Articles
Pengaruh Penerapan Musik Tradisional terhadap Tingkat Stres dan Produktivitas Ternak Ayam Petelur:
Alamsyah, Alamsyah
ARMADA : Jurnal Penelitian Multidisiplin Vol. 3 No. 5 (2025): ARMADA : Jurnal Penelitian Multidisplin, Mei 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram
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DOI: 10.55681/armada.v3i5.1619
Penelitian ini bertujuan untuk menganalisis pengaruh penerapan musik tradisional terhadap tingkat stres dan produktivitas ternak ayam petelur. Stres pada ayam petelur dapat menurunkan kesehatan serta hasil produksi telur, sehingga pencarian metode alami untuk mengurangi stres sangat penting dalam budidaya ayam. Musik tradisional dipilih sebagai stimulasi lingkungan yang potensial karena dianggap memiliki frekuensi dan ritme yang menenangkan. Metode penelitian yang digunakan adalah eksperimen dengan desain randomized control trial, melibatkan dua kelompok ayam petelur, yaitu kelompok perlakuan yang diperdengarkan musik tradisional selama 8 jam per hari dan kelompok kontrol tanpa musik. Tingkat stres diukur melalui parameter fisiologis seperti kadar kortisol dan perilaku ayam, sedangkan produktivitas dinilai berdasarkan jumlah dan kualitas telur yang dihasilkan selama periode penelitian 30 hari. Hasil penelitian menunjukkan bahwa ayam yang mendengarkan musik tradisional memiliki penurunan signifikan kadar kortisol dan menunjukkan perilaku yang lebih tenang dibandingkan kelompok kontrol. Selain itu, produktivitas telur pada kelompok musik tradisional meningkat secara signifikan, baik dari segi jumlah maupun kualitas telur. Temuan ini mengindikasikan bahwa penerapan musik tradisional dapat menjadi metode efektif untuk mengurangi stres dan meningkatkan produktivitas ternak ayam petelur secara alami. Implikasi dari penelitian ini dapat menjadi dasar untuk pengembangan praktik budidaya ayam yang lebih berkelanjutan dan ramah terhadap kesejahteraan hewan.
Pengaruh model pembelajaran kolaboratif terhadap keterampilan menulis mahasiswa Program Studi Pendidikan Bahasa Jerman
Fatimah, Syarifah;
Alamsyah, Alamsyah;
Lestuny, Carolina
J-EDu: Journal - Erfolgreicher Deutschunterricht Vol 5 No 2 (2025): J-EDu: Journal - Erfolgreicher Deutschunterricht
Publisher : Universitas Pattimura
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DOI: 10.30598/J-EDu.5.2.105-113
Writing skills are one of the most challenging aspects of foreign language learning, including German. Students of the German Language Education Study Program, Faculty of Languages and Literature, Universitas Negeri Makassar, experience difficulties in producing grammatical and coherent texts. This study aims to determine the effect of applying the collaborative learning model on improving students’ writing skills. The research method used was an experimental design with a one-group pretest-posttest design. The participants were 16 second-semester students. The instrument used was a writing test assessed with a standardized rubric. Data analysis using a paired-sample t-test showed a significant improvement, with the average pre-test score increasing from 53.07 to 72.13 (tcount = 10.00 > ttable = 1.75). These findings indicate that the collaborative learning model is effective not only in enhancing the technical aspects of writing (grammar, vocabulary, and structure) but also in improving students’ motivation and social interaction. It is concluded that the collaborative learning model has a significant effect on students’ writing skills and can serve as an effective alternative teaching strategy in higher education.
The Relationship Between Phubbing Behavior and Student Empathy at Garudaya Bontonompo Vocational School
Muhrawati, Muhrawati;
Alamsyah, Alamsyah;
Putri, Rukiana Novianti
EDUTREND: Journal of Emerging Issues and Trends in Education Vol. 2 No. 3 (2025): EDUTREND: Journal of Emerging Issues and Trends in Education
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani
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DOI: 10.59110/edutrend.503
This study investigates the relationship between phubbing behavior and students’ empathy in the context of digital learning at SMKS Garudaya Bontonompo, Indonesia. The research employs a quantitative correlational design with a sample of 135 students selected through simple random sampling. Data were collected using two standardized instruments: the Phubbing Behavior Scale and the Empathy Scale, both of which demonstrated high reliability coefficients. Descriptive and inferential statistical analyses were conducted using SPSS version 23.0. The results indicate a strong and significant negative correlation (r = –0.806, p < 0.05) between phubbing behavior and empathy, suggesting that increased smartphone-centered activity corresponds with decreased emotional understanding. These findings emphasize the need for balanced digital engagement in educational settings and highlight the psychological impact of smartphone dependency on social-emotional development. The study recommends integrating social-emotional learning (SEL) and digital citizenship education into classroom practices to foster empathy and responsible technology use among students.
