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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Imam Cholissodin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Dokumen SAMBAT Online Menggunakan Metode Naive Bayes dan Seleksi Fitur Berbasis Algoritme Genetika Tony Faqih Prayogi; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Integrated Community Asking Application System (SAMBAT) Online is one of application that becomes an eGov system in Malang City to provide a place for the people of Malang City to voice their aspirations towards problems that exist for the good of the city itself. All complaints that enter through SAMBAT Online have been grouped based on the existing parts and later will be sorted manually and forwarded to the respective Regional Work Unit (SKPD) so that they can be immediately followed up. But because of the number of complaints received so long enough to be processed by each SKPD. Therefore a system was created for the classification of SAMBAT Online documents. In this study implemented a naive bayes method and genetic algorithm-based feature selection for the SAMBAT Online document classification. The implementation process itself consists of preprocessing, term weighting, Feature Selection using genetic algorithms and the classification process using naive bayes method. The results of the tests that have been done, obtained the highest accuracy of 89.79% in the test of 49 data test with the parameter value of generations 70, population size 20, crossover rate 0.8 and mutation rate 0.2.
Implementasi Algoritme Extreme Learning Machine (ELM) Untuk Prediksi Harga Emas Bagi Investor Laila Restu Setiya Wati; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

There are a variety of investing one is gold. Plain gold made into a long-term investment, since the benefits of investing in gold is easily exchanged, no taxes and investing in gold because it has properties that are resistant to inflation. The nature of the resistance it that make interested investors to invest. Tough investors get information mengenahi changes up and down the gold price with the issue so that investors desperately need information for predictions as a consideration of when to buy and sell gold in order to get the profit in accordance with the perancanaan that have been made. This research uses algorithms Extreme Learning Machine (ELM) for predicting the price of gold. Testing in predicting model algorithms so that the gold price to ELM produce gold price predictions with optimal. Test analysis results by using the best of previous testing variables produce the Mean Absolute Percentage Error (MAPE) of 0.29%, best of MAPE generated less than 10% indicates that Extreme Learning Machine (algorithms ELM) good to be implemented in doing the predictions of the gold price.
Penerapan Multi Travelling Salesman Problem Pada Optimasi Pendistribusian Bantuan Sosial Beras Sejahtera Studi Kasus: Perum Bulog Subdivre Malang Muhammad Nadzir; Imam Cholissodin; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Distribution is an economic activity that bridges the production and consumption processes. The distribution process is distributing goods from producers to consumers. Bantuan Sosial Beras Sejahtera (Bansos Rastra) Program has a goal to improving the quality of service for the poor through fulfilling food needs. The distribution system of Bansos Rastra sending goods to each Distribution Point by Perum Bulog in accordance with the data distribution request. In the process of distributing goods, it is necessary to calculate the route distance in order to minimize travel time with the problems used in processing document data is Multi Traveling Salesman Problem (m-TSP) with Genetic Algorithm. From the evaluation results, the distribution routes for each warehouse are recommended by meeting the limits made. Based on the research carried out, the optimal parameters obtained were the size of the optimal number of generations of 300 generations, the optimal size of the population is 90 populations. The crossover probability value is 0.1 and the probability of mutation is 0.9 so that it gets the best average fitness value of 2.583. The final evaluation results produce the best chromosomes with a difference in the predicted distance that is more efficient than the actual distance so that the distribution process of Bansos Rastra can be more optimal.
Penentuan Seleksi Atlet Taekwondo Menggunakan Algoritme Support Vector Machine (SVM) Uswatun Hasanah; Imam Cholissodin; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Martial arts is an art that in use for defense or defense, protecting one's self when subjected to danger. Taekwondo is a martial art that originated in Korea that uses the hands and feet with the rules and ethics of discipline. Public physical exercise in a prepare taekwondoin i.e. MTH, 300 meter run, run back and forth 6 meters, side kick, back kick, front kick, Crescent kick, block, punch, horses, jump rope, push ups, sit ups, pull ups, backing up, triple hops. The purpose of this research can apply the algorithm support vector machine in determining the selection athlete taekwondo. On this research uses data sets that 116 has 16 parameters. Then the data is divided into training data and test data which used the method with a K-Fold Cross Validation, with k = 10. The result of the implementation of the algorithm of support vector machine for determination of taekwondo athletes in the classification selection qualify and do not qualify for the best accuracy results obtained with the parameters used, namely a comparison ratio data = 90%: 10%, a parameter λ ( lamda) = 10, the parameter γ (gamma) = 0.001, the parameter C (constant) = 1, parameter ϵ (epsilon) = 0.001 maximum iterations, 30. So the average accuracy is obtained that is 100%.
