Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

2.76

P-Index

This Author published in this journals

All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Bulletin of Electrical Engineering and Informatics Proceeding International Conference on Information Technology and Business Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics International conference on Information Technology and Business (ICITB) JAIS (Journal of Applied Intelligent System) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Emerging Science Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology Sewagati Jurnal Penelitian IPTEKS

Riyanarto Sarno

Departemen Informatika Institut Teknologi Sepuluh Nopember Surabaya

Author-ID : 373697

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

Published : 80 Documents Claim Missing Document

Claim Missing Document

Articles

Title

Process Mining in Supply Chains: A Systematic Literature Review Bambang Jokonowo; Jan Claes; Riyanarto Sarno; Siti Rochimah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Performance analysis and continuous process improvement efforts are often supported by the construction of process models representing the interactions of the partners in the supply chain. This study was conducted to determine the state of the art in the process mining field, specifically in the context of cross-organizational process. The Systematic Literature Review (SLR) method is used to review a collection of twenty-one papers that are classified according to the Artifact framework of Hevner, et al. and within the Process Mining framework of Van der Aalst. In the reviewed papers, the authors conducted a variety of techniques to establish the event log, which is then used to perform the process mining analysis. Eight of the reviewed papers focus on the definition of concepts or measures. Five of the papers describe models and other abstractions that are used as a theoretical basis for process mining in the context of supply chains. The majority twenty of papers describe some kind of informal method or formal algorithm to perform process mining analysis. Nine of the papers that propose a formal algorithm also present an accompanying software implementation. Eight papers discuss the data preparation challenges and twelve papers discuss process discovery techniques.

Incorporating Index of Fuzziness and Adaptive Thresholding for Image Segmentation Umi Salamah; Riyanarto Sarno; Agus Zainal Arifin; Anto Satriyo Nugroho; Ismail Eko Prayitno Rozi; Puji Budi Setia Asih
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Binary Segmentation of an image played an important role in many image processing application. An image that was having no bimodal (or nearly) histogram accompanied by low-contrast was still a challenging segmentation problem to address. In this paper, we proposed a new segmentation strategy to images with very irregular histogram and had not significant contrast using index of fuzziness and adaptive thresholding. Index of fuzziness was used to determine the initial threshold, while adaptive thresholding was used to refine the coarse segmentation results. The used data were grayscale images from related papers previously. Moreover, the proposed method would be tested on the grayscale images of malaria parasite candidates from thickblood smear that had the same problem with this research. The experimental results showed that the proposed method achieved higher segmentation accuracy and lower estimation error than other methods. The method also effective proven to segment malaria parasite candidates from thickblood smears image.

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion Fika Hastarita Rachman; Riyanarto Sarno; Chastine Fatichah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Music has lyrics and audio. That’s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. ‘ooh’, ‘ah’, ‘yeah’, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.

Music fingerprinting based on bhattacharya distance for song and cover song recognition Riyanarto Sarno; Dedy Rahman Wijaya; Muhammad Nezar Mahardika
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

Synonym based feature expansion for Indonesian hate speech detection Imam Ghozali; Kelly Rossa Sungkono; Riyanarto Sarno; Rachmad Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1105-1112

Online hate speech is one of the negative impacts of internet-based social media development. Hate speech occurs due to a lack of public understanding of criticism and hate speech. The Indonesian government has regulations regarding hate speech, and most of the existing research about hate speech only focuses on feature extraction and classification methods. Therefore, this paper proposes methods to identify hate speech before a crime occurs. This paper presents an approach to detect hate speech by expanding synonyms in word embedding and shows the classification comparison result between Word2Vec and FastText with bidirectional long short-term memory which are processed using synonym expanding process and without it. The goal is to classify hate speech and non-hate speech. The best accuracy result without the synonym expanding process is 0.90, and the expanding synonym process is 0.93.

Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs Fauzan, Hermawan; Sarno, Riyanarto; Saikhu, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4214-4227

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study Haryono, Agus Tri; Sarno, Riyanarto; Sungkono, Kelly Rossa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp861-869

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.

Aspect-based sentiment analysis: natural language understanding for implicit review Suhariyanto, Suhariyanto; Sarno, Riyanarto; Fatichah, Chastine; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6711-6722

The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.

Improving cyberbullying detection through multi-level machine learning Salsabila, Salsabila; Sarno, Riyanarto; Ghozali, Imam; Sungkono, Kelly Rossa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1779-1787

Cyberbullying is a known risk factor for mental health issues, demanding immediate attention. This study aims to detect cyberbullying on social media in alignment with the third sustainable development goal (SDG) for health and well-being. Many previous studies employ single-level classification, but this research introduces a multi-class multi-level (MCML) algorithm for a more detailed approach. The MCML approach incorporates two levels of classification: level one for cyberbullying or not cyberbullying, and level two for classifying cyberbullying by type. This study used a dataset of 47,000 tweets from Twitter with six class labels and employed an 80:20 training and testing data split. By integrating bidirectional encoder representations from transformers (BERT) and MCML at level two, we achieved a remarkable 99% accuracy, surpassing BERT-based single-level classification at 94%. In conclusion, the combination of MCML and BERT offers enhanced cyberbullying classification accuracy, contributing to the broader goal of promoting mental health and well-being.

