Analisis Kinerja Perilaku Mobile Robot Penghindar Halangan dengan Fungsi Keanggotaan Non Linear pada Kendali Logika Fuzzy Sugeno

Mobile robot banyak diaplikasikan pada berbagai aspek kehidupan. Navigasi robot merupakan salah satu sistem yang mampu melakukan navigasi yang terdiri dari aktivitas pergerakan seperti menghindari halangan (obstacle avoidance). Navigasi robot mencakup berbagai aktivitas yang saling terkait seperti aktuasi, persepsi dan eksplorasi. Penentuan navigasi yang baik menjadikan robot dapat melakukan eksplorasi yang bebas dari tabrakan dengan penghalang atau robot lain. Penelitian ini dikembangkan dengan menggunakan metode kendali logika Fuzzy dengan fungsi keanggotaan non linear, karena metode logika Fuzzy memiliki kemampuan untuk lebih merepresentasikan dunia nyata. Penelitian ini menghasilkan perancangan model kendali logika Fuzzy dan kemudian diterapkan pada suatu aplikasi perangkat lunak yang dapat mengendalikan robot hingga sukses menghindari halangan dengan baik dalam lingkungan virtual kompleks yang spesifik, dimana fungsi keanggotaan non linear dapat mengendalikan robot untuk menghindari halangan pada lingkungan virtual spesifik yang kompleks dengan lebih smooth dan lebih baik

Sistem Navigasi Robot Hexapod Menggunakan Behavior Dan Learning Vector Quantization

Robot banyak digunakan dalam berbagai bidang pekerjaan, salah satunya seperti pada mobile robot jenis berkaki untuk navigasi di dalam lingkungan yang kompleks. Robot berkaki atau dikenal dengan robot hexapod merupakan robot yang meniru pola gerak makhluk hidup. Pada penelitian ini telah dirancang robot hexapod yang dilengkapi dengan lima buah sensor jarak ultrasonik untuk bernavigasi di dalam lingkungan. Adapun tujuan robot hexapod ini bernavigasi untuk aplikasi robot patroli yang bekerja di lingkungan yang sulit diprediksi. Sistem navigasi pada robot hexapod menggunakan metode behavior based dan Learning Vector Quantization (LVQ). Pada robot hexapod memliki lima behavior, diantaranya behavior bergerak maju, mengikuti dinding kiri, mengikuti dinding kanan, mengikuti jalur koridor dan menghindar halangan. Pada masing-masing behavior tersebut untuk bernavigasi menggunakan LVQ. Salah satu behavior akan diaktifkan jika robot menerima masukan (stimuli) nilai jarak tertentu dari sensor jarak. Percobaan dilakukan dalam arena dengan kondisi yang telah ditentukan. Pada percobaan pertama, posisi robot dalam kondisi tanpa ada halangan, dan aksi robot tersebut berjalan maju sampai mendeteksi dinding atau halangan, dan selanjutnya robot melakukan aksi manuver kiri atau kanan berdasarkan behavior yang aktif. Pada percobaan kedua, posisi robot berada pada jalur koridor dan bernavigasi mengikuti jalur koridor tersebut. Selanjutnya pada percobaan ketiga, robot berada dalam lingkungan kompleks dimana robot tersebut ditempatkan pada posisi yang berbeda-beda, dan robot dapat bernavigasi dengan baik tanpa menabrak objek atau dinding

Sentiment Analysis Using PSEUDO Nearest Neighbor and TF-IDF TEXT Vectorizer

Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it

Expert System to Diagnose Disease in Toddlers Using Dempster Shafer Method

oai:ojs.sjia.ilkom.unsri.ac.id:article/27Children, especially toddlers at the age of two months to five years old are more susceptible to disease. Limited information about diseases that attack children makes it difficult for parents to predict the disease that will suffer from their children. Therefore we need an expert system  that can predict the disease suffered by children, and the method used in this study is the Dempster Shafer method. The Dempster Shafer method can be implemented into an expert system to combine separate symptoms (evidence) in calculating the probability of a disease. Based on the test results using 250 test data, the accuracy of the expert system for diagnosing diseases in children under five years old using Dempster Shafer method is 94%.Keywords : Expert System, Dempster Shafer, Disease in Toddler

Classification of Emotions on Twitter using Emotion Lexicon and Naïve Bayes

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion in the sentence is using the Emotion Lexicon. However, the lexicon-based method is not good at classifying data because not every word contains emotion. So, there's a need to combine it with other classification method such as Naive Bayes. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f- measure of 59%

