bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengembangan Computer Based Testing (CBT) pada Pembelajaran Teknologi Informasi dan Komunikasi untuk kelas VIII SMP
Authors: Akmal Syahril , Syafril Syafril , Hendri Nofri 
Published: August 2023

The background of this research is because of processing in evaluation at Junior High School 16 Padang which still used the old method or conventional. This evaluation is still have problem to determine the students ability directly in other word, this method needed the long time for getting students’ score. It this because of teacher must search students’ score manually. Based on the problem above appears the purpouse of this research is to make Computer Based Testing (CBT) product in TI&K Teaching Learning process at eighth grade of Junior High School. This research used Research and Development (R & D) method. Which concist in 10 Steps. Although, these steps is more minimilize to be 5 (five) steps, such as; (a) planning, (b) developing the first product, (c) validation (material and product), (d) hypoteses for determining of practical CBT, and (e) the last result of product, is created a tool to evaluate in learning process. Based on the result of data analysis pointed that CBT product is can be categoriezed to use. To result of part all answers can be categorized to use. And to the practical CBT also can be categorized is good or can be used in learning. From the result of this research can be concluded that CBT product is can be used as a tool in evaluation on learning process.
Keputusan Pembelian Ditinjau Dari Citra Merek Dan Gaya Hidup Konsumen Rm Sambel Alu Bandar Lampung
Authors: Dinata Yazid Hajrian , Wakhid Ali Abdul , Purnama Rahmad 
Published: August 2023

Purchasing decisions in urban communities are not only based on product taste or quality, but also other factors such as brand image and lifestyle which most local culinary entrepreneurs pay little attention to. This shows that urban people with high mobility want a product that is able to answer their problems, namely food products that are fast, practical and prestige. This study aims to reveal and analyze consumer purchasing decisions in a review of brand image and lifestyle, is there a positive and significant relationship between brand image and lifestyle and purchase decisions at RM Sambel Alu Bandar Lampung consumers.This research is a quantitative study that uses a psychological measurement tool with a Likert scale model as a method of collecting research data. Purchase decision scale with Cronbach alpha (α = 0.815), brand image scale with Cronbach alpha (α = 0.813), and lifestyle scale with Cronbach alpha (α = 0.936). The population in this study were consumers of RM Sambel Alu with certain criteria determined by the researcher. To ensure that the sample truly represents the entire population, the researcher chose purposive sampling and accidental sampling techniques as a reference in sampling. Analysis of the research data was carried out using a two-predictor regression analysis technique which in the calculation process was assisted by the SPSS 21 computer program. The results of the study show that there is a positive and significant relationship between brand image and lifestyle and consumer purchasing decisions of RM Sambel Alu. The results of the regression analysis show that the two independent variables make an effective contribution (SE) of 36.2% to purchasing decisions (R2=0.362; R=0.601; F=27.459; sig. 0.000 (p0.01). Meanwhile, the remaining 63.8% can be influenced by variables other than this research.X1-Y=0.360; sig.0.000 (p0.01) means that there is a significant positive relationship between brand image and purchasing decisions.X2-Y= 0.581; sig.0.000 (p0.05) means there is a positive relationship significant relationship between lifestyle and purchasing decisions. Keywords: Purchase Decision, Brand Image, Lifestyle, Consumers, Local Culinary.
A One Health Initiative For Air Pollution: Student-Living Gardens
Authors: Cameron-Hamilton Emily , Dawydenko Kaylyn , Dawson Phoebe 
Published: August 2023

Air pollution is one of the largest issues facing our planet to date. It leads to a variety of severe consequences including an increased incidence of respiratory illness in humans and non-human animals, damage to plants, and exacerbation of climate change. An enormous contributor to air pollution is the livestock farming industry which, in addition to its negative environmental impacts, detrimentally affects the mental health and well-being of non-human animals through various unnatural practices. However, air pollution may be mitigated by planting gardens at homes located in the student-living area of Queen’s University in Kingston, Ontario. These gardens would include vegetables, low-maintenance plants, and edible native species which would remove toxins from the air and provide multiple additional benefits to humans, non-human animals, and the environment. One of the greatest benefits of the proposed gardens would be the provision of vegetables and edible native species, allowing students to consume more plant-based foods and stray away from livestock consumption. The gardens would also increase ecosystem biodiversity, which would not only make plant life more resilient but also help create new opportunities for reliable food sources and habitats. If the success of the proposed initiative were to be proven within the area, additional strategies may be introduced in Kingston to further reduce air pollution and perhaps inspire other university communities to undergo similar changes.
Evaluation of Caspase 8 Role as a Gene and Protein in Chronic Myeloid Leukemia Incidence
Authors: Mohamed Minarose Nabeel , Altaee Maha Fakhry 
Published: August 2023

