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

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Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM) Arsya Monica Pravina; Imam Cholisoddin; 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

With the increasing use of Twitter, social media that works in real-time for the public can convey complaints and appreciation to airlines, it is necessary to create a system that can classify a tweet containing opinions including what is the best class, in this study there are positive and negative classes. This is done so that it can help airline companies in terms of evaluating service improvements and can help people choose the right airline. Thus a sentiment classification with Lexicon Based features which is able to receive information in languages other than Indonesian (in this study used in English) is done to conduct sentiment analysis. Use the support vector machine algorithm to classify. The results of this study show optimal parameters and the effect of using Lexicon Based Features. By using parameter C is 10 and the learning rate is 0.03 also used Lexicon Based Features with an iteration of 50 times giving accuracy 40%, precision 40%, recall 100%, and f-measure 57,14%.
Optimasi K-Means untuk Pengelompokan Data Kinerja Akademik Dosen menggunakan Particle Swarm Optimization Dian Werdiningsih Dwi Rahmawati; Imam Cholisoddin; Nurudin Santoso
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

Lecturers are teachers and tutors for students. In addition to teaching and guiding, lecturers are also required to assist in developing science as well as potential and expertise in itself. Variables or criteria that exist in the lecturer include Education, research, devotion, administration, and support. One of the difficulties faced by universities is the ideal grouping of assignments to lecturers. Assignment of lecturers related to the committee, further study, positions, filler of an event held from internal and external universities and others. So it takes a system that can classify the academic performance of lecturers using K-Means algorithm combined with Particle Swarm Optimization algorithm. The Particle Swarm Optimization algorithm is useful in optimizing the centroid on the K-Means algorithm. The data used is data from the Faculty of Computer Science (FILKOM), Universitas Brawijaya 2016. The data is obtained from GJM FILKOM. The results of this study obtained K-Means algorithm combined with Particle Swarm Optimization algorithm better 3.28% compared with stand-alone K-Means algorithm, where the cluster quality is determined using Silhouette Coefficient method.
Prediksi Nilai Cryptocurrency Bitcoin menggunakan Algoritme Extreme Learning Machine (ELM) Rahmat Faizal; Budi Darma Setiawan; Imam Cholisoddin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bitcoin is one of cryptocurrency which is popular among people due to decentralized management, well-maintained confidentiality, and easy process. But, this type of cryptocurrency is extremely volatile which makes the owner feel aggrieved. Lots of actions have been taken to overcome this by seeing the statistic movement over and over, Taking actions without considering the future prospect, or make the the asset being untouched until the considered time. Those are inneficient regarding the goal is to get the profit.Therefore, the need of system which can predict the value of Bitcoin accurately and efficiently so it can help decreasing the risk of losing and could give another consideration on trading cryptocurrency Bitcoin. This research has a purpose to obtain the value of cryptocurrency Bitcoin using Extreme Learning Machine (ELM) algorithm. Based on the implementation and analysis conducted using Bitcoin Data from May 1th, 2018 until August 1th, 2018, it can be obtained that the smallest error value using Mean Average Percentage Error (MAPE) is 2,657% with the number of features is 2, the number of hudden neuron is 4, and the percentage of training data is 90%, also the range of with range [-1.8, 1.8].
Klasifikasi Penyakit Kulit Kucing menggunakan Metode Support Vector Machine Yuwilda Wilantikasari; Imam Cholisoddin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cats are one of the most popular pets in the world. However, health is a matter of concern in the nurturing of cats. Indonesia has a relatively high humidity of air, hence parasites and fungi can multiply and spread which could cause skin diseases. The scarce knowledge of cat owners about cat skin disease and some symptoms that have similarities to various types of cat skin disease are difficult to identify. With these problems, an intelligent system that can classify cat skin diseases based on symptoms is proposed. This intelligent system also aims to help medical teams especially in the field of veterinary medicine in providing a diagnosis of cat skin diseases. Support Vector Machine method can be applied to skin disease classification problems using a limited dataset of 240 with 14 parameter. This study uses five classes of classes: scabies, cat flea, abscesses, dermatitis, and fungi. SVM performances gave an accuracy of 98.745% with parameter value on sequential training SVM, , y = 0.01, C = 10, = 0.01, iteration = 100 and the ratio data of 90%:10%.
