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All Journal Journal of Environmental Engineering and Sustainable Technology Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JOURNAL OF APPLIED BUSINESS ADMINISTRATION Indonesian Journal of Electrical Engineering and Computer Science
Muhammad Tanzil Furqon
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Social Sciences

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Pelatihan Feedforward Neural Network dengan Particle Swarm Optimization dalam Memprediksi Pertumbuhan Penduduk Kota Malang Andini Agustina; Muhammad Tanzil Furqon; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is the fourth most populous country in the world. With a large population, Indonesia is not immune to population problems. This happens because the rate of population growth is not accompanied by the provision of clothing, food, and shelter. In other words, the amount of population growth is not balanced with the availability of natural resources, services, and existing facilities. Therefore, predicting population growth is expected to help the government to overcome population problems. This paper will be using Feedforward Neural Network trained by Particle Swarm Optimization (PSO). PSO algorithm is considered to be able to overcome the weaknesses of the Backpropagation algorithm in training networks. In this study, the predicted error rate is calculated using Mean Average Percentage Error (MAPE). The smallest MAPE results obtained were 0,1599% using 6 input neurons, 4 hidden neurons, 1 output neurons in the network architecture, and the dataset used is the population of Malang City from January 2009 to June 2019. The MAPE results showed that PSO is able to train Feedforward Neural Network to predict the population growth of Malang City.
Implementasi Metode Linear Discriminant Analysis (LDA) Untuk Klasifikasi Pengambilan Mata Kuliah Pilihan Ayu Anggrestianingsih; Agus Wahyu Widodo; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Faculty of Computer Science is one of the best faculties at Brawijaya University. The Faculty of Computer Science (FILKOM) has several majors, one of which is the Informatics major which is most in demand by FILKOM students. When students enter semester 5, they are required to choose interests that are in accordance with their abilities. There are a variety of fields of study offered, one of which is artificial intelligence, which in this study, will focus on taking electives of interest in the field of Artificial Intelligence, to help students choose interests in accordance with their abilities. In this study, using the Linear Discriminant Analysis (LDA) method in order to get good accuracy for taking courses. The training data used as many as 30 data, where each class consists of 15 data for yes classes and 15 data for classes not with 5 elective courses. Then obtained accuracy values ​​for each elective course such as 40% Fuzzy Logic, 40% Decision Support System, 80% Digital Image Processing, 20% Evolution Algorithm and 40% Expert System
Perbandingan Metode Exponential Smoothing Untuk Peramalan Penjualan Produk Olahan Daging Ayam Kampung (Studi Kasus : Ayam Goreng Mama Arka) Dzar Romaita; Fitra Abdurrachman Bachtiar; Muhammad Tanzil Furqon
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

Ayam Goreng Mama Arka is a culinary business of Ayam Kampung meat products that are currently developing. Uncertain customer demand is a challenge for Ayam Goreng Mama Arka to determine the right sales strategy in order to maximize profits and minimize losses Therefore, a customer demand forecasting system is made using the Exponential Smoothing method. In this research, comparing the accuracy of 3 methods of Exponential Smoothing, namely: Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES) using Mean Absolute Error (MAE). The data used are sales results from January 2019 to November 2019 for 5 products,namely ayam goreng laos, ayam bakar kecap, ayam bumbu rujak, ayam frozen laos, and ayam frozen rujak. Based on the test results for one of the products namely ayam bakar kecap, the smallest value obtained in TES method when a=0.3, b=0.2, g=0.7 with value of 2.45, while the largest MAE value obtained in DES method when a=0.3, b=0.1 with value of 2.74. Overall, forecasting with TES show the best result for 3 products, the smallest MAE value is 2.45 which obtained in ayam bakar kecap products, so it can be concluded that the most accurate method for forecasting ayam kampong meat products is the Triple Exponential Smoothing method.