Chronic Myeloid Leukemia )CML( is a type of clonal hematopoietic stem cell disease marked by cytogenetic abnormalities induced by the growth and division of cells carrying the Philadelphia chromosome. The current research was carried out in Iraq to examine the link between Caspase 8 gene expression and Caspase 8 protein and the development of chronic myeloid leukemia (CML) in 100 samples (50 patients and 50 controls). There were differences in the expression of this gene between healthy controls and studied patients. The relationship between CML onset with age and gender was investigated in comparison to controls. The results revealed significant rises in the mean of Caspase 8 expression level (∆Ct) of patient groups in comparison to the related ∆Ct means in the healthy group, as well as significant differences in gene expression folding (2-∆∆Ct) of Caspase 8 gene, which reached (2.778) in CML patients, Caspase 8 protein levels were (0.1059 ng/ml) and There were considerable variations between CML patients and control groups. On the other hand, the results for gender and age of CML patients compared to controls indicated no significant differences for genders of CML patients and controls, and significant differences P <0.0001 for age of CML patients and controls, this result suggests that CML may afflict people of different ages and affect both men and women.
Intelligent Task Scheduling Using Bat and Harmony Optimization
Authors: Almahdi Wijdan Abd , Lafta Hussein Attia , Ali Yossra Hussain 
Published: August 2023

Cloud computing describes computer services provided through the internet and includes a wide range of virtualization resources. Because cloud computing is made up of a sizable number of heterogeneous autonomous systems with an adaptable computational architecture, it has been widely adopted by many businesses. The scheduling and management of resource utilization, however, have become more difficult as a result of cloud computing. Task scheduling is crucial, and this procedure must schedule tasks on the virtual machine while using the least amount of time possible. Utilizing an effective scheduling strategy enhances and expedites cloud computing services. Optimization techniques are used to resolve cloud scheduling problems. The purpose of this research is to address tasks distribution within the system to improve system performance overall and reduce task execution time. Two well-known optimization algorithms (the Bat, and Harmony search algorithms) were used in this approach as well as a combination approach Bat Algorithm Harmony Search ( BAHS ) that integrates the two. When compared to the other algorithms used for task scheduling, the (BAHS) method was chosen because it is flexible and produces effective results. Tests were run on a dataset that was created randomly. The suggested algorithm results were compared to the other popular algorithms in the field. The results show that the suggested swarm-based scheduling techniques can produce more accurate results than those of the competing algorithms in terms of the makespan , mean, and standard division.
Analisis Nilai Tambah Usaha Pengolahan Serai Wangi (Cymbopogon Nardus L) Di Kecamatan Rambatan Kabupaten Tanah Datar
Authors: Permata Wulan , Sari Rina , Raesi Syahyana 
Published: August 2023

