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Journal : Building of Informatics, Technology and Science

Penerapan Metode Dempster Shafer Dalam Mendiagnosa Penyakit Tumor Hipofisis Roberto Valentinus Manurung; Muhammad Syahrizal; Murdani Murdani
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.232 KB) | DOI: 10.47065/bits.v3i2.176

Abstract

Pituitary tumors are relatively common intracellular neoplasms, and constitute 10-15% of all intracial neoplasms. These types of tumors often have difficulties and not infrequent recurrence, even though surgery has been done. Although there have been many studies on pituitary tumors, the pathogenesis of the occurrence of these tumors is not entirely clear. It is generally considered that pituitary neoplasms are primary pituitary tumors. Biomolecular electrons show that pituitary tumors, both functional and non-functional, originate from the growth of one clone (monoclonal). The Dempster-Shafer method is used to look for inconsistencies in effects and also new facts that will change existing attitudes, using the Dempster-Shafer method that allows a person to be safe in the work of an expert. This study aims to apply the Dempster-Shafer method to expert systems to diagnose the risk level of a person's pituitary tumor based on factors and symptoms of a pituitary tumor. The problem experienced by the community is the difficulty of specialist pituitary tumors due to the limited time of specialists to work, besides that in terms of very expensive costs for them or those whose economies cannot diagnose or consult with tumor specialists.
Animal Caregiver Selection by Applying ARAS Method Decision Support System and Entropy Weighting Utomo, Dito Putro; Syahrizal, Muhammad; Hondro, Rivalri Kristianto; Saputra, Imam
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7048

Abstract

Animal care is a profession that is responsible for taking good care of animals or checking the condition of animals for the purpose of animal health. To get a qualified animal care worker according to the company's needs, it takes quite a long time, because animal care workers who apply for a job at a company must first go through several tests in order to meet the criteria required by the company. So that animal care workers are needed and required to care for animals, maintain their health and pay attention to the nutrition of the animals. So that there is no extinction of protected animals. The current animal care worker acceptance procedure in the wildlife park is that applicants submit identity files, if they pass the applicant's files, they take an interview test and the last test, the applicant must practice in the field directly to find out how well the applicant is able to adapt to animals. In calculating the value, problems often occur in this acceptance process. A decision support system (DSS) is an interactive information system that provides information, modeling, and data manipulation. In this case, the author uses the Entropy method and the ARAS (Additive Ratio Assessment) method to solve it. The Entropy method can be used to calculate weights based on data characteristics in the criteria, the higher the variation between data in the criteria, the higher or more important the weight of the criteria. While the ARAS (Additive Ratio Assessment) method is used for ranking. The use of the Entropy method as a weighting aims to ensure that the weighting process is carried out based on objective value assignment. In the application of the Entropy Method, a weighting of the criteria value is produced where the criteria with the highest to lowest values ​​are Certificate, Work Experience, Age, Interview and Education with the highest value being 0.768 and the lowest being 0.021. Then the process of applying the selection of animal nurses using the ARAS method obtained the result that alternative A1 was selected as an animal nurse with a value obtained of 0.0816.
Co-Authors Abdul Karim Ade Septi Rezeki Anggreani Binjori Advent Halawa Afri Nirmalasari Halawa Agung Dermawan Agus Minta Riang Zega Aida Sopia Aisyah, Sity Akbar, Aswin Akmal, Oktafiana Alwin Fau Amanda Pratama Amanudin Harahap Ananda, Rizky Anggia Arif Anri Muda Siregar Antonius Antonius Ardian Fadly Telaumbanua Ari Gunawan Rambe Armansyah Ritonga Asma Asma Baehaqi Berry Richard Ornos Sinambela Buang, Nursahar Cahyo, Robi Dwi Daulay, Nelly Khairani Dermawan, Agung Devi Purnama Sari Didi Prawira Suprayogi Dito Putro Utomo Dona Roni Tambunan Dwina Pri Indini Dwina Priindini Ebenezer Bangun Efidoren L Nainggolan Eka Gustina Bancin Ekawati, Yuni Andri Endhika Endhika Enzel Febrianti Telaumbanua Fadillah, Rizkah Fadlina Fince Tinus Waruwu Fitri Handayani Lubis Fuady Mahbub Garuda Ginting Halawa, Advent Hanif, Faizira Nur Hapipuddin Hapipuddin Haryati Haryati Hasanah, Nurul Riska Helfrida Hotmaria Sihite Hetty Rohayani Hotni Rotua Br Hutapea Hutabarat, Sumiaty Adelina Ida Mayanju Pandiangan Ilhamsyah Ilhamsyah Imam Saputra Intan Maharani Iwan Setia Budi Jamillah Nasution Jaya, Andi Eka Putra Karto Wijaya Karto Wijaya, Karto Kennedi Tampubolon Kevin Yanto Sarumaha Khairun Nisa Ulfa Khairunnisa Khairunnisa Kristian Siregar Kurnia Ulfa Lince Tomoria Sianturi Liza Handayani Lumban, Romayani Malango, Monika Stefani Manik, Lastri Mardin, Mardin Marta Zega Masriani . Matondang, Hasiholan Maulidza, Dwi Maya Sari Mei Warni Zendrato Mesran, Mesran Miya Putri Daulay Muammar Muammar, Muammar Muhammad Fajar Rizky Muhammad Rezki Wasallam Muhammad Sayuthi Murdani Murdani Murdani Murdani Murdani Murdani Murdani, Murdani Nelly Astuti Hasibuan Ngajudin Nugroho Nola Dita Puspa NS, Eka Widya Nugroho, Very Sapto Nurainun Hasanah Sinaga Nurlela Nurlela Nurlinda, Andika Oktavian Laksamana Suryo Ongah, Harun Pagan, Darmandra Mirza Pradana, Ari Purba, Rohan Kristini Purnama Sari, Dian Putri Ramadhani Rahayul Kahfi Rakhmaji, Iqbal Rangkuti, Fiqri Hidayat Renta Gracia Tampubolon Ridha Ismadiah Rika Irwanti Ritonga, Devi Rivalri K. Hondro Rivalri Kristianto Hondro Rizka Rahayu Pasaribu Rizky Maulana Pribadi, Rizky Maulana Roberto Valentinus Manurung Robiah Al-Adawiyah Rombekila, Aryani Ronda Deli Sianturi Sabrina, Elisa Saidi Ramadan Siregar Samsuri, Syahrul Sarwandi Wandi Septa Fenly Sinaga shara, yuni Siagian, Edward Robinson Simangunsong, Santi Sinurat, Sinar Siregar, Anggi Jaya Maulana Sitorus, Jepri Saprianto Situmorang, Dharma Bakti Soeb Aripin Suha Alvita Supiya Supiya Surya Darma Nasution Teresia Teti Ernawati Ndruru Tesa Aurelia Siregar Thesa Noveninta Ginting Ulfa, Kurnia Vina Winda Sari Wardi, Syah Yuanda, Richa Yuni Shara