Hari Ginardi

Decision mining for multi choice workflow patterns

Decision mining is combination of process mining and machine learning technique to retrieve information about how an attribute in a business process affects a cases route choice. It identifies decision point by looking for XOR-splits in petri-net workflow model and analyzing rules for each choice based on available attributes using decision tree. Problem emerges when decision mining technique is used on a workflow that does not use either XOR or AND splits, for example OR-split gateway logic. OR-split does not have explicit representation in petri nets and it makes decision mining algorithm cannot find its decision point. Workflow pattern that uses OR-split as its splitting logic is multi choice. Multi choice does not have its own explicit representation in form of petri net and it is problematic to apply decision mining to this workflow pattern. To make multi choice can be analyzed by decision miner some modification needs to be applied to the petri net representation of this pattern. This paper proposes modification of OR-split gateway representation in petri net. The new representation of OR-split uses combination the existing XOR-split and AND-split to make the model easier to be analyzed using decision miner. The proposed modification do not affect the conformance of event log and process model, but will allow each choice branch to be checked by decision miner.

Clustering of ERP business process fragments

Business process management technology at present has been developed and applied both in small and in large scale. Many companies and organizations use, for instance, Enterprise Resource Planning (ERP) or other business process-oriented system. In this paper, a clustering method in business process model based on its similarity is proposed. This clustering aims to group some similar business processes to form a common business process. A new business process, as a result, can be composed based on similar common business process in order to increase reusability. It is done according to similarity value among business processes that is by calculating the similarity based upon structural and behavioral similarity method. Meanwhile, the clustering process uses a graph partition approach. This research then shows that the clustering result of business process is precise at certain threshold value.

Linking spatial information of cultural heritage metadata with geo linked open data

This paper propose a method for automatically linking spatial information that embedded in cultural heritage metadata to geo linked open data. The spatial information that embedded in a cultural heritage object could be used to enrich information of the other objects. For example, by knowing the spatial relation between two or more CH objects, we could conclude the condition of civilization in the past. Since most of the spatial information might had been documented incompletely, it needs to utilize open geo datasets as a dictionary to identify any place names and its other related information that would be beneficial to enrich CH metadata. However, linking spatial information to geo linked open data could be very troublesome if it done manually. For the experimental purposes, we built a prototype to test the feasibility of our method. As the result, our method are considered could give contribution in generating CH metadata along with their enriched spatial information.

PENGENALAN PENY AKIT NODA PADA CITRA DAUN TEBU BERDASARKAN CIRI TEKSTUR FRACTAL DIMENSION CO-OCCURRENCE MATRIX DAN L*a*b* COLOR MOMENTS

Penyakit yang menyerang tebu dapat disebabkan oleh bakteri, jamur maupun virus. Penyakit noda merupakan penyakit pada tanaman tebu yang disebabkan oleh jamur dengan menampakkan lesi atau bercak pada permukaan daun. Penyakit noda tersebut dapat menghambat proses fotosintesis yang akan berakibat menurunkan produksi gula karena mempengaruhi pertumbuhan tebu. Upaya pengendalian dini dapat dilakukan dengan mengenali jenis penyakit melalui lesinya yang bermanfaat dalam menentukan tindakan penanganan yang tepat. Lesi yang disebabkan oleh penyakit noda masing-masing dapat dikenali secara visual karena memiliki ciri warna dan tekstur yang unik. Tetapi pengamatan secara visual memiliki beberapa kekurangan seperti subjektifitas dan kurang akurat. Penelitian ini mengusulkan pengenalan penyakit noda tanaman tebu yang terdiri dari noda cincin, noda karat, dan noda kuning berdasarkan fitur tekstur yang merupakan kombinasi dari konsep Gray Level Co-Occurrence Matrix (GLCM) dan dimensi fraktal yang dinamakan Fractal Dimension Co-Occurrence Matrix (FDCM). Sedangkan fitur warna didapatkan dari perhitungan statistik col or moments pada citra L*a*b*. Kombinasi fitur tersebut menghasilkan 12 fitur warna dan 6 fitur tekstur yang kemudian digunakan sebagai masukan klasifikasi k-Nearest Neighbor (KNN). Pengenalan penyakit noda pada tanaman tebu menggunakan metode tersebut dapat menghasilkan akurasi tertinggi 90%.