Procentage of E-commerce Utilization among UIN Maulana Malik Ibrahim Malang Students

 This study aims to discover the influence of e-commerce on student life, both in terms of understanding and its use. The variables analyzed include the level of understanding, the experience of a transaction through e-commerce, reasons for choosing e-commerce, e-commerce knowledge sources, and a list of e-commerce sites they have used. Respondents were students of the State Islamic University of Maulana Malik Ibrahim Malang. A simple random sampling technique used in this study. As for the data collection, using a questionnaire technique. The obtained data will be processed and analyzed. Generally, the results of the study explain that most students know ecommerce, but the use is still not optimal, even very less

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues

Metode Steganografi Citra Digital

Steganografi merupakan teknik penyembunyian data dalam media. Dalam penyembunyiannya, pesan rahasia disisipkan pada media pembawa(carrier file) antara lain, teks, gambar, audio dan video. Salah satu carrier file dalam steganografi adalah gambar/citra digital, yang merupakan media yang paling sering digunakan dalam pertukaran data melalui internet. Dalam review literatur ini, akan dijelaskan tentang metode steganografi pada citra digital, seperti LSB, MSB, DCT, DWT, Spread Spectrum dan  BPCS. Termasuk penjelasan tentang perbandingan berupa kelebihan dan kelemahan dari masing-masing metode. Dengan melihat dan mempelajari beberapa metode tersebut diharapkan pengembangan yang dilakukan akan lebih baik dan dapat menutupi kekurangan sebelumnya

Can Multinomial Logistic Regression Predicts Research Group using Text Input?

While submitting proposals in SISINTA, students often confuse or falsely submit their proposals to the less relevant or incorrect research group. There are 13 research groups for the students to choose from. We proposed a text classification method to help students find the best research group based on the title and/or abstract. The stages in this study include data collection, preprocessing data, classification using Logistic Regression, and evaluation of the results. Three scenarios in research group classification are based on 1) title only, 2) abstract only, and 3) title and abstract. Based on the experiments, research group classification using title-only input is the best overall. This scenario gets the most optimal results with accuracy, precision, recall, and f1-score successively at 63.68%, 64.91%, 63.68%, and 63.46%. This result is sufficient to help students find the best research group based on the text titles. In addition, lecturers can comment more elaborately since the proposals are relevant to the research group’s scope

THE EFFECT OF GOOGLE SKETCHUP AND NEED FOR ACHIEVEMENT ON THE STUDENTS’ LEARNING ACHIEVEMENT OF BUILDING INTERIOR DESIGN

This study aims to find out the effect of the Google SketchUp application and the need for achievement on students’ learning achievements of building interior design. Quasi-experimental research was conducted at the Vocational High School (VHS) with a 2 x 2 factorial design. The Google SketchUp application was used in the experimental group, while the PowerPoint Slides were used in the control group. The sample consisted of 56 VHS students, study program of modeling design and building information. The instruments used are need for achievement tests and learning achievement tests with reliability coefficients of 0.916 and 0.671. To test the hypothesis using multiple variance analysis techniques and the Tukey test. The results show that the Google SketchUp application is more effective than the PowerPoint in the learning of building interior design. Students who classified with a high need for achievement earn higher learning achievement compared to the lower one. There is an interaction between the Google SketchUp application and the students’ need for achievement. For students who have a high need for motivation, using the Google SketchUp application is more effective than using PowerPoint Slides. On the other side, the students who have a low need for motivation, the use of the Google SketchUp application does not differ significantly compared to the use of PowerPoint Slides. This finding is very useful for vocational teachers as an effort to improve the learning process of building interior design. However, it is also possible that these findings can apply more broadly to student learning in other skills competencies in VHS. These findings contribute to the management of vocational education as an effort to implement VHS revitalization. Furthermore, it also can be used as a consideration by the Head of VHS and decision-makers in the Ministry of Education and Culture

Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models

Social informatics and CDIO: revolutionizing technological education

Social informatics is an interdisciplinary area that examines how information and communication technologies (ICT) and the complex web of social and cultural contexts interact and change over time. This study not only helps with the design and use of ICT but also shows how these technologies significantly affect society and culture. It encourages new ideas, collaborations between different fields, and policymaking insights, which drives technological innovation and a better knowledge of how ICT affects society. The Conceive, Design, Implement, operate (CDIO) educational system stands out as a new and innovative teaching method. It emphasizes active learning and gives engineering students both technical and social skills. Its use in social informatics ushers in a new era of education that combines innovation and technology to help students become strong and independent. Future study on CDIO programs in social informatics education has the potential to augment the technical proficiency and social consciousness of graduates, thereby rendering them significant contributors to the field

Perbandingan Metode Prediksi pada Bidang Bisnis dan Keuangan

Pada era globalisasi ini peran teknologi telah merambah ke berbagai bidang seperti di bidang bisnis dan keuangan. Ekonomi dunia yang selalu berubah-ubah menuntut para investor dan pebisnis untuk dapat mengambil keputusan tepat dan cepat. Peran teknologi diperlukan untuk menghadapi perubahan-perubahan yang terjadi sehingga dapat meminimalisir kekhawatiran investor akan kerugian yang akan dialami. Oleh karena itu, diperlukan suatu pemanfaatan teknologi untuk memprediksi perubahan-perubahan yang akan terjadi dikemudian hari. Salah satu teknologi yang dapat dimanfaatkan adalah dengan menggunakan teknik dan metode data mining. Metode ini memungkinkan untuk melakukan prediksi berdasarkan data sebelumnya pada periode waktu tertentu. Usaha untuk mendapatkan hasil prediksi yang akurat masih terus dilakukan. Maka dari itu perlu adanya pengetahuan mengenai berbagai macam metode yang digunakan dalam prediksi untuk dapat menentukan metode yang tepat dari berbagai kasus dan menghasilkan hasil yang akurat

Face Images Classification using VGG-CNN

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%)

Stemming javanese affix words using nazief and adriani modifications

Stemming is the process of finding a basic word with several stages of affix removal. The main reason for stemming is to check spelling and machine translation and to support the effectiveness of the retrieval process. This study uses the Nazief and Adriani algorithm for stemming Javanese-influenced words. The first step taken is data collection and making a basic word dictionary. Then do the stemming process. Before stemming, modifications are made to the rules. The rules of the Nazief and Adriani algorithm, which are based on the morphology rules of the Indonesian language, are modified to suit the morphological rules of the Javanese language. Of the 366 words that were tested, it produced 351 correct basic words and 15 basic words that experienced errors. The results show that this algorithm can be used for stemming Javanese with an accuracy value of 95.9%