Identification Types Of Student Learning Modalities In Physics Subjects With Expert Systems Using Bayes Theorem Method

Learning modality is a person's way of absorbing and processing information effectively and efficiently. This study aims to determine the results of the identification types of student learning modalities in physics subjects with an expert system using the Bayes theorem method, and the accuracy of the Bayes theorem method in identifying types of student learning modalities in physics subjects. This study uses the Bayes theorem method because it can produce a parameter estimate by combining information from the sample and other information that has been previously available to determine the results of the learning modality. This study uses 21 characteristics of learning modalities, 3 types of learning modalities, and 30 test cases obtained from an expert physics teacher at SMA Sumsel Jaya Palembang. Based on the tests that have been carried out, the results show that the system has an accuracy of 90% in identifying types of student learning modalities in physics subjects. It can be concluded that the Bayes theorem method can be used to identify types of student learning modalities in physics subjects

Prediction of the Number of New Cases of Covid-19 in Indonesia Using Fuzzy Time Series Model Chen

Coronavirus Diseases 2019 (Covid-19) is a disease caused by a virus that originated in Wuhan, China. This virus infects people rapidly to the country of Indonesia. According to the latest Covid-19 Development Team in Indonesia, as of 09/08/2021, there were around 3,686,740 people who were confirmed positive for Covid-19. With the numbers continuing to grow, predictions of new cases of Covid-19 in Indonesia were made using the time series method. The method used by the researcher is Chen's Fuzzy Time Series. The purpose of the researcher is to forecast, to find out the prediction of the number of new cases of Covid-19 in Indonesia using the FTS Chen method into software. In addition, in order to provide information to predict, so that the government knows and can make decisions. To measure the performance of the method, the Mean Absolute Percentage Error (MAPE) is used as a measure of the level of accuracy of the forecasting performed. The test data used were 363 data with several variations of parameters  & . From the results of the analysis that was tested by the researcher, with 50 trials of parameter input, better accuracy results were obtained at input  = 135135 and  = 2000 with MAPE is 35.55006797 (35%)

Pengenalan Motif Kain Songket Pada Citra Kamera Smartphone Dengan Beragam Sudut Pandang Menggunakan CNN

Songket  Palembang  memiliki  motif  yang  beragam  sehingga  dibutuhkan mesin  pengenal  yang  dapat  membantu  orang  awam  mengenali  motif  ini.  Mesin pengenal  harus  mampu  mengenali  motif  dengan  variasi  transformasi spatial, noisedan blur. Dalam penelitian ini, CNN mampu mengklasifikasi motif songket dengan akurasi 93%. Arsitektur CNN yang digunakan menggunakan 2.22 MB memori GPU saat    inference.    Penggunaan    Dropout    memberikan    efek    regularisasi,    yaitu meningkatkan  akurasi  pada  data  uji  dan  penggunaan  momentum  dengan  nilai  0.9 mengurangi  waktu  training  2x  lebih  cepat.  Layer  konvolusi  CNN  pada penelitian ini tidak dapat mengekstrak fitur penting pembeda antar kelas, tidak seperti layer konvolusi CNN pretrain yang sudah dilatih dengan dataset yang besar sehingga menghasilkan akurasi 100% untuk klasifikasi songke

PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader

Sosialisasi dan Pelatihan Bebras Challenge Untuk Siswa SMP di Kota Palembang

Berpikir komputasional (Computational Thinking) adalah metode yang bisa menyelesaikan persoalan (solving problem) dengan menerapkan teknik ilmu komputer (informatika). Jenjang pendidikan Sekolah Menengah Pertama (SMP) dirasa sebagai jenjang yang tepat untuk mulai diperkenalkannya kasus problem solving. Bebras Challenge menyajikan soal-soal yang mendorong siswa untuk berpikir kreatif dan kritis dalam menyelesaikan persoalan yang biasa terjadi pada kehidupan sehari-hari mereka dengan menerapkan konsep-konsep berpikir komputasional. Maka dari itu diselenggarakan sosialisasi dan pelatihan Bebras Challenge untuk siswa SMP di palembang agar dapat membantu siswa meningkatkan kemampuan pemecahan masalah yg mereka miliki serta menambah wawasan, minat, kreativitas dan inovasi siswa terkait bidang teknologi informasi dan komputasi.Setelah mengikuti kegiatan ini, para siswa dan guru pembina SMP di palembang memiliki wawasan dan kemampuan menggunakan computational thinking dalam menyelesaikan problem dalam bentuk soal-soal yang tentunya juga dapat diterapkan pada penyelesaian masalah dalam kehidupan sehari-hari