Penelitian ini dilaksanakan di Kecamatan Rambatan Kabupaten Tanah Datar dan terdapat 2 usaha pengolahan daun serai wangi yaitu usaha Bapak Mukhyar dan usaha Bapak Arinal. Penelitian ini bertujuan untuk mengetahui proses pengolahan daun serai wangi menjadi minyak serai wangi dan menganalisis besarnya nilai tambah serta distribusi nilai tambah dari kegiatan usaha pengolaham daun serai. Metode yang digunakan adalah metode survei. Analisis data yang digunakan untuk menganalisis besarnya nilai tambah menggunakan metode nilai tambah Hayami. Hasil penelitian menunjukkan bahwa dari kegiatan pengolahan serai wangi ini memberikan nilai tambah sebesar Rp. 371,35/Kg dengan rasio nilai tambah 27% untuk usaha penyulingan Bapak Mukhyar, dan nilai tambah sebesar Rp. 363,07/Kg dengan rasio nilai tambah 25% untuk usaha penyulingan Bapak Arinal, yang dikategorikan termasuk rasio nilai tambah sedang karena memiliki persentase 15 %-40%. Rendemen usaha Bapak Mukhyar memiliki rendemen 0.85% sedangkan untuk usaha Bapak Arinal 0.88%. Distribusi nilai tambah pada usaha penyulingan Bapak Mukhyar dan Bapak Arinal yang terbesar diperoleh sumbangan input lain sebesar 37,12% untuk usaha Bapak Mukhyar, sedangkan usaha Bapak Arinal sebesar 42,02%. Untuk keuntungan perusahaan yakni sebesar 36,42% untuk usaha Bapak Mukhyar sedangkan usaha penyulingan Bapak Arinal sebesar 42,01%. Distribusi nilai tambah terkecil terdapat pada pendapatan tenaga kerja 26,46% untuk usaha Bapak Mukhyar, 15,97% untuk usaha penyulingan Bapak Arinal, sehingga diharapkan agar perusahaan dapat lebih memperhatikan pendistribusian nilai tambah tenaga kerja dengan menyesuaikan upah dan jumlah jam kerja sesuai dengan standar yang ada. Pemerintah juga diharapkan untuk menjaga kestabilan harga minyak serai wangi sehingga dapat meningkatkan keuntungan perusahaan yang berimbas kepada para pekerja dalam usaha pengolahan serai wangi tersebut.
Modifikasi Fonem Vokal Pada Stemming Kata Tidak Baku
Authors: Iskandar Ahmad Fikri , Utami Ema , Hidayat Wahyu , Budi Agung Prasetio , Hartanto Anggit Dwi 
Published: August 2023

<p class="Abstrak">Bahasa Indonesia termasuk bahasa yang paling populer digunakan di dunia. Bahasa Indonesia dapat berupa bahasa baku dan tidak baku. Bahasa tidak baku dapat dikarenakan oleh penyerapan dari bahasa asing atau bahasa daerah. Penyerapan ini dapat terjadi perganti huruf vokal. Kontribusi pada penelitian ini adalah melakukan modifikasi fonem pada huruf vokal untuk mengembalikan kata tidak baku ke dalam bentuk kata dasar yang baku disebut sebagai <em>Modified Vocal Phonemes</em><em> Non Formal</em>. Percobaan dilakukan dengan 60 kata tidak baku yang sudah dilakukan <em>preprocessing</em> pada penelitian sebelumnya terlebih dahulu. Penelitian ini membandingkan hasil algoritma dengan algoritma pada penelitian sebelumnya. Algoritma <em>Modified Vo</em><em>cal Phonemes Non Formal</em> telah berhasil melakukan <em>stemming</em> dengan presisi 90.00% dengan 54 kata tidak baku yang sukses dikonversi ke kata dasar sesuai dengan Kamus Besar Bahasa Indonesia (KBBI) dan 6 kata masih belum berhasil dikonversi.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Indonesian is one of the most popular languages spoken in the world. Indonesian can be standard and non-standard language. Non-standard language can be caused by absorption of foreign languages or village languages. This absorption can occur as a substitute for vowels. The contribution to this research is to modify the phonemes of vowels to return non-formal words into formal root forms known as Modified Vocal Phonemes in Non-Formal. The experiment was carried out with 60 non-formal words that have been preprocessed in the previous research. This research will compare the results of the algorithm with the algorithm in previous research. Algorithm Modified Vocal Phenomes Non-Formal has succeeded in performing stemming with 90.0% precision with 54 words that were successfully converted to base words according to the Big Indonesian Dictionary and 6 words were still not converted.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
Klasifikasi Sinyal Phonocardiogram Menggunakan Short Time Fourier Transform dan Convolutional Neural Network
Authors: Amal Muhammad Alwi Adnan , Zulherman Dodi , Widadi Rahmat 
Published: August 2023

