Cancer patients with comorbidities face various life problems, health costs, and quality of life. Therefore, determining comorbid diseases would significantly affect the treatment of cancer patients. Because cancer disease is very complex, we can represent the relationship between cancer and its comorbidities as a network. Furthermore, the network analysis can be employed to determine comorbidities as a community detection problem because the relationship between cancer and its comorbidities forms a community. This study investigates which community detection algorithms are more appropriate to determine the comorbid of cancer. Given different community findings, this study attempted to analyze the modularity generated by the algorithm to decide the significant comorbid diseases. We retrieved lung cancer comorbid data on the basis of text mining manuscripts in PubMed, searched through disease ontologies, and calculated disease similarity. We investigate 20 algorithms using five modularity metrics and 16 fitness function evaluations to determine the significant comorbid diseases. The results show the five best modularity algorithms, namely label propagation, spinglass, Chinese whispers, Louvain, RB Pots. These five algorithms found significant comorbidities: blood vessels, immune system, bone, pancreas, and metabolic disorders, atrial cardiac septal defect, atrial fibrillation respiratory system, interstitial lung, and diabetes mellitus. The fitness function justifies the results of the community algorithm, and the ones that have a significant effect are average internal degree, size, and edges inside. This study contributes to more comprehensive knowledge and management of diseases in the healthcare context.

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Abstrak Sebagian besar lembaga pelatihan atau balai latihan kerja menggunakan penjadwalan dengan model batch, yang artinya sebuah jadwal digunakan bersama-sama untuk sekelompok orang, tanpa melihat karakteristik masing-masing peserta pelatihan atau ketersediaan waktu mereka. Namun penjadwalan model batch seperti itu belum tentu efektif untuk setiap orang dan belum sesuai dengan prinsip student center learning. Tulisan ini menawarkan alternatif solusi bagi lembaga pelatihan atau balai latihan kerja yang memerlukan penjadwalan dengan keragaman materi pelatihan dan ketersediaan waktu setiap peserta yang berbeda-beda. Solusi berupa pembuatan perangkat lunak aplikasi penjadwalan dengan arsitektur model-view-controller. Perangkat lunak yang dibuat mampu menjadwalkan lebih dari 300 peserta pelatihan dengan jumlah instruktur lebih dari 14 orang dan materi pelatihan berjumlah lebih dari 18 macam dengan tingkat kedalaman yang beragam.Kata Kunci : penjadwalan pelatihan, slot waktu dan materi berbeda-beda  Abstract It is common for training institutions or vocational training centers using batch scheduling model, which means that a timetable be used together for a group of people, regardless of their individual needs or their time availabilities. Scheduling in batch model like that was not necessarily effective for every class member and not in accordance with the principle of student center learning. This paper offers an alternative scheduling solution for training institutions or vocational training centers that each participant has special needs in timetable, course interest, and course level. Our solution was implemented by model-view-controller architecture. The software could be used to schedule of more than 300 trainees with more than 14 instructors and more than 18 kinds of training materials.    Keyword : training scheduling, different timetables and course material Â