An artificial neural network for redundant robot inverse kinematics computation

A redundant manipulator can be defined as a manipulator that has more degrees of freedom than necessary to determine the position and orientation of the end effector. Such a manipulator has dexterity, flexibility, and the ability to maneuver in presence of obstacles. One important and necessary step in utilizing a redundant robot is to relate the joint coordinates of the manipulator with the position and orientation of the end-effector. This specification is termed as the direct kinematics problem and can be written as x = f(q) where x is a vector representing the position and orientation of the end-effector, q is the Joint vector, and f is a continuous non-linear function defined by the design of the manipulator. The inverse kinematics problem can be stated as: Given a position and orientation of the end-effector, determine the joint vector that specifies this position a q = f -1(x). and orientation. That is, For non-trivial designs, f -1 cannot be expressed analytically. This paper presents a solution to the inverse kinematics problem for a redundant robot based on multilayer feed-forward artificial neural network

SIMULASI DAN ANALISIS MULTIPLE OBJECT TRACKING BERBASIS PENGOLAHAN CITRA DIGITAL DAN PARTICLE SWARM OPTIMIZATION

Object tracking merupakan suatu bidang pada computer vision yang mempelajari cara melacak suatu objek yang bergerak pada suatu ruang. Objek yang dilacak merupakan objek yang sudah ditentukan. Dalam pengaplikasiannya di dunia nyata elacakan suatu objek bergerak sangat berguna untuk berbagai hal seperti: pengenalan gerakan, pelacakan kendaraan, pelacakan manusia yang dalam beberapa kasus melacak atlit olahraga, dan augmented reality. Dalam aplikasinya, merealisasikan suatu sistem object tracking memiliki beberapa tantangan antara lain adanya noise, kekacauan oklusi, dan perubahan dinamis dalam gerakan objek. Sehingga pada tugas akhir ini, dirancang sebuah sistem multiple object tracking dengan metode particle swarm optimization. Sistem pelacakan ini bekerja dengan masukan secara non-real time berupa video yang berisi objek bergerak yang telah direkam sebelumnya. Jumlah objek terbanyak yang dilacak oleh sistem dalam satu kali pengujian mencapai 4 objek. Objek yang akan dilacak menjadi masukan sistem yang kemudian cirinya akan diekstrak menggunakan histogram warna. Selanjutnya objek tersebut akan dilacak dengan metode particle swarm optimization. Proses pelacakan dilakukan dengan cara membangkitkan random partikel pada area dekat dengan objek dan membandingkan histogram warna partikel dengan objek target menggunakan bhattacarya coefficient yang merupakan proses observasi untuk menghitung kemungkinan dari partikel tersebut yang mempunyai kesamaan histogram dengan objek target. Pengujian pada sistem ini dengan menggunakan parameter yang diubah-ubah yaitu jumlah partikel, jumlah objek dan kondisi perekaman objek dengan mengukur tingkat akurasi, waktu yang dibutuhkan untuk pelacakan serta frame rate. Tingkat akurasi terbaik pada pelacakan dengan jumlah partikel 50 di setiap swarm tetapi jumlah partikel yang semakin banyak akan menyebabkan waktu komputasi semakin lama. Sistem dapat berjalan dengan baik pada pengujian multiple objek. Dibandingkan dengan pelacakan menggunakan metode HOG, particle swarm optimization memiliki waktu pemrosesan yang jauh lebih singkat dengan akurasi yang tidak jauh berbeda. Pada proses pelacakan menggunakan PSO, jumlah objek akan mempengaruhi waktu yang dibutuhkan untuk memproses pelacakan. Kata kunci: multiple object tracking, particle swarm optimization, histogram warna

