 and 0.621134 and parameter value are 0,6, 1.8 and 2.4. Get the best fitness value of the forecasting, is 0.445334.
Optimasi Peramalan Jumlah Kasus Penyakit Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Dengan Algoritma Genetika Gilang Ramadhan; Budi Darma Setiawan; Marji Marji
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 number of disease cases has increased and decreased every month. This has an impact on the unbalanced of medicine availability such as, lack of supply of medicine, waste of medicine, medicine that are not on target, damaged medicine and so on. Therefore forecasting on number of disease cases is needed to determine the number of disease cases within a certain time. One of forecasting method that can be used is backpropagation neural network method. This method can be optimized using genetic algorithm to produce optimal results. The optimized parameters are weight and bias which will be used in backpropagation algorithm. The purpose of this study is to forecast the number of disease cases at Puskesmas Rogotrunan, Lumajang using backpropagation method optimized by genetic algorithm. From this study the optimal parameters of genetic algorithm are population=180, combination of cr and mr respectively 0,4 and 0,6, generation=100. The optimal parameters of backpropagation algorithm are total data=16, input neuron=6, iteration=1000, alfa=0,1. Accuray obtained with MSE=87,2 with data test of the number of disease cases in january to desember 2016. From the value of MSE obtained using backpropagation method optimized by genetic algorithm can be used to forecast the number of disease cases.
Implementasi Algoritme Shazam untuk Mengidentifikasi Hadis dan Surah dalam Al-Quran Menggunakan Suara Bahruddin El Hayat; Imam Cholissodin; Marji Marji
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

For muslims, understanding Al-Qur'an and Hadis are an obligation because those two is the basic of Islam. In the learning process, a person usually will begin by memorizing the pronounciation and the surah's or hadis's name. After the person can pronounce the surah or hadis fluently, then they will continue to understand the meaning and the content of the surah or hadis. The problem is sometimes a person can forget the name of surah or hadis when another person says a verse from the surah or hadis. So, a solution is needed to handle the problem. In this research, the writer offers a solution to build a system that can identify the name of surah or hadis in Al-Qur'an by taking an input in the form of a sound file with WAV extension using Shazam algorithm. The identification process is done by doing these following actions: numeric value extraction, conversion, feature extraction, filtering and matching. The result is the name and information of the surah or hadis. The best accuracy from identifying surah and hadis from Al-Qur'an is 82% in the testing phase using a test data with 15 second duration, chunk size=4096 and range=60.
Optimasi Penjadwalan Bimbingan Skripsi Menggunakan Algoritme Genetika (Studi Kasus : Fakultas Ilmu Komputer Universitas Brawijaya) Lilis Damayanti; Imam Cholisoddin; Marji Marji
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

Thesis consultation is an activity that must be done for students who are taking thesis. Usually students who will conduct guidance will meet their lecturer or contact the lecturer before. Because the lecturer also has time to teach and perform other activities related to the campus. The number of students in Fakultas Ilmu Komputer Universitas Brawijaya (FILKOM UB) which they want to do guidance to make the students queue in front of lecturers room while the lecturer has a hectic schedule. Therefore it is necessary for the system to schedule thesis consultation. This research applies the concept of solution obtained using genetic algorithm. Genetic algorithm is a search algorithm that aims to find the optimal solution. The result of genetic parameters obtained in the optimal solution is population size 70, number of generation of 2500, combination of cr and mr value is 0,4 and 0,6. This built system resulted in an optimal thesis guidance schedule with a fitness value of 1,0305.
Klasifikasi Jenis Kanker Berdasarkan Struktur Protein Menggunakan Algoritma Naive Bayes Tawang Wulandari; Marji Marji; Lailil Muflikkah
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