Klasifikasi Status Gizi pada Balita Menggunakan Metode Extreme Learning Machine dan Algoritme Genetika Nabila Lubna Irbakanisa; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nutritional problem is one of serious problems. Because nutrition does not only concern in survival, but also relates to the quality of someone's life. In this case, the examination of child nutrient by medical personnel is generally done by archiving, namely by recording manually, and then analyzed. But by doing the analysis manually, it makes the vulnerability of inaccuracy in identifying nutritional status, and takes longer time because it is less practical. Based on these problems, the authors apply the Extreme Learning Machine (ELM) method and Genetic Algorithm to classify nutritional status in toddlers quickly and accurately. In this research, Genetic Algorithms used for finding the best input weight, which will then be used to determine the value of nutritional status using ELM. After testing, obtained an average accuracy of ELM - Genetic Algorithm is 72.3529% with the number of popsize is 100, 34 iterations, crossover rate 0.6, mutation rate 0.4, and 2 hidden neuron. While the accuracy obtained from the ELM is 67.6471%. The result also shows that the addition if Genetic Algorithm on ELM can improve the accuracy.
Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Dengan Menggunakan Algoritme Hybrid Discrete Particle Swarm Optimization (Studi Kasus: MI Salafiyah Kasim Blitar) Ana Holifatun Nisa; Imam Cholissodin; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The policy of using school buses as a means of transportation to take students from school to home is very helpful for their role as parents. Moreover the distance between home and school is quite far and the age of students is still young. The constraints of the system between school transport can not be separated from the name of the efficiency of the time needed, but also the comfort of the students and the trust of the parents. With the optimization of the problems of the delivery route from this school bus, it is expected to minimize problems that can occur, including: traffic accidents due to the use of private vehicles; reduce fears of parents; so that it can increase student satisfaction with the optimization of delivery time. The algorithm used to optimize the Traveling Salesman Problem (TSP) problem is Hybrid Discrete Particle Swarm Optimization (HDPSO). In this study using data from students of Blitar's Salafiyah MI MI, which in the process of going to the house were divided into 2 groups, namely: the first group of 20 people and the second group of 24 people. From the results of testing the system compared to the actual data, the biggest difference was obtained on the second day of 2,69 Km (10,7%) in the first cluster and 22,8 (41%) Km in the second cluster.
Implementasi Komputasi Paralel GPU pada Algoritme Cellular Automata Menggunakan CUDA® Muhammad Rizal Ma'rufi; Imam Cholissodin; Eriq Muh. Adams Jonemaro
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Video games industry is expanding fast in this modern era. This expansion needs to be balanced with quality and production time from creator. Procedural Content Generation (PCG) is one of innovations that suit with that condition. In PCG world, there are many algorithms can be used, one of the most well know in PCG is cellular automata (CA) algorithm. But CA has efficiency problem, but it can be solved with parallel computing. In this research, parallel computing is implemented using CUDA® technology. But there is another problem, that is the map result from CA is not always giving good result, sometimes it cannot be played due to isolated cave in map, but it can be solved using flood fill algorithm. In this research, the implementation will go through few steps, implementation of CA CPU, CA GPU and Flood Fill. Then those implementations will be integrated with Unity® game engine, then time to implement it to game. The game implementations will use Unity® game engine with C# as scripting language. CA CPU, CA GPU and Flood Fill algorithms computation time will be tested to know their performances. From the test result, CA GPU algorithm has better performance than CA CPU up to 227 times. In the other hand, flood fill algorithm that being used in this research has very high computation time, so the map size is very limited.
Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto dengan Algoritme Genetika pada Peramalan Harga Emas untuk Stock Trading Ficry Agam Fathurrachman; Fitra Abdurrachman Bachtiar; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Investors and stock traders need knowledge of forecasting when the price of gold will rise or will decline to minimize the risk in investing. This forecasting requires an appropriate method in order to give good results. FIS Tsukamoto is used to forecast the price of gold based on existing exchange rate data. The parameters used by Tsukamoto FIS are the currency rates of USD / GBP, CHF / USD, JPY / USD, EUR / USD based on the previous three days and the price of gold based on the previous day. To maximize Tsukamoto FIS performance, Tsukamoto FIS membership function will be optimized using Genetic Algorithm. The chromosome representation used is real-coded with a double data type. The reproduction of the crossover method used is one-cut point, while the mutation method used is random mutation. In the selection process, the method used is elitism selection to get the best individuals. Based on parameter testing carried out with 10 experiments each parameter, the best population size is 180, combination of cr = 0.9 and mr = 0.1, and the best number of generations is 325, the best fitness value is 8.6972. The Root-Mean Squared Error (RMSE) value obtained before optimization is 13.3611, while after optimization it is obtained that the smaller RMSE value is 12.5801. These results indicate an increase in the value of accuracy in Tsukamoto FIS after being optimized using Genetic Algorithm.
