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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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Prediksi Nilai Harga Patokan Batu Bara (HPB) Untuk Merek Dagang Gunung Bayan I dengan Metode Extreme Learning Machine (ELM) Evilia Nur Harsanti; Muhammad Tanzil Furqon; Putra Pandu Adikara
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

Coal is a fossil fuel that is often used by industrial companies as a source of energy and power as a raw material for steelmaking. Coal is obtained by industrial companies through a sale and purchase transactions conducted with coal mining companies. Price is a major factor in the transaction process, because industrial companies need to design an expenditure budget every month before making a transaction. Budget design is done to maximize the company's money to meet all the needs of the company. Therefore, the prediction of coal price will be very beneficial for industrial companies that will buy coal products to know the estimated price in the future. The method used to make the prediction process is the method of Extreme Learning Machine (ELM). ELM has the advantage of fast computing time and small error rate, so ELM does not require a long time in the learning process. Based on the result of research, the best Means Absolute Percentage Error (MAPE) score is 3,926804% for training process and 7,360343% for testing process.
Sistem Pendukung Keputusan Pemilihan Skuad Utama Tim Bola Voli Menggunakan Metode AHP-TOPSIS Hangga Eka Febrianto; Muhammad Tanzil Furqon; Sigit Adinugroho
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

Exercise is one of the physical activities that a person does to maintain and mprove the quality of health. One of the most widely played sports is volleyball. Brawijaya University, which is one of the major universities in Malang, currently also has several volleyball teams organized by UB Volleyball Activity Unit (UABV-UB). In every year UABV-UB always receives new member registration for students who want to join. Seeing the development and the amount of nterest then this makes UABV-UB having difficulty in choosing the players. So in this to solve the problem is used method AHP-TOPSIS. The AHP method is used for weighting which consists of making matched pair matrices, calculating matrix normalization, computing consistency test and producing krteria weight. While Topsis consists of paired normalization process of alternative data, after calculating the weighted normalization value of AHP and paired normalization process TOPSIS. The weighted normalization value will be used to find the positive and negative deal solution value as well as the distance between positive and negative deal solutions. The value is used to calculate the preference value of each alternative. Then do a ranking against the preference value. The result of system accuracy obtained from the test result is 85.7%.
Rancang Bangun Aplikasi Pengelolaan Bahan Baku Kafe Menggunakan Metode Analytical Hierarchy Process (AHP) dan Push Notification Julita Gandasari Ariana; Aryo Pinandito; 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

To maintain quality of service, a café must pay attention to several aspect. One of them is the availability of the menu served by the café. The availability of the menu also depends on the availability of the ingredients needed to make it. Therefore, the café manager need to make sure that the ingredients is enough and resupplied fast enough when needed. Push Notification is a technology which allow information to be delivered to stakeholder via instant alert on their smartphone. This concept can be used by café manager to monitor the availability of ingredients, so that any insufficient ingredients can be resupplied fast. Analyical Hierarch Process (AHP) is used to determine the priority of ingredients that needed to be resupplied first. Ingredient stock, the number of menu using corresponding ingredient, and ingredient price are used to determine supply priority. The result of using both method reach 68% in accuracy, and 80% in delivery rate of push notification.
Optimasi Parameter Support Vector Machine (SVM) dengan Particle Swarm Optimization (PSO) Untuk Klasifikasi Pendonor Darah Dengan Dataset RFMTC I Gusti Ngurah Ersania Susena; 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

Blood donation is one of voluntary humanitarian activities. Blood is one of the most important substances that humans have in the human life cycle. In carrying out blood donation activities, monitoring the stock availability of blood bags is usually a major problem. To know ammount stock of blood bag we need a system that can predict the behavior of blood donors. RFMTC (Recency, Frequency, Monetary, Time, Churn Probability) is a modified RFM method in order to see the behavior of donors who can donate their blood or not to donate again. Therefore, SVM-PSO method needed to know classification of blood donors behavior. With SVM techniquesto find hyperplane that is the dividing line between data classes. Then the PSO technique to find the range of input parameters that SVM needed to get the optimal hyperplane value. This research uses 748 data from UCI dataset with 4 main features and 2 classes. Based on the test that has been done obtained the accuracy of 90% with the value of learning rate SVM small and the value of the number of PSO particles are low.
Peramalan Status Siaga Banjir Berdasarkan Data Curah Hujan (ARR) dan Tinggi Muka Air (AWLR) Menggunakan Metode Fuzzy Time Series (Studi Kasus: Perum Jasa Tirta I) Arina Rufaida; Muhammad Tanzil Furqon; Bayu Rahayudi
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

