50 research outputs found
Aplikasi Sistem Informasi Perpustakaan FT-UMM
No full textSurat Pencatatan Ciptaan yang dikeluarkan oleh Kementrian Hukum dan Hak Asasi Manusia atas Program Komputer yang berjudul Aplikasi Sistem Informasi Perpustakaan FT-UMM yang diciptakan oleh Merinda Lestandy, Bayu Adiputra Pratama, Muhammad Nasar, Amrul Faruq, Lailis Syafa’ah, dan Muhammad Irfan
Buku Panduan Aplikasi Monitoring Kandang Closed House Berbasis Mobile
Full text linkPatent dengan jenis: Buku Panduan/Petunjuk, Berjudul Buku Panduan Aplikasi Monitoring Kandang Closed House Berbasis Mobile, yang ditulis oleh: Mahar Faiqurahman, Merinda Lestandy, Novendra Setyawan, Basri Noor Cahyadi, Lailis Syafa’ah, dan Zulfatman
Optimasi Routing Pada Metropolitan Mesh Network Menggunakan Adaptive Mutation Genetic Algorithm (AMGA)
Full text linkPada jaringan dinamis dan sangat besar seperti Metropolitan Mesh Network (MMN),
routing menjadi sangat kompleks karena banyak potensi dalam pertengahan perjalanan suatu
paket dapat terhalang sebelum mencapai tujuannya. Selain itu, pengguna pun dapat masuk
dan keluar dari topologi jaringan. Sehingga dibutuhkan algoritma routing yang baik dan
mampu menekan waktu dalam update jaringan ataupun jika terjadi kesalahan dalam
jaringan. Permasalahan routing dapat direpresentasikan sebagai masalah jalur terpendek
untuk memudahkan penyelesaiannya. Pada paper ini dihasilkan bahwa Adaptive Mutation
Genetic Algorithm (AMGA) mampu mengoptimalkan routing pada MMN dengan
menentukan probabilitas mutasi sebesar 0.25, probabiltas crossover sebesar 0.75, batas
generasi sebesar 50 dan ukuran populasi (nind) sebesar 100 sehingga mampu mengurangi
atau menghindari adanya premature convergence
Undergraduate researchers report only moderate knowledge of scholarly communication: they must be offered more support
No full textUndergraduate students are increasingly participating in the scholarly communication process, mostly through formal research experiences. However, Catherine Fraser Riehle and Merinda Kaye Hensley, having surveyed and interviewed university students, reveal that undergraduate researchers have only moderate levels of confidence in their knowledge of scholarly communications, especially publication and access models, author and publisher rights, determining the impact of research, and research data management. Moreover, students revealed that to receive specific guidance in these areas was rare. There is much opportunity for faculty members, graduate students, librarians, and research programme coordinators to collaborate and develop learning interventions in these areas
UAV Image Classification of Oil Palm Plants Using CNN Ensemble Model
Full text linkBasal Stem Rot (BSR), caused by Ganoderma boninense, is one of the most destructive diseases affecting oil palm plantations in Southeast Asia. Early detection of this disease is crucial to prevent its widespread transmission and to maintain plantation productivity. This study proposes an image classification approach using ensemble learning with three Convolutional Neural Network (CNN) architectures: DenseNet161, ResNet152, and VGG19, to detect BSR-infected oil palm trees through aerial imagery captured by Unmanned Aerial Vehicles (UAVs). The dataset used consists of 7,348 annotated images classified into two categories: healthy and unhealthy. Experimental results show that the DenseNet161 model outperformed the others, achieving a validation accuracy of 91.75% and a validation loss of 0.0307. The ensemble CNN approach demonstrated improved classification accuracy and holds significant potential for implementation in automated and precise plant health monitoring systems. This research provides a valuable contribution to AI-based agricultural technology, particularly in disease management for oil palm plantations.Basal Stem Rot (BSR), caused by Ganoderma boninense, is one of the most destructive diseases affecting oil palm plantations in Southeast Asia. Early detection of this disease is crucial to prevent its widespread transmission and to maintain plantation productivity. This study proposes an image classification approach using ensemble learning with three Convolutional Neural Network (CNN) architectures: DenseNet161, ResNet152, and VGG19, to detect BSR-infected oil palm trees through aerial imagery captured by Unmanned Aerial Vehicles (UAVs). The dataset used consists of 7,348 annotated images classified into two categories: healthy and unhealthy. Experimental results show that the DenseNet161 model outperformed the others, achieving a validation accuracy of 91.75% and a validation loss of 0.0307. The ensemble CNN approach demonstrated improved classification accuracy and holds significant potential for implementation in automated and precise plant health monitoring systems. This research provides a valuable contribution to AI-based agricultural technology, particularly in disease management for oil palm plantations
