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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Martabe : Jurnal Pengabdian Kepada Masyarakat The IJICS (International Journal of Informatics and Computer Science) Informatika Journal of Applied Engineering and Technological Science (JAETS) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Computer Science and Information Technologies Ihsan: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) International Journal Of Science, Technology & Management (IJSTM) Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Sains Teknologi dan Sistem Informasi Proceeding International Seminar of Islamic Studies Jurnal Minfo Polgan (JMP) Prosiding Snastikom sudo Jurnal Teknik Informatika Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Blend Sains Jurnal Teknik Wahana TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi International Journal of Economic, Technology and Social Sciences (Injects) Jurnal Pengabdian Barelang Jurnal Ilmu Komputer dan Sistem Informasi Hanif Journal of Information Systems Electronic Integrated Computer Algorithm Journal Economic: Journal Economic and Business Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Pengabdiaan Masyarakat Larisma Al'Adzkiya International of Computer Science and Information Technology Journal AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method Arif Ridho Lubis; Santi Prayudani; Muharman Lubis; Al Khowarizmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1273-1281

Abstract

The tea plants (Camellia Sinensis) are small tree species that use leaves and leaf buds to produce tea harvested through a monoculture system. It is an agriculture practice to cultivate one types of crop or livestock, variety or breed on a farm annually. Moreover, the emergence of pests, pathogens and diseases cause serious damages to tea plants significantly to its productivity and quality to optimum worst. All parts of the tea plant such as leaves, stems, roots, flowers and fruits are exposed to these harm lead to loss of yield 7 until 10% per year. The intensity of these attacks vary greatly on particular climate, the degree slope and the plant material used. Therefore, this study analyzes tea leaves as a common part used in recipes to create unique taste and flavor in tea production, especially in agro-industry. The decision making method used is Fuzzy Mamdani Inference as one of model with functional hierarchy with initial input based on established criteria. Fuzzy logic will provide tolerance to the set of value, so that small changes will not result in significant category differences, only affect the membership level on the variable value. Previous method using probabilities have shown 78% tea leaves have been attacked by category C (Gray Blight) while using Mamdani indicated 86% of tea leaves have been infected. In this case, this result pointed out that Fuzzy Mamdani Inferences have more optimal result compare to the previous method.
Analysis of linear congruent methods and multiplicative random number generator in computer-based test Amrullah Amrullah; Al-Khowarizmi Al-Khowarizmi; Firahmi Rizky
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1521-1532

Abstract

This research focuses on the implementation of computer - based test exams in high schools which face the problem of not having differences in exam questions which results in weak security and validity of exam results. Therefore, a randomization method is needed to overcome this problem. The method used in random ization is linear congruent methods and multiplicative random number generator. There are 101 random questions, but only 40 questions are displayed for each student with a reference value of Xn and C, 2 will be added for each package of exam questions and to avoid question code=0, the calculation results will be added 1. The linear congruent methods (LCM) results achieve 100% accuracy, while the method The multiplicative random number generator (MRNG) only achieved 62.5% accuracy in randomizing the exam que stions. This accuracy comparison highlights the difference in the ability of the two methods to generate random permutations of test item packages. LCM randomization accuracy ensures that each student will receive a different set of test questions in a con sistent manner. However, the low accuracy of randomization using MRNG indicates a weakness in generating permutations of exam question packages. The results of this study show that the LCM method is better than the MRNG in conducting exams.
Deep neural networks approach with transfer learning to detect fake accounts social media on Twitter Arif Ridho Lubis; Santi Prayudani; Muhammad Luthfi Hamzah; Yuyun Yusnida Lase; Muharman Lubis; Al-Khowarizmi Al-Khowarizmi; Gabriel Ardi Hutagalung
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp269-277

Abstract

The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.
Co-Authors Abdulbasah Kamil, Anton Ade Haikal Adidtya Perdana, Adidtya Adila Mawaddah Meuraxa Aisar Novita Ajulio Padly Sembiring Akbar Idaman Al Hamidy Albara Albara, Albara Amrullah Amrullah Amrullah Andy Satria Angkat, Fhatiya Alzahra Aulia Jannah Bela Bela Budi Kurniawan Hutasuhut Chindy Yovita Sukma Dalimunthe, Yulia Agustina Diana, Has Dicky Apdilah Edy Rahman Syahputra Efendi, Syahril Elveny, Marischa Fadhilah, Ulfa Faradillah, Yanty Farid Akbar Siregar Fatma Sari Hutagalung FAUZI . Fauzi Fauzi Faza, Sharfina Ferry Fachrizal - Firahmi Rizky Frainskoy Rio Naibaho Gabriel Ardi Hutagalung Habibi Ramdani Safitri Halim Maulana Hapzi Ali Harefa, Hafid Rahman Hariani, Pipit Putri Hasanuddin Hasanuddin Hasdiana Herman Mawengkang Hutagalung , Fatma Sari Hutagalung, Fatma Sari Idham Kamil Ilham Ramadhan Nasution Indah Purnama Sari Indah Purnama Sari Indah Purnama Sari Irvan, Irvan Ismail Hanif Batubara Julham Julham Julham Julham Lubis, Arif Ridho Lubis, Mhd Muchlisin M. Iqbal Tanjung M.Pd, Akrim Mahyuddin K. M Nasution Mandra Saragih Manurung, Asrar Aspia Marah Doly Nasution MD, Pipit Putri Hariani Mhd Faris Pratama Mhd. Basri Mhd. Basri Michael J Watts Muhammad Basri Muhammad Luthfi Hamzah Muharman Lubis Muhathir Muhathir Muhathir, Muhathir Mulkan Azhari Mulkan Azhari Mutiara Akbar Nasution Nadeak, Nurhalimah Nasution, Tia Alfi Sahara Niken Aprilina Oris Krianto Sulaiman Permatasari, Dhyta Pipit Putri Hariani MD Pradesyah, Riyan Pradesyah, Riyan Prayudani, Santi Putri, Berlianda Oktariani Jelita Putri, Wan Hafizah Ainun Syah Qadri, Habib Al Rahmad B.Y Syah Rahmad Syah Rahmat Mushlihuddin Ramadhani, Fanny Romi Fadillah Rahmat Salma, Riza Sarah Purnamawati Sari Hutagalung, Fatma Septiana Dewi Andriana, Septiana Dewi Sibarani, Theofil Tri Saputra Simanungkalit, Ahmad Hazazi Siregar, Ananda Afifah Siregar, Muhammad Rizky Pratama Suherman Suherman Tessya Fakhta Tri Nasution Vicky Rolanda Wasesa, Istikha Ruchitra Hayudirga Watts, Michael J. Yoshida Sary Yuyun Yusnida Lase Zhafirah, Zhahrah