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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Infotech: Jurnal Informatika & Teknologi Reputasi: Jurnal Rekayasa Perangkat Lunak JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Penelitian dan Pengabdian Masyarakat Jotika
Ali Mustopa, Ali
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Sistemasi: Jurnal Sistem Informasi

 1 
Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results Ali Mustopa; Hendri Mahmud Nawawi; Sarifah Agustiani; Siti Khotimatul Wildah
Sistemasi: Jurnal Sistem Informasi Vol 11, No 2 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (171.973 KB) | DOI: 10.32520/stmsi.v11i2.1966

Abstract

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of  pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment.
Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Ali Mustopa; Agung Sasongko; Hendri Mahmud Nawawi; Siti Khotimatul Wildah; Sarifah Agustiani
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Application of the Finite State Automata (FSA) Method in Indonesian Stemming using the Nazief & Adriani Algorithm lady agustin fitriana; Ali Mustopa; Muhammad Rifqi Firdaus; Rizka Dahlia
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4038

Abstract

Language is a communication tool commonly used in everyday life. Each country has a different language with predetermined rules. For instance, in the Indonesian language, there are approximately 35 official affixes mentioned in the Big Indonesian Dictionary. These affixes include prefixes (prefixes), infixes (insertions), suffixes (suffixes), and confixes (a combination of prefixes and suffixes). In Information Retrieval, there is a stemming process, which is the process of converting a word form into a base word or the process of transforming variant words into their base form. The theory of language and automata is the foundation of the computer science field that provides the basis for ideas and models of computer systems. In the implementation of the research, several stages were carried out, such as explaining the Nazief & Adriani stemming algorithm, finite state automata, creating pseudocode, and testing using a web-based system, resulting in affixed words becoming the correct base words with 20 affixed words. The results obtained from reading this web-based system, the base word "cinta" (love) used as a test yielded accurate results in accordance with the concept of the Nazief & Adriani stemming algorithm. There are some weaknesses in stemming from suffixes, and the solution is to perform stemming from the prefix position (Prefix).
Co-Authors Abdul Hamid Abdul Latif Abdul Latif Abdul Latif Agung Sasongko Agus Junaidi Agustiani, Sarifah Ahmad Fachrurozi Andi Saryoko Antonius Yadi Kuntoro Desyandri Desyandri Ganda Wijaya Hendri Mahmud Nawawi Hermanto Hermanto Lady Agustin Fitriana Laksono Trisnantoro M Rangga Ramadhan Saelan Maysaroh, Maysaroh Mochammad Abdul Azis Muhammad Fahmi Julianto Muhammad Iqbal Muhammad Iqbal Muhammad Rezki Muhammad Rifqi Firdaus Muhammad Sony Maulana Nanik Wuryani Nuraeni Herlinawati Pribadi, Denny Rabiatus Sa’adah Rahmat Hidayat Rangga Sanjaya Risdiansyah, Deni Rizka Dahlia Rizky Ade Safitri Rusda Wajhillah Siti Khotimatul Wildah Sopiyan Dalis Suharyanto Suharyanto Umi Khultsum Wahyu Nugraha Wahyutama Fitri Hidayat Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira Yudhistira