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All Journal Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer 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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Implementasi Association Rule Mining Untuk Menentukan Menu Paket Makanan Dengan Algoritma FIN Menggunakan Nodesets (Studi Kasus R.M. Lesehan Nova Sragen) Riski Nova Saputra; Muhammad Tanzil Furqon; Indriati Indriati
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

Restaurant of Lesehan Nova Sragen has a variety of menu items are quite a lot of 116 food menu items and 44 drink menu items. Due to the high variations of menu items makes consumers take longer to select order menu items. Author provides solutions to restaurant's owner to create a menu package based on consumer history in selecting menu items. So as to improve restaurant service to consumers. To create package menu author using FIN algorithm. Fin algorithm is used to perform mining frequent itemset to sales transaction data. Fin algorithm is implemented on automatic package menu builder system. Based on test results, minimum value of support = 11 has resulted in proportional package menu variation ,and has been representative with consumer choice. Variation number of resulting package menu is 6 variations of package menu.
Perbandingan Algoritme K-Means Dengan Algoritme Fuzzy C Means (FCM) Dalam Clustering Moda Transportasi Berbasis GPS Rahman Syarif; Muhammad Tanzil Furqon; 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

Transportation has become a basic necessity for today's society. But often the need for transportation is not followed by information on the availability of transportation in a certain place. In this case, data from GPS can be used to group the available modes of transportation and provide information on the number of each mode of transportation scattered in a certain place and time. Algorithm used to group modes of transportation in this research is K-Means and Fuzzy C Means (FCM). These two algorithms then compared which one with the better result. The transportation mode grouping on the K-Means algorithm is obtained from the smallest distance of the transport mode data with the cluster center. Whereas in the FCM algorithm, grouping is obtained from the greatest degree values. After 10 times testing, obtained an average of K-Means accuracy of 58.46154 and 70.86538 for FCM algorithm. While for the silhouette Coefficient value obtained an average of 0.4582670 for K-Means and 0.440682 for FCM algorithm. From the results, it was concluded that the FCM algorithm is superior to K-Means.
Sistem Pendukung Keputusan Penentuan Prioritas Perbaikan Jalan Menggunakan Metode AHP-TOPSIS (Studi Kasus: Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Ponorogo) Firdaus Rahman; Muhammad Tanzil Furqon; Nurudin 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

Road infrastructure has an important role in supporting the economic, social and cultural sectors. Approximately 49.12% of the total district road length in 2013 is included in the good category, 24.16% included in the medium category, slightly damaged 16.64% and heavily damaged 10.08%. The strategic target of the Directorate General of Highways is the stability of the road area reaches 70%. In the implementation of road improvements by the Office of Public Works and Spatial Planning of the Ponorogo District, it is necessary to consult the priority of road improvement. Modifying road priority priority with AHP (Analytical Hierarchy Process) method - TOPSIS (Preferential Technique with Equalization with Ideal Solution) combines several interrelated factors in road improvement process including: good road conditions, road conditions, defective road conditions, road conditions severely damaged, daily traffic, access, secrets of segments and regent policies. From this study. The low level of system required in the case of there are still some individual interests so there is still mismatch target in the implementation of existing roads.
Klasifikasi Kualitas Susu Sapi Menggunakan Algoritme Support Vector Machine (SVM) (Studi Kasus: Perbandingan Fungsi Kernel Linier dan RBF Gaussian) Arif Indra Kurnia; Muhammad Tanzil Furqon; Bayu Rahayudi
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

