Adaptive Lighting System for Presence Detection and Indoor Room Brightness Control

According to the survey, 10% of the electricity used is for lights. Adaptive lighting is a term used to describe innovations that reduce energy consumption for lighting. Generally speaking, adaptive lighting is a highly developed system built with built-in sensors to react automatically without the assistance of the people to make a decision. The research aims to develop an adaptive lighting system with two main functions presence detection and fuzzy logic implementations for automatic brightness adjustments. Increased sensitivity in detecting the presence and movement of items, monitoring the lighting conditions in the room, and consideration of the system's energy efficiency, which was not the main emphasis of the previous study, are some improvements over earlier studies. The device was tested for five days to calculate the energy consumption efficiency of a 4.5 W bulb for 10 hours in total. With a 98.4 % accuracy rate, the adaptive lighting system has proven 74% more efficient than regular lighting

Perbandingan Convolutional Neural Network VGG16 dan ResNet34 pada Sistem Klasifikasi Sampah Botol

Hampir semua botol minuman kemasan yang beredar di masyarakat terbuat dari bahan plastik dikarenakan plastik merupakan bahan yang murah dan mudah dibentuk. Plastik adalah bahan non-organik yang sulit diuraikan sehingga botol plastik dapat menyebabkan pencemaran lingkungan. Sehingga diperlukan suatu solusi yang efektif untuk mengatasi kerusakan lingkungan yang disebabkan oleh sampah botol plastik. Salah satu solusi yag dapat dilakukan yaitu melakukan klasifikasi dan daur ulang sampah botol plastik. Pengklasifikasian sampah botol plastik dan sampah botol bukan plastik ke dalam kategori yang ditentukan sesuai dengan persyaratan kemudian didaur ulang agar dapat diolah kembali agar tidak merusak lingkungan. Artikel ini mengusulkan model VGG16 dan ResNet34 berbasis deep learning menggunakan CNN (Convolutional Neural Network) untuk mengidentifikasi dan mengklasifikasikan sampah botol. Berdasarkan hasil pengujian menggunakan Convolutional Neural Network, arsitektur VGG16 memiliki akurasi sebesar 90% dan ResNet34 memiliki akurasi sebesar 50% pada klasifikasi botol plastik dan bukan botol plastik. Masing-masing arsitektur menggunakan 10 epoch, 32 batch, 1655 gambar

Aspect-Based Analysis of Telkomsel User Sentiment on Twitter Using the Random Forest Classification Method and Glove Feature Expansion

In this modern era, people certainly very easy to access social media, one of which is Twitter. Twitter is usually used by the public in expressing opinions regarding current issues, product reviews, and many other things positive, negative, or neutral opinions, or can be interpreted as sentiment. This study aims to analyze the aspect-based sentiment of Telkomsel users on Twitter using random forest classification and the extension of the Glove feature. This study uses signal aspects and service aspects with a total dataset of 16988 data. A Random forest can be classified as relevant and accurate for sentiment analysis with the greatest accuracy of 80.37% in the signal aspect and 80.12% in the service aspect, and the expansion feature is proven to be able to increase the performance value of this study by 13.15% in the signal aspect. and 5.37% in the service aspect

Computer vision for sports

We explore theories and applications of Computer Vision (CV) in sports. We use the method proposed included: object, research question, search process, inclusion and exclusion, quality assessment, data collection, data analysis, and characteristics of the article. We review it based on problem, methods, interpretation, finding, and future work. We analyze it based on categories: recognition, motion, detection, classification, identification, and automation. Process CV in sports included computing technology, capture motion, multi-scenarios, application of statistical sports, output prediction, object measurement, performance, and object adjudication. We found that Machine Learning (ML) and Deep Learning (DL) were widely used on CV in sports. DL approach has more advantages than the ML approach because the DL approach is supported by high-performance computing and high-quality image datasets. The implication of this research is an artificial feature-based, multi-scenarios, syntaxis method, rapid prototype, indoor localization, and gaze method as big challenge and new potential research for CV in sports.

TATOPSIS: A decision support system for selecting a major in university with a two-way approach and TOPSIS

Several problems can occur when students feel they have made the wrong choice of major in university. Choosing a major is one of the problems that students often face. Therefore, this study aims to develop a Decision Support System (DSS) to help students find majors that match their interests and abilities. This DSS proposes a two-way approach by considering students and the major's requirements, standards, and characteristics. The DSS utilizes the TOPSIS method; therefore, it is called TATOPSIS, which stands for Two-way Approach TOPSIS. It showed that the two-way approach in Scenario 1 (without score normalization) and Scenario 3 (with score normalization) shows better agreement results in 78.33% and 73.33% than the two-way approach for Scenario 2, Scenario 4, and the student-one-way approaches

