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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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SISTEM PAKAR DIAGNOSA PENYAKIT SAPI POTONG DENGAN METODE NAIVE BAYES Indriana Candra Dewi; Arief Andy Soebroto; Muhammad Tanzil Furqon
Journal of Environmental Engineering and Sustainable Technology Vol 2, No 2 (2015)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (965.786 KB) | DOI: 10.21776/ub.jeest.2015.002.02.2

Abstract

In order to produce quality beef, one of the important factors in maintenance of cattle is to maintain the health of livestock to stay fit. One way to provide an understanding of the breeders is to use expert system. An expert system is one of the artificial intelligence which is adopting of the expert knowledge that used to solve problem that usually can only be solved by expert in the field. Expert systems can be allowed to extend the working range of experts so that expert knowledge can be acquired and used anywhere. In this expert system use a Naive Bayes method as inference methods for diagnosing the disease. Types of diseases that can be recognized by expert system are 11 types of disease while symptoms that can be recognized the expert system are 20 types of symptom. The results of testing the accuracy of the 26 test case data, have generated the level of conformity percentage of 96,15%.
PENCARIAN PASAL PADA KITAB UNDANG-UNDANG HUKUM PIDANA (KUHP) BERDASARKAN KASUS MENGGUNAKAN METODE COSINE SIMILARITY DAN LATENT SEMANTIC INDEXING (LSI) Setyoko Yudho Baskoro; Achmad Ridok; Muhammad Tanzil Furqon
Journal of Environmental Engineering and Sustainable Technology Vol 2, No 2 (2015)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.427 KB) | DOI: 10.21776/ub.jeest.2015.002.02.4

Abstract

Indonesia is a country of law. As law states, Indonesian have regulations that govern the relationship between the communities, one of them is criminal law. Set of rules of criminal law is written in the Kitab Undang-undang Hukum Pidana (KUHP), which contains hundreds of clause which regulate the relationship between the community based on values, norms, and specific rules that focuses on the interests of the public. In this paper, information retrieval used to search the clause of the KUHP based on a description of the crime, using Latent Semantic Indexing (LSI). LSI adopts techniques in mathematical dimension reduction process Singular Value Decomposition (SVD). This system use 60 clause as training data, and 6 query or crime description as test data. In each of the data clause of the KUHP contained data such as clause number, clause, and the clause contents. The system will calculate and determine the relevant clause is based on  query or  description of the crimes that has been entered. Cosine similarity used to calculate the similarity or proximity clause KUHP with query. The performance of the system is shown by the test results of Mean Average Precision (MAP) value at each k-rank is 5, 10, 20, 30, 40, 50, and 59, with the highest performance is in k-rank 40 with MAP 0.8944.
PENGEMBANGAN SISTEM PAKAR DIAGNOSA PENYAKIT SAPI POTONG DENGAN METODE FUZZY K-NEAREST NEIGHBOUR Restia Dwi Oktavianing Tyas; Arief Andy Soebroto; Muhammad Tanzil Furqon
Journal of Environmental Engineering and Sustainable Technology Vol 2, No 1 (2015)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (104.109 KB) | DOI: 10.21776/ub.jeest.2015.002.01.8

Abstract

Early detection and treatment of cow disease is an important thing for increasing productivity of beef. The dependence of the existence of an expert or veterinarian is too high. It is caused by a lack of knowledge of the breeder about cow disease. This is a condition in which an expert is needed. However, An expert or veterinarian is not always there every encountered, especially in country areas. Those problems can be solved by expert systems. This expert system using fuzzy K-Nearest Neighbour method to process the diagnosis. The results show the functional validation testing and system expertise by 100% and accuracy test variation k, variations training data and m by 97.56%.
CLUSTERING THE POTENTIAL RISK OF TSUNAMI USING DENSITY-BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE (DBSCAN) Muhammad Tanzil Furqon; Lailil Muflikhah
Journal of Environmental Engineering and Sustainable Technology Vol 3, No 1 (2016)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.906 KB) | DOI: 10.21776/ub.jeest.2016.003.01.1

Abstract

Tsunami is one of the deadliest natural disaster that causing devastating property damage and loss of life. Therefore, this triggers many scientist to do researches in tsunami mitigation disaster, such as analyzing the potential risks caused by tsunami. The process of analyzing the potential risk caused by tsunami can be done by grouping the data of tsunami based on characteristics of the previous tsunami events. DBSCAN (Density-based Spatial Clustering of Application with Noise) is a popular clustering method and can be used to do the task. The algorithm do the clustering processes using density-based concept that able to detect outlier/noise and clusters irregular shapes. It was proved in this research where the evaluation method using Silhouette Coefficient on the DBSCAN clustering result gave highest value 0.96056649 for ε and minPts value of .1 and 0.1.
PETA POTENSI DALAM MENCIPTAKAN KEMANDIRIAN EKONOMI DESA Edriana Pangestuti; Inggang Perwangsa Nuralam; Muhammad Tanzil Furqon; Hanifa Maulani Ramadhan
JOURNAL OF APPLIED BUSINESS ADMINISTRATION Vol 2 No 2 (2018): Journal of Applied Business Administration - September 2018
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.142 KB) | DOI: 10.30871/jaba.v2i2.1018

