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

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The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning) Sensa Gudya Sauma Syahra; Yunita Sari; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.69906

Abstract

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.
Komputasi Tingkat Kesehatan Instalasi Listrik pada Gedung Fauzun Atabiq; Yohanes Suyanto
JURNAL INTEGRASI Vol 9 No 1 (2017): Jurnal Integrasi - April 2017
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v9i1.262

Abstract

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.
Music Genre Identification Using SVM and MFCC Feature Extraction Septian Yogi Yehezkiel; Yohanes Suyanto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 12, No 2 (2022): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.70898

Abstract

 Indonesia  is a very diverse country because it has a vast territory and is occupied by millions of people from various tribe. Therefore, traditional music in Indonesia is also diverse because each region has its own culture and art.  In this study, the author used the Support Vector Machine(SVM) pattern recognition  to identify the Indonesian traditional music genre. This genre identification system is able to produce an accuracy of 83% using MFCC.Keywords : traditional music identification, Mel Frequency Cepstral Coefficient, Support Vector Machine.
Offensive Language and Hate Speech Detection using BERT Model Amalia, Fadila Shely; Suyanto, Yohanes
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.99841

Abstract

Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.
Analisis Perbandingan Metode Similarity untuk Kemiripan Dokumen Bahasa Indonesia pada Deteksi Kemiripan Teks Bahasa Indonesia Pawestri, Sheraton; Suyanto, Yohanes
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7648

Abstract

Ease of accessing information brings diverse benefits, including the ability to develop models that can detect similarities between documents, a plagiarism-checking system, automatic summarization, classification, etc. The various benefits of word similarity detection make research on similarity detection between documents an important area to develop. However, studies regarding similarity detection specifically for Indonesian language documents are still relatively small and the performance can still be developed. Therefore, this research aims to conduct a comparative analysis of the performance of Doc2Vec compared to the Jaccard Coefficient, Cosine Similarity, and Euclidean Distance in detecting the similarity of documents with Indonesian text. Three datasets are used in this analysis, with the first dataset consisting of 200 news from Google News, the second dataset from IndoNLU, and the third dataset from TaPaCo. The findings from this study show that overall Cosine Similarity has better performance than Jaccard Coefficient and Euclidean Distance for average performance. The superior performance was with accuracy of 0.98, precision of 0.84, recall of 0.95, and F-1 score of 0.89, with the model formed in 10.56 seconds using the Cosine Similarity algorithm on the Google News dataset. This is because doc2vec is better suited to datasets with higher dimensions than datasets that only contain a few words.
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