Prediksi Rating Otomatis Berdasarkan Review Restoran pada Aplikasi Zomato dengan menggunakan Extreme Learning Machine (ELM) Diajeng Tania Ananda Paramitha; Imam Cholisoddin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this modern culture, technology advancement are growing better than we ever discovered before. One of the apps we use to search for information about restaurant in Jakarta are known as Zomato. Zomato is an application that provides various information about a restaurant from it facility, price, review, and rating. Users of The Zomato App can input various information that people haven't aware of about the restaurant into the app. Besides of inputting information into the app, Users of The Zomato App can also input a review and rating of a specific restaurant. The data review is used as an information about the restaurant for the potential customer from The Zomato App but sometimes the data review doesn't yet include a restaurant rating. This lack of misinformation will surely make the restaurant owner to occure some difficulties such as improving the restaurant services status for future outcomes. This research helps to classifying the review into the rating. Test protocol of this research are using a prediction with Extreme Learning Machine (ELM) Methods as it core. The prediction process however are build from a several steps such as pre-processing, word weighting with TF-IDF, and Extreme Learning Machine (ELM) Method calculations. Test result of The ELM parameter provides accuracy result 80,01% with k=10 amount hidden neuron 25 Interval weights -0.5 until 0,5 using function activation Sigmoid biner. We have come to conclusion were ELM method could positively solve the prediction problem exquisitely.
Optimasi Komposisi Menu Makanan bagi Penderita Penyakit Diabetes Melitus Tipe 2 dan Komplikasinya menggunakan Hybrid Algoritme Genetika dan Simulated Annealing Muhammad Jibril Alqarni; Imam Cholisoddin; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes mellitus is a common disease among the people. One of the active preventive things to deal with type 2 diabetes mellitus is to do regular exercise, and eat nutritious foods and have adequate nutrition for 1 day. In getting enough calories or energy according to the needs of patients, calculations can be done manually. But if the process is done manually, it will take a long time so that if this is implemented in a health institution, it will be very inefficient given the large number of patients in the queue. This problem can be solved by using an artificial intelligence system using a genetic algorithm that is performed hybridization with simulated annealing. Simulated Annealing can help the genetic algorithm come out of optimum local conditions, due to its nature that can accept solutions that are not better or better than the previous solution. Simulated Annealing was successfully added to help the genetic algorithm out of optimum local conditions, this was indicated by the highest fitness value of 0.998, with the percentage difference between the patient's actual needs of calories by 0.18%, carbohydrate by 0.20%, protein by 0.69% and the last is fat at 0.27%.
Optimasi Kombinasi Bahan Makanan untuk Mencegah Stunting pada Balita dengan menggunakan Algoritme Genetika Tria Melia Masdiana Safitri; Imam Cholisoddin; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of good nutrients provided to kids in Indonesia has been a common problem and has yet to be solved. The lack of nutritional intake can cause malnutrition and stunting (the lack of height growth). Indonesia has become the fifth biggest country in terms of the numbers of toodlers suffering stunting, whose numbers have risen to nearly 9 million toddler. Stunting in toddlers can be caused by many factors, one of which is the lack of knowledge possessed by the parents of the combination of a variety of food ingredients which must be fed to the toddler in order to provide a perfectly balanced supply of nutrients. Providing a single dish does not always provide a perfectly balanced supply of nutrients. Providing a stable supply of perfectly balanced nutrients can be achieved by a variety of food ingredient combinations that contain roughly the same amount of nutrients. In this research, a recommendation of food compositions was provided through the course of 7 days using a genetic algorithm to assist parents in combining food ingredients which corresponded with a toddler's nutritional needs. Based on the testing parameters of the genetic algorithm, the optimal results from the combination of Cr:Mr test was 0,7:0,3 and the fitness results had an average score of 97,412. The testing results on the optimal size of the population was discovered to be 90 with an average fitness score of 96,95. Testing of the most optimal number of generations that need to be generated were 350 generations with an average fitness score of 96,664. The result of the test was capable of saving the parents expenses as much as 35,77% with the average cost of a dish was Rp.19269,21.