Implementasi Regresi Linier Berganda Untuk Prediksi Jumlah Peminat Mata Kuliah Pilihan Nur Kholida Afkarina; Agus Wahyu Widodo; Muhammad Tanzil Furqon
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

Higher education is a continuation of education at a higher level after completing secondary education. With so many elective courses with each interest, it makes students have difficulty in knowing the number of interested ones. To overcome the problems that exist, this study predicts the number of interested subjects with multiple linear regression methods. Therefore we need a system to predict the number of interested subjects. There are two features used, namely the average student score in the previous year, the number of interested ones in the previous year. The method used is multiple linear regression. The training data used to determine the number of interested parties in taking courses is the 2013-2017 student data. As for the test data using student data for 2018-2019. From this research, the predicted MAPE score of Fuzzy Logic (2017) is 61.52% and in 2016 is 49.64%
Implementasi Metode Modified K-Nearest Neighbor untuk Klasifikasi Status Gunung Berapi Fikar Cevi Anggian; Nurul Hidayat; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 12 (2019): Desember 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia has numerous disaster-prone area, therefore, it is known as a 1001 disaster country. Natural disasters are inevitable, nevertheless, the impact can be minimized with proper anticipation. One of the disasters that often occur in Indonesia is volcanic eruption. Indonesia has 127 active volcanoes that are ready to erupt anytime. Indonesia is also known to contribute to around 30% of the world's volcanoes, and they are located near the residential areas. Casualties are often found in every volcanic eruption due to lack of anticipation from residents who live nearby the volcano. To minimize life and material loss, early warning is needed to provide quick and accurate notification of the volcano. This research used the Modified K-Nearest Neighbor method to classify volcano status. The data used are 110 data obtained from the official government agency that authorized to issue volcanic status, known as Pusat Vulkanologi dan Mitigasi Bencana Geologi (PVMBG). The test was carried out using various k values, namely 3,5,6,7, and 9. The highest accuracy obtained in this research was 86.87%, and the average accuracy was 82.87%.
Pembentukan Daftar Stopword menggunakan Zipf Law dan Pembobotan Augmented TF - Probability IDF pada Klasifikasi Dokumen Ulasan Produk Destin Eva Dila Purnama Sari; Yuita Arum Sari; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stopword is an insignificant word contained in a sentence. Stopword was used to help the text preprocessing stage, especially in the stopword removal stage. Digital library was often used at this stage to get a stopword list. However, not all stopword lists in the digital library were words that were not important in the data. The main focus in this research was to find out forming stopword list and word weighting on the document classification of product review using the Zipf Law method. The method used for word weighting was Augmented Term Frequency - Probability Inverse Document Frequency. The document classification process aimed to find out the effect of forming stopword list and word weighting. Document classification using the Support Vector Machine algorithm and Polynomial Kernel. The output of the research was the result of classification accuracy. Based on the result of classification accuracy, there was an effect of forming a stopword list and weighting of words on the classification result. The best accuracy result of the document classification was found at a percentage of 15% for forming stopword list taken from term that has low constant result. The resulting accuracy consisted of a precision value of 0.73, a recall value of 0.7 and a f-measure value of 0.63.
Prediksi Laju Pertumbuhan Penduduk Menggunakan Metode Support Vector Regression (Studi Kasus: Kota Malang) Arynda Kusuma Dewi; Muhammad Tanzil Furqon; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Population growth rate is a changes population every year in a region. The high population growth rate in Indonesia is something important because it has impact on the economic, social, politic and national defense. Therefore, related parties such as Dinas sosial and BKKBN analyze the factors which related with population growth rate, so it can make some policies to realize balance of population growth. Beside that, population growth prediction is also used by Dispendukcapil to make other budget plans and other needs. In this study, population growth rate is predicted using Support Vector Regression method by comparing the performance of linear kernels with Gaussian kernel RBF used population growth dataset time series in March 2013 until December 2018. The steps to predict population growth rate begin with data normalization, SVR training to get the update lagrange multiplier value and SVR testing to get prediction results and error rates using MAPE. The test results obtained by the MAPE value using a linear kernel 0.0985% and 0.38192% using the Gaussian RBF kernel.
