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Journal : Journal of Environmental Engineering and Sustainable Technology

PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIBIT UNGGUL SAPI BALI MENGGUNAKAN METODE K-NEAREST NEIGHBOR Indra Ekaristio P; Arief Andy Soebroto; Ahmad Afif Supianto
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 (865.329 KB) | DOI: 10.21776/ub.jeest.2015.002.01.7

Abstract

Bali cattle is an Indonesian native cattle that have a characteristic of the color of his skin. Bali cattle skin color can indicate the quality of the Bali cattle. The classification of the quality of Bali cattle directly is difficult because the human eye has a limited ability to see colors. A decision support system that is able to classify the quality of Bali cattle is based on a digital image of the skin color can help to overcome these limitations. The system will classify Bali cattle into three classes, namely Good (Seeds Superior), Average and Poor. System applying the K-Nearest Neighbor algorithm for the classification process is based on the average features and standard deviation of the red, green, and blue (RGB). This research tested a method to obtain the best value of K, the best image size, and the amount of training data best that will be used. Male Bali cattle using a value of K = 3, image size = 128×128 pixel, and the amount of training data = 45. While the female Bali cattle using a value of K = 6, image size = 64×64 pixel, and the amount of training data = 30. The results of testing the accuracy of the system for male Bali cattle is 100%, while the results of testing the accuracy of the system for female Bali cattle is 66.67%.
PEMILIHAN ALTERNATIF SIMPLISIA MENGGUNAKAN METODE WEIGHTED PRODUCT (WP) DAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) Febrianita Indah Perwitasari; Arief Andy Soebroto; Nurul Hidayat
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 (114.94 KB) | DOI: 10.21776/ub.jeest.2015.002.01.4

Abstract

Nowadays, people tend to consume organic stuff for meal and medication because of its condition of being secure and inexpensive price. Simplisia is organic material which is not yet processed in order to cure the illness. The part that is used from the whole part to the each piece of simplisia, such as leaves, flowers, fruits, and so on. Simplisia has been being used for solution to the illness, especially at Poli Obat Tradisional RSUD Dr. Soetomo. There are many variants of illness that can be cured by simplisia and there are many variants of simplisia than can be used to cure the illness, which are all usually made the people confused which one is the best variant to cure. Regarding of choosing the alternatives, there is more than one method in Decision Support System that can be used to solve the problem. In this research, there will be two methods that aim at finding the best alternative of simplisia, which are Weighted Product (WP) and Simple Additive Weighting (SAW). Comparison research is used to decide which method as the best method on giving simplisia for the illness. The test scenario is comparing between the result which is given by the system and by the doctor. The accuracy of the result for WP method is 89% and SAW method is 89%.
IMPLEMENTASI FUZZY INFERENCE SYSTEM (FIS) METODE TSUKAMOTO PADA SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KUALITAS AIR SUNGAI Galuh Mazenda; Arief Andy Soebroto; Candra Dewi
Journal of Environmental Engineering and Sustainable Technology Vol 1, No 2 (2014)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.785 KB) | DOI: 10.21776/ub.jeest.2014.001.02.4

Abstract

Water was one resource that has a very important function for life and human life. River was the main channel as water flowing from upstream to downstream, has many domestic and industrial activity along the stream. The flow dynamics lead to changes in the quality and quantity of the river significantly. Water quality was maintained by analyzing the quality of the river water. Decision Support System (DSS) was a system designed to simplify the determination of water quality officer in making decisions. Inputs are parameter water quality test that consists of physical parameters and chemical parameters.The process of water quality analysis was conducted using Fuzzy Inference System Tsukamoto method. Fuzzy tsukamoto method used to determine the water quality of the river into four (4) classes which meet quality standards (good condition), lightly polluted, contaminated medium, and heavy polluted. The results of tested scenarios obtained an accuracy rate between the results of the calculation method of Fuzzy Tsukamoto with the calculated water quality STORET method at 90%.
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%.
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%.
SISTEM PENDUKUNG KEPUTUSAN DETEKSI DINI PENYAKIT STROKE MENGGUNAKAN METODE DEMPSTER-SHAFER Deby Putri Indraswari; Arief Andy Soebroto; Eko Arisetijono Marhaendraputro
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 (570.575 KB) | DOI: 10.21776/ub.jeest.2015.002.02.6