Berdasarkan laporan American Heart Association, penyakit kardiovaskular menjadi penyebab kematian global tertinggi. Phonocardiogram (PCG) dan electrocardiogram (ECG) biasanya digunakan untuk mendeteksi penyakit jantung. Penggunaan sinyal PCG memberikan hasil prediksi yang lebih baik pada deteksi penyakit jantung bila dibandingkan dengan ECG. Tetapi, penggunaan PCG secara elektronik membutuhkan analisis sinyal yang kompleks untuk mengklasifikasikan kondisi jantung. Penelitian ini bertujuan merancang suatu sistem klasifikasi sinyal PCG berdasarkan metode ekstraksi fitur menggunakan Short Time Fourier Transform (STFT) dan metode klasifikasi menggunakan Convolutional Neural Network (CNN). Pengujian rancangan sistem menggunakan dataset sekunder dengan 2.575 rekaman PCG normal dan 665 rekaman PCG abnormal dalam format wav. Pengujian kinerja menggunakan variasi Hamming, Hann dan Blackman-Harris Window pada bagian ektraksi fitur dan variasi jumlah layer konvolusi pada bagian klasifikasi. Berdasarkan hasil pengujian, penggunaan hamming window pada proses ekstraksi fitur dan 4 layer konvolusi pada proses klasifikasi memberikan hasil terbaik dengan tingkat akurasi 88,11%. Penelitian ini membuktikan bahwa penggunaan hamming window pada bagian ekstraksi fitur dan 4 layer konvolusi pada bagian klasifikasi sebagai bentuk model terbaik sistem klasifikasi PCG berdasarkan STFT dan CNN. AbstractAccording to a report by the American Heart Association, cardiovascular disease is the leading global cause of death. Phonocardiogram (PCG) and electrocardiogram (ECG) are commonly used to detect heart disease. The use of PCG signals provides better predictive results in the detection of heart disease when compared to ECG. However, the use of PCG electronically requires complex signal analysis to classify heart conditions. This study aims to design a PCG signal classification system based on the extraction method using the Short Time Fourier Transform (STFT) and the classification method using the Convolutional Neural Network (CNN). The system design test used a secondary dataset with 2,575 normal PCG records and 665 abnormal PCG records in wav format. Performance testing uses variations of Hamming, Hann and Blackman-Harris Window in the feature extraction section and variations in the number of convolution layers in the classification section. Based on the test results, the use of a hamming window in the feature extraction process and 4 convolution layers in the classification process gives the best results with an accuracy rate of 88.11%. This study proves that the use of a hamming window in the feature extraction section and 4 convolution layers in the classification section is the best form of the PCG classification system based on STFT and CNN.
Penerapan Algoritma Deep Learning Convolutional Neural Network Dalam Menentukan Kematangan Buah Jeruk Manis Berdasarkan Citra Red Green Blue (RGB)
Authors: Yanto Budi , Rouza Erni , Fimawahib Luth , Hayadi B.Herawan , Pratama Rinanda Rizki 
Published: August 2023

