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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 Metode Support Vector Machine (Svm) Untuk Klasifikasi Rumah Layak Huni (Studi Kasus: Desa Kidal Kecamatan Tumpang Kabupaten Malang) Weni Agustina; Muhammad Tanzil Furqon; Bayu Rahayudi
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

House is an important part in the aspect of life. A habitable house that is good to be used is clean, safe, and comfortable. Lack of knowledge about the function of house in the society, more difficult to imply the realization of a habitable house. Government gets difficulty in assessing the habitable house. In fact, the unpreety house has high income. Government's assistance often misplaced, many people complain because of this. To overcome the problem, then the government needs a system that classifies habitable house and inhabitable house. The system for classification of habitable house was made using the Support Vector Machine (SVM) method. this study uses 160 data that is divided into two types that are habitable and inhabitable. The method used is Support Vector Machine (SVM) method is a good classification method. Support Vector Machine (SVM) method is linear, but SVM method can also be used to solve non-linear problem. The experiment result shows an average accuracy of 98,75% using K-fold Cross Validation test method with k = 10, and SVM method parameters are = 0,5, = 0,001, = 1, = 2, maximum iteration = 10 iteration and using the Polynomial of degree kernel.
Sistem Pendukung Keputusan Pemilihan Varietas Unggul Jagung Hibrida Menggunakan Metode AHP-SMART R Moh Andriawan Adikara; Muhammad Tanzil Furqon; Achmad Arwan
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

Corn proudction in Indonesia is still continue to rise. Although in 2016 Indonesian corn production is currently at 17 million tons, it is still behind the United States with 365 million tons in corn production. The amount of corn production will affect the level of exports and imports that directly affect the country's economy. Increased corn production can be done in various ways, one of which is the improvement of cultivation techniques with the use of superior varieties. According to available data from the Indonesian Ministry of Agriculture, there are 100 hybrid corn varieties, but of the many varieties of corn, there are still varieties that have not been able to increase the corn's production significantly. Selection of corn varieties becomes a problem because there are many criteria to consider. Solutions from the selection of corn varieties can be solved by decision support system method called AHP and SMART method. The AHP method is used to give weighting to the criteria used in the SMART method. The SMART method is used to rank superior corn varieties. Both methods are selected for being able to produce accurate and fast computing decisions. The result of the system is in the form of rank of corn varieties ranging from the best to the worst.System validation is done by using Spearman Rank correlation with = 0,99754 which means a relationship between result system and expert result is near perfect.
Penerapan Algoritme Fuzzy K-Nearest Neighbor (FK-NN) Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia Tania Oka Sianturi; 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

One of the psychiatric diseases that affects many Indonesians is schizophrenia. Schizophrenia causes a person to sustain delusions, hallucinations, chaotic thoughts, and behavioral changes. According to Riset Kesehatan Dasar (Riskesdas) in 2013, prevalence of schizophrenia is 1.7% per 1000 people or about 400,000 people. For very wide territory of Indonesia with total population around 237 million, the number of psychologists or psychiatrists about 616 people is still very small. With this limitation, a system that can be used to assist paramedics in diagnosing and classifying psychiatric illnesses of schizophrenia. In this study applied fuzzy K-nearest neighbor algorithm to diagnose and classify psychiatric illness of schizophrenia. Types of schizophrenia used in this study are paranoid schizophrenia, hebephrenic schizophrenia, catatonic schizophrenia, undifferentiated schizophrenia, and simple schizophrenia. The classification process consists of three processes are the fuzzy initialization process, the K-nearest neighbor algorithm process, and the fuzzy K-nearest neighbor algorithm process. The testing consists of the effect of K value and the effect of K-Fold. Based on the test results on the K value, obtained the highest accuracy of 38.33% at K=5. The effect of K-Fold test results obtained the highest average accuracy of 34.17% at K-Fold= 10.
Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal Ratna Ayu Wijayanti; 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