Implementasi Logika Fuzzy Mamdani Dalam Game Simulasi Memancing

Game simulasi memancing merupakan sebuah permainan yang menirukan kegiatan memancing. Game simulasi memancing terlihat menarik apabila game tersebut benar-benar mempresentasikan kejadian nyata secara detail. Untuk memenuhi hal tersebut, game memancing dapat disisipkan hal-hal yang berkaitan dengan habitat dan perilaku ikan yang ada didunia nyata. Perilaku ikan berupa waktu makan ikan dan kedalaman air. Penelitian ini bertujuan untuk mencari suatu model yang dapat digunakan dalam menentukan suatu jenis ikan berdasarkan habitat dan perilaku ikan. Metode yang digunakan dalam penentuan ikan tersebut adalah dengan menggunakan logika fuzzy Mamdani dengan agregasi operator OR (max), dengan metode mean of maximum sebagai defuzzifikasi, sedangkan masukan berupa waktu pemancingan dan kedalaman umpan pancing. Dari hasil pengujian game didapatkan bahwa tingkat akurasi sebesar 86.9% dalam menentukan kelompok ikan yang didapat yaitu diurnal atau nokturnal

CLASSIFICATION OF BENTHIC HABITAT BASED ON OBJECT IN SHALLOW WATERS OF KARANG LEBAR AND LANCANG ISLAND

Teknik klasifikasi berbasis objek (OBIA) merupakan salah satu teknik pemetaan habitat bentik selain metode konvensional (berbasis piksel). Pemetaan metode OBIA dengan memanfaatkan algoritma machine learning terbatas pada perairan Karang Lebar dan Pulau Lancang. Penelitian ini bertujuan untuk mengetahui performa algoritma machine learning (support vector machine (SVM), decision tree (DT), random forest (RF), dan k-nearest neighbour (KNN) dalam mengklasifikasikan habitat bentik perairan dangkal berdasarkan objek menggunakan data satelit Sentinel-2. Metode klasifikasi yang digunakan adalah metode OBIA dengan dua tingkatan analisis. Hasil analisis Agglomerative Hierarchial Clustering diperoleh sebanyak 6 kelas habitat bentik yaitu karang, patahan karang (rubble), lamun, pasir rubble, dan pasir. Tingkat pertama adalah memisahkan darat, laut dangkal dan laut lebih dalam. Tingkat kedua adalah klasifikasi menggunakan algoritma machine learning, hasil klasifikasi menunjukkan alogritma SVM mendapatkan nilai akurasi yang lebih tinggi dibandingkan algoritma lainnya dengan akurasi sebesar 84% di perairan Karang Lebar, kemudian pada perairan Pulau Lancang mendapatkan akurasi sebesar 80% dengan algoritma SVM. Habitat dasar perairan dangkal Karang Lebar dan Pulau Lancang mampu dipetakan dengan baik menggunakan metode OBIA. Perbedaan tingkat akurasi antara perairan Karang Lebar dan Pulau Lancang disebabkan oleh tingkat kekeruhan perairan.The object-based classification technique (OBIA) is one of the benthic habitat mapping techniques besides the conventional (pixel-based) method. The mapping of the OBIA method using machine learning algorithms is limited to the waters of Karang Lebar and Lancang Island. This study aims to determine the performance of machine learning algorithms (support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN)) in classifying shallow water benthic habitats based on objects using Sentinel satellite data. -2. The classification method used is the OBIA method with two levels of analysis. A total of 6 benthic habitat classes were obtained from field observations and Agglomerative Hierarchial Clustering analysis, namely coral, rubble, seagrass, rubble sand, and sand. The results obtained include the first level separating land, shallow sea and deeper sea. The second level is classification using a machine learning algorithm, the results of the classification show that the SVM algorithm gets a higher accuracy value than other algorithms with an accuracy of 84% in Karang Lebar waters, then in Lancang Island waters it gets an accuracy of 80% with the SVM algorithm. The bottom habitat of the shallow waters of Karang Lebar and Lancang Island can be well mapped using the OBIA method. The difference in the level of accuracy between the waters of Karang Lebar and Pulau Lancang is caused by the level of turbidity of the waters

