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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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Implementasi Algoritme Extreme Learning Machine (ELM) untuk Prediksi Beban Pemanasan dan Pendinginan Bangunan Alif Fachrony; Imam Cholissodin; Edy Santoso
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

Energy conservation is a very important thing as the growth of the times and technology. Making energy-efficient buildings needs to be done by optimizing the use of tools for cooling and heating the building without affecting the health and comfort of the user of the building. Energy-efficient buildings can be achieved by calculate heating (HL) and cooling (CL) loads. HL and CL are the heat flow rates to be taken or added from the building to maintain relative air temperature and humidity of the building under desired conditions. The prediction of HL and CL will be used in calculating the power loads of heater or air conditioner. Currently HL and CL calculations still have constraints such as very complex calculations, time consuming because many disciplines are involved and it use very varied parameters. It needs learning machine to predict HL and CL easily, and quickly. The author uses the algorithm Extreme Machine Learning (ELM) to predict HL and CL. In the test analysis using ELM algorithm performed using binary sigmoid activation function, 3 input, 1 hidden neurons, 2 output targets and 130 dataset, the best Mean Absolute Error Percentage (MAPE) is 24.73% and it takes 0.0176 seconds to complete the process.
Implementasi Algoritme Average Time Based Fuzzy Time Series Untuk Peramalan Tingkat Inflasi Berdasarkan Kelompok Pengeluaran Mohammad Angga Prasetya Askin; Imam Cholissodin; Sigit Adinugroho
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

Inflation is a condition in which the sale price of goods or services experienced a general increase or decrease in economic activity. This affects the people of the country so that the effect is enormous. But in determining the rate of inflation is still experiencing difficulties in predicting inflation. Therefore, this study aims to determine / predict the rate of inflation by expenditure category by the Average Time Based Fuzzy Time Series method. This study uses scenarios based on consecutive monthly data, consecutive years, and the mean divisor of the difference. Inflation expense category data obtained from Indonesia Central Bureau of Statistics (BPS) and predicted results obtained is the average value of RMSE 0.486 in data month 15, the average value of RMSE 0.335 in the data year 3, and the last average RMSE 0.314 in the value of divisor 1.9 for consecutive month data categories and the mean RMSE 0.336 in the divisor value 2 for the consecutive year data categories.
Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa Menggunakan Extreme Learning Machine Dan Improved-Particle Swarm Optimization Bayu Andika Paripih; Imam Cholissodin; Putra Pandu Adikara
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

Milk is source of protein which is contain all of easy digested and required nutrition. Milk production by dairy cows are low so Indonesian need of milk can't be fulfilled. PE goat can produce qualify milk cow and it also suitable to be cultivated at Indonesia so they can be alternative of milk source. Produced milk quality is affected by given feed. This research uses Extreme Learning Machine and Improved-Particle Swarm Optimization to search best feed composition so the goat can produce good milk. Parameter calibration for building model are hidden node = 9, population size 70, maximum iteration 40 with fitness value 0.973892. Parameter calibration for searching feed composition are population size = 90 and maximum iteration 20 with fitness value 38,51344218.
Implementasi Extreme Learning Machine Untuk Deteksi Dini Infeksi Menular Seks (IMS) Pada Puskesmas Dinoyo Kota Malang Fikhi Nugroho; Imam Cholissodin; Suprapto Suprapto
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

Sexually Transmitted Infections (STI) is a major public health problem in the world. Incidence of STI cases in many developing countries such as failure in diagnosing and provide treatment at an early stage can lead to serious complications. The required input parameters consist of 39 features consisting of 2 sexes, 9 risk factors, and 29 symptoms. The process of identifying early identification of STI symptoms in this case will implement Extreme Learning Machine (ELM). The implementation of ELM itself does not require IMS rules related to the exact rules but rather compares the results of both determinations. Thus, if there is a change of calculation or identification provisions, it does not affect the calculation of ELM. The ELM method is used to determine STI disease to a number of 17 classes. The best results of the three test scenarios of accuracy between ELM calculations and expert diagnosis results were 36,36% for the 90:10 ratio, 50% for 100 hidden layers, and 31.82% for the weight range of -1 to 0.
Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine Wanda Athira Luqyana; Imam Cholissodin; Rizal Setya Perdana
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