Flood is a condition where water flow is higher than normal water level so it flooded the surrounding area. Floodwaves flow from upstream to downstream and interact with increasing water capacity of the estuary. Floods can occur due to high rainfall, overflow from the river, the destruction factor of Watershed (DAS). From that point on, a system that is able to forecast to make it easier to analyze the flood alert status in the future. Regression method used in this research is Fuzzy Time Series. The FTS method is a model usually used to forecast data in sequence. This research has a goal to forecast flood alert in Kambing Station DAS Brantas . The results of the test show the prediction of flood alert on the water level data (AWLR) that is in December 2016 got the error value (RMSE) of 2.89 and rainfall data (ARR) in February 2015 got the error value (RMSE) of 16.0. Both data resulted flood alert forecasting that shows Normal.
Implementasi Metode Support Vector Machine Untuk Klasifikasi Jenis Penyakit Malaria Tryse Rezza Biantong; Muhammad Tanzil Furqon; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Malaria is a disease transmitted by female Anopheles mosquitoes infected by a parasite (protozoa) originating from the genus Plasmodium. There are four species of protozoa parasites that commonly attack humans, including: Plasmodium vivax which causes malaria tertiana, Plasmodium falciparum causes malaria tropica, Plasmodium malariae causes malaria quartana, and Plasmodium ovale causes malaria ovale. These four malaria cases almost have the same symptoms, so it is not easy to distinguish between one to another. Therefore, a system that can classify these types of malaria based on the symptoms is needed. Classification is the creation of a model that is used to classify an object into a predetermined class based on the same characteristics. One of the classification method is Support Vector Machine (SVM). Therefore the SVMs classification algorithm using the RBF kernel is being used in this study. The data used were 200 data taken from Dinas Kesehatan Kabupaten Nabire, Papua. In this test used K-fold Cross Validation with the K-fold values = 10. The best accuracy results generated by this system is 72.5% with the value of the parameter λ=0.1, σ=1, γ=0.001, C=0.1, ε=1.10-5, itermax=50 data on the ratio of 80% training data : 20% testing data.
Peringkasan Teks Otomatis Pada Artikel Berita Hiburan Berbahasa Indonesia Menggunakan Metode BM25 Desy Andriani; Indriati Indriati; Muhammad Tanzil Furqon
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

One of the most often activitiy carried out by Indonesian internet users is reading news. More than 50% of Indonesian internet users use the internet to read news. However, problems will arise if the content of the article is a long text so that the reader needs time to read and understand the contents of the article. One way that users can still read and understand the contents of articles quickly is by reading the summary. Therefore we need an automatic text summarization system in entertainment news articles with the aim of emphasizing the main information and helping the reader get the main information from the text quickly and don't need to read the entire contents of the text or document. This study uses the BM25 method which is a method of weighting sentences that sort sentences based on terms that appear in each sentence in the document. BM25 is using tf idf weighting for word weighting and the relationship between terms and each sentence in the document is influenced by free parameters k1 and b. Based on the test results it was found that summarizing the text with the BM25 method obtained the best average precision result, recall and f-measure values ​​when the value of the compression rate used was 30%. Where the average values ​​of precision, recall, and f-measure are 0,730, 0,738 and 0,734.
Penerapan Metode Neighbor Weighted K-Nearest Neighbor Dalam Klasifikasi Diabetes Mellitus Dendry Zeta Maliha; Edy Santoso; Muhammad Tanzil Furqon
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

Diabetes mellitus is a critical illness caused by abnormal irregular insulin secretion in an increase in blood sugar. Diabetes mellitus can increase glucose in the body, resulting in complications that can lead to several risks, namely heart disease, stroke, kidney failure, death and blindness According to the World Health Organization (WHO), as many as 300 million people in the world will be affected by diabetes by 2025 In addition there are some diseases that have early symptoms that are almost similar to diabetes mellitus, if you make a mistake to analyze it will be fatal in people with diabetes mellitus. Therefore an application is needed that can facilitate the classification of diabetes mellitus. In this study propose the application of the Neighbor Weighted K-nearest Neighbor method in the classification of diabetes mellitus. The NWKNN method uses weighting in the data class. The results showed the average accuracy using the value of K = 15 and the value of E = 2 obtained an accuracy of 92.3% in the training data of 130 data divided into 10 fold and test data as many as 13 data in each fold.
Sistem Perkiraan Penggunaan Listrik Rumah Tangga Menggunakan Logika Fuzzy (Studi Kasus: PLN Area Pasuruan) Mochamad Ali Fahmi; Muhammad Tanzil Furqon; Sutrisno Sutrisno
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

In one of Indonesia's regions, Pasuruan, in recent years there has been a rapid increase in economic growth, resulting in a large increase in electricity demand to exceed the scenario originally planned by the government. The electricity system in the city of Pasuruan itself is a complex electricity system where there are difficulties in estimating the amount of electricity that can affect the readiness of the generating unit to provide electricity supply to consumers. Based on these constraints, it is necessary to estimate long-term electricity use, especially for the household sector in planning the addition of new power plants, expansion of the distribution network and planning requirements for the operation of electricity generation, so that the power generated is in accordance with load requirements. In this study Fuzzy Logic method is used to estimate or forecast. Data that were used as many as 70 historical data from January 2012 to October 2017 obtained from PLN Pasuruan Area. The results of the implementation and accuracy testing in this study got the best parameter value with the lowest MSE value of 1.602823095 and MAPE 3,84%. The test is done to get the best number of fuzzy sets at 16, while the worst value is 7 fuzzy sets.
Diagnosis Hama Penyakit Tanaman Bawang Merah Menggunakan Metode Neighbors Weighted K-Nearest Neighbors (NWKNN) Masayu Vidya Rosyidah; Budi Darma Setiawan; Muhammad Tanzil Furqon
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

Red onions is a a plant that is a successfull export commodity in Indonesia. Red onions have many benefits, can be a seasoning cook to medical ingredients. Behind that, during the planting period these plants often run the risk of crop failure caused by pests and diseases of red onions. In addition, there is still a lack of understanding of farmers in controlling pests and diseases causing inevitable losses. One way to overcome this problem is to build a system that can diagnose pests and diseases on red onions, namely the expert system. The expert system that is built to diagnose pests and diseases on red onions in this study using the Neighbors Weighted K-Nearest Neighbors (NWKNN) method with parameters k =2 and e = 4 produces an accuracy of 100%.
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