Prediksi Siswa Putus Sekolah Swasta Menggunakan Algoritma Bayesian Network (Studi Pada : SMA Islam Al Wahid Kepung)

Masalah siswa putus sekolah di SMA Islam Al Wahidmembawa dampak kepada sekolah antara lain berkurangnya bantuan operasional yang diterima, berkurangnya jumlah rombongan belajar, dan hutang biaya siswa. Mempertimbangkan dampaknya bagi sekolah, penelitian ini bertujuan mengembangkan sistem prediksi dini siswa putus sekolah. Penelitian menggunakan Bayesian Network (BN) dengan tujuan mengetahui faktor yang paling berpengaruh, di mana tugas tersebut tidak dapat diselesaikan menggunakan naive bayes. Jumlah data yang digunakan dalam penelitian ini berjumlah 77 siswa dengan 18 siswa berlabel putus sekolah. Hasil dari penelitian ini menghasilakn sebuah model dengan akurasi bernilai 0,935 dan nilai area under curve sebesar 0,948. Struktur BN memperlihatkan bahwa faktor nilai rerata, mengikuti ekstrakurikuler, dan penghasilan ayah merupakan faktor yang paling berpengaruh terhadap siswa putus sekolah. Struktur BN memperlihatkan bahwa faktor nilai rerata, mengikuti ekstrakurikuler, dan penghasilan ayah merupakan faktor yang paling berpengaruh terhadap siswa putus sekolah

Large-scale integrated infrastructure for asynchronous microservices architecture

Integrated large-scale business activities increasingly rely on the use of remote resources and services across multi-platform applications. Microservice in previous research has become a solution, but this approach still leaves a data loss problem. This research methodology proposed an architecture of data transmission managed by messaging service to prevent data loss in handling many requests to deliver a multiplatform architecture, handling the plugin services, and enabling escalation based on the requirement. As a result, this research successfully implements large-scale multiplatform Single Sign-On (SSO) infrastructure for asynchronous microservices architecture. The system test results show that the developed system can handle up to 2000 requests with 20 concurrent requests

Data scaling performance on various machine learning algorithms to identify abalone sex

This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling

Spatial Skyline Query Based on Surrounding Environment Untuk Data Streaming Menggunakan Apache-Spark

Previous research on Spatial Skyline Query Based on Surrounding Environment left a challenge in finding skyline objects that support the use of mobile devices. This study introduces a method that allows users to search for spatial objects dynamically. Cloud-based streaming data services are currently available to support the dynamic search of spatial skyline objects. Under these conditions, streaming data requires a longer processing time. This study aims to examine the effectiveness and efficiency of Apache-Spark in developing Spatial Skyline Query Based on Surrounding Environment in processing streaming data. Further implementation of the developed algorithm can provide better location access for users on mobile devices. Comparative analysis of algorithm execution time is performed by comparing algorithm processing on a single processor and cluster computing using various evaluation parameters. The test results on each parameter show that the computation time of the proposed algorithm on a single computation is not as good as the previous algorithm. However, in cluster computing, the proposed algorithm is superio

Edge Detection Analysis using Roberts, Sobel, Prewitt and Canny Methods

Edge identification in a digital image is overgrowing in line with advances in computer technology for image processing. Edge detection becomes vital in recognizing the object of an image because the edge of the object in the image contains critical information. The information obtained can be either the size or shape of the object in the image, so the edge quality must be good so that the information contained in it is not lost. This study uses edge detection with the Roberts, Sobel, Prewitt, and Canny methods. The assessment method uses visual analysis, PSNR, Histogram, and Contrast. The study shows that the calculation of PSNR on the Roberts method has the highest value, with an average of 44.19 dB. Sobel, Prewitt, and Canny operators have PSNR values above 30 dB to classify it as a good image. The histogram value with the highest value is the Sobel operator, with an average histogram value of 22.06. In contrast, the highest contrast value is the Canny operator has an average contrast value of 5.08. The Roberts and Canny operators have the best image quality.Border identification in a digital image is overgrowing in line with advances in computer technology for image processing. Edge detection becomes vital in recognizing the object of an image because the edge of the object in the image contains critical information, the information obtained can be either the size or shape of the object in the image so the edge quality must be good so that the information contained in it is not lost. This study uses edge detection with the Roberts, Sobel, Prewitt, and Canny method. The analysis shows that the calculation of PSNR on the Robetrs method has the highest value with an average of 44.19 dB, Sobel, Prewitt and Canny operators have PSNR values above 30 dB so that it is classified as a good image. The histogram value with the highest value is the Sobel operator with an average histogram value of 22.06, while the highest contrast value is the Canny operator has an average contrast value of 5.08. Based on testing, it can be concluded that the Roberts and Canny operators have the best image quality