Instagram is the most popular social media in these recent days. The users who start from kids, teenagers to adults, have the role in boosting the popularity of Instagram. However, this social media could not be seperated from the dangers of cyberbullying which is done often by the users, especially in the comment column. The dangers of cyberbullying are certainly worried many people because of the impact it has. Therefore, a sentiment analysis in Instagram comment column can be done in order to find out the sentiments in each comment. Sentiment analysis is a branch of text mining science which is used to extract, understand, and cultivate the data. This research used Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM) classification method to examine the sentiments in each comment. Data consisted of 400 data which taken offline have a total 1799 features. The comment document is divided into 70% of training data and 30% of test data. Based on the tests performed, the best parameters obtained in the SVM method are the degree of polynomial kernel 2, the average of learning rate of 0.0001, and the maximum number of iterations which is 200 times. From these result, it obtained that the highest accuracy is 90%, 50% in the training data composition and 50% composition of test data.
Optimasi Susunan Bahan Makanan Bagi Anak Penderita Attention Deficit Hyperactivity Disorder (ADHD) Menggunakan Algoritme Genetika Muhammad Taufan; Imam Cholissodin; Putra Pandu Adikara
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

Proper nutrition can reduce symptoms of Attention Deficit Hyperactivity Disorder (ADHD). However, children are easily bored when given the same food constantly. In addition, food prices are also considered by parents. In this study sought the optimal solution of the problem of food ingredient preparation using genetic algorithm. To solve this problem, we use an integer permutation representation with chromosome length of 12 genes per day. These genes are a representation of a diet consisting of carbohydrates, proteins, and fats. The crossover method used is one-cut point, while for mutation method using exchange mutation. For the selection stage, elitism selection method is used. From the results of tests that have been done, we obtain optimal parameters that is 50 generations with an average fitness value obtained 13,928. The final result obtained is the composition of the food ingredient in accordance with the number of days desired.
Peramalan Pemakaian Air Pada PLTGU Di Pembangkitan Listrik Jawa Bali Unit Gresik Menggunakan Extreme Learning Machine Dengan Optimasi Algoritme Genetika Heny Dwi Jayanti; Imam Cholissodin; Edy Santoso
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

Water is an absolute necessity every day that has an important role. One of the utilization of seawater used for the industrial sector is PLTGU in PT Pembangkitan Jawa Bali. This makes the electricity industry has treated the sea water into fresh water called desalination process. However, in the PLTGU process often experience problems in water treatment such as the occurrence of leaking pipe due to corrosion, the difference of water filling treatment, and the long-time desalination process resulted in unstable turbine performance. With some problems that arise, then needed a solution. In this study, researchers have proposed a water forecasting system using the method of extreme learning machine (ELM) with the optimization of Genetic Algorithm. The genetic algorithm is used to optimize the input weight values obtained randomly on the ELM method. Meanwhile, to represent chromosomes using real code. At the reproduction stage using extended intermediate crossover method and random mutation method. The result of ELM test method and genetic algorithm resulted in average MAPE value of 0.428 with a parameter value of crossover rate (Cr) value 0.4 and mutation rate (Mr) equal to 0.6, popsize amount 200, number of generation 1000, and training data amount 80% of the entire dataset. From the results obtained MAPE, shows that the combined ELM method with genetic algorithm able to minimize the error value in forecasting compared with the ELM method.
Klasifikasi dan Rekomendasi Jurusan Kuliah Bagi Pelajar SMA Menggunakan Algoritme Naive Bayes-WP Restu Fitriawanti; Imam Cholissodin; Ratih Kartika Dewi
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

