Article Per Year (5 Year)
p-Index From 2021 - 2026
2.931
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering 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
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
Check
Articles

Found 11 Documents
Search
Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1   2 
Gap analysis business process model by using structural similarity Afrianda Cahyapratama; Kelly Rosa Sungkono; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp124-134

Abstract

Gap analysis process model is a study that can help an institution to determine differences between business process models, such as a model of Standard Operating Procedure and a model of activities in an event log. Gap analysis is used for finding incomplete processes and can be obtained by using structural similarity. Structural similarity measures the similarity of activities and relationships depicting in the models.  This research introduces a graph-matching algorithm as the structural similarity algorithm and compares it with dice coefficient algorithms. Graph-matching algorithm notices parallel relationships and invisible tasks, on the contrary dice coefficient algorithms only measure closeness between activities and relationships. The evaluation shows that the graph-matching algorithm produces 76.76 percent similarity between an SOP model and a process model generating from an event log; while, dice coefficient algorithms produces 70 percent similarity. The ability in detecting parallel relationships and invisible tasks causes the graph-matching algorithm produces a higher similarity value than dice coefficient algorithms.
Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision M. Jupri; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp75-87

Abstract

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.
A comparative study of sentiment analysis using SVM and SentiWordNet Mohammad Fikri; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp902-909

Abstract

Sentiment analysis has grown rapidly which impact on the number of services using the internet popping up in Indonesia. In this research, the sentiment analysis uses the rule-based method with the help of SentiWordNet and Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) as feature extraction method. Since the number of sentences in positive, negative and neutral classes is imbalanced, the oversampling method is implemented. For imbalanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 56% and 76%, respectively. However, for the balanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 52% and 89%, respectively.
Process Mining: Measuring Key Performance Indicator Container Dwell Time Bambang Jokonowo; Riyanarto Sarno; Siti Rochimah; Bagus Priambodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp401-411

Abstract

The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The L* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.
Comparative method of moora, copras and topsis based on weighting of best worst method in supplier selection at ABC mining companies in Indonesia Ryco Puji Setyono; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp890-899

Abstract

Supplier selection is essential to the business. The risks posed by suppliers will have a significant impact on the company's performance. Each business has distinct supplier features to mitigate this.These characteristics are divided into criteria in supplier selection. In this study, the criteria in supplier selection will be weighted by best worst method (BWM) and comparing the three ranking methods, complex proportional assessment (COPRAS), multi-objective optimization berdasarkan analisis rasio (MOORA) and technique for order preference by similarity to ideal solution (TOPSIS). The sample in this study is an ABC manufacturing company engaged in mining from Indonesia. From the results of the study, there were 16 criteria using the Delphi Method. These criteria are divided into four main criteria, namely service, quality, cost and time. From the results of weighting BWM, the price sub criteria on cost criteria have the greatest weight for ABC companies. The results of the weighting are then carried out by supplier ranking by comparing the COPRAS, MOORA and TOPSIS approaches. In comparing these three methods, approaches are used based on accuracy and complexity. The COPRAS method has the highest accuracy and lowest complexity according to the ABC company.
Developing Corpora using Wikipedia and Word2vec for Word Sense Disambiguation Farza Nurifan; Riyanarto Sarno; Cahyaningtyas Sekar Wahyuni
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1239-1246

Abstract

Word Sense Disambiguation (WSD) is one of the most difficult problems in the artificial intelligence field or well known as AI-hard or AI-complete. A lot of problems can be solved using word sense disambiguation approaches like sentiment analysis, machine translation, search engine relevance, coherence, anaphora resolution, and inference. In this paper, we do research to solve WSD problem with two small corpora. We propose the use of Word2vec and Wikipedia to develop the corpora. After developing the corpora, we measure the sentence similarity with the corpora using cosine similarity to determine the meaning of the ambiguous word. Lastly, to improve accuracy, we use Lesk algorithms and Wu Palmer similarity to deal with problems when there is no word from a sentence in the corpora (we call it as semantic similarity). The results of our research show an 86.94% accuracy rate and the semantic similarity improve the accuracy rate by 12.96% in determining the meaning of ambiguous words.
Performance analysis of wireless sensor network with load balancing for data transmission using xbee zb module Ahmad Yusuf Ardiansyah; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp88-100

Abstract

In general, research in the field of wireless sensor network (WSN) has never discussed the reliability aspect of network routing with router devices that can find new routes when damage occurs. To date, overloaded routers will be ignored without any response that gives control which can reduce the quality of network performance. Therefore, we propose research using the AODV routing, and Mesh routing algorithm to find other routes as an alternative when problems occur and using the Round Robbin based xbee algorithm on providing load balance control carried out by the router. The experiments on the performance of non-balancing networks and balancing were conducted. Both trials used quality of service (QoS) parameters as a guarantee of performance to be more effective and in line with expectations. Measurements performed by testing the parameters of packet loss, delay, throughput, and fault tolerance. The network performance in finding other alternative routes has been successfully carried out by transmitting 100 packets from the end device node to the coordinator node via the router based on distance variations from 0 to 100 meters. The recovery time required by the dead router to find another route was 10 seconds, this was related to the parameter delay, and fault tolerance. The experimental results of the non-balancing system showed an average 20 % packet loss in one transmission, meanwhile the packet loss was smaller than the previous experiment by 37%. Therefore, the WSN with balancing system was proven to be more effective that could improve QoS performance by 17%.
Temperature effect of electronic nose sampling for classifying mixture of beef and pork Sinarring Azi Laga; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1626-1634

Abstract

Strong demand and strong price of raw foodstuffs like beef was commonly used in conventional markets by beef dealers to commit fraud in order to gain larger income. The fraud has been in the form of combining beef and pork. In Indonesia, this has been a issue of food health in recent years. Via scent, some food safety concerns can be expected. By using electronic nose that is equipped with electrochemical and air sensors  such as temperature sensors, strain, and humidity to find the pure beef or mixed beef. According to its selectivity, the sensor can detect gas to make small icurrents that are the result of chemical sensor and gas interactions with oxygen .In this study, the classification method k-NN, SVM, Naïve Bayes, and random forest was used in 5 different meat variations with a ratio of 0%, 10%, 50%, 90% and 100% with temperatures of -22°C, room temperature, and 55°C. The results showed the effect of temperature on increasing the accuracy, which is at a temperature of -22°C. The lower the temperature, the more stable the value obtained by electronic nose. At a temperature of -22°C, the method that produces the highest accuracy is the Random Forest method.
Single nucleotide polymorphism based on hypertension potential risk prediction using LSTM with Adam optimizer Lailil Muflikhah; Imam Cholissodin; Nashi Widodo; Feri Eko Herman; Teresa Liliana Wargasetia; Hana Ratnawati; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1126-1139

Abstract

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.
Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose Dava Aulia; Riyanarto Sarno; Shintami Chusnul Hidayati; Alfian Nur Rosyid; Muhammad Rivai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp264-275

Abstract

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.
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