PEMANFAATAN PROCESS AWARE INFORMATION SYSTEMS UNTUK MENINGKATKAN TINGKAT PELAYANAN

Perkembangan bidang ilmu sistem informasi mengarah pada i) dari programming ke assembling; ii) dari berorientasi data ke berorientasi proses; iii) dari design ke redesign yang berkesinambungan. Hal tersebut berpengaruh pada infrastruktur sistem, lapisan aplikasi generik, lapisan aplikasi domain-specific dan lapisan aplikasi tailor-made. Berdasarkan perkembangan ini kemudian lahir Process Aware Information Systems (PAIS), yang didefinisikan sebagai sebuah sistem informasi yang mengelola dan melaksanakan operasi-operasi yang melibatkan manusia dan sumber daya informasi berdasarkan model proses. Proses model umumnya direpresentasikan secara visual. PAIS bertujuan untuk menjembatani kebutuhan proses bisnis yang secara dinamis berubah dan kebutuhan perubahan pada perangkat lunak yang terkait. Oleh karena itu sebuah PAIS seharusnya dapat mengakomodasi adanya perubahan manajemen dan Business Process Reenginering (BPR). Pada seminar ini dibahas topik yang berkaitan dengan PAIS, antara lain process mining, business process modeling, workflow management dan pengukuran kinerja proses bisnis

MEASURING BUSINESS PROCESS SIMILARITY USING PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA) AND GREEDY GRAPH MATCHING

AbstractThe business process is a set of activities and tasks performed to achieve the goals of an organization. The business process model can be reused as a business process management effort into a repository. To solve the problem, it is necessary to measure the business process model that has similarity or similarity in terms of activity or process. From several business process models that have similarity can be identified as the main business process model, which has the primary function of the same activity. Business process model matching is the one of technique that can be used to identify, to measure the similarity of a set of business process models. The graph matching approach fit to identify the similarity of processes or activities in the business process model. The technique of matching the graph with Greedy graph matching shows similar results with an 89% precision value based on measuring the similarity of the graph building structure. Another approach in graph matching is a semantically or a text-based. Probabilistic Latent Semantic Analysis (PLSA) is one of the semantic approaches to measure the similarity of text in documents. PLSA measures the linkage of words in the document to identify any similarity of topics in the document. Measuring PLSA in business process matching analysis is by comparing text labels on each node in the business process. This research measures the similarity of business process models by combining two similarity analysis techniques based on semantics using PLSA and structural with Greedy. A graph matching technique by computing the semantics of each label on activities that are related to other activity labels. Structurally, connected activities are related to the same process or the same function. The result of this research is to know the effectiveness of business process which has activity relation.Keywords : Business Process, BPMN, Graph Similarity, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph MatchingProses bisnis adalah serangkaian aktivitas dan tugas yang dilakukan untuk mencapai tujuan dari sebuah organisasi. Model proses bisnis dapat digunakan kembali sebagai upaya manajemen proses bisnis tersebut ke dalam sebuah repositori. Dalam repositori berisi ratusan hingga ribuan model proses bisnis dengan model yang sama maupun berbeda. Hingga dapat terjadinya duplikasi dan penumpukkan data. Untuk mengatasi permasalahan tersebut, perlunya dilakukan pengukuran terhadap model proses bisnis yang memiliki kesamaan atau kemiripan dalam hal aktivitas ataupun proses. Beberapa model proses bisnis yang memiliki kemiripan (similarity) dapat diidentifikasi sebagai model proses bisnis utama, yaitu memiliki fungsi dan aktivitas yang sama. Mencocokkan model proses bisnis merupakan salah satu teknik untuk mengidentifikasi, mengukur kemiripan dari kumpulan model proses bisnis. Pendekatan pencocokkan graf (graph matching) cocok untuk mengidentifikasi kemiripan proses atau aktivitas dalam model proses bisnis. Teknik mencocokkan graf dengan Greedy graph matching menghasilkan nilai presisi sebesar 89% berdasarkan pengukuran kemiripan struktur graf. Pendekatan lain dalam pencocokkan graf ialah secara semantik atau teks. Probabilistic Latent Semantic Analysis (PLSA) merupakan salah satu pendekatan semantik untuk menghitung kemiripan teks dalam dokumen. Perhitungan PLSA dalam analisis pencocokkan proses bisnis adalah dengan membandingkan label teks pada tiap node (label) proses bisnis. Penelitian ini mengukur kemiripan model proses bisnis dengan menggabungkan dua teknik analisis kemiripan berdasarkan semantik menggunakan PLSA dan struktural dengan Greedy. Teknik pencocokkan graf dengan menghitung semantik dari setiap label aktivitas yang saling memiliki keterkaitan atau hubungan. Secara struktural, beberapa aktivitas saling terhubung memiliki keterkaitan proses atau fungsi yang sama. Hasil penelitian ini adalah untuk mengetahui efektifitas dari proses bisnis yang memiliki keterkaitan aktivitas.Kata Kunci : Proses Bisnis, BPMN, Kemiripan Graf, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph Matching

