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All Journal Journal of Enviromental Engineering and Sustainable Technology Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muhammad Tanzil Furqon
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science

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SURVEY BOAT PROTOTYPE DESIGN FOR REMOTE AREA Muhammad Tanzil Furqon; Muammar Kadhafi; Sunardi Sunardi; Seftiawan Samsu Rijal; Muhammad Dafa'anh Murya Tsani
Journal of Environmental Engineering and Sustainable Technology Vol 8, No 1 (2021)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2021.008.01.3

Abstract

The water survey activities in remote areas have many challenges, such as cost, transportation to location, access to the location, boat for water survey, manpower, and other factors. This research proposes the method of water survey to overcome the difficulties encountered in surveying in remote areas. The mini boat prototype was designed and fabricated by using a control system to operate in the water. The propulsion power prediction was carried out, by comparison, the similarity to the fullscale ships with 9.5 knots of velocity. Furthermore, the wave-making resistance was simulated by software and confirmed by the trial. The trial has shown that the boat was going well on the water and the measurement equipment in a safe condition. Aktifitas survei perairan pada wilayah terpencil menemui banyak tantangan, seperti biaya yang mahal, transportasi menuju lokasi, akses jalan, perahu untuk melakukan survei dan tenaga manusia untuk membawa peralatan tersebut. Penelitian ini bertujuan menawarkan metode survei perairan untuk mengatasi kesulitan saat melakukan survei pada wilayah terpencil. Survei dilakukan dengan menggunakan kapal mini yang telah didesain dan dibuat prototipenya. Pengoperasian kapal di air menggunakan sistem kendali jarak jauh. Prediksi kecepatan menggunakan perbandingan kemiripan dengan kapal sebenarnya dengan kecepatan yang diperoleh sebesar 9.5 knot. Selanjutnya, karakteristik gelombang yang terbentuk akibat gesekan dengan lambung kapal disimulasikan menggunakan software dan terkonfirmasi melalui pengujian performa kapal bahwa air tidak masuk kedalam kapal sehingga alat ukur yang berada diatas kapal tetap aman.
Sistem Pendukung Keputusan Penentuan Tingkat Keparahan Autis Menggunakan Metode Fuzzy K-Nearest Neighbor Robbiyatul Munawarah; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 7 (2017): Juli 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1457.182 KB)

Abstract

Autistic or Autistic Spectrum Disorders (ASD) is a general term referring to a neurodevelopmental disorder that is well known among Indonesian. Many researches on autism detection have been done by designing artificial intelligence systems with a variety of techniques used to make it easier for society to predict this kind of disorder. However, we hardly ever seen a system that can determine the severity of autism. In fact, the progress of the research in this field is no longer focused on whether a child is autistic individual or not, but rather to questioning about “Is there anything in autistic children that makes them different from one another?” as the ‘severity' label appear to give them spesific class under certain behaviour they shown. To make it easier to determine the severity of autism, decision support system will be designed using one of data mining method called Fuzzy K-Nearest Neighbor (FK-NN). Fuzzy K-Nearest Neighbor (FK-NN) is K-Nearest Neighbor method combine with Fuzzy theory that gives value of membership on every predicted data.. There are 14 symptoms and 3 types of severity used as a parameter in the development of the system. The output of this decision support system is autism severity level. The results of the system shows that the average maximum accuracy is 90.83% while the average minimum accuracy is 82.50%. Based on those results, the uses of Fuzzy K-Nearest Neighbor (FK-NN) method can be implemented in our daily life.
Clustering Data Kejadian Tsunami Yang Disebabkan Oleh Gempa Bumi Dengan Menggunakan Algoritma K-Medoids Daniel Alex Saroha Simamora; Muhammad Tanzil Furqon; Bayu Priyambadha
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 8 (2017): Agustus 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (983.063 KB)

Abstract

Tsunami is a natural events caused by sudden alteration in sea surface vertically, causing displacement of a large volume of water. Underwater volcano eruption, earthquake that is centered under the sea, and submarine landslide are some of the causes of sudden sea level change. Tsunami have occurred many times and causing many damages and fatalities. Tsunami often occurred so suddenly and cannot be predicted is the main reason for so many damages and fatalities, and the lack of knowledge and awareness are also worsen the effect of tsunami. K-Medoids is one of many clustering method which is applied to the dataset which have outlier. Subject in this research is a clustering application using K-Medoids to cluster the tsunami event which caused by earthquake dataset. Dataset used in this research come from the tsunami events database from the official site of National Oceanic and Atmospheric Administration (NOAA). The outcome from this research is a system that able to do clustering process on the tsunami events dataset using K-Medoids method. From the test, it is showed that the best number of clusters for tsunami events dataset is 2 clusters.
Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Hotspot) Dyang Falila Pramesti; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (840.407 KB)

