Rice is a staple food source for most countries in the world, including Indonesia. The problem of rice disease is a problem that is quite crucial and is experienced by many farmers. Approximately 200,000 - 300,000 tons per year the amount of rice attacked by pests in Indonesia. Considerable losses are caused by late-diagnosed rice plant diseases that reach a severe stage and cause crop failure. The limited number of Agricultural Extension Officers (PPL) and the Lack of information about disease and proper treatment are some of the causes of delays in handling rice diseases. Therefore, with the development of information technology and computers, it is possible to identify diseases by utilizing Artificial Intelligence, one of which is by using recognition methods based on image processing and pattern recognition technology. The purpose of this research is to create a Machine Learning model by applying the model architecture from Resnet101 combined with the model architecture from the author. The model proposed in this study produces an accuracy of 98.68%.
Implementasi Sistem Penjaminan Mutu Internal di Universitas Muhammadiyah Malang dilaksanakan oleh Badan Kendali Mutu Akademik. Prinsip kerjanya mengacu pada siklus penetapan standar, pelaksanaan standar, evaluasi pelaksanaan standar, pengendalian standar dan peningkatan standar. Siklus penjaminan mutu internal tersebut menuntut kedinamisan sehingga mendorong pemanfaatan teknologi informasi sebagai tools dalam pelaksaannya. Sampai dengan saat ini, telah ditetapkan 43 dokumen mutu, dengan jumlah mahasiswa 35.466 orang mahasiswa, jumlah dosen 614 orang dosen, yang tersebar di 55 program studi dan program profesi. Apabila seluruh siklus SPMI dilaksanakan secara manual, akan membutuhkan sumberdaya manusia, waktu dan biaya yang sangat besar. Guna mengatasi masalah tersebut telah dibangun SIMUTU. SIMUTU berhasil menyediakan 13 evaluasi mutu dengan pengguna Badan Kendali Mutu Akademik, Mahasiswa, Program Studi, Laboratorium, Perpustakaan dan Dosen. Hasil pengujian fungsionalitas pada prototype SIMUTU menunjukkan bahwa seluruh fitur beroperasi sesuai dengan kebutuhan dan rancangan.
Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.
Various studies on the Learning Management System (LMS) have not examined the suitability of LMS features with the educational standards applicable in a country/region. This study aims to measure the suitability of LMS features with the National Higher Education Standards/ Standar Nasional Pendidikan Tinggi (SN-Dikti) in Indonesia using the Feature-Oriented Domain Analysis (FODA) method. This research identifies explicitly LMS features in the assignment and assessment functions. Besides, this study recommends previous LMS features for future LMS development based on the assessment standards applicable in Indonesia. The results of the analysis in this study found the suitability of the three LMS and recommended LMS features for Lecturer and Student users.
Abstract-Syllabus and lesson plan (RPS and RPP in Indonesian), which consist of topics and plan for conducting a subject during a period of time, are essential elements for teaching and learning activity. Therefore, in order to conduct subject successfully, syllabus and learning plan should be revised before class started. However, the revising activity is not a simple activity and sometimes becomes a complex activity that takes time. Consequently, in many cases, teachers tend to regret doing this activity; and if do so the maximal result will not be achieved. Beberapa tahapan yang wajib dilakukan dosen dalam penyusunan RPS dan RPP ialah sebagai berikut [2]. Dosen mengidentifikasi Capaian Pembelajaran Lulusan (CPL) yang dibebankan pada mata kuliah. Dosen wajib merumuskan capaian pembelajaran mata kuliah (CPMK) yang bersifat lebih khusus merujuk pada CPL yang dibebankan pada mata kuliah tersebut. Dosen diwajibkan menyusun Kemampuan Akhir yang Diharapkan (KAD), atau pada referensi lain dinamakan sub CPMK, sebagai representasi kemampuan akhir lulusan yang direncanakan pada tiap tahap pembelajaran. KAD dirumuskan berdasarkan CPMK. Dosen diharuskan menentukan indikator dan kriteria pencapaian KAD sebagai bagian dari perencanaan pembelajaran. Instrumen penilaian pembelajaran selanjutnya disusun berdasarkan indikator pencapaian kemampuan akhir tiap tahapan belajar. Dosen dapat