Developing Distributed System with Service Resource Oriented Architecture

 Service oriented architecture (SOA) is a design paradigm in software engineering for an enterprise scale which built in a distributed system environment. This paradigm aims at abstracting of application functionality as a service through a protocol in web service technology, namely simple object access protocol (SOAP). However, SOAP have static characteristic and oriented by the service methode, so have restrictiveness on creating and accessing for big numbers of service. For this reason, this reasearch aims at combining SOA with resource oriented architecture (ROA) that is oriented by the service resource use representational state transfer (REST) protocol in order to expand scalability of service. This combination is namely service resource oriented architecture (SROA). SROA can optimize distributing of applications and integrating of services where is implemented to develop the project management software. To realize this model, the software is developed according with framework of Agile model driven development (AMDD) to reduce complexities on the whole stage processing of software development

AHP-TOPSIS for analyzing job performance with factor evaluation system and process mining

Job performance is a type of assessments which refers to scalable actions, behaviour and outcomes that employees engage in or bring out linked with and contribute to organizational goals.This research employed the Factor Evaluation System (FES) method to analyze the job performance due to the common usage of the method. In analyzing employees, FES consists of nine factors; however, those nine factors are considered to be insufficient. Hence, the researchers used the process mining method to improve FES. Process mining analyzes job performance in details. The steps taken in process mining consist of time stamp, case, activity, and resources of employee. This means that the method can be continuously used, since the researcher provides weight for each factor. The weight of each factor is obtained from Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution. The result shows that FES with process mining are good for job performance but AHP-TOPSIS is considered to be incompatible for usage compared to the real work because the priority of the FES factors from the method is inconsistent with the priority factor made by manager of the warehouse officer

MEASURING BUSINESS PROCESS SIMILARITY USING PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA) AND GREEDY GRAPH MATCHING

AbstractThe business process is a set of activities and tasks performed to achieve the goals of an organization. The business process model can be reused as a business process management effort into a repository. To solve the problem, it is necessary to measure the business process model that has similarity or similarity in terms of activity or process. From several business process models that have similarity can be identified as the main business process model, which has the primary function of the same activity. Business process model matching is the one of technique that can be used to identify, to measure the similarity of a set of business process models. The graph matching approach fit to identify the similarity of processes or activities in the business process model. The technique of matching the graph with Greedy graph matching shows similar results with an 89% precision value based on measuring the similarity of the graph building structure. Another approach in graph matching is a semantically or a text-based. Probabilistic Latent Semantic Analysis (PLSA) is one of the semantic approaches to measure the similarity of text in documents. PLSA measures the linkage of words in the document to identify any similarity of topics in the document. Measuring PLSA in business process matching analysis is by comparing text labels on each node in the business process. This research measures the similarity of business process models by combining two similarity analysis techniques based on semantics using PLSA and structural with Greedy. A graph matching technique by computing the semantics of each label on activities that are related to other activity labels. Structurally, connected activities are related to the same process or the same function. The result of this research is to know the effectiveness of business process which has activity relation.Keywords : Business Process, BPMN, Graph Similarity, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph MatchingProses bisnis adalah serangkaian aktivitas dan tugas yang dilakukan untuk mencapai tujuan dari sebuah organisasi. Model proses bisnis dapat digunakan kembali sebagai upaya manajemen proses bisnis tersebut ke dalam sebuah repositori. Dalam repositori berisi ratusan hingga ribuan model proses bisnis dengan model yang sama maupun berbeda. Hingga dapat terjadinya duplikasi dan penumpukkan data. Untuk mengatasi permasalahan tersebut, perlunya dilakukan pengukuran terhadap model proses bisnis yang memiliki kesamaan atau kemiripan dalam hal aktivitas ataupun proses. Beberapa model proses bisnis yang memiliki kemiripan (similarity) dapat diidentifikasi sebagai model proses bisnis utama, yaitu memiliki fungsi dan aktivitas yang sama. Mencocokkan model proses bisnis merupakan salah satu teknik untuk mengidentifikasi, mengukur kemiripan dari kumpulan model proses bisnis. Pendekatan pencocokkan graf (graph matching) cocok untuk mengidentifikasi kemiripan proses atau aktivitas dalam model proses bisnis. Teknik mencocokkan graf dengan Greedy graph matching menghasilkan nilai presisi sebesar 89% berdasarkan pengukuran kemiripan struktur graf. Pendekatan lain dalam pencocokkan graf ialah secara semantik atau teks. Probabilistic Latent Semantic Analysis (PLSA) merupakan salah satu pendekatan semantik untuk menghitung kemiripan teks dalam dokumen. Perhitungan PLSA dalam analisis pencocokkan proses bisnis adalah dengan membandingkan label teks pada tiap node (label) proses bisnis. Penelitian ini mengukur kemiripan model proses bisnis dengan menggabungkan dua teknik analisis kemiripan berdasarkan semantik menggunakan PLSA dan struktural dengan Greedy. Teknik pencocokkan graf dengan menghitung semantik dari setiap label aktivitas yang saling memiliki keterkaitan atau hubungan. Secara struktural, beberapa aktivitas saling terhubung memiliki keterkaitan proses atau fungsi yang sama. Hasil penelitian ini adalah untuk mengetahui efektifitas dari proses bisnis yang memiliki keterkaitan aktivitas.Kata Kunci : Proses Bisnis, BPMN, Kemiripan Graf, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph Matching

