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Jurnal Komtika (Komputasi dan Informatika)
Published by Universitas Muhammadiyah Magelang
ISSN : 25802582     EISSN : 2580734X     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 8 Documents
 1 
Search results for , issue "Vol 5 No 1 (2021)" : 8 Documents clear
Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN) Sari, Yuslena; Alkaff, Muhammad; Arif Rahman, Muhammad
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.4605

Abstract

Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.
Implementasi Aplikasi Monitoring Nilai dan Kegiatan Siswa Berbasis Android dengan Metode Prototype Juniawan, Fransiskus Panca; Sylfania, Dwi Yuny; Rian Chrisna Putra, Rendy; Sulaiman, Rahmat
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5119

Abstract

The number of smartphone and internet users in Indonesia is currently very large. This is become the basis for the use and development of mobile-based applications for the advantage of education. However, not all High Schools in Indonesia have a mobile-based system. Another problem is that most of them still use conventional methods in implementing teaching and learning activities, and reporting learning outcomes to parents. This is also still the case at SMA Negeri 1 Pangkalanbaru, Bangka Tengah. This problem is what we want to raise and solve by developing applications that can solve these problems. The research was developed using the Prototype method which consists of stages of Data Collection, Rapid Planning, Prototype Design, and Prototype Testing. By using the UML tool, results are obtained in the form of parents who can monitor grades, school information, and school announcements. In addition, students can take attendance online, register for extracurricular activities, and view announcements. From the testing results it is known that the system performance is running well as it should.
Analisis Keamanan Sistem Informasi Menggunakan Sudomy dan OWASP ZAP di Universitas Duta Bangsa Surakarta Hariyadi, Dedy; Nastiti, Faulinda Ely
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5134

Abstract

Peretas saat ini tidak hanya menyerang instansi pemerintah seperti pada tahun 2019 melainkan sudah melakukan serangan ke instansi pendidikan. Hal ini sesuai dengan pantauan dan identifikasi Badan Siber dan Sandi Negara bahwa instansi pendidikan telah diserang sebanyak 38% pada tahun 2020. Sebagai wujud tindakan preventif terkait dengan serangan siber pada instansi pendidikan perlu dilakukan sebuah tindakan analisis keamanan informasi terhadap sistem-sistem yang terpasang. Pada artikel ini diusulkan tahapan teknis melakukan analisis keamanan informasi menggunakan perangkat lunak dengan lisensi Free Open Source Software, yaitu Sudomy dan OWASP ZAP. Menggunakan kedua perangkat lunak tersebut didapatkan hasil analisis potensi-potensi celah keamanan pada sistem informasi yang terpasang pada Universitas Duta Bangsa.
Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM) Herlawati, Herlawati; Nidaul Khasanah, Fata; Dina Atika, Prima; Sari, Rafika; Handayanto, Rahmadya Trias
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5139

Abstract

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes Nurmalasari, Maulidya Dwi; Kusrini, Kusrini; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5140

Abstract

Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.
Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes Fitriana, Frizka; Utami, Ema; Al Fatta, Hanif
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5185

Abstract

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.
Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika Rahman, Arif; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5188

Abstract

Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.
Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes Setiawan, Hendrik; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5189

Abstract

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm

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