PERAN GURU PENDIDIKAN AGAMA ISLAM DALAM MENCIPTAKAN BUDAYA RELIGIUS DI UPT SPF SMPN 18 MAKASSAR
Ramadhani, Fadhila;
Nashir, Ahmad;
Alamsyah, Alamsyah
Jurnal Kinerja Vol 2 No 1 (2024): Kinerja : Jurnal Manajemen Pendidikan Islam
Publisher : LPPM Universitas Islam 45
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DOI: 10.33558/kinerja.v2i1.9483
The aims of this study were: to find out the learning interest of children with special needs (slow learning) at the 1st storey Mallengkeri Inpres Elementary School. the role of Islamic religious education teachers in increasing interest in learning children with special needs (Slow Learning) in learning Islamic religious education at the Mallengkeri Pres. Elementary School with Grade 1. The type of research used is a qualitative method. Data collection techniques in this study are observation, interviews, and documentation. The data sources in this study are primary and secondary data sources. Meanwhile, the objects in this study were school principals, Islamic religious education teachers, slow learning students at Grade I Mallengkeri Pres Elementary School. The results showed that slow learning students at Grade I Pres Mallengkeri Grade VI Elementary School had a high interest in learning. can be characterized by the level of craftsmanship and activeness of slow learning students in the learning given by Islamic religious education teachers at school. learning strategies carried out by Islamic religious education teachers in dealing with slow learning children at Grade I Mallengkeri Elementary School Grade VI, namely by implementing various strategies in learning such as using a student-centered learning approach with an individual system and a remedial approach, then the methods used by the teacher in Slow learning students are lecture methods, questions and answers, exercises, demonstrations with picture books about the practice of prayer and ablution, reward punishment and assignment methods.
Strategi Komunikasi Dinas Perhubungan Kabupaten Buton Utara Dalam Mensosialisasikan Keselamatan Berlalu Lintas
Suandi, Fikry;
Alamsyah, Alamsyah;
Sukma, Agus Hitopa Sukma;
Pranawukir, Iswahyu
Brand Communication Vol. 4 No. 3 (2025): Media Sosial dan Peradaban Manusia
Publisher : Prisani Cendekia Institute
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This study aims to determine the communication strategies implemented by the North Buton Regency Transportation Agency in its Traffic Safety Socialization activities and to identify the factors that hinder its implementation. This study uses qualitative research methods that produce descriptive data in the form of written or spoken words from people and observed behavior. Data were obtained using interview, observation, documentation, and triangulation methods from the source of the results of the Communication Strategy research conducted by the Safety Guidance Team (Bimkos Team) of the North Buton Regency Transportation Agency in the socialization program by identifying the communication targets, reviewing the message objectives through the material delivered and traffic order videos displayed in the socialization activities. Using face-to-face communication media (direct) with presentations and various official online media of the North Regency pioneer student community. Traffic Safety Guidance (Bimkes Team) as a communicator in communication plays a role in persuading its audience with an active speaking method in socialization activities. As well as the attractiveness and credibility of sources in the traffic safety socialization program. The communication strategy implemented by the Bimkos Team is intended to minimize the high level of accidents that occur. The results of the study show an increase in awareness and concern among students to comply with traffic regulations for the sake of mutual safety, but there are still obstacles that prevent this socicoalization program from running optimally. The obstacles experienced by the North Buton Regency Transportation Agency in implementing the Traffic Safety Socialization program come from internal and external factors
Strategi Komunikasi Pemasaran Digital Dalam Membangun Brand Awareness Brodo Melalui Akun Instagram
Arian, Vabrian Prima Dana;
Alamsyah, Alamsyah;
Sukma, Agus Hitopa;
Pranawukir, Iswahyu;
Nanda Barizki, Rezzi
Brand Communication Vol. 4 No. 3 (2025): Media Sosial dan Peradaban Manusia
Publisher : Prisani Cendekia Institute
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Penelitian ini bertujuan untuk mengetahui dan menganalisis strategi komunikasi pemasaran digital yang dilakukan oleh Brodo dalam upaya membangun brand awareness brand Brodo melalui akun Instagram. Demi meningkatkan penjualan komunikasi pemasaran yang dilakukan perusahaan sangatlah penting dan harus dilakukan sesuai dengan strategi yang tepat. Metode penelitian yang digunakan adalah pendekatan kualitatif dengan teknik wawancara mendalam kepada pihak manajemen dan konsumen Brodo dan dokumentasi. Hasil penelitian menunjukkan bahwa Strategi Komunikasi Pemasaran Digital Dalam Membangun Brand awareness Brodo Melalui Akun Instagram, maka didapatkan kesimpulan bahwa strategi komunikasi pemasaran digital Brodo melalui Instagram terbukti efektif dalam meningkatkan brand awareness dengan mengedepankan identitas brand yang kuat, visual yang konsisten, serta pendekatan interaktif melalui konten edukatif, storytelling, dan kolaborasi dengan KOL. Brodo juga menghadapi beberapa hambatan, seperti perubahan algoritma Instagram yang memengaruhi jangkauan konten, keterbatasan sumber daya dalam menghasilkan konten secara konsisten, serta persaingan dengan brand lokal lainnya yang semakin aktif di media sosial. Penelitian ini menegaskan bahwa strategi komunikasi pemasaran digital yang tepat di Instagram dapat secara efektif meningkatkan brand awareness dan mendorong keputusan pembelian.