Abstract

Forest / land wildfire is one of the disasters that occur every year in some countries in the world. This incident got more attention from the government because it caused many losses both in the economic, ecological, and social. Indonesia is a country with a high rate of forest / land wildfire disasters. Indonesia suffered losses of up to Rp 209 trillion by 2015. As a result of losses incurred an early prevention is needed, which one can be done by grouping areas with potential forest fires by utilizing hotspot data. Forest wildfires are marked by the detection of fire spots by satellites indicated as hot spots. This research uses hotspot data with parameter of latitude, longitude, brightness, frp (fire radiative power), and confidence by using K-Medoids method. K-Medoids method is a clustering method that serves to split the dataset into groups. The advantages of this method is able to resolve the weakness of K-Means method that is sensitive to outlier. The result of this research shows that the use of K-Medoids method can be used for the process of hot spot data clustering with the best silhouette coefficient in amount of 0.56745 on the use of 2 clusters by using 7352 data. The results of the clustering analysis showed that using 2 clusters resulted in a group of data with the potential of high potential with an average brightness of 344.470K with average confidence of 87.18% and medium potential with average brightness of 318.800K with Average confidence of 58.73%.
Identifikasi Diagnosis Gangguan Autisme Pada Anak Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Jojor Jennifer BR Sianipar; Muhammad Tanzil Furqon; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.102 KB)

Abstract

Autism is a neurological disorder that shows significant result as a lack of ability to form social relationships, normal communication, and behavior in children. This symptoms generally appear before children reach the age of 3 years. It is not classified as a psychiatric disease because autism is a disorder that occurs malfunction of children's brain and it is manifested on children's behaviour. Some research states that autism causes as the neurodevelopmental disorder that causes abnormalities in children's brain structure. Different experts mentioned that autism in children caused by the kind of food they consumed or they living environment that contain many harmful substances that shows in children's behaviour. Therefore, the system for the identification of autism disorders in children will be create to help identifies autism disorder by using the method of Modified K-Nearest Neighbor (MKNN). It is one of classification method based on the appearance of largest classes in data training. There are 14 symptoms from 4 aspects that are used as parameters in the development of the system. The output of the system is showing whether a child is autistic individuals or not. Based on the testing that has been done on the system that using Modified K-Nearest Neighbor (MKNN), maximum accuracy shows 100% accuracy while minimum accuracy is 92%. Based on those results, the uses of Modified K-Nearest Neighbor (MKNN) method can be implemented in our daily life.
Implementasi Metode Fuzzy Subtractive Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Vianti Mala Anggraeni Kusuma; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1258.645 KB)

Abstract

Abstract Forest is the habitat for all kinds of animals and plants, forests have a very big function to maintain the balance of nature, as the supplier of the oxygen requirement for living on earth, and the natural resources that provide a variety of materials for human needs. But at this moment the existence of forest diminishing due to illegal logging by humans or by forest fires are becoming more frequent. Forest fires this gives very bad impact, extinction of some species of plants and animals, the smoke is detrimental to health even low and so forth. So to be able to help deal with the issue made a system that can manage data hotspots (hotspots) with Fuzzy subtractive clustering. Parameter data used in the development of the system: brightness temperature and FRP (Fire Radiative Power). The result of clustering which illustrates the potential of forest fires, which are grouped in the high potential and low potential. The test results showed the best coefficient silhouette value of 0.45 and the results of the cluster is formed by two clusters using radius values ​​0.2, accept ratio 0.5, reject ratio 0.15. The results of the analysis in the determination of the potential for forest fires result is a high potential with an average brightness value of 335.727⁰K, FRP 57.248 and average confidence 83.47%. While medium potential with an average brightness value of 318.934⁰K, FRP 23.330 and average confidence 58.08%. Keywords: Clustering, Hotspot, Fuzzy subtractive clustering, Silhouette Coefficient
Sistem Pendukung Keputusan untuk Rekomendasi Wirausaha Menggunakan Metode AHP-TOPSIS (Studi Kasus Kab. Probolinggo) Ghulam Mahmudi Al Azis; Imam Cholissodin; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1122.954 KB)

Abstract

Along with the growing number of Indonesian population, also raises the competition in looking for work. The limited job vacancy impacts the increasing number of unemployed every year. Until February 2016, the labor force in Indonesia reached 127 million people with an overall unemployment rate of 5.5% or 7 million people. To overcome this increasing number of unemployment, needed step solution that is in the form of decision support for entrepreneurship. Decision support systems can be used to recommend an entrepreneur for unemployment or everyone. AHP method and TOPSIS method is one of the methods of decision support system that can be combined with calculating the weight of criterion using AHP method then continued by calculating the value of preference for ranking from entrepreneurial alternative using TOPSIS method. The AHP-TOPSIS method will recommend the results of 3 entrepreneurs with the highest preference value. In accordance with the test results, that these application can help to recommend entrepreneurs in helping decisions support for user to choose an entrepreneur.
Implementasi Metode Improved K-Means untuk Mengelompokkan Titik Panas Bumi Al-Mar'atush Shoolihah; Muhammad Tanzil Furqon; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1051.2 KB)