Kidney failure is a condition that the kidneys can not function properly. Worldwide cases of kidney failure are on the rise every year is chronic renal failure. In Indonesia the disease sufferers of chronic kidney failure are categorized as very high. According to data from the penetri (Union of Netrologi Indonesia) was estimated at 70 thousand kidney failure chronic disease sufferers. To help knowing the status of kidney function someone, we made an intelligent system using support vector machine (SVM) algorithm for classification of risk of kidney failure and using one-againts-all strategy. The flow of research those are using correlation analysis to look at the relationships between features, with normalization for data values are at the same interval, the calculation kernel RBF, do the training process with sequential training, then use one-againts-all for the process ofclassification. This study The final test result of this research obtained the average value of accuracy is 83,998% and the highest accuracy is 98,33% using the ratio of data 80%: 20%, with the parameter value of λ (lambda) = 1, γ (gamma) = 0,0001 , σ for kernel RBF = 2, C (Complexity) = 0,0001 and the number of iterations =100. Based on these results it can be concluded that the SVM algorithm and strategy one-againts-all can be used for classification of risk of kidney failure.
Penerapan Algoritme Support Vector Machine (SVM) Pada Pengklasifikasian Penyakit Kucing Jumerlyanti Mase; Muhammad Tanzil Furqon; Bayu Rahayudi
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

Kucing merupakan hewan peliharaan yang sering ditemukan di masyarakat. Pemeliharaan kucing memerlukan perhatian yang besar agar kucing tidak terserang penyakit yang dapat membahayakan kucing, pemiliknya ataupun orang yang melakukan interaksi langsung dengan kucing tersebut. Penyakit pada kucing biasanya disebabkan oleh virus, bakteri atau jamur. Kemiripan gejala yang muncul pada penyakit kucing membuat masyarakat umum sulit mendeteksi penyakit yang menyerang kucing tersebut. Sehingga dibutuhkan sistem yang dapat membantu pengklasifikasian terhadap gejala penyakit yang timbul pada kucing untuk mendiagnosis penyakitnya dengan tepat. Sistem yang digunakan untuk pengklasifikasian penyakit kucing ini mengunakan algoritme Support Vector Machine (SVM) dengan menerapkan strategi One-Against-All untuk permasalahan multi class. Penelitian ini menggunakan 220 data dengan 9 hasil klasifikasi yaitu Scabies, Gastritis, Helminthiasis, Rhinitis, Dermatitis, Dermaphytosis, Otitis, Enteritis dan kucing sehat. Hasil akurasi yang dihasilkan oleh sistem ini dengan menggunakan perbandingan rasio data 90% : 10% dan kernel RBF adalah 80,2%. Dengan hasil akurasi yang baik, maka penelitian ini dapat diterapkan untuk membantu melakukan pengklasifikasian penyakit kucing dengan menggunakan algoritme Support Vector Machine (SVM).
Clustering Titik Panas Bumi Menggunakan Algoritme Affinity Propagation Barik Kresna Amijaya; Muhammad Tanzil Furqon; Candra Dewi
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

Forest and land fires are catastrophic and can disrupt the activity of living things around the fire location. Forest and land fires should be prevented by knowing the cause of the fire. One of the ways of fire prevention is to monitor the hotspot. The hotspot is an area where the temperature is relatively higher compared to the area around which the satellite is detected. The area is represented in a point that has certain coordinates. hotspot needs to be grouped or clustered to know the similarity of each point and easy to do monitoring. Clustering is the process of grouping data into clusters, so that objects that exist within a cluster have a high similarity with each other and very different from the objects that exist in other clusters. Affinity Propagation method is a method used to perform data grouping by specifying the exemplar as data centers. Affinity Propagation performs clustering by searching for responsibility value and availability of each data to find the right exemplar. In this research has done clustering using Affinity Propagation with the best silhouette coefficient value that is 0.317818 with 125 data and formed 44 clusters.
Klasifikasi Pendonor Darah Menggunakan Metode Support Vector Machine (SVM) Pada Dataset RFMTC Erwin Bagus Nugroho; Muhammad Tanzil Furqon; Nurul Hidayat
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