Abstract

Stroke is a neurological function disorders caused by impaired blood flow in the brain. Stroke is the third most common cause of death in developed countries, after heart disease and cancer. This causes a stroke to watch. Early prevention through medical examination needs to be done to reduce the high rate of risk of stroke. The detection of the risk of stroke is determined when knowing the criteria of risk factors is complete and structured. But sometimes the detection of the risk of stroke is difficult to determine if there are risk factors that have forgotten or not structured so that doctors can experience problems or ambiguous to make diagnosis. To overcome the problem of semi-structured pattern, it can be solved using decision support systems (DSS) with intelligent computing. SPK early detection of stroke constructed using methods Dempster Shafer. In the study can detect the level of risk of stroke is high risk, medium, and low with 8 input risk factors. Based on the data used in this system is obtained accuracy of 90%. So that it can be concluded that SPK is constructed with Dempster Shafer method to function well for detecting stroke.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIBIT UNGGUL SAPI BALI MENGGUNAKAN ALGORITMA SIMPLIIFIED SEQUENTIAL MINIMAL OPTIMIZATION (SSMO) PADA SUPPPORT VECTOR MACHINE (SVM) Eugenius Yosep Korsan N; Arief Andy Soebroto; Imam Cholissodin
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 (338.686 KB) | DOI: 10.21776/ub.jeest.2015.002.01.6

Abstract

Balai Pembibitan Ternak Unggul (BPTU) Sapi Bali di Jembrana, Bali merupakan sebuah tempat pembudidayaan Sapi Bali yang memiliki kualitas unggul.Sapi Bali merupakan jenis sapi yang memiliki ciri khas yang unik. Ciri khas tersebut terletak pada warna kulit Sapi Bali yang mengalami perubahan sesuai dengan jenis kelamin dan usianya. Pemilihan bibit unggul Sapi Bali di BPTU dilakukan dengan berbagai macam cara. Salah satunya melihat pola warna kulit secara langsung yang terdapat pada tubuh Sapi Bali. Proses pemilihan bibit unggul Sapi Bali rentan terjadinya kesalahan yang dilakukan oleh para peternak (human error) dikarenakan jumlah Sapi Bali yang banyak di BPTU Sapi Bali. Pemilihan bibit unggul diklasifikasikan ke dalam tiga kelas yaitu Baik (Bibit Unggul), Sedang, Buruk. Untuk itu, perlu dibutuhkan suatu sistemyang mampu menghasilkan klasifikasi bibit unggul Sapi Bali berdasarkan warna kulit yang diambil menggunakan citra digital.Pada sistem tersebut, akan menerapkan algoritma SimplifiedSequential Minimal Optimization (SSMO)dengan kernel Radial Basis Function (RBF)  untuk proses training data dan metode One-Against-All untuk proses klasifikasi berdasarkan fitur rata-rata dari nilai red, green dan blue (RGB). Hasil dari skenario pengujian didapatkan rata-rata tingkat akurasi untuk empat skenario pengujian Sapi Bali Jantan dan Betina sebesar 97.50% dan 67.50%.
HYBRID OF ADABOOST ALGORITHM AND NAÏVE BAYES CLASSIFIER ON SELECTION OF CONTRACEPTION METHODS Faridah, Nurul; Dewi, Candra; Soebroto, Arief Andy
Journal of Environmental Engineering and Sustainable Technology Vol 8, No 2 (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.2021.008.02.6

