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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

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

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Articles

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Performance Measurement Based on Coloured Petri Net Simulation of Scalable Business Processes Abd. Charis Fauzan; Riyanarto Sarno; Muhammad Ainul Yaqin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Business process is also a complex area which receives much attention in recent years especially in increasing productivity and saving cost. Meanwhile, situation at the company allows existing business processes to be enlarged. This paper proposed the performance measurement based on coloured petri net simulation of scalable business processes, which has purpose to compare the performance of scalable business processes. For experiments, this paper uses real-world business processes. Then compare it to some business processes that have been enlarged. The result shows that scalable business processes influence the performance of business process. This paper provides feedback to business process developers for determine appropriate business processes based on the performance through coloured petri net simulation.

Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO Kholed Langsari; Riyanarto Sarno
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

The estimation of software effort is an essential and crucial activity for the software development life cycle. Software effort estimation is a challenge that often appears on the project of making a software. A poor estimate will produce result in a worse project management. Various software cost estimation model has been introduced to resolve this problem. Constructive Cost Model II (COCOMO II Model) create large extent most considerable and broadly used as model for cost estimation. To estimate the effort and the development time of a software project, COCOMO II model uses cost drivers, scale factors and line of code. However, the model is still lacking in terms of accuracy both in effort and development time estimation. In this study, we do investigate the influence of components and attributes to achieve new better accuracy improvement on COCOMO II model. And we introduced the use of Gaussian Membership Function (GMF) Fuzzy Logic and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating and optimizing the COCOMO II model parameters. The proposed method is applied on Nasa93 dataset. The experiment result of proposed method able to reduce error down to 11.891% and 8.082% from the perspective of COCOMO II model. The method has achieved better results than those of previous researches and deals proficient with inexplicit data input and further improve reliability of the estimation method.

Discovering Process Model from Event Logs by Considering Overlapping Rules Yutika Amelia Effendi; Riyanarto Sarno
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of the discovered process models is a must. Nowadays, using process execution data in the past, process models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete information, there are some cases that are overlapping in process model. Moreover, the rules which are generated from existing method are not suitable with the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.

CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities Kelly R. Sungkono; Riyanarto Sarno
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

An intentional process model is known to analyze processes deeply and provide recommendations for the upcoming processes. Nevertheless, the discovery of intentions is a difficult task because the intentions are not recorded in the event log, but they encourage the executable activities in the event log. Map Miner is the latest algorithm to depict the intentional process model. A disadvantage of this algorithm is the inability to determine strategies that contain same activities with the different sequence with other strategies. This disadvantage leads failure on the intentional process model. This research proposes an algorithm for discovering an intentional process model by considering the sequence of activities and CHMM (Coupled Hidden Markov Model). The probabilities and states of CHMM are utilized for the formation of the intentional process model. The experiment shows that the proposed algorithm with considering the sequence of activities gets an appropriate intentional process model. It also demonstrates that an obtained intentional process model using proposed algorithm gets the better validity than an intentional process model using Map Miner Method.

Performance Improvement of Business Process Similarity Calculation using Word Sense Disambiguation Endang Wahyu Pamungkas; Riyanarto Sarno; Abdul Munif
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Similarity calculation between Business Process Models (BPM) has an important role in the process of managing BPM repository. One of its uses is to facilitate the searching process of a model in the repository. Similarity calculation between business processes is closely related with semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database, such as WordNet, to find the semantic meaning of words. The problem in WordNet is that this lexical database contains terms wich have more than one meaning or polysemous. Selecting the wrong meaning will decrease the accuracy of similarity calculation process. In this study, we will try to improve the accuracy of similarity calculation of business processes using Word Sense Disambiguation (WSD). The main purpose is to eliminate the ambiguity of polysemous words before calculating the similarity value. WSD is performed by unsupervised methods based on the value of graph connectivity. Then, we used a lexical database that is focused in the business and industry field. The results from this study is able to achieve higher accuracy of the sense selection process for terms especially terms that are related to business and industrial domains. It will also increase the accuracy of similarity value calculation between the business process models.

Business Process Anomali Detection using Multi-Level Class Association Rule Learning Fernandes Sinaga; Riyanarto Sarno
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Recently, Business Process Management System (BPMS) is widely used by companies in order to manage their business process. The company’s business process has a possibility to have changes which can cause some variations of business process. These variations might be contain some anomalies. Any anomalies that can make some losses for the company can be regarded as a fraud. There were some research have done to detect anomalies in business process. But, there is some issues that still need improvement especially on the accuracy. This paper proposed Multi-Level Class Association Rule Learning method (ML-CARL) to detect business process anomalies accurately. This method is supported by the process mining method which is used to analyze the anomalies in process. From the experiment, ML-CARL method can detect anomalies with an accuracy of 0.99 and better than ARL method in previous research. It can be concluded that ML-CARL method can increase the accuracy of business process anomaly detection.

Fuzzy MADM Approach for Rating of Process-Based Fraud Solichul Huda; Riyanarto Sarno; Tohari Ahmad
Journal of ICT Research and Applications Vol. 9 No. 2 (2015)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2015.9.2.1

Process-Based Fraud (PBF) is fraud enabled by process deviations that occur in business processes. Several studies have proposed PBF detection methods; however, false decisions are still often made because of cases with low deviation. Low deviation is caused by ambiguity in determining fraud attribute values and low frequency of occurrence. This paper proposes a method of detecting PBF with low deviation in order to correctly detect fraudulent cases. Firstly, the fraudulence attributes are established, then a fuzzy approach is utilized to weigh the importance of the fraud attributes. Further, multi-attribute decision making (MADM) is employed to obtain a PBF rating according to attribute values and attribute importance weights. Finally, a decision is made whether the deviation is fraudulent or not, based on the PBF rating. Experimental validation showed that the accuracy and false discovery rate of the method were 0.92 and 0.33, respectively.

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

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

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

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.

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

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