Blood donation is a process of taking blood from a person voluntarily to blood transfusions for patients in need. Blood from donors can't be used after 42 days. The only way to meet the demand for blood bags are having regular blood donations from a healthy donor. In Indonesia 2013, there is 2,476,389cc shortage of blood bags, where the ideal blood availability is 2.5% of the population. These problems required a system that can predict the behavior of donors in order to anticipate the shortage of blood bags. Regency, Frequency, Monetary, Time, Churn Probability (RFMTC) is a modification of the RFM method modified that used to predict the blood donor behavior to donate or not to donate bloods. The method for classifying the behavior of donors in this research are Support Vector Machine (SVM) method. Data that was used in this research is 748 which is divided into training data and test data. The accuracy result got best accuracy based on 50%: 50% data ratio, using linear kernel and parameter value λ (lambda) = 2, Gamma (γ), = 0.5, Epsilon (ε) = 0.005, and C (complexity) = 20. The result of SVM method accuracy on blood donor classification is 72.64%.
Prediksi Intensitas Curah Hujan Menggunakan Metode Jaringan Saraf Tiruan Backpropagation Defanto Hanif Yoranda; Muhammad Tanzil Furqon; Mahendra Data
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

The intensity of rainfall is quite difficult to predict. Many things can be the factor of rainfall, such as temperature, wind speed, humidity, air pressure, and others. This rainfall factor is a major component that is difficult to predict and calculated, therefore rainfall forecasting is a very interesting thing to discuss, because it will be very useful for various things. Many forecasting methods can be used for forecasting, such as the Backpropagation Neural Network used in this study. This research will use time-series data, monthly rainfall data obtained from Kab. Ponorogo. The best result of this research is test MAPE of 20.28% obtained from training using data from Balong rain gauge station. The training process uses 10 neurons on the input layer, training data from 1997 to 2015, test data in 2016, 40 neurons on the hidden layer, a MAPE limit of 20%, and a maximum of 200000 iterations. Test MAPE is classified as not very well and too high due to there are many 0 values in the data.
Implementasi Algoritme Modified-Apriori Untuk Menentukan Pola Penjualan Sebagai Strategi Penempatan Barang Dan Promo Vania Nuraini Latifah; Muhammad Tanzil Furqon; Nurudin Santoso
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

Sales pattern is one of the methods that can be used to determine sales strategy such as the products placement and promo, by seeing on how often an item purchased simultaneously in a retail store. Data mining is used for analyzing the big data to find inter-data connection and to generate useful informations for the users. So, this study used sales transaction data to determine sales pattern by using association rule and algorithm Modified-Apriori. Association rule is a method used for finding unique connection hid in big data by using the calculation of the value of support and confidence. Algorithm modified Apriori is the development of the Apriori algorithm which searches frequent itemset and joining and pruning process, then as a result, it produces faster time efficiency by using HashMap technique instead of Apriori algorithm. The results obtained from this study are the highest value of the minimum support is 9% and the highest value of minimum confidence is 80%. The length of the itemset are 2-itemset and 3-itemset. Test which used lift ratio generates rule which has value of more than 1.
Penerapan Algoritme Modified K-Nearest Neighbour Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia Anjelika Hutapea; 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

Schizophrenia is a disease that has a soul crack or a splitting of personality. Problems of mental disorder almost in all countries in the world. Schizophrenia has 5 types: Paranoid, Hebephrenic, Catatonic, Unspecified and Simplex.The similarity of symptoms causes the paramedic difficulty to determine the class of Schizophrenia. Therefore, a system that aims to Schizophrenia classification by applying one of the classification method Modified K-Nearest Neighbor (MKNN). The system will perform the step by calculating the distance of assymetric binary, the calculation of the validity value and weight voting value in order to get the final result that will be used to determine the classification based on the value of K that has been determined. The testing of this system consist of testing the effect of K value and testing the influence of K-Fold value. The result of testing the effect of K value gives the greatest accuracy equal to 37,045% at K=7 and K-Fold=10. The result of testing the effect of K-Fold value gives the greatest accuracy of 28,4462% at K-Fold=5.
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