Optimasi Feeder Vehicle Routing Problem pada Distribusi Pengiriman Barang dengan menggunakan Multiple Travelling Salesman Problem dan Algoritme Genetika Maya Novita Putri Riyanto; Imam Cholisoddin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The increase in population is directly proportional to the increase in demand for goods. The sales block is a concept of shipping goods by dividing the area based on the number of fleets, but the number of goods in the Motorcycle fleet is depleted, so the goods must be taken to the Car fleet, this problem is called the Feeder Vehicle Routing Problem. Researchers will optimize the Feeder Vehicle Routing Problem (FVRP) on the distribution of freight shipments using Multiple Traveling Salesman Problems (MTSP) and Genetic Algorithms. In the Genetic Algorithm chromosome representation based on the MTSP concept, a cluster division is made to map the shipping routes, including the destination Shop, the condition of reloading or not reloading, the selection of the Car to reload, the Position of the Car, and the time interval for changing the reloading items. Crossover Reproduction Stage uses Partial Mapped Crossover and Extended Intermediate Crossover, while the reproduction of mutations using the method. Calculate distance values ​​using the Haverseine Formula, then calculate fitness values ​​and save with Elitism. The test results get the greatest fitness value 1.35926 assessment of population size 50, Cr value of 0.5 and Mr 0.6 in generation 100, besides fitness convergence is available at around values ​​1.25 to 1.3. Producing with a size of 50, or more than 50, and producing 100, and a value of Cr 0.5 and Mr 0.5, resulting in a fitness value of 1.266594973.
Optimasi Penjadwalan Perkuliahan menggunakan Hybrid Discrete Particle Swarm Optimization (Studi Kasus: STAI Ma'had Aly Al-Hikam Malang) Achmad Choirur Roziqin; Imam Cholisoddin; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Generally, timetabling is done by using conventional tables or spreadsheets. As a result, its affect the quality of timetable and it could drain time and energy if data is considered in thousands. Based on these problems, it requires an intelligent system that not only automates its process but also optimizes the result. Particle Swarm Optimization (PSO) is a popular metaheuristic algorithm to solve multiparameter optimization problems. Discrete PSO is used in this study because of combinatorics problems. Various strategies are also used in this method such as transposition method for particle movement, guided random strategies, and particle's position repair strategies. The strategies is expected to improve timetabling result. With the various strategies that have been used, this study will use “Hybrid Discrete PSO” approach. The test results showed the combination of parameters that resulting the best fitness are b_loc=1, b_glob=0,8, b_rand=0, number of particle is 200 and number of iteration is 40. The resulting fitness is 0,018896357 with the total execution time is 34 minutes 16 seconds 358 miliseconds.
Co-Authors Achmad Choirur Roziqin Arsya Monica Pravina Bayu Rahayudi Budi Darma Setiawan Candra Dewi Diajeng Tania Ananda Paramitha Dian Werdiningsih Dwi Rahmawati Dimas Prenky Dicky Irawan Edy Santoso Ilham Romadhona Ivarianti Sihaloho Lilis Damayanti Marina Debora Rindengan Marji Marji Maya Novita Putri Riyanto Muhammad Faris Mas'ud Muhammad Jibril Alqarni Nirzha Maulidya Ashar Nurudin Santoso Putra Pandu Adikara Rahmat Faizal Ratih Kartika Dewi Rizal Rudiantoro Sarah Aditya Darmawan Sema Nabillah Dewi Tibyani Tibyani Tria Melia Masdiana Safitri Wayan Firdaus Mahmudy Yuwilda Wilantikasari