Hybrid neural machine translation with statistical and rule based approach for syntactics and semantics between Tolaki-Indonesian-English languages

Machine Translation (MT) incorporates syntax lexical extraction and semantics to predict accurate results. Indonesian have many factors compared to English that related with syntax, especially morphophonemic factors in the language study. These factors are influenced by Lexical type and function while effected MT to frequently mistranslate sentences containing these factors. Meanwhile, semantic extraction is heavily reliant on syntaxis extraction results to predict accurate Lexical translations. In this study, we propose a hybrid statistical and rule-based for MT method that can solve syntaxis and semantic Indonesian problems that conducted the Local Languages in it, particularly Tolaki. First, we developed lexical extraction techniques in Statistical and Rule Based Approach to compile into hybrid MT. This lexical extraction technique is divided into three major tasks: morphophonemic extraction, Lexical Function, and Lexical type extraction. Then we forecast each output of forwards and backwards translations. We compare the predicted output to find accurate translations. Following that, we update the Lexical type based on the actual Lexical function for the translation updating process, which we mark as incorrect translation. Finally, we evaluated MT in both directions. As a result, the proposed method received significant evaluation results, with a percentage success of Indonesian-Tolaki to English translation achieved Precision 0.7231; Recall 0.7; F1-measure: 0.7114; Accuracy: 0.7417 and percentage of success English to Indonesian-Tolaki translation Precision: 0.7119; Recall: 0.7167; F1-measure: 0.7143; Accuracy: 0.7083

Repair and Replacement Strategy for Optimizing Cost and Time of Warranty Process using Integer Programming

Warranty is an assurance issued by a company as the manufacturer to guarantee that its product is damage-free within a specified period. The warranty process is usually carried out when a complaint or damage regarding the product is received. The warranty process consists of two decisions that the company establishes to handle the process. The occurring problem is in the warranty process; there is not any standard established to determine the cost to incur for the warranty process. In this research, integer programming method was used to do optimization on repair and replacement strategy in warranty process. Before doing optimization, mathematical model must be created. Using that mathematical model, the results show that the costs of the warranty process decrease by 16.97%, while the time increases by 13.9%. So, with this method company will be increase the profit

Asynchronous agent-based simulation and optimization of parallel business

A Port Container Terminal (PCT) involves complex business processes which are carried out by at least four organizations, namely PCT Operator, Customer, Quarantine and Customs. Each organization produces event log data from the activities. The event log data from the four organizations contain synchronous and asynchronous activities. In this research, the four organizations are represented by four agents. By simulating this log data using agent based simulation, we get the performance of the current business process. The performance indicators gathered are time and cost which are needed to do the activity (task). After the simulation is complete, we found Asynchronous Waiting Time (AWT). AWT is waiting time which happens because the agent in the simulation cannot do the newly assigned task because the agent is still working on the other task. Therefore, we parallelize the task performed by the agent so that the agent can do multiple tasks at a time. After we parallelize the task, we perform an optimization process using Stochastic Multicriteria Adaptability Analysis 2 (SMAA-2). Thus, the optimal amount of task an agent can do simultaneously is analyzed. This study result shows that parallelization can reduce AWT of the current system and the optimization process using SMAA-2 shows the most optimal number of multiple tasks an agent can do simultaneously

Process Discovery untuk Streaming Event Log menggunakan Model Markov Tersembunyi

Process discovery adalah teknik penggalian model proses dari rangkaian aktivitas yang tercatat dalam event log. Saat ini, sistem informasi menghasilkan streaming event log dimana Online Heuristic Miner adalah algoritma process discovery yang mampu menghasilkan model proses dari streaming event log. Algoritma Online Heuristic Miner memiliki kelemahan yaitu ketidakmampuan mengatasi incomplete trace. Incomplete trace adalah rangkaian aktivitas pada event log yang terpotong di bagian awal ataupun di bagian akhir. Incomplete trace mengakibatkan proses tidak dapat ditampilkan secara utuh dalam model proses. Algoritma yang memanfaatkan Model Markov Tersembunyi digunakan untuk membentuk model proses yang dapat menangani incomplete trace. Algoritma yang memanfaatkan Model Markov Tersembunyi terdiri atas gabungan dari metode pembentukan model proses serta metode yang dimodifikasi. Metode yang dimodifikasi adalah metode Baum- Welch, Backward serta Viterbi. Metode Backward dan Viterbi yang dimodifikasi digunakan untuk memperbaiki incomplete trace sedangkan metode Baum-Welch yang dimodifikasi dan metode pembentukan model proses digunakan untuk membangun model proses dari Model Markov Tersembunyi. Hasil uji coba menunjukkan bahwa dengan adanya perbaikan incomplete trace, nilai kualitas dari sisi fitness, presisi, generalisasi, dan simplicity model proses dari algoritma yang memanfaatkan Model Markov Tersembunyi lebih tinggi dibandingkan model proses dari algoritma Online Heuristic Miner

Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO

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