Prediksi Pertumbuhan Penduduk di Kota Malang menggunakan Metode Extreme Learning Machine (ELM) Inas Hakimah Kurniasih; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 2 (2020): Februari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dinas Kependudukan dan Pencatatan Sipil (Dispendukcapil) in Malang City is tasked with provide public services in terms of civil registration such as making electronic Kartu Tanda Penduduk (e-KTP) and birth certificates. Dispendukcapil prepared this by planning for the target needs and predicting the population in the next 5 years, but the error value is unknown. This research helps predict with a small error value using Extreme Learning Machine (ELM) method and calculates the error value using Mean Absolute Percentage Error (MAPE). Based on the results of testing implementation and analysis, using data from 2009 to 2019 obtained MAPE error value of 0.498% and runtime 1.166 seconds with the use of 3 input neurons, 5 hidden neurons, binary sigmoid, as well as 50 training data and 66 testing data. Then, in implementation of testing using data from 2012 to 2019 obtained MAPE error value of 0.117% and runtime 1.227 seconds with the use of 3 input neurons, 6 hidden neurons, binary sigmoid, as well as 70 training data and 4 testing data.
Implementasi Metode Extreme Learning Machine (ELM) untuk Memprediksi Jumlah Debit Air yang Layak Didistribusi (Studi Kasus: PDAM Kabupaten Gowa Makassar) Putri Indhira Utami Paudi; Muhammad Tanzil Furqon; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PDAM Gowa Regency, Makassar City is a company under the government that carries out the process of water production and continues to distribute the PDAM water to home residents. If there is a lot if water produced, it means theres is also a large amount of water that available for PDAM, so it can fulfill the public's requirement and can even to add customers. However, the seasonal change factor can take effect the discharge of water produced. So, the main problem is the uncertainty of water production which will certainly have an impact of the PDAM water distribution that will be distributed to home residents. But not all the water produced can be distributed because it has to go through several stages of water quality checking, so that the water that's distributed is in accordance with the standarts set by the government. Therefore, preduction of a proper flow of water distributed by PDAM is needed, with the aim that PDAM can adjust the proper flow of water distributed to customers. This research applies Extreme Learning Machine (ELM) method to forecast using single variable dan multivariate data types. The process of applying the ELM methods are normalizing, process of training and testing, denormalizing, and evaluating the prediction results using Mean Percentage Absolute Error (MAPE). Depend on the application of the ELM method and the testing process, it produces the best conditions of single data variable when using 7 input neurons, 4 hidden neurons, 20 training data and 5 testing data to produced an average MAPE of 3.938%, while using the multivariate data, the average MAPE was 13.081% using 4 hidden neurons, 30 training data and 5 testing data.
Sistem Prediksi Pertumbuhan Jumlah Penduduk Kota Malang menggunakan Metode K-Nearest Neighbor Regression Diajeng Sekar Seruni; Muhammad Tanzil Furqon; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Population growth rate in Indonesia keeps growing each and every year. The rapid growth of population may give impacts on many aspects of the country, such as economic development, quality of life and health issues of the residents, and even educational problems. To anticipate the negative effects of population growth, projection of future population is needed as to help the government to develop city development plans. K-Nearest Neighbor (KNN) is one of many methods that could be used to predict future values, be it for classification or regression. KNN Regression is a KNN algorithm used for regression or forecasting problems. In this study, the KNN Regression method is implemented to forecast future population of Malang city, using a time series of monthly population growth consisting of 73 datas in total. The forecasting method starts with preprocessing the time series, calculate the distance between each training and testing data, and estimate the predicted value based on k nearest neighbors. From the testings done in this study, the lowest Mean Absolute Percentage Error (MAPE) value obtained is 0,02526%.
