Widi Hastomo’s research while affiliated with Institute of Business and Information Technology University of the Punjab and other places

Kampanye tumbler telah berhasil meningkatkan kesadaran siswa dan komunitas sekolah tentang dampak negatif limbah plastik terhadap lingkungan. Melalui kampanye ini, pola konsumsi siswa dan seluruh entitas sekolah mengalami perubahan positif, dengan lebih banyak orang beralih dari penggunaan plastik sekali pakai ke penggunaan tumbler yang ramah lingkungan. Komitmen siswa terhadap penggunaan tumbler mencapai 93%, yang membuktikan pentingnya pendidikan dalam membentuk perilaku berkelanjutan dan gaya hidup ramah lingkungan. Kampanye ini berhasil meningkatkan pemahaman tentang pentingnya tumbler sebagai alternatif pengganti plastik sekali pakai, serta diharapkan dapat mendorong sekolah untuk menerapkan kebijakan pengurangan limbah plastik di lingkungan sekolah. Kegiatan pengabdian masyarakat ini merupakan integrasi kampanye tumbler dalam dunia pendidikan, di mana sekolah memiliki kesempatan untuk melibatkan siswa sebagai agen perubahan. Melalui edukasi yang menyeluruh, siswa dapat memahami dampak besar dari perubahan kecil yang mereka lakukan, seperti beralih menggunakan tumbler. Namun, dampak kampanye ini belum dapat diukur secara menyeluruh karena hanya melibatkan dua kelas (X-XI). Agar kegiatan ini dapat berkelanjutan, partisipasi aktif dari guru sangat diperlukan. Kampanye selanjutnya dapat melibatkan orang tua dan masyarakat sekitar sekolah, serta menggunakan konten visual yang menarik untuk menyampaikan pesan lebih efektif.

Artikel ini membahas pelaksanaan kegiatan pengabdian masyarakat melalui pelatihan komputer dasar dan Microsoft Office bagi guru Pendidikan Usia Dini (PAUD). Tujuan dari kegiatan ini adalah untuk meningkatkan keterampilan teknologi informasi guru-guru PAUD sehingga mereka dapat lebih efektif dalam mengelola administrasi dan mendukung proses pembelajaran. Pelatihan ini mencakup materi pengenalan komputer, penggunaan dasar Microsoft Word, Excel dan PowerPoint, serta penggunaan internet dan email, selain itu pengelolaan email menggunakan Microsoft Outlook. Hasil dari pelatihan menunjukkan peningkatan signifikan dalam keterampilan teknologi informasi bagi peserta.

This research compares the performance of two popular datasets, Labelled Faces in the Wild (LFW) and faces94, in the task of face recognition using Convolutional Neural Networks (CNN) algorithms. The LFW dataset is known for its high variation in pose, lighting, and expression, while faces94 is more structured with more uniform lighting and pose conditions. CNNs were chosen for their ability to extract important features from face images for classification. In this study, a CNN model was trained on both datasets and its performance was evaluated using accuracy, precision, and recall metrics. The experimental results showed that the model trained on the faces94 dataset achieved higher accuracy compared to the model trained on the LFW dataset. However, the model on the LFW dataset demonstrated better resilience to variations in lighting and pose conditions. These findings indicate that while a more structured dataset like faces94 can produce a model with high accuracy under testing conditions similar to the training data, a dataset with greater variation like LFW is more suitable for real-world applications involving diverse conditions. This study provides important insights into the selection of datasets for developing robust and accurate face recognition systems.

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.

Kualitas permukaan baja yang diproduksi sangat penting untuk meningkatkan daya saing dalam industri baja. Tingginya tingkat cacat pada permukaan baja merupakan masalah serius yang berdampak pada kualitas keluaran. Pengendalian yang masih dilakukan secara manual dan visual saat ini hanya dapat dilakukan oleh orang-orang dengan bakat dan keahlian tertentu. Pengamatan dengan metode konvensional ini memerlukan waktu yang lama, lamban, dan presisi yang rendah. Saat ini, perkembangan teknik pembelajaran mendalam memungkinkan deteksi cacat permukaan baja secara otomatis dengan tingkat akurasi yang tinggi. Arsitektur Xception digunakan dalam pekerjaan ini untuk menerapkan strategi pembelajaran mendalam. Teknik permutasi dan augmentasi digunakan untuk mengatasi ketidakseimbangan data. Model yang dikembangkan dapat membedakan empat jenis cacat pada permukaan baja. Koleksi 7.095 foto permukaan baja digunakan dalam prosedur pelatihan. Jika dibandingkan dengan tidak menggunakan transfer learning, hasil pengukuran kinerja proses pelatihan dengan menggunakan transfer learning (Imagenet) menunjukkan hasil yang lebih baik. Pelatihan pembelajaran transfer menghasilkan skor akurasi masing-masing sebesar 94,9% dan 97,7% untuk data pelatihan dan validasi. Sedangkan hasil penilaian nilai kerugian untuk data latih dan validasi masing-masing sebesar 19,4% dan 14,4%.

span lang="EN-US">A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.</span

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.