Abstract

Disaster is an incident or a series of incidents that threaten and disturb people's lives and livelihoods caused by both natural and / or non-natural factors. One of the disasters that happen is fire. Fire is a flame that occur either in small or large size, burning in an unexpected area and difficult to control. Therefore, early prevention is needed. one of the way is with geothermal point which is detected by the satellite. It is used as the indicator of land and forest fires in a region, so that the more geothermal point exist, the more potential for landfill incidents in a region. Hence, it is necessary to implement a system that can cluster the geothermal point data that has the potential in causing fire with farious status such as high, middle, and low potential. Improved K-Means is one of the most popular clustering methods and it can be used for geothermal point grouping. This algorithm performs clustering process based on the maximum distance as the cluster center and the cluster center distance will be calculated with the other data to be grouped. The calculation is done continuously until the data clustering does not change. That case is proven in this research where the evaluation result that uses silhouette coefficient give the highest point of 0.908000874 for the value of cluster 2 and the amount of data 700.
Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto menggunakan Algoritma Genetika untuk Diagnosis Autisme pada Anak Indra Eka Mandriana; Candra Dewi; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (947.843 KB)

Abstract

Autism is a developmental disorder that cause children to experience social disruption in certain areas, such as communications, social interaction, emotional and behavioral symptoms that is difficult to be identified. According to research in autism, the number of children who suffered from autism is estimated to grow every year around the world, including in Indonesia. This research implement Fuzzy Tsukamoto method to optimized genetic algorithm in order to diagnose autism in children, by optimizing the constraints on all fuzzy variables.Chromosome representation that is used in this research is real code genetic algorithm which every chromosome will initialize the limitations on all fuzzy variables. Method that is used to the process of crossover is extended intermediate crossover and random mutation for mutation process while selection method used elitism selection. Based on the results, the system obtained the most optimal parameters on a method of CARS in a population of 50, 200 generations, as well as the combination of Cr = 0.8 and Mr = 0.1 with the fitness of 1, while on the CHAT population method 10, 100 generations, as well as the combination of Cr = 0.9 and Mr = 0.1 with fitness by 1
Aplikasi Perencanaan Wisata di Malang Raya dengan Algoritma Greedy Akhmad Eriq Ghozali; Budi Darma Setiawan; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (909.386 KB)

Abstract

Malang raya is one of regions which becomes the main objective place to visit because it has many tourism places. The thing which has to be noticed is determining the tourism schedule, every tourist must choose the shortest distance and time to be able to reach that destination because they can save the time. To reach that destination, it is used greedy algorithm with knapsack problem to assist the optimation process against searching the shortest traveling time and how many tourism places which can be visited from the possessed time. Time allocation which is possessed by the user to tour is used as an integrity in calculating this application, while the traveling time at each tourism locations which are also used as an integrity is time data which is gotten from google maps. With thats data, the application with greedy algorithm will calculate the most optimal location to be visited with the time which belongs to the user. According to the result of testing application with ten sample of problem cases gets accuracy result 90% from two models of greedy algorithm calculation in searching location which can be visited by the allocation time which is owned. While the result of optimal tour accuracy that is visited is 0% from the first model of calculation and 80% from the second calculation.
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 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 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 Dyang Falila Pramesti Dzar Romaita Edy Santoso Eky Cahya Pratama Elan Putra Madani Erwin Bagus Nugroho Evilia Nur Harsanti Fahmi Achmad Fauzi Fajar Pradana Fatwa Ramdani, Fatwa Fikar Cevi Anggian Firdaus Rahman Fitra Abdurrachman Bachtiar Gabriel Mulyawan Ghulam Mahmudi Al Azis Guntur Syafiqi Adidarmawan Hangga Eka Febrianto 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 Indriati Indriati Issa Arwani Jojor Jennifer BR Sianipar Julita Gandasari Ariana Jumerlyanti Mase Kadhafi, muammar 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 Dafa'anh Murya Tsani 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 Ihsani Fadilah Ofi Eka Novyanti Oky Krisdiantoro 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 Ridho Ghiffary Muhammad Rifaldi Raya Rifwan Hamidi Rimba Anditya Kurniawan Riski Nova Saputra Riza Rizqiana Perdana Putri Robbiyatul Munawarah Romlah Tantiati Satrio Hadi Wijoyo Seftiawan Samsu Rijal Silvia Aprilla Sunardi Sunardi 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 Wilis Biro Syamhuri Yuita Arum Sari