PENERAPAN DEEP LEARNING UNTUK PREDIKSI KASUS AKTIF COVID-19
No full textCoronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%
Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes
Full text linkCOVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtainedfrom 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80
Klasifikasi pendonor darah potensial menggunakan pendekatan algoritmepembelajaran mesin
No full textAbstract – Blood donation is the process of takingblood from someone used for blood transfusions.Blood type, sex, age, blood pressure, and hemoglobinare blood donor criteria that must be met andprocessed manually to classify blood donor eligibility.The manual process resulted in an irregular bloodsupply because blood donor candidates did not meetthe criteria. This study implements machine learningalgorithms includes kNN, naïve Bayes, and neuralnetwork methods to determine the eligibility of blooddonors. This study used 600 training data divided intotwo classes, namely potential and non-potentialdonors. The test results show that the accuracy of theneural network is 84.3 %, higher than kNN and naïveBayes, respectively of 75 % and 84.17 %. It indicatesthat the neural network method outperformscomparing with kNN and naïve Baye
PENINGKATAN KOMPETENSI GURU TK ABA 16 MALANG MELALUI PELATIHAN DAN PENDAMPINGAN DI BIDANG TIK (TEKNOLOGI INFORMASI DAN KOMPUTER
Full text linkPemanfaatan teknologi dalam kehidupan sehari-hari tidak dapat dihindari. Perkembangan teknologi
pada era globalisasi saat ini sangat pesat. Untuk itu, masyarakat dituntut untuk melakukan suatu
perubahan di setiap kegiatannya. Terutama bagi para guru diharapkan dapat mengikuti perubahan
tersebut dalam meningkatkan kualitas kegiatan belajar mengajar. Perkembangan Teknologi
Informasi dan Komunikasi (TIK) telah berpengaruh terhadap berbagai aspek kehidupan manusia.
Hal ini mendorong era baru peradaban manusia dari era industri ke era informasi. Berdasarkan hasil
wawancara dan observasi pada TK ABA 16 Malang kami melihat masih ada guru yang beranggapan
tidak menggunakan komputer dan IT (Teknologi Informasi) dalam proses pembelajaran bukan hal
mengganggu jalannnya pelajaran karena guru merasa tidak mendapatkan fasilitas komputer saat
mengajar, jadi inilah yang membuat guru merasa tidak perlu untuk tahu cara menggunakan
komputer. Jika dilihat dari kenyataannya ini terjadi pada guru-guru yang sudah berusia tua, walaupun
yang guru junior pun masih ada yang gagap pada kemajuan IT. Dari permasalahan tersebut, maka
perlu diadakan kegiatan pelatihan dan pemahaman bagi guru untuk mengoptimalkan fasilitas TIK
yang ada untuk menunjang keefektifan pembelajaran dan kompetensi guru. Setelah pelaksanaan
pelatihan, tim pengusul akan memonitoring dan mengevaluasi pemahaman guru-guru tentang TIK
yang ditentukan dengan standar keberhasilan pemahaman yaitu pencapaian skor harapan senilai 7
dari jumlah skor hasil observasi. Dari skor tersebut maka dapat disimpulkan bahwa guru sudah
meningkat kompetensinya atau belum setelah mengikuti pelatihan IT mengenai penggunaan TIK
sebagai media mengajar. Pelaksanaan evaluasi dilakukan dengan mengamati peningkatan
kompetensi guru setelah satu minggu mengajar menggunakan IT melalui lembar observasi
Sentiment Analysis of Covid-19 Vaccine Tweets Utilizing Naïve Bayes
No full textCOVID-19 is acknowledged as a transmitted from one person to another through contact, coughing, and
sneezing. Twitter has served as one of the media outlets to raise awareness regarding COVID-19 problems. One of the
government's objectives, based on the rising distribution, is pursued to preserve immunizations in stock. Hence, the vaccine
information has become adequately available. However, immunization has sparked a range of reactions, including support
and objection for vaccination. Attempts require a mechanism to distinguish tweets addressing immunization-related
information. One notable method includes sentiment analysis, expressing a statement's negative, neutral, and positive
feelings. A total of 5200 datasets were employed, with 4000 datasets classified as neutral, 300 datasets as negative, and
900 datasets as positive. The Naïve Bayes method and the TF-IDF (Term Frequency Inverse Document Frequency) word
weighting strategy are proposed to model the COVID-19 vaccine dataset, by comparing the three models of: Gaussian,
Multinomial, and TF-IDF (Term Frequency Inverse Document Frequency). According to study employing Naïve Bayes,
the best model employing Bernoulli Naive Bayes is 80% with a data splitting of 30%