Kanker adalah beberapa sel tubuh yang mulai membelah tanpa berhenti dan menyebar ke jaringan sekitarnya. Setiap tahun terdapat ribuan kasus baru kanker yang menyerang warga Indonesia. Terlambatnya deteksi dini meyebabkan banyak kasus kematian akibat kanker. Faktor penyebab kanker adalah faktor genetik dan lingkungan yang dapat merubah struktur DNA. Perubahan DNA tersebut merugikan proses pembelahan sel dan menguntungkan proses mutasi. Pada proses mutasi dapat menghasilkan gen p53, perubahan genetik tersebut paling umum ditemukan pada kanker manusia. Dari permasalahan tersebut dibutuhkan sistem untuk mengklasifikasikan jenis kanker yang diderita oleh pasien. Salah satu metode yang digunakan adalah Naive Bayes. Naive Bayes merupakan sebuah pengklasifikasian probabilitas sederhana yang mengaplikasikan Teorema Bayes dengan asumsi ketidaktergantungan yang tinggi. Algoritma tersebut diketahui telah banyak digunakan dalam bidang kedokteran. Algoritma ini diterapkan pada hal-hal yang berhubungan dengan diagnosa medis. Diagnosa dilakukan dengan cara melihat gejala-gejala yang berkaitan kemudian melihat probabilitas kemungkinan dari penyakit. Pengujian dilakukan dengan menggunakan 5 dataset yaitu 320, 400, 480, 588 dan 848 data dari data total sebanyak 848 data. Data dibagi menjadi data latih dan data uji. Data uji diambil 10% hingga 60% dari dataset. Hasil akurasi yang didapatkan pada pengujian 848 data dengan persentase data uji 60% didapatkan akurasi sebesar 79,17%.
Clustering Pasien Kanker Berdasarkan Struktur Protein Dalam Tubuh Menggunakan Metode K-Medoids Laily Putri Rizby; Marji Marji; Lailil Muflikhah
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

Kanker merupakan penyakit yang kerap menjadi momok bagi sebagian besar orang memang telah memakan banyak korban. Semakin berkembangnya zaman semakin banyak virus yang tersebar di masyarakat. Kanker adalah istilah yang digunakan untuk menggambarkan ratusan penyakit berbeda dengan fitur tertentu yang sama. Kanker dimulai dengan perubahan dalam struktur dan fungsi sel yang menyebabkan sel membelah dan menggandakan diri tanpa terkontrol. Umumnya kanker dinamai sesuai organ dan jenisnya tempat pertama kali ia berkembang. Mutasi gen yang paling sering ditemukan pada kanker manusia adalah Gen P53. Gen P53 merupakan gen penekan tumor yang mengkode atau mengekspresikan protein 53. Dari berbagai banyak data yang ada perlu dilakukan proses klusterisasi yaitu pengelompokkan jenis kanker berdasarkan kelasnya. Salah satu metode klustering yang mulai banyak digunakan adalah metode K-Medoids. K-medoids atau dikenal pula dengan PAM (Partitioning Around Medoids) menggunakan metode partisi clustering untuk mengelompokkan sekumpulan n objek menjadi sejumlah k cluster. Algoritma ini menggunakan objek pada kumpulan objek untuk mewakili sebuah cluster. Objek yang terpilih untuk mewakili sebuah cluster disebut medoid. Pada penelitian clustering pasien kanker menggunakan metode K-Medoids ini menunjukkan nilai persentase kualitas cluster sebesar 77% pada percobaan pada nilai k 14 dan menggunakan 116 data.
Peramalan Produksi Kelapa Sawit Menggunakan Jaringan Syaraf Tiruan Dengan Metode Backpropagation (Studi Kasus PT.Sandabi Indah Lestari) Retiana Fadma Pertiwi Sinaga; Budi Darma Setiawan; Marji Marji
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 Big Private Plantation companies in Indonesia is PT. Sandabi Indah Lestari located in Bengkulu Province. PT.Sandabi Indah Lestari designs a budget every year to spend on production process conducted once every week. Each production process of course requires a separate cost, if the production can not change production costs, the company will incur losses. Therefore, it is necessary to forecast the output of palm oil production to be a reference for the production results remain stable or even increased. Forecasting results can later be used by the company to improve production and do not lose from budget planning targets that have been made. This research uses backpropagation method combined with nguyen widrow algorithm. From the test results with the number of 260 data train, the amount of test data 12 test data, the value of learning rate 0.4, the number of hidden layer 5 neurons, the error limit of 0.001, and the maximum iteration of 900 yields MAPE (Mean Absolute Percentage Error) value of 10,0047 %.
Penerapan Algoritme Particle Swarm Optimization-Learning Vector Quantization (PSO-LVQ) Pada Klasifikasi Data Iris Ilham Romadhona; Imam Cholisoddin; Marji Marji
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