Cow milk has a lot of animal protein and have benefit for children and whoever in process for grow up. Cow milk contains good essential amino acids. Malang Animal Health Laboratory as the unit executor in east java Animal Husbandry Department do a test in kesmavet for efforts to secure milk as a farm product with appropriate testing in suitable with the Indonesian National Standard (SNI). The classification of cow milk quality is still using organoleptic (smell, taste, color) that are linguistic, so that variable and parameter are uncertain and become themain obstacle of expert in determining good milk quality. To resolve this issue, this can be done with schizophrenia classification using support vector machine (SVM) algorithm, which SVM performace is more suitable than other classification methods. In this study there are 269 data that is divided into two data that is data training and data testing with three classification result, that is low, medium, and hight. The result in this paper get the best acuracy based K-Fold Cross Validation as much 10 fold, with Kernel RBF and Kernel Linear with value λ (lambda) = 0,0001, C (complexity) = 1, γ (gamma) =0,0001, maximum iteration = 30 and σ kernel RBF= 10. The highest accuracy using SVM method in cow milk quality classification use Kernel RBF was 96% and the highest accuracy use Kernel Linear was 62%.
Sistem Pendukung Keputusan Penentuan Prioritas Pemeliharaan Jalan Menggunakan Metode PROMETHEE II (Studi Kasus: Dinas Pekerjaan Umum Dan Penataan Ruang Kabupaten Ponorogo) Mahardhika Hendra Bagaskara; Muhammad Tanzil Furqon; Sutrisno Sutrisno
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

Road infrastructure has an important role in supporting the economic, social and cultural sectors. The government as the road operator must prioritize the maintenance of the road periodically in accordance with the minimum service standards set. The strategic target of Directorate General of Highways one of them is the stability of the road that reaches 70%. Due to the number of broken roads, the number of complaints from the public, and the limited budget, the government must have priority on which roads should receive maintenance. The selection of road segments implemented by Ponorogo District Office of PUPR can be done by applying PROMETHEE II method to consider several alternatives and get the best alternative ranking based on the aspect of steady road conditions, unsteady road conditions, LHR, access and inter-district liaison. The method of Promethee II performs calculations with several stages: weighting, multicriteria preference index calculation for 4 types of preference ie, usual, quasi, linear, and the level and calculation of leaving flow, entering flow, and netflow. Based on the test, the highest accuracy on the use of the usual and quasi type of preference is 55.56% and the lowest accuracy on the use of linear preferences type is 45.83%. The degree of accuracy in testing is influenced by the weights used for each of the criteria and the type of preferences used in the calculation process.
Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Exponential Smoothing (Studi Kasus : PDAM Kota Malang) Bossarito Putro; Muhammad Tanzil Furqon; Satrio Hadi Wijoyo
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 a natural resource that needed by all living things. Humans, animals, and plants need water to survive. Unlike animals and plants, humans need clean water to survive. Becoming a challenge for all PDAM Indonesia to keep sufficient number of demand for clean water supply, not to mention PDAM Malang City. Prediction done in this research use Exponential Smoothing method. Exponential Smoothing is a method that continuously performs forecasting improvements by taking the average value of smoothing past values from time expanding data in exponential way. In this research, we will compare three Exponential Smoothing methods: Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES) used to obtain prediction result and evaluate prediction result with Mean Absolute Percentage Error (MAPE). The smallest MAPE was obtained when using Single Exponential Smoothing (SES) method when value ɑ = 0.2 with MAPE value 3.992, Double Exponential Smoothing (DES) method when value ɑ = 0.1 with MAPE value 4.932, and Triple Exponential Smoothing method TES) when the value of ɑ = 0.1, β = 0.1, and γ = 0.6 with MAPE value of 6.733. With the MAPE value below 10, the Exponential Smoothing method to predict the amount of water requirement included into the category is very good.
Klasifikasi Penyakit Skizofrenia dan Episode Depresi Pada Gangguan Kejiwaan Dengan Menggunakan Metode Support Vector Machine (SVM) Silvia Aprilla; Muhammad Tanzil Furqon; Mochammad Ali Fauzi
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

Psychiatric disorders are disorders of the human brain that is not normal or different from people in general. There are many types of psychiatric disorders. Schizophrenia and Depression are a type of psychiatric disorders suffered by many people. There are also types of Schizophrenia and Depression, one type of disease in each is Schizophrenia Hebephrenic and Psychotic Depression. According to data in the soul hospital of Dr. Radjiman Wediodiningrat Lawang, both of these diseases are included in the top 10 diagnoses of outpatient and outpatient illnesses in 2017 which reached over 22.000 people. Due to a large number of patients affected by the disease, soul hospital needed a system that can classify Schizophrenia Hebephrenic and Psychotic Depression Disease. Classification is the manufacture of a model that used to make a group for an object with the same characteristics into a determined class. To classify the disease used support vector machine (SVM) algorithm with the polynomial of degree 2 kernel. The data used are 200 data taken from soul hospital of Dr. Radjiman Wediodiningrat Lawang. This data consists of 80% data training and 20% data testing. The test method used is K-fold cross-validation. Based on the results of testing SVM parameters obtained the highest average accuracy is 79% with the value of γ = 0,00001, λ = 0,1, C = 0,01, max iteration = 150, and ɛ = 1.10-10.
Implementasi Algoritme Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Dengan Gejala Demam Nurul Ihsani Fadilah; Bayu Rahayudi; Muhammad Tanzil Furqon
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