Abstract

Stunting is a growth failure in children. Stunting can be avoided by adjusting birth spacing or implementing a Family Planning program by using appropriate contraception. Therefore, it is necessary to develop appropriate and rapid contraceptive selection techniques to assist family planning programs. This study develops a model for determining contraceptive methods using a Naïve Bayes Classifier. In addition, an Adaboost algorithm was used to handle the independent between attributes on Naïve Bayes. The performance evaluation of model was measured by combining k-fold cross validation and confusion matrix. Based on the results testing was obtained an optimal parameter of learning rate was 0.1 and the number of iterations was 50. The evaluation using optimal parameters produce the best accuracy of 87.5%, precision of 87.6%, recall of 87.5%, and f1-measure of 87.5%. This result was better than applying the Naïve Bayes without implementing Adaboost, which had 70% accuracy. The used of Adaboost was proven to increase the accuracy of Naive Bayes by 17.5%.
Co-Authors Achmad Arwan Achmad Ridok Adam Hendra Brata Ade Wija Nugraha Adi Setyo Nugroho Admaja Dwi Herlambang Agi Putra Kharisma Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Mustafirudin Ahmad Shofi Nurur Rizal Aizul Faiz Iswafaza Alfarisi, Muhammad Asnin Ali Akbar Alysha Ghea Arliana Amira Ibtisama Ana Kusuma Ardani Andreas Tommy Christiawan Andri Wijaya Kusuma Asrul Syawal Asrul, Divanda Arya Inasta Asus Maizar Suryanto H Austenita Pasca Aisyah Baghaz, Renanda DSP Bambang Gunadi Brilliansyach, Raihan Fikri Candra Dewi Candra Dewi Canny Amerilyse Caesar Catur Ari Setianto Dama Yuliana Deby Putri Indraswari Denny Sagita Rusdianto Destyana Ellingga Pratiwi Destyana Ellingga Pratiwi Dhea Azahria Mawarni Dian Eka Ratnawati Djoko Pramono Dwi Cindy Herta Turnip Dwi Puri Cemani Dzikrullah, Muhammad Aulia Fachruz Edy Santoso Eka Miyahil Uyun Eko Ari Setijono Marhendraputro Eko Arisetijono Elza Fadli Hadimulyo Enggar Septrinas Enggarsita Auliasin Eugenius Yosep Korsan N Evi Irhamillah Azza Faisal Roufa Rohman Faizatul Amalia Fajar Pradana Fauziah Mayasari Iskandar Febrianita Indah Perwitasari Fendy Yulianto Ferdy Wahyurianto Fildzah Amalia Galuh Mazenda Guruh Prayogi Willis Putra Habib Yafi Ardi Hanafi, Andy Hastian Bayu Hendra Darmawan Herman Syantoso Himawan Sutanto I Gede Adi Brahman Nugraha I Putu Bagus Arya Pradnyana Ibnu, Mohammad Ibrahim Kusuma Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indra Ekaristio P Indriana Candra Dewi Indriati Indriati Indriati Indriati Ishak Panangian Sinaga Ismiarta Aknuranda Issa Arwani Issa Arwani Karmia Larissa Br Pandia Khoifah Inda Maula Khrisna Widhi Dewanto Krisna Wahyu Aji Kusuma Lailatul Rizqi Ramadhani Lailil Muflikhah Laode Muhamad Fauzan Latifah Hanum Mahdi Fiqia Hafis Maria Tenika Frestantiya Maria Tenika Frestantiya Maria Tenika Frestantiya, Maria Tenika Maya Febrianita Mohammad Imron Maulana Muh. Arif Rahman Muhammad Iqbal Kurniawan Muhammad Rois Al Haqq Muhammad Rouzikin Annur Muhammad Tanzil Furqon Muhammad Taruna Praja Utama Mutia Ayu Sabrina Nadya Rahmasari Nadya Sylviani Niftah Fatiha Armin Niken Hendrakusma Wardani Nizar Rahman Kusworo Nurannisa, Nadhira Nuriya Fadilah Nurudin Santoso Nurul Faizah Nurul Faridah, Nurul Nurul Hidayat NURUL HIDAYAT Nurul Hidayat Nurul Hidayat Odhia Yustika Putri Priyambadha, Bayu Randy Cahya Wihandika Raymond Gunito Farandy Junior Rekyan Regasari Restia Dwi Oktavianing Tyas Reynald Daffa Pahlevi Ridwan Fajar Widodo Rio Andika Dwiki Adhi Putra Rio Arifando Risda Nur Ainum Riski Ida Agustiyan Risqi Nur Ifansyah Rivaldy Raihan Syams Rizal Setya Perdana Rizal Setya Perdana Saiful Kirom, Muhammad Ihsan Santoso, Nurudin Sativandi Putra Satrio Agung Wicaksono Sitepu, Yosua Christiansen Stefan Levianto Sukamto, Anjas Pramono Surya Wirawan SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Teddy Syach Pratama Thareq Ibrahim Tiara Rossa Diassananda Tryse Rezza Biantong Vasya, M Azka Obila Vicky Virdus Vivien Fathuroya, Vivien Wayan Firdaus Mahmudy Welly Purnomo Wijaya, Aldi Rahman Wildan Ziaulhaq Wildan Ziaulhaq Wildansyah Maulana Rahmat Yearra Taufan Ardy Rinaldy Yusril Iszha Eginata Zaien Bin Umar Alaydrus Ziya El Arief Ziya El Arief Ziya El Arief, Ziya El