Abstract

Law Number 6 of 2014 concerning village stipulation is meant in developing village potential and assets in order to develop together, and advance the economy of rural communities. This is an opportunity for every village to develop every potential they have. It needs to be highlighted that Tawang Argo Village, Karangploso Subdistrict, Malang Regency is experiencing maximixing the facilities available in Universitas Brawijaya Educational Forest (Hutan UB). This study aims to map the potential that is capable and developed in Tawang Argo Village. This study uses a qualitative approach and strengthens SWOT, EFAS, and IFAS analysis. Several factors found from the results of this study include: 1) lack of product innovation; 2) land conditions in the highlands that are not supported by economic commodities; 3) lack of creativity in the community; and 4) small market opportunities. Imaging which shows that Tawang Village is very appropriate to build House of Mushrooms as a potential solution that exists. In cultivating mushroom kumbung not by the condition of the residents' land, having their own planting media with temperature regulation can also be planted on limited land. The results of mushroom kumbung can be sold in the form of raw materials and processed to add value and be developed into a product that is competitive and builds the independence of the village economy.
Neighbor Weighted K-Nearest Neighbor for Sambat Online Classification Annisya Aprilia Prasanti; M. Ali Fauzi; Muhammad Tanzil Furqon
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp155-160

Abstract

Sambat Online is one of the implementation of E-Government for complaints management provided by Malang City Government.  All of the complaints will be classified into its intended department. In this study, automatic complaint classification system using Neighbor Weighted K-Nearest Neighbor (NW-KNN) is poposed because Sambat Online has imbalanced data. The system developed consists of three main stages including preprocessing, N-Gram feature extraction, and classification using NW-KNN. Based on the experiment results, it can be concluded that the NW-KNN algorithm is able to classify the imbalanced data well with the most optimal k-neighbor value is 3 and unigram as the best features by 77.85% precision, 74.18% recall, and 75.25% f-measure value. Compared to the conventional KNN, NW-KNN algorithm also proved to be better for imbalanced data problems with very slightly differences.
Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi) Raditya Rinandyaswara; Yuita Arum Sari; Muhammad Tanzil Furqon
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022934707

Abstract

Stopword Removal merupakan bagian dari tahapan preprocessing teks yang bertujuan untuk menghapus kata yang tidak relevan didalam suatu kalimat berdasarkan daftar stopword. Daftar stopword yang biasa digunakan berbentuk digital library yang daftarnya sudah tersedia sebelumnya, namun tidak semua kata-kata yang terdapat didalam digital library merupakan kata yang tidak relevan dalam suatu data tertentu. Penelitian ini menggunakan daftar stopword yang dibentuk dengan algoritme Term Based Random Sampling. Dalam Term Based Random Sampling terdapat 3 parameter yaitu Y untuk jumlah perulangan pengambilan kata random, X untuk jumlah pengambilan bobot terendah dalam perulangan Y, dan L sebagai persentase jumlah stopword yang ingin digunakan. Sehingga penelitian ini ditujukan untuk mencari kombinasi terbaik dari 3 parameter tersebut serta membandingkan stopword Term Based Random Sampling dengan stopword Tala dan tanpa proses stopword removal dalam analisis sentimen tweet mengenai kuliah daring dengan menggunakan metode Naïve Bayes. Hasil evaluasi dengan stopword Term Based Random Sampling mendapatkan akurasi tertinggi dengan X, Y, L sebesar 10, 10, 40 dengan macroaverage accuracy sebesar 0,758, macroaverage precision sebesar 0,658, macroaverage recall sebesar 0,636, dan macroaverage f-measure sebesar 0,647. Berdasarkan hasil pengujian disimpulkan bahwa semakin besar X, Y, L maka semakin tinggi kemungkinannya untuk hasil evaluasi turun. Hasil pengujian membuktikan bahwa Term Based Random Sampling berhasil mendapatkan akurasi lebih tinggi dibandingkan dengan stopword Tala maupun tanpa menggunakan proses stopword removal. AbstractStopword Removal is part of the text preprocessing stage which aims to remove irrelevant words in a sentence based on the stopword list. The stopword list that is commonly used is in the form of a digital library whose list is already available, but not all words contained in the digital library are irrelevant words in certain data. This study uses a stopword list formed by the Term Based Random Sampling algorithm. In Term Based Random Sampling, there are 3 parameters, namely Y for the number of random word retrieval repetitions, X for the lowest number of weights in Y repetitions, and L as the percentage of the number of stopwords you want to use. So this research is aimed at finding the best combination of these 3 parameters and comparing the Term Based Random Sampling stopword with the stopword tuning and without the stopword removal process in the analysis of tweet sentiment regarding online lectures using the Naïve Bayes method. The results of the evaluation with the Term Based Random Sampling stopword get the highest accuracy with X, Y, L of 10, 10, 40 with a macroaverage accuracy of 0.758, a macroaverage precision of 0.658, a macroaverage recall of 0.636, and a macroaverage f-measure of 0.647. Based on the test results, it is concluded that the greater the X, Y, L, the higher the probability that the evaluation results will decrease. The test results prove that Term Based Random Sampling is successful in obtaining higher accuracy than stopword tuning or without using the stopword removal process.
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%.
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