Abstrak: Sekolah merupakan salah satu sumber penghasil sampah. Untuk itu diperlukan sosialisai dan edukasi sejak dini mengenai sampah sebagai langkah membangun kultur zero waste di sekolah. Pengabdian masyakarat dilaksanakan di SMP dan SMK Faradisa Islamic School berlokasi di Bambu Apus, Pamulang, Tangerang Selatan . Tujuan kegiatan ini adalah mengedukasi sampah sejak dini. Sekolah dipilih sebagai mitra karena merupakan instansi tempat belajar siswa. Dengan adanya sosialasi mengkampanyekan sekolah tanpa sampah diharapkan bisa mereduksi sampah dilingkungan sekitar. Penggunaan metode pada pengabdian ini berupa penyuluhan dan praktik. Metode penyuluhan dengan memberikan materi disatu waktu, sedangkan praktik dilakukan dengan memberikan tumbler gratis kepada peserta. Dengan adanya pembagian tumbler gratis diharapkan memberikan budaya dan kebiasan tidak menggunakan botol plastik sekali pakai. Pengabdian masyakarat dilaksanakan di SMP dan SMK Faradisa Islamic School berlokasi di Bambu Apus, Pamulang, Tangerang Selatan. Pengabdian masyarakat ini dihadiri oleh 50 peserta yang merupakan siswa SMK Faradisa Islamic school. Capaian pengabdian masyarakat ini yaitu peningkatan softskill berupa pengetahuan mengenai sampah dan dampak terhadap lingkungan. Sedangkan peningkatan hardskill berupa tindakan konkrit berupa penggunaan tumbler sebagai kampanye sekolah bebas tanpa sampah. Berdasarkan monitoring dan evaluasi kegiatan, sebesar 98,8% sangat setuju dan setuju bahwa kegiatan ini memberikan manfaat terhadap mitra.Abstract: Schools are one source of waste production. For this reason, early socialization and education regarding waste is needed as a step to build a zero waste culture in schools.Community service is carried out at Faradisa Islamic Middle School and Vocational School located in Bambu Apus, Pamulang, South Tangerang. The aim of this activity is to educate waste from an early age. The school was chosen as a partner because it is an institution where students study. With the outreach campaigning for schools without waste, it can reduce waste in the surrounding environment. The methods in this community service are counseling and practicing. The counseling method is by providing material at one time, while practice is carried out by giving free tumblers to participants. With the distribution of free tumblers, and it will provide a culture and habit of not using single-use plastic bottles. Community service is carried out at SMP and SMK Faradisa Islamic School located in Bambu Apus, Pamulang, South Tangerang. This community service was attended by 50 participants who were students of Faradisa Islamic School Vocational School. The achievement of this community service is the increase in soft skills in the form of knowledge about waste and its impact on the environment. While increasing hard skills in concrete actions in using a tumbler as a free school campaign without waste. Based on activity monitoring and evaluation, 98.8% strongly agreed and agreed that this activity provided benefits to partners.

The purpose of this research was to apply exploratory data analysis techniques to building energy meters, such as electricity, cold, heat, and steam meters. A thorough understanding of energy usage patterns becomes increasingly vital in an era of growing awareness of energy management and sustainability. Trends, patterns, and anomalies can be identified in building energy meter data using meticulous data exploration approaches, which can give significant insights for increasing energy efficiency. Exploratory data analysis combined with machine learning approaches may be was used to reveal hidden patterns of energy usage and examine the links between relevant factors. The findings of this exploratory data analysis gave vital insights into building energy use trends. Some significant and hidden information that was crucial for understanding energy usage within a certain time frame in each building was discovered via the investigation of the data used in this study. Steam had the highest use, whereas electricity had the lowest. Utilities were more popular before 5 a.m., followed by healthcare, with daytime use hours beginning around 10 a.m., depending on the area. During the working day, the industry needs more energy. Places of worships use more energy on weekends. There was a significant relation between the number of floors and spaces per level of a building and the height meter reading between May and October. There is a significant association between the kind of buildings used for schools, workplaces and high energy use. This study significantly contributed to the management of the energy and sustainability domains. Using exploratory data analysis and machine learning approaches to building energy meters could optimize energy usage, minimize running costs, and enhance overall energy efficiency. This research is still very open to be continued using other methods, to obtain other hidden information.