Sentiment analysis of wayang climen using naive bayes method

This research focuses on sentiment analysis of Wayang Climen performances in Indonesia using the Naïve Bayes algorithm. Wayang, a traditional puppet show, holds cultural significance and has persisted alongside modern entertainment options. The study collected public comments from Dalang Seno and Ki Seno Nugroho's YouTube channels, classified them into positive, negative, and neutral sentiments, and employed a translation process to align comments with program language objectives. Preprocessing steps included case folding, removing punctuation, tokenizing, stopword removal, and post-tagging. To address data class imbalances, resampling was performed using the Synthetic Minority Oversampling Technique (SMOTE). The Naïve Bayes algorithm was utilized for data classification, exploring various translation scenarios. Evaluation involved the confusion matrix method and metrics like accuracy, precision, recall, and f-measure. Results demonstrated that the Dalang Seno train data scenario outperformed Ki Seno Nugroho's, with higher precision, recall, accuracy, and f-measure values. Additionally, the translation scenario from Indonesian to English yielded the most effective results. In conclusion, this study highlights the suitability of the Naïve Bayes algorithm for sentiment analysis in the context of Wayang Climen performances, with practical implications for understanding public sentiment in the digital age

Desain Bus Trans Sarbagita Sebagai Sarana Transportasi Umum Modern Yang Ramah Lingkungan

Provinsi Bali mempunyai masalah kemacetan yang berat dan kompleks. Salah satunya karena transportasi umum yang mulai ditinggalkan masyarakat karena kualitas yang rendah. Maka saat ini pemerintah Bali mulai memprakarsai bus rapid transit sebagai salah satu solusi kemacetan yang diberi nama Trans Sarbagita. Sarbagita merupakan akronim dari Denpasar, Badung, Gianyar, dan Tabanan yang merupakan kabupaten dan kota yang berada dalam satu kawasan geografis dan ekonomi. Namun desain Bus Trans Sarbagita masih banyak mempunyai kekurangan. Sehingga dirasa perlu melakukan redesain dengan mempertimbangkan teknologi BRT di dunia yang sudah mulai berkembang. Setelah dilakukan redesain diharapkan orang-orang menjadi lebih tertarik menggunakan transportasi umum. Pada akhirnya Trans Sarbagita akan menjadi ikon baru pariwisata Bali dan menjadi sarana transportasi umum yang berkualitas bagi masyarakat. ======================================================================================================= Province of Bali has a heavy and complex congestion problems. For one thing, public transportation began to be abandoned by society because of its poor quality. So this time the Bali government began to initiate a bus rapid transit as a solution to congestion, named Trans Sarbagita. Sarbagita an acronym of Denpasar, Badung, Gianyar and Tabanan regency and city which is located in one geographic region and economy. However the design of Bus Trans Sarbagita still have many deficiencies. Thus perceived need to do a redesign considering BRT technology in the world which have already begun. After conducting redesign expected that people are becoming more interested in using public transport. Trans Sarbagita Ultimately will become the new icon of tourism in Bali and be a means of public transport which qualified for the public

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

Performance of Ensemble Classification for Agricultural and Biological Science Journals with Scopus Index

The ensemble method is considered an advanced method in both prediction and classification. The application of this method is estimated to have a more optimal output than the previous classification method. This article aims to determine the ensemble's performance to classify journal quartiles. The subject of agriculture was chosen because Indonesia is an agricultural country, and the interest of researchers in this field shows a positive response. The data is downloaded through the Scimago Journal and Country Rank with the accumulation in 2020. Labels have four classes: Q1, Q2, Q3, and Q4. The ensemble applied is Boosting and Bagging with Decision Tree (DT) and Gaussian Naïve Bayes (GNB) algorithms compiled from 2144 instances. The Boosting meta-ensembles used are Adaboost and XGBoost. From this study, the Bagging Decision Tree has the highest accuracy score at 71.36, followed by XGBoost Decision Tree with 69.51. The third is XGBoost Gaussian Naïve Bayes with 68.82, Adaboost Decision Tree with 60.42, Adaboost Gaussian Naïve Bayes with 58.2, and Bagging Gaussian Naïve Bayes with 56.12 results. This paper shows that the Bagging Decision Tree is the ensemble method that works optimally in this subject classification. This result suggests that the ensemble method can still fail to produce an ideal outcome that approaches the SJR system