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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JUITA : Jurnal Informatika JURNAL INTEGRASI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Tekno

Yohanes Suyanto

Universitas Gadjah Mada

Author-ID : 398071

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

Published : 15 Documents Claim Missing Document

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Articles

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Implementasi Reduksi Keadaan Rangkaian Digital Sekuensial Metode Bagan Implikasi Ari Setiyani, Theresia Prima; Suyanto, Yohanes
Jurnal Tekno Vol 16 No 2 (2019): Jurnal TEKNO
Publisher : Direktorat Riset dan Pengabdian pada Masyarakat Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jtekno.v16i1.622

The implementation of state reduction in sequential digital circuits is made for learning the topic of state reduction. The method used for state reduction is an implication chart. This method starts with reading the transition table state and transfered into the array structure. Based on this array structure a table or chart of initial implications is arranged. The next process is to change the contents of the table if there are cells that meet the requirements to be declared as identical or not identical. This process is repeated continuously until there is no change in cell contents. The state of reduction implementation is made using the Python programming language and PHP. The results of the implementation are successful for the state transition table with 1 input and 1 output.  

Uji Kemiripan Hasil Sintesis Suara Menggunakan Metode Jarak Mahalanobis Yohanes Suyanto; Th. Prima Ari Setiyani
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2018
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Telah dilakukan pengujian kemiripan sistem penerapan intonasi pada sintesis ucapan Bahasa Indonesia terhadap suara referensi menggunakan metode jarak Mahalanobis. Sistem ini akan mensintesis ucapan dengan pola intonasi yang diperoleh dari ekstraksi frekuensi dasar suara berita online. Teks untuk sintesis ucapan diperoleh dengan melakukan transkripsi suara berita online. Hasil sintesis dibandingkan dengan suara berita online asli menggunakan metode jarak Mahalanobis. Hasil penelitian menunjukkan bahwa nilai jarak Mahalanobis antara suara asli dan suara sintesis adalah rata-rata 0,94. Hasil ini lebih baik daripada suara sintesis dengan intonasi kaidah standar yaitu 1,20.

A New Approach in Query Expansion Methods for Improving Information Retrieval Lasmedi Afuan; Ahmad Ashari; Yohanes Suyanto
JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

This research develops a new approach to query expansion by integrating Association Rules (AR) and Ontology. In the proposed approach, there are several steps to expand the query, namely (1) the document retrieval step; (2) the step of query expansion using AR; (3) the step of query expansion using Ontology. In the initial step, the system retrieved the top documents via the user's initial query. Next is the initial processing step (stopword removal, POS Tagging, TF-IDF). Then do a Frequent Itemset (FI) search from the list of terms generated from the previous step using FP-Growth. The association rules search by using the results of FI. The output from the AR step expanded using Ontology. The results of the expansion with Ontology use as new queries. The dataset used is a collection of learning documents. Ten queries used for the testing, the test results are measured by three measuring devices, namely recall, precision, and f-measure. Based on testing and analysis results, integrating AR and Ontology can increase the relevance of documents with the value of recall, precision, and f-measure by 87.28, 79.07, and 82.85.

Perbandingan PSNR, Bitrate, dan MOS pada Pengkodean H.264 Menggunakan Metode Prediksi Temporal Ari Haryadi; Yohanes Suyanto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 2, No 2 (2012): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

AbstrakStandar 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 AbstractH264 / 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, interframe

Sintesis Suara Bernyanyi Dengan Teknologi Text-To-Speech untuk Notasi Musik Angka dan Lirik Lagu Berbahasa Indonesia Jonathan Jonathan; Yohanes Suyanto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 10, No 1 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, 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.

Sintesis Taganing Adaptif Menggunakan Metode Pitch Shifting by Delay-Line Based untuk Standardisasi Gondang Batak Toba Pasto Juni Ansen Malau; Yohanes Suyanto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 10, No 2 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.37659

This research using pitch shifting by delay line based method which consist of two main stage. The first stage is called analysis stage (framing, windowing, pre-emphasis and de-emphasis and FFT) that can detect the value of fundamental frequency of each taganing’s gendang. Then, this fundamental frequncy from each gendang will be classified into keyboard tones. The second one is called synthesis stage that will process the fundamental frequency become a new desire signal by creat an upward pitch change or a downward pitch change by delay line based method. Result of this research is created new signals as standard tones of each taganing’s gendang. The evaluation of synthesis output is using comparation method between fudamnetal frequency value of signal output as result of synthetis stage and the fundamental frequency value of keyboard standard’s tone. From the results of the system, it can be concluded that taganing synthesis tone have 98.87% accuration rate.

Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network MHD Theo Ari Bangsa; Sigit Priyanta; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51646

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%.

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection Auliya Rahman Isnain; Agus Sihabuddin; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51743

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%.

Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning Annisaa Utami; Yohanes Suyanto; Agus Sihabuddin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.58919

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%.

Attention-Based BiLSTM for Negation Handling in Sentimen Analysis Riszki Wijayatun Pratiwi; Yunita Sari; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.60733

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.

Co-Authors Agus Sihabuddin Ahmad Ashari Amalia, Fadila Shely Ari Haryadi Ari Setiyani, Theresia Prima Fauzun Atabiq Isnain, Auliya Rahman Jonathan Jonathan Lasmedi Afuan MHD Theo Ari Bangsa Pasto Juni Ansen Malau Pawestri, Sheraton Riszki Wijayatun Pratiwi Sensa Gudya Sauma Syahra Septian Yogi Yehezkiel Sigit Priyanta Th. Prima Ari Setiyani Utami, Annisaa Yunita Sari

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