Currently Iris flowers are easily found in around the world with various species. In Greek Iris mean the goddess of the rainbow because Iris species has reached 260 to 300 various species with colorful and light flowers. Because of the large number of Iris species, it is necessary to classify the Iris species. To solve the problem, used the Learning Vector Quantization (LVQ) algorithm which will be optimization using the Particle Swarm Optimization (PSO) algorithm was used to classify species into Sentosa Iris, Virginica Iris and Versicolor Iris category where the species previously recorded on Iris dataset. Then the result of this study was compared with the classification using LVQ algorithm. The average accuracy obtained with PSO-LVQ algorithm is 93.334%, whereas the average accuracy with LVQ algorithm is 84.268%. The differece in accuracy is 9.066% it is mean PSO-LVQ algorithm give more a good provides result than LVQ algorithm.
Implementasi Performance Improved Holt-Winters Untuk Prediksi Jumlah Keberangkatan Domestik di Bandar Udara Soekarno Hatta Revinda Bertananda; Budi Darma Setiawan; Marji Marji
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

Air transportation in Indonesia is experiencing a rapid increase. Given the developments that occur, it's not impossible that in the future air transport will be a superior transportation again. But every flight in an airport doesn't always carry the same number of passengers each month. The number of these unconfirmed passengers should always be predictable so that the airport can determine policies to adjust the increase or decrease the number of passengers in the future. Prediction done in this research using Performance Improved Holt-Winters method. This method can predict time series data that has a data pattern with seasonal variation. In its calculations, Performance Improved Holt-Winters method involves trend and seasonality and is based on three smoothing equations: overall smoothing (level), trend smoothing, and seasonal smoothing. The data used in this study is the data of domestic departure at Soekarno Hatta airport from January 2012 to December 2017 which obtained from the official website of Central Bureau of Statistics Indonesia (www.bps.go.id). From the results of tests that have been done, the result of the smallest MAPE value is 2,976% with the parameter value α (alpha) = 0,04; β (beta) = 0,002; Υ (gamma) = 0,1; the number of training data = 60, and testing data = 12.
Optimasi Komposisi Makanan Bagi Penderita Obesitas Pada Orang Dewasa Menggunakan Algoritme Particle Swarm Optimization (PSO) Shinta Anggun Larasati; Imam Cholissodin; Marji Marji
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