Secara umum proses menentukan jeruk manis layak (bagus) dan tidak layak (busuk) masih banyak menggunakan cara manual. Cara manual dilakukan berdasarkan pengamatan visual secara langsung pada buah yang akan diamati. Pengamatan dengan cara ini memiliki beberapa kelemahan yaitu adanya keterbatasan visual manusia, di pengaruhi oleh kondisi psikis pengamatannya serta memakan waktu yang lama terutama bagi perkebunan besar. Untuk itu, diperlukan suatu algoritma untuk menentukan buah jeruk manis dengan sistem terkomputerisasi yang dibuat menggunakan algoritma Convolutional Neural Network (CNN) merupakan salah satu algoritma dari deep learning yang merupakan pengembangan dari Multilayer Percepton (MLP) yang mampu mengolah data dalam bentuk dua dimensi, misalnya gambar serta mampu melakukan klasifikasi pada citra dengan kelas–kelas yang lebih banyak atau besar. Sistem ini dirancang dan dibangun menggunakan bahasa pemrograman matlab versi R2018a, dengan 100 dataset gambar jeruk manis menunjukan tingkat akurasi sebesar 96% untuk training 92% untuk testing yang dinilai telah mampu melakukan klasifikasi kelayakan buah jeruk manis dengan sangat baik. Pada pengujian menggunakan data baru dari 10 citra jeruk manis dihasilkan 9 citra jeruk manis dengan nilai benar dan 1 citra jeruk manis dengan nilai salah. AbstractIn general, the process of determining appropriate (good) and unfit (rotten) sweet oranges still uses manual methods. The manual method is carried out based on direct visual observation of the fruit to be observed. Observations in this way have several weaknesses, namely the presence of human visual limitations, being influenced by the psychological condition of the observations and taking a long time, especially for large plantations. For this reason, an algorithm is needed to determine sweet oranges with a computerized system created using the Convolutional Neural Network (CNN) algorithm, which is one of the deep learning algorithms, which is the development of Multilayer Perceptron (MLP), which is able to process data in two-dimensional form, for example. Images as well as being able to classify images with more or larger classes. This system is designed and built using the Matlab programming language version R2018a, with 100 sweet orange image datasets showing an accuracy rate of 96% for training 92% for testing which is considered to have been able to classify the feasibility of sweet oranges very well. In testing using new data from 10 images of sweet oranges, 9 images of sweet oranges were generated with the correct value and 1 image of sweet oranges with a false value.
EpCare: Prototipe Sistem Detektor Pre-Iktal Pasien Epilepsi Berbasis Fitur CSI dari Sinyal EKG 1 Kanal Menggunakan AD8232
Authors: Wulandari Diah P. , Kurniawan Arief , Nathanael Alvin , Purnami Santi W. , Juniani Anda L. , Islamiyah Wardah R. 
Published: August 2023

Kejang epilepsi dapat terjadi di sembarang waktu dan tempat, dan dalam kondisi tertentu dapat menyebabkan cedera fatal. Oleh karena itu, kebutuhan akan perangkat wearable yang dapat mengirimkan peringatan kepada pengguna akan kejang yang akan datang adalah penting. Perangkat ini harus dapat merasakan kelainan pada sinyal biomedis pengguna dan mengirimkan peringatan sebelum kejang. Penelitian ini mengembangkan sistem yang mendeteksi kondisi pre-iktal pasien epilepsi berdasarkan fitur Cardiac Sympathetic Index (CSI) dari sinyal Elektrokardiogram (EKG). Listrik jantung pasien diukur menggunakan 3 elektroda yang dihubungkan ke AD8232 untuk mewakili sinyal 1 kanal. Algoritma Pan-Tompkins diimplementasikan untuk mendapatkan interval RR dari sinyal EKG. Kemudian fitur CSI dihitung berdasarkan nilai RR-interval. Distribusi setiap 100 interval RR dijadikan sebagai dasar untuk menentukan nilai ambang batas CSI. Ketika nilai CSI melebihi ambang batas ini, sistem akan mengirimkan peringatan ke aplikasi seluler, yang disebut EpCare. Eksperimen dilakukan pada dua kelompok data, yaitu kelompok data primer dari non-penderita epilepsi dan kelompok data sekunder dari penderita epilepsi. F-measure dari eksperimen yang menggunakan ambang batas dari orang normal sebesar 0.64, sedangkan F-measures dari eksperimen yang menggunakan ambang batas individual penderita epilepsy sebesar 0.50. AbstractEpileptic seizures may occur at anytime and anywhere, and in certain conditions may lead to fatal injury. Therefore, the need for wearable device that can alert user to an impending seizure is important. This device should be able to sense abnormality in user’s biomedical signals and send alert prior to seizure. This research develops a system that detects pre-ictal condition of epilepsy patient based on Cardiac Sympathetic Index (CSI) feature from Electrocardiogram (ECG) signals. Patient’s heart electricity is measured using 3 electrodes which are connected to AD8232 to represent 1 channel signal. Pan-Tompkins algorithm is implemented to obtain RR intervals of ECG signals. Then, CSI feature is calculated based on the values of RR-intervals. A distribution of every 100 RR-intervals is made as basis to determine a threshold value of CSI. When CSI value exceeds this threshold, system will send alert to a mobile application, called EpCare. Experiments were conducted on two groups of data, which are primary one from non-epileptic persons, and secondary one from epileptic patients. F-measures of experiments used threshold of non-epileptic person is 0.64, while F-measures of experiments used individual threshold of epileptic person is 0.50.