Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration.               This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods.               The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance.  The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%

Klasifikasi Suara Paru-Paru Berdasarkan Ciri MFCC

Paru-paru merupakan organ utama sistem pernapasan pada manusia,berfungsi untuk menukarkan oksigen dari udara dengan karbon dioksida dari darah. Deteksi adanya gangguan pernapasan dan gangguan pada paru-paru dapat dilakukan melalui berbagai cara; melihat rekam medis, pemeriksaan fisik, pendeteksian dengan x-ray dan juga auskultasi pernapasan. Pemrosesan sinyal digital dapat digunakan sebagai salah satu cara untuk mendeteksi adanya gangguan pada paru-paru berdasarkan suara yang dihasilkan. Pada penelitian ini dilakukan pengklasifikasian suara paru-paru pada kelas normal, crackle, wheeze, dan crackle-wheeze dengan menggunakan metode Mel Frequency Cepstral Coefficient (MFCC) dan Convolutional Neural Network (CNN).Pengamatan dilakukan dengan melakukan variasi pada ekstraksi ciri MFCC dengan menggunakan MFCC 8 dan 13 koefisien, jumlah frame 50 dan 60, dan lebar frame yang digunakan 0,1, 0,15 dan 0,2 detik. Hasil ekstraksi ciri kemudian diterapkan pada sistem klasifikasi CNN, serta menggunakan confusion matrix unutk mendapatkan nilai akurasi dan presisi. Nilai akurasi dan presisi tertinggi didapatkan sebesar 71,85% dan 65,70% pada MFCC 13 koefisien dengan rata-rata 71,18%. Berdasarkan hasil tersebut, sistem yang telah dibuat dapat mengiklasifikasi suara paru-paru normal, crackle, wheeze dan crackle-wheeze dengan cukup baik

Attention-Based BiLSTM for Negation Handling in Sentimen Analysis

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%

Perbandingan PSNR, Bitrate, dan MOS pada Pengkodean H.264 Menggunakan Metode Prediksi Temporal

Abstrak Standar pengkodean H.264/AVC merupakan hasil perumusan Joint Video Team (JVT), H.264/AVC didesain untuk menjawab kebutuhan akan tingkat kompresi yang tinggi maupun untuk dapat diimplementasikan pada berbagai aplikasi. Pada tugas akhir dilakukan perbandingan nilai PSNR, bitrate, dan MOS untuk masing-masing video dengan karakteristik yang berbeda.. Penelitian ini dilakukan menggunakan software referensi pengkodean video JM18.3. Hasil pengujian video foreman, hall, news, waterski, carphone, dan lobby, bitrate yang dihasilkan untuk setiap sequence pada setiap Quantization Parameter (QP) dipengaruhi oleh karakteristik sequence. Untuk hasil pengujian PSNR, diperoleh kesimpulan bahwa semakin besar nilai Quantization Parameter akan menghasilkan PSNR yang semakin kecil. Berdasarkan penilaian ITU-T, untuk dapat mencapai kualitas excellent ( >37 dB), rata-rata nilai parameter kuantisasi yang memenuhi untuk keenam video tersebut berada pada QP 28. . Kata kunci— H.264/AVC, bitrate, PSNR, prediksi temporal, interframe     Abstract H264 / AVC coding standard is developed by Joint Video Team (JVT), H.264/AVC was designed, either to meet the needs of high  compression level, or to be implemented on various application. This final paper compares Peak-to-peak Signal to Noise Ratio (PSNR), bitrate and Mean Opinion Score (MOS) for each videos with different characteristics using library JM 18.3. Tests result on foreman, hall, news, waterski, carphone and lobby videos show that the bitrate produced in each sequence for every Quantization Parameter (QP) is influenced by the sequence characteristics. As for PSNR, it is concluded that higher QP produces smaller PSNR. Based on ITU-T scoring for excellent quality (PSNR >37 dB), the quantization parameters of the evaluated videos that meet the standard are 28.   Keywords—H.264/AVC, bitrate, PSNR, temporal prediction, interfram

Komputasi Tingkat Kesehatan Instalasi Listrik pada Gedung

Kesehatan instalasi listrik gedung adalah kondisi mengenai baik buruknya integritas instalasi listrik sistem instalasi listrik pada suatu gedung. Tujuan dari penelitian ini adalah mengembangkan sebuah teknik evaluasi kesehatan instalasi listrik untuk menentukan tingkat kesehatan instalasi listrik pada sebuah gedung/bangunan. Sebuah sistem komputasi kesehatan instalasi listrik telah dimodelkan untuk mengevaluasi tingkat kesehatan instalasi kelistrikan pada sebuah gedung berdasarkan parameter-parameter instalasi. Dengan melakukan ekivalen analisis terhadap beberapa parameter instalasi seperti; usia instalasi, pembebanan circuit breaker, pembebanan pada kabel, ketidakimbangan beban instalasi dan temperatur pada panel di setiap panel instalasi listrik yang ada pada gedung, kondisi kesehatan instalasi listrik sebuah gedung dapat ditentukan. Dari implementasi yang dilakukan pada instalasi listrik Gedung S2/S3 FMIPA UGM pada bulan Maret - April 2016, hasil penelitian memperlihatkan bahwa tingkat kesehatan instalasi listrik Gedung S2/S3 FMIPA UGM secara keseluruhan adalah di atas 7.0, atau secara garis besar menunjukkan bahwa kondisi kesehatan instalasi listrik pada gedung tersebut adalah baik

Sintesis Suara Bernyanyi Dengan Teknologi Text-To-Speech untuk Notasi Musik Angka dan Lirik Lagu Berbahasa Indonesia

Singing is a work of art that can not be separated from human life. It then makes a research about develop the art of singing by technology will brings a useful impact for such a wide aspect of human life. This research is trying to synthesize singing voice with TTS (text-to-speech) technology, as it capability to produce sound with certain pronunciation at certain frequency of sound. Inputs that used in the system are texts of song in TXT format that contain the information of numbered musical notation and lyrics in Indonesian. These inputs will converted to a phonetic transcription, for then synthesize of song voice can done based on the transcription. In general, the system made successfully synthesize song voices with some feature that based on the convention of numbered musical notation. Based on 30 people of respondents, the song voice synthesized has 81.71% of accuracy with 6.24% of deviation standard. The syntax of song text also reputed as a user-friendly convention with only up to 3 times re-compilation done to synthesize 8 bar of song text by each of respondents without any error

The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning)

The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data

Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%