Infectious disease in humans have one of the general indications, that is Fever. There are three diseases with symptoms of fever that transmission of disease occurs by the media Arthropod-borne disease, such as dengue fever, malaria, and typhoid. The disease has almost the same clinical symptoms, that is difficult to make a diagnosis of the disease suffered by the patient. Because of a large number of patient and a high risk of death in this disease, need a system that can distinguish these three diseases quickly and precisely. To solve the problem, the system is needed to classify the disease with fever symptoms using the Support Vector Machine (SVM) algorithm. This research uses 130 datasets that have 15 parameters. The dataset is divided into train data and test data by using K-Fold Cross Validation method, with k=10. The final result from SVM algorithm implementation for disease classification with symptoms of fever is the accuracy of the system capabilities in classifying dengue fever class, malaria class, and typhoid class. So, the best average value of accuracy in this implementation is 99.23%, using k-fold cross validation, with k=10, division of data ratio=90%:10%, and the parameters used are lamda=0.5, gamma=0.01, C(Complexity)=1, epsilon=0.0001, maximum iteration=20.
Optimasi Rute Multiple Travelling Salesman Problem Pada Distribusi Es Batu Dengan Algoritme Artificial Bee Colony (ABC) Muhammad Aghni Nur Lazuardy; Imam Cholissodin; Muhammad Tanzil Furqon
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

The distribution is done to improving the productivity of the company. A strategy in the process of distribution is required primarily in determining the distribution route. An optimal route is essential in product distribution especially ice cubes. A company needs to send its products to multiple addresses, because the numbers of shipping addresses and varying distances creates a problem such as needing a long time to reach the destination. In solving these problem need a system that has a purpose to help the distribution process with the number of sales more than one, the problem is named Multiple Travelling Salesman Problem (M-TSP). One of the methods that can solve the problem of M-TSP is Artificial Bee Colony (ABC) algorithm which compared to other algorithm based on swarm intelligence. The initial process of ABC algorithm looks for random ice cubes distribution routes based on customer's ordering data. Furthermore swapping and insertion route is done then taken the route with optimal fitness. The last is comparison with the initial route whether the result is better or not. The test result show the numbers of optimal parameters are 23 size problems, 80 pop sizes, 10 limits, and 600 iterations. From these parameters obtained average fitness value based on system optimization of 0,078163 and manual selection of routes the sales goes through obtain average fitness value of 0,043472, with the result that path selection can be optimized by system.
Prediksi Harga Pasar Daging Sapi Di Kota Malang Dengan Menggunakan Metode Extreme Learning Machine (ELM) Cusen Mosabeth; Muhammad Tanzil Furqon; Randy Cahya Wihandika
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

Beef is one of the basic needs whose existence is greatly increased in Indonesia. The need to consume beef is very sharp in proportion to the increase in population and the awareness of the importance of consuming very high nutritious foods. Basically the need for animal protein cannot be replaced with other proteins. Estimating future consumer demand by making production plans a challenge for an industry. This makes predictions play an important role. Effective and efficient design must be supported by an accurate prediction system. ELM Is an artificial neural network consisting of feed-forward with one or hidden layer-forwad neural. Therefore, in this study the author uses the Extreme Learning Machine (ELM) method. The experimental results showed that the ELM method had a good error measured by the Mean Absolute Percentage Error (MAPE) error rate of 0.344% using the ratio of the training data 90%: 10%, the input weight range between -1 and 1, the number of neurons in the hidden layer 7, then use the binary sigmoid activation function, and use the number of features 3. The results are proved by using the method of Extreme Learning Machine can predict the price of beef with accurate and precise and get the price of beef in the future.
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 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 Yuita Arum Sari