Penerapan Model Deep-CNN Untuk Meningkatan Akurasi Klasifikasi Bahasa Isyarat Alfabet Menggunakan Algoritma Convolutional Neural Network
Al-Hafizh, Fadhl;
Alamsyah, Alamsyah
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 1 (2024): Volume 47 Nomor 1 Tahun 2024
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/4x9r3p15
Technological developments in the era of artificial intelligence have led to the development of computer systems capable of identifying sign language. Sign language is the primary means of communication for deaf and hard of hearing people used by millions of people around the world. This research aims to improve accuracy in alphabetic sign language recognition by using the Deep-CNN model. The method in this research starts from the selection of sign language datasets based on previous research. The dataset used in this study comes from Kaggle regarding American Sign Language which contains each training and test class representing a label (0-25) This dataset contains 27,455 training data and 7,172 test data. This research uses the Python programming language in performing data splitting, scaling, data augmentation, training, and evaluating. The architectural model built in this research is the Deep CNN architecture which is implemented to carry out the process of improving the accuracy of sign language recognition classification. The test results show an increase in the accuracy of alphabetic sign language classification compared to previous research. The increase in accuracy in the value of the Deep CNN model built managed to reach an accuracy rate of 99.72%. The model that has been built is the best model among previous research models.
Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet50V2 Untuk Mengidentifikasi Penyakit Pneumonia
Izzulhaq, Muhammad Agil;
Alamsyah, Alamsyah
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 1 (2024): Volume 47 Nomor 1 Tahun 2024
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/p532ny06
Pneumonia is a disease that infects the respiratory tract, disrupting the normal function of the human body. Viruses and bacteria are known as common causes of pneumonia. Identification of Pneumonia can use Convolutional Neural Network (CNN). CNN is an effective artificial neural network architecture for image analysis, inspired by how the human brain processes visual information. CNNs are capable of understanding the hierarchical features in images, from lines and angles to complex shapes and objects. This research aims to use ResNet50V2, a popular CNN architecture, to classify X-ray images as either normal or indicative of pneumonia, with the goal of creating an accurate and efficient diagnostic tool. The research method involves using X-ray image datasets for training, validation, and testing, using the ResNet50V2 CNN architecture. The test results show that ResNet50V2 achieves a pneumonia classification accuracy of 93.26%. This study innovatively explores alternative CNN architectures for pneumonia classification, focusing on ResNet50V2.
Peningkatan Hiperparameter Framework Deep Learning VGG-16 untuk Pendeteksian Tumor Otak pada Teknologi MRI
Alamsyah, Alamsyah;
Aulia, Ahmad Bagas Aditya Ilham
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 2 (2024): Volume 47 Nomor 2 Tahun 2024
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/n0vrqm85
Detection of human brain tumors through medical images still has limitations, so an accurate method is needed. This study aims to improve the ability of the modified VGG-16 model through hyperparameter adjustment in detecting human brain tumor MRI images. The dataset used comes from the Brain MRI Tumor Dataset on Kaggle, with four categories of brain tumors. The VGG-16 model was adjusted to improve accuracy, adjust brightness and contrast in data augmentation, and add a classification layer. Hyperparameters set include learning rate, batch size, epoch, and optimizer. The results showed an accuracy of 95.63%, precision 95.69%, recall 95.58%, and F1 score 95.57%. The applied model shows potential in improving the accuracy and efficiency of brain tumor diagnosis using MRI technology. Thus, the modification of the VGG-16 model in this study provides improved performance in brain tumor MRI image detection compared to previous studies.
Optimalisasi Algoritma Naïve Bayes untuk Klasifikasi Tweet Berbahasa Indonesia dalam Mengatasi Hate Speech di Platform X
Haikal, Muhammad;
Alamsyah, Alamsyah
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 2 (2024): Volume 47 Nomor 2 Tahun 2024
Publisher : Universitas Negeri Semarang
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DOI: 10.15294/zphjtr75
Hate speech is a form of expression used to express hatred and is often destructive, aimed at opposing individuals or certain groups for various reasons. Cases of hate speech are frequently found on social media, especially during election seasons, which occur regularly. To combat hate speech, monitoring actions are needed by censoring words that have the potential to offend and attack personal elements, such as ethnicity, religion, and race. Previous research has generally focused only on sentiment analysis of tweets to determine their positive or negative weights. This research continues the study on hate speech by developing the application of the Naive Bayes algorithm, specifically the Multinomial and Gaussian variants, along with an automatic censorship system aimed at improving classification accuracy. This system is implemented on social media with the hope of significantly reducing the amount of hate speech. From 13,169 tweets collected as the dataset, the data was classified into 12 categories with the highest accuracy rate being 90%. The test results are stored in the form of a hate speech dictionary that contains inappropriate words, allowing the algorithm to detect and automatically censor tweets containing hate speech