Co-Authors Abas Saritua Gultom Abu Wildan Mucholladin Achmad Arwan Achmad Ridok Adinda Chilliya Basuki Adinugroho, Sigit Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Eriq Ghozali Al-Mar'atush Shoolihah Aldion Cahya Imanda Amalia Luhung Andini Agustina Anindya Celena Khansa Kirana Anjelika Hutapea Annisya Aprilia Prasanti Annisya Aprilia Prasanti Ardisa Tamara Putri Arief Andy Soebroto Arif Indra Kurnia Arina Rufaida Arinda Rachman Arjun Nurdiansyah Arya Perdana Arynda Kusuma Dewi Aryo Pinandito Aryu Hanifah Aji Asfie Nurjanah Audi Nuermey Hanafi Ayu Anggrestianingsih Barik Kresna Amijaya Bayu Rahayudi Bayu Rahayudi Bossarito Putro Brillian Ghulam Ash Shidiq Budi Darma Setiawan Candra Dewi Cusen Mosabeth Daniel Alex Saroha Simamora David Bernhard Defanto Hanif Yoranda Dendry Zeta Maliha Destin Eva Dila Purnama Sari Desy Andriani Diajeng Sekar Seruni Dian Eka Ratnawati Dwi Yana Wijaya Dyan Dyanmita Putri Dyang Falila Pramesti Dzar Romaita Edy Santoso Eko Ari Setijono Marhendraputro Eky Cahya Pratama Elan Putra Madani Erwin Bagus Nugroho Evilia Nur Harsanti Fadhilla Puji Cahyani Fahmi Achmad Fauzi Fajar Pradana Fatwa Ramdani, Fatwa Fernando Parulian Saputra Fikar Cevi Anggian Firdaus Rahman Fitra Abdurrachman Bachtiar Gabriel Mulyawan Ghulam Mahmudi Al Azis Guntur Syafiqi Adidarmawan Hangga Eka Febrianto Hanifa Maulani Ramadhan Hanifah Khoirunnisak Hugo Ghally Imanaka Humam Aziz Romdhoni I Gusti Ngurah Ersania Susena Imam Cholissodin Iman Harie Nawanto Imaning Dyah Larasati Inas Hakimah Kurniasih Indra Eka Mandriana Indri Monika Parapat Indriana Candra Dewi Indriati Indriati Inggang Perwangsa Nuralam Issa Arwani Jojor Jennifer BR Sianipar Julita Gandasari Ariana Jumerlyanti Mase Kevin Nadio Dwi Putra Khaira Istiqara Laila Diana Khulyati Lailil Muflikhah Listiya Surtiningsih Luthfi Faisal Rafiq M. Ali Fauzi Mahardhika Hendra Bagaskara Mahendra Data Maria Sartika Tambun Marji Marji Masayu Vidya Rosyidah Mochamad Ali Fahmi Muh. Arif Rahman Muhamad Fahrur Rozi Muhammad Aghni Nur Lazuardy Muhammad Iqbal Mustofa Muhammad Rafif Al Aziz Muhammad Riduan Indra Hariwijaya Muhammad Wafiq Naufal Sakagraha Kuspinta Nindy Deka Nivani Novanto Yudistira Nur Kholida Afkarina Nurdifa Febrianti Nurudin Santoso Nurul Hidayat Nurul Hidayat Nurul Ihsani Fadilah Ofi Eka Novyanti Oky Krisdiantoro Pangestuti, Edriana Pricielya Alviyonita Priyambadha, Bayu Putra Pandu Adikara Putri Indhira Utami Paudi R Moh Andriawan Adikara Raden Rafika Anugrahning Putri Raditya Rinandyaswara Rahman Syarif Randy Cahya Wihandika Ratna Ayu Wijayanti Restia Dwi Oktavianing Tyas Ridho Ghiffary Muhammad Rifaldi Raya Rifwan Hamidi Rimba Anditya Kurniawan Riski Nova Saputra Riza Rizqiana Perdana Putri Rizal Setya Perdana Robbiyatul Munawarah Romlah Tantiati Satrio Hadi Wijoyo Setyoko Yudho Baskoro Silvia Aprilla Sutrisno Sutrisno Tania Oka Sianturi Taufan Nugraha Teri Kincowati Tryse Rezza Biantong Ulva Febriana Vandi Cahya Rachmandika Vania Nuraini Latifah Vera Rusmalawati Vianti Mala Anggraeni Kusuma Weni Agustina Wildan Afif Abidullah Wildan Ziaulhaq Wildan Ziaulhaq Wilis Biro Syamhuri Yuita Arum Sari