Prediksi Harga Cabai Rawit di Kota Malang Menggunakan Algoritme Extreme Learning Machine (ELM) Galih Ariwanda; Imam Cholissodin; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cayenne is a commodity for food that cannot be separated from the daily needs of people in Indonesia. Cayenne for the people in Malang City is consumed to maintain metabolism and body temperature to keep warm and vitamin C which can help maintain the health of the human body. Prices of cayenne in Malang City always fluctuate changes every day. Fluctuation changes that make the price of cayenne are difficult to predict well. In addition, the prices given by traders are always varied, cayenne pepper is also one of the contributing commodities of inflation and prevents the difference in prices obtained by consumers and farmers so that they are not harmed by each other. Therefore, it is necessary to predict the price of cayenne in Malang so that consumers and the government can take preventive measures against the existing problems. The prediction process is divided into several process including pre-processing, normalization of data, predictions using the Extreme Learning Machine algorithm, and the results of errors with MAPE. Based on the results of testing using cayenne price data from January 1, 2017 to December 31, 2018 in Malang City, the smallest MAPE value was 2.087% with 2 features, the number of neurons in the hidden layer was 5, the percentage of training data and testing data 90%:10%, and the activation function is Binary Sigmoid.
Klasifikasi Penyakit Kanker Serviks dengan Extreme Learning Machine Uke Rahma Hidayah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cervical cancer is the second most common cancer in Indonesia after breast cancer. The number of deaths from cervical cancer in Indonesia continues to increase every year due to delays in making diagnoses and examinations. To detect cervical cancer, a laboratory examination using Visual Inspection with Acetic Acid (IVA) or pap smears is needed which requires specialist internal medicine and several considerations of features to get accurate diagnosis. Sometimes, how to analyze features by doctor with one another produces different results. Therefore, a classification process is needed to make a diagnosis of cervical cancer with high accuracy results so that it is expected to be able to match the diagnosis results of medical personnel. This study uses cervical cancer risk classification data with feature selection based on expert interviews. This study uses the Extreme Learning Machine algorithm to carry out the classification process and measure the results of algorithm performance with accuracy values ​​from the calculation of confusion matrix. Based on the test results obtained the optimal parameters are as many as 11 hidden neurons, the activation function is binary sigmoid, and the fold on training and testing data is fold 1st which produces an accuracy of 91.76%.
Co-Authors Achmad Arwan Adam Syarif Hidayatullah Adhipramana Raihan Yuthadi Adhitya Wira Castrena Adinugroho, Sigit Ageng Wibowo Agus Wahyu Widodo Aldino Caturrahmanto Alfen Hasiholan Alif Fachrony Ana Holifatun Nisa Anandita Azharunisa Sasmito Andika Eka Putra Andriko Hedi Prasetyo Anggi Novita Sari Anim Rofi'ah Annisa Alifia Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiansyah Setiajati Arief Andy Soebroto Arina Indana Fahma Arsti Syadzwina Fauziah Atika Anggraeni Aulia Dinia Aulia Herdhyanti Aulia Jasmin Safira Azmi Makarima Yattaqillah Bahruddin El Hayat Bana Falakhi Bayu Andika Paripih Bayu Rahayudi Benita Salsabila Bisma Anassuka Bondan Sapta Prakoso