Each year high school students will be faced with a final choice to determine what direction will be selected for future education. Each choice will determine the future of the voter, and this is something that is difficult enough to be determined by most high school students, because they do not have information and images related to education in college. In addition, the child is still not aware of the interests and abilities on him. Based on the problems above the selection of majors as early as possible should start to be considered because choosing faculty and majors with precisely very difficult, if one chose the department will result in learner in the learning process in lectures, because less comfortable with the materials in the lecture and probably a lot of less-liked material. This will affect the child's achievement index (IP) that can be below the standard and worse the discharge of the student (DODrop Out) because it is declared not able to follow the education that followed. So the classification and recommendation of college majors for high school students who based on academic grades wrote can help high school students to determine the proper choice. The calculation of the study is calculated separately for the Naive Bayes algorithm used to classify student learner data into the faculty class and Weighted Product (WP) is used to help determine the exact majors based on the majors in the faculty predetermined by the Naive Bayes algorithm. By using the Naive Bayes-WP algorithm, the system's average accuracy reaches 82%.
Peramalan Jumlah Pemakaian Air di PT Pembangkit Jawa Bali Unit Gresik dengan Extreme Learning Machine dan Ant Colony Optimization Anim Rofi'ah; Imam Cholissodin; Candra Dewi
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

PT. PJB Unit Gresik using seawater as a steam power plant. Water has advantages such as it is high availability and environmentally friendly. However, seawater requires a refining process in order to be used. Using seawater as a power plant often experiences water-reduction problems caused by certain problems, such a pipeline leakage, tempering, and removal of gases that still contain water so that additional water is required to keep the turbin working. To anticipate the lack of water that can inhibit the process, an intelligent system required to estimate the amount of water that generation process needed. One of forecasting method is Extreme Learning Machine (ELM), to maximize forecasting results with optimization algorithm Ant Colony Optimization that can be used in the optimization input weight and bias of ELM parameters. After optimization process for ELM parameters, then the next process is training and testing to get forecasting result. This study uses 103 data. Based on the research, the optimal parameter number of ants is 40, the parameter range of the input weight is 0 to 1, the using 82 of training data and 21 testing data (80%: 20%), and the maximum iteration is 500. From these parameters obtained the MAPE value for ELM-ACO is 0.170% with 3799.200 ms running time and for the ELM algorithm the MAPE value is 4.851% with 162.400 ms, so the optimization of ELM parameters can improve the forecasting results.
Diagnosis Penyakit Ikan Koi Menggunakan Metode Naive Bayes Classifier Yudo Juni Hardiko; Nurul Hidayat; Imam Cholissodin
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

Koi fish (Cyprinus carpio) is a type of freshwater ornamental fish that is widely cultivated because it has an attractive body shape and color. Koi morphology is almost similar to other fish species, koi body covered by two layers of skin, the outer skin (epidermis) and the skin (dermis). Epidermis is useful as a protective skin from the outside environment or as protection such as impact, dirt, and pest. Disease attacks and parasitic infections are a common problem faced by fish farmers. Diseases that often attack koi caused by pathogens in the form of bacteria, fungi, or viruses. The pathogens that live in the body of koi is very harmful because it will indirectly affect the color of koi fish. Koi fish diseases generally have some common symptoms that are almost the same as excessive mucus, punctured wounds or lumps on the body of fish and koi fish so menyediri. With so many diseases that have the same symptoms it makes fish farmers difficult to diagnose diseases in koi fish. Many methods can be used to create an system one of them is by using the method of Naive Bayes Classifier. In this system receive input in the form of data koi fish disease symptoms and the data is then processed using the method of Naive Bayes the results of system output in the form of diagnosis of diseases and treatment of disease outcomes that are diagnosed. Based on the accuracy testing of 20 data yields an accuracy of 90%.
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