Obesity occurs due to buildup of fat in the body is very high. Thus causing weight gain be not ideal. Obesity can also cause disease complications, some of which can endanger lives. To get the ideal body weight and the minimum cost incurred, the patient needs to control the amount of food intake is by regulating the composition of food that enters the body. The research was done by optimizing food composition for obese people in adults using Particle Swarm Optimization Algorithm (PSO). In this study, the initial particle formation of the particle random based on the amount of food so there is no need to convert that into food index. The results displayed by the program is actual body weight, ideal weight, nutritional status, energy needs, the needs of protein, fat and carbohydrate needs needs. While the test results obtained the optimal parameters such as the number of particles = 80, the number of iterations based on testing convergence of 703, = 0,4, = 0,7 c1i = 1,5 and c1f = 0,3, c2i = 0,3 and c2f = 1,5. The results of the program with the first patient parameters produce an average difference between the actual data with the data from the program registration -2,08% and it can reduce the cost of expenditure up to 6,85%. While the second patient the average of the actual data difference with data from the program amounted to -1,06% and it can reduce the cost of expenditure up to 5,93%.
Co-Authors Achmad Burhannudin Adam Hendra Brata Adhikari, Basanta Prasad Adhiyatma Mugiprakoso Afifah, Nadiyah Hanun Agi Putra Kharisma Agung Kurniawan Agustian, Moch. Alfredo Barta Ahmad Fauzan Rahman Ahmad, Baihaqi Aldy Satria Andika Harlan Andini Agustina Anita Sulistyorini Annisa Selma Zakia Ardhimas Ilham Bagus Pranata Arifin, Maulana Muhamad Asti Melani Astari Atika Anggraeni Audi Nuermey Hanafi Bagus Abdan Aziz Fahriansyah Bahruddin El Hayat Baihaq, Firda Barlian, Salwa Isna Bayu Rahayudi Bayu Septyo Adi Budi Darma Setiawan Budi Darma Stiawan Cahyo Adi Prasojo Candra Ardiansyah Choirul Anam Cindy Puspita Sari Cindy Rizki Amalya Dani Irawan Daud, Nathan Dea Widya Hutami Dewi Yanti Liliana Dian Eka R Dian Eka Ratnawati Djoko Kustono Dwi Yana Wijaya Dyva Agna Fauzan Edy Santoso Edy Santoso Edy Santoso Endang Wahyu Handamari Erwin Komara Mindarta Fanani, Erianto Fatih Kamala Nurika Gilang Ramadhan Gustian Ri'pi Hadi, Moch. Sholihul Handoyo, Samingun Hary Suswanto Hasan Ismail Ilham Romadhona Imam Cholisoddin Imam Cholissodin Imam Muda Nauri Imran Imran Indriati Indriati Indriati Indriati Issa Arwani Istiana Rachmi Istiqomah, Mutiara Titian Januar Dwi Amanda Jeffrey Simanjuntak Kenty Wantri A Kohei Arai Kurnianingtyas, Diva Lailatul Fitriah Lailil Muflikah Lailil Muflikhah Lailil Muflikkah Laily Putri Rizby Laksono Trisnantoro Leni Istikomah Liana Shanty Wato Wele Keaan Lilik Zuhriyah Lilis Damayanti Luthfi Faisal Rafiq M Chandra Cahyo Utomo M. Alfian Mizar Made Bela Pramesthi Putri Mahmudi, Wayan Firdaus Maududi, Affan Al Michael Adrian Halomoan Mochammad Pratama Viadi Mountaz, Lotu Muchammad Harly Muhamad Altof Muhamad Hilmi Hibatullah Muhammad Fakhri Mubarak Muhammad Hafidzullah Muhammad Indra Harjunada Muhammad Ramanda Hasibuan Muhammad Rizkan Arif Muhammad Robby Dharmawan Muhammad Tanzil Furqon Muhammad, Naufalsyah Falah Muzdalifah Yully Ayu Nonny Aji Sunaryo Nurul Hidaya Nurul Hidayat Nurul Hidayat Okvio Akbar Karuniawan P. P. S, Gladis Viona Pangestu, Wiyan Dwi Panji Prasuci Saputra Paryono Permadani , Anda Permatasari, Adelia Pratitha Vidya Sakta Prawidiastri, Firnadila Pricielya Alviyonita Rafely Chandra Rizkilillah Ratih Kartika Dewi Ratna Candra Ika Razaq, Hilal Nurfadhilah Retiana Fadma Pertiwi Sinaga Revinda Bertananda Riana Nurmalasari Ricky Irfandi Ricky Marten Sahalatua Tumangger Rizqi Addin Arfiansyah Rosalinda, Nadia Ryan Mahaputra Krishnanda Sabrina Hanifah Sari, Resti Novita Shinta Anggun Larasati Sri Wahyuni Sri Widyarti Sumarli Sumarli, Sumarli Supraptoa Supraptoa Supriyadi Supriyadi Sutrisno Sutrisno Swandy Raja Manaek Pakpahan Syarif Suhartadi Tahtri Nadia Utami Tawang Wulandari Tika Dwi Tama Usman Adi Nugroho Wayan Firdaus Mahmudy Wulandadi, Retno Yamlikho Karma Yayuk Wiwin Nur Fitriya Yuita Arum Sari Yusufrakadhinata, Muhammad Zulianur Khaqiqiyah