Co-Authors A.A. Ketut Agung Cahyawan W ABDUL MUNIF ABDUL MUNIF Adhatus Solichah Ahmadiyah Adhatus Solichah Ahmadiyah, Adhatus Solichah Afina Lina Nurlaili Afrianda Cahyapratama Agung Wiratmo Agus Tri Haryono, Agus Tri Agus Zainal Arifin Ahmad Saikhu Ahmad Yusuf Ardiansyah Ahmadiyah, Adhatus Solichah Ainul Yaqin Alfian Ma’arif Alief Yoga Priyanto Andrean Hutama Koosasi Anggraini, Ratih Nur Esti Anto Satriyo Nugroho Ardy Januantoro Arifin, Mohammad Nazir Aziz Fajar Azzam Jihad Ulhaq Azzam Jihad Ulhaq Bagus Priambodo Bagus Setya Rintyarna Bambang Jokonowo Bilqis Amaliah Buliali, Joko Lianto Cahyaningtyas Sekar Wahyuni Chastine Fatichah Chastine Fatichah Chastine Fatihah Danica Virlianda Marsha Daniel Oranova Siahaan Dava Aulia Dedy Rahman Wijaya Dewi Rahmawati Dieky Adzkiya Dini Adni Navastra Dwi Sunaryo Dwi Sunaryono Dwo Sunaryono Edi Faisal Effendi, Yutika Amelia Endang Wahyu Pamungkas Faisal Rahutomo Faizal Anugrah Bhaswara Fajar, Aziz Farza Nurifan Fauzan Prasetyo Fauzan, Hermawan Feri Eko Herman Fernandes Sinaga Fika Hastarita Rachman Fony Revindasari Gabriel Sophia Gelu, Leonard Peter Gita Intani Budiawati HANA RATNAWATI Hendra Darmawan Hermawan Hermawan Hidayat, Husnul Hidayati, Shintami Chusnul I Gusti Agung Chintya Prema Dewi Ida Ayu Putu Sri Widnyani Imam Cholissodin Imam Ghozali Imam Ghozali Imam Mukhlash Imam Riadi Ismail Eko Prayitno Rozi Isnaini Nurul Kurnia Sari Isnaini Nurul KurniaSari Jan Claes Johanes Andre Ridoean Joko Buliali Kartini Kartini Kelly Rosa Sungkono Kelly Rossa Sungkono Kholed Langsari Kholed Langsari Lailil Muflikhah Langsari, Kholed M. Jupri Margo Pudjiantara Mochammad Faris Ponighzwa Rizkanda Mohammad Fikri Mohammad Nazir Arifin Muhammad Ainul Yaqin Muhammad Nezar Mahardika Muhammad Nicko Rahmadano Muhammad Rivai Muhammad Suzuri Hitam Muhammad Taufiqulsa’di Muhammad Taufiqulsa’di Nashi Widodo Navinda Meutia Navinda Meutia Nurlaili, Afina Lina Nurul Fajrin Ariyani Nurul Fajrin Ariyani Nurul Fajrin Ariyani Peter Gelu Pradipta Ghusti Puji Budi Setia Asih Purwono, Purwono R.V Hari Ginardi Rachmad Abdullah Rachmad Abdullah Rahmawati, Dewi Ratih Nur Esti Anggraeni Ratih Nur Esti Anggraini Ratih Nur Esti Anggraini, Ratih Nur Esti Rizky Widhanto Herlambang Rosyid, Alfian Nur Ryco Puji Setyono Salsabila, Salsabila Sarwosri Sarwosri Setiaputra G, Riswandy Shintami Chusnul Hidayati Shintami Chusnul Hidayati Shintami Chusnul Hidayati Sholiq Sinarring Azi Laga Siti Maimunah Siti Maimunah Siti Rochimah Solichul Huda Suhariyanto Suhariyanto Suhariyanto Suhariyanto Sungkono, B.J. Santosa Tohari Ahmad Tyas, Salsabila Mazya Permataning Umi Salamah Untoro, Meida Cahyo Widya Nilam Rumana Widyasari Ayu Wibowo Yutika Amelia Zahrul Zizki Dinanto Zahrul Zizki Dinanto

Title Search

Found 9 Documents Search Journal : International Journal of Electrical and Computer Engineering

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search
Journal : International Journal of Electrical and Computer Engineering