Brendy Oscar Munthe Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Budi Santoso Candra Dewi Cindy Cynthia Nurkholis Citra Nadya Dwi Irianti Daisy Kurniawaty Danastri Ramya Mehaninda Daneswara Jauhari Daniel Agara Siregar Dellia Airyn Diah Priharsari Dian Eka Ratnawati Dieni Anindyasarathi Dinda Adilfi Wirahmi Diva Kurnianingtyas Dyah Ayu Wahyuning Dewi Edy Santoso Ega Ajie Kurnianto Elisa Julie Irianti Siahaan Ellita Nuryandhani Ananti Elmira Faustina Achmal Ema Agasta Ema Rosalina Eriq Muh. Adams Jonemaro Ersya Nadia Candra Fahri Ariseno Faizatul Amalia Faturrahman Muhammad Suryana Fayza Sakina Maghfira Darmawan Febriyani Riyanda Felicia Marvela Evanita Fendra Gunawan Ficry Agam Fathurrachman Fikhi Nugroho Fildzah Amalia Firda Priatmayanti Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Ariwanda George Alexander Suwito Ghulam Mahmudi Al Azis Gregorius Dhanasatya Pudyakinarya Guruh Adi Purnomo Gusti Reza Maulana Heny Dwi Jayanti Heru Nurwarsito Himawat Aryadita Holiyanda Husada Husin Muhamad I Gusti Ayu Putri Diani Ibnu Rasyid Wijayanto Ichwanda Hamdhani Ika Oktaviandita Indriati Indriati Irma Lailatul Khoiriyah Ishak Panangian Sinaga Istiana Rachmi Izzatul Azizah Jeffrey Junior Tedjasulaksana Khairinnisa Rifna Khairiyyah Nur Aisyah Komang Anggada Sugiarta Kresentia Verena Septiana Toy Kukuh Wicaksono Wahyuditomo Laila Restu Setiya Wati Lailil Muflikhah Leni Istikomah Liwenki Jus'ma Olivia M. Ali Fauzi M. Khusnul Azhari Mahendro Agni Giri Pawoko Marji Marji Maulana Ahmad Maliki Maulana Putra Pambudi Mauldy Putra Pratama Mentari Adiza Putri Nasution Michael David Moch Bima Prakoso Moh. Ibnu Assayyis Mohammad Aditya Noviansyah Mohammad Angga Prasetya Askin Mohammad Toriq Muhammad Aghni Nur Lazuardy Muhammad Dio Reyhans Muhammad Fahmi Hidayatullah Muhammad Fuad Efendi Muhammad Halim Natsir Muhammad Hasbi Wa Kafa Muhammad Hidayat Muhammad Maulana Solihin Hidayatullah Muhammad Nadzir Muhammad Rizal Ma'rufi Muhammad Rois Al Haqq Muhammad Shafaat Muhammad Syafiq Muhammad Tanzil Furqon Muhammad Taufan Mukh. Mart Hans Luber Nabila Lubna Irbakanisa Nabilla Putri Sakinah Nadia Natasa Tresia Sitorus Nadia Siburian Nadiah Nur Fadillah Ramadhani Nining Nahdiah Satriani Noerhayati Djumaah Manis Novanto Yudistira Novirra Dwi Asri Nur Afifah Sugianto Nur Firra Hasjidla Nurul Hidayat Nurul Inayah Obed Manuel Silalahi Panji Husni Padhila Priscillia Vinda Gunawan Putra Pandu Adikara Putri Ratna Sari Radita Noer Pratiwi Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Renata Rizki Rafi` Athallah Restu Fitriawanti Reyvaldo Aditya Pradana Reza Aprilliana Fauzi Rien Difitria Rinindya Nurtiara Puteri Rio Cahyo Anggono Riski Ida Agustiyan Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Ramadhan Rosintan Fatwa Rowan Rowan Sabrina Nurfadilla Salsabila Multazam Sandya Ratna Maruti Sari Narulita Hantari Satria Habiburrahman Fathul Hakim Sayyidah Karimah Shafira Eka Aulia Putri Shelly Puspa Ardina Shibron Arby Azizy Shinta Anggun Larasati Siti Mutdilah Sofi Hidyah Anggraini Stefanus Bayu Waskito Supraptoa Supraptoa Sutrisno Sutrisno Tara Dewanti Sukma Tibyani Tibyani Timothy Bastian Sianturi Tobing Setyawan Tony Faqih Prayogi Tusiarti Handayani Tusty Nadia Maghfira Uke Rahma Hidayah Uswatun Hasanah Utaminingrum, Fitri Vergy Ayu Kusumadewi Veronica Kristina Br Simamora Vinesia Yolanda Vivilia Putri Agustin Vivin Vidia Nurdiansyah Wahyu Bimantara Wanda Athira Luqyana Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Yessica Inggir Febiola Yoseansi Mantharora Siahaan Yudha Ananda Kresna Yudo Juni Hardiko Yuita Arum Sari Yunico Ardian Pradana Yusuf Afandi Zanna Annisa Nur Azizah Fareza