memilih dan mengembangkan model/metode/strategi pembelajaran agar relevan dengan tujuan pembelajaran. Selain itu, dosen juga perlu menyelaraskan materi pembelajaran serta mengembangkan dan melakukan evaluasi pembelajaran.Permasalahan yang begitu kompleks dalam perancangan RPS dan RPP mengakibatkan tidak sedikit dosen yang enggan memperbarui rancangan RPS dan RPP di setiap semester. Padahal, salah satu bagian dari tugas utama dosen adalah merancang pembelajaran yang mengikuti permkembangan keilmuan yang sangat dinamis dari waktu ke waktu [3]. Di sisi lain, dunia kerja menuntut kompetensi lulusan yang senantiasa relevan dengan kebutuhan kerja.Berdasarkan analisis masalah yang telah dilakukan dan penggalian informasi dari pakar-pakar pendidikan, perlu dikembangkan tools perancangan perangkat pembelajaran yang adaptif dengan peraturan dan kebijakan pendidikan
Considering the problem of maternal and under-five mortality rates, and the high number of pregnancies at risk is not just a matter of the health world. The role of information technology that is developing very rapidly can be used as a solution to the problem of risky pregnancy. What's more, computers often change functions to turn off routine human work and decision making. Then to overcome this case two algorithms will be applied namely: (a) Decision Tree C5.0 Algorithm, (b) K-Medoids Clustering. Commercial Version 5.0 (C5.0) method for processing the analysis variables used. The use of C5.0 in this case is for attribute selection so that it produces very powerful features. After doing the selection of new data features will be grouped using K-Medoids for analysis so that they can be used as a reference for handling this case. The application of these two methods is also so that the decisions that are made later are more targeted to reduce or overcome the problem of high-risk maternal pregnancy. AbstrakMengingat permasalahan angka kematian ibu dan balita, dan tingginya angka kehamilan beresiko tidak hanya masalah dunia kesehatan saja. Peranan Teknologi informasi yang berkembang sangat pesat dapat dijadikan soluli terhadap permasalahan kehamilan beresiko. Terlebih lagi, computer sering kali berubah fungsi untuk mengatikan pekerjaan manusia yang bersifat rutinitas maupun pengambilan keputusan. Maka untuk mengatasi kasus ini akan diterapkan dua algoritma yaitu: (a) Algoritma Decision Tree C5.0, (b) K-Medoids Clustering. Metode Commercial Version 5.0 (C5.0) untuk mengolah variabel-variabel analisa yang digunakan. Penggunaan C5.0 pada kasus ini untuk melakukan seleksi atribut sehinga menghasilakan fitur yang sangat berpengauh. Setelah melalukan seleksi fitur data yang baru akan dikelompokkan menggunakan K-Medoids untuk di analisa agar dapat dijadikan acuan untuk penanganan pada kasus ini. Penerapan kedua metode ini juga agar keputusan yang nanti diambil lebih tepat sasaran untuk mengurangi atau mengatasi masalah kehamilan ibu yang beresiko tinggi.
Herbal leaves are a type that is often used by people in the health sector. The problem faced is the lack of knowledge about the types of herbal leaves and the difficulty of distinguishing the types of herbal leaves for ordinary people who do not understand plants. If any type of plant is used, it will have a negative impact on health. Automatic classification with the help of technology will reduce the risk of misidentification of herbal leaf types. To make identification, a precise and accurate herbal leaf detection process is needed. This research aims to facilitate the classification model of herbal leaf images with a higher accuracy value than previous research. Therefore, the proposed method in this classification process is one of the Transfer Learning methods, namely Convolutional Neural Network (CNN) with a pretrained VGG16 model. This research uses a dataset of herbal leaves with a total of 10 classes: Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri and Sirih. The performance of the results of the proposed classification method on the test dataset using Classification Report shows an increase in the results of the previous research accuracy value from 82% to 97%. This research also applies Image Data Generator in the augmentation process which aims to improve the image of herbal leaves, reduce overfitting, and improve accuracy.
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