Judul: Pengembangan Sistem Predictive Maintenance Dengan Algoritma Prophet dan Decision Tree Pada Mesin Hot Press di PT Corinthian Industries Indonesia
Abstrak: A machine maintenance system is important in maintaining machine productivity and production productivity. PT Corinthian Industries Indonesia uses preventive maintenance as its machine maintenance system. The system still causes sudden damage because sometimes machine maintenance is not carried out at the right time according to its condition. An example is a Hot-press machine which has several breakdowns within a month. This project aims to develop a better maintenance system, namely a predictive maintenance system with the Prophet algorithm and a C4.5 decision tree based on a mobile application. The system initiates maintenance in response to certain machine conditions or performance degradation. Temperature and vibration are some parameters that can be measured as a marker of machine condition. This project develops a predictive maintenance system featuring real-time OEE calculations, machine condition detection from temperature and vibration sensors, and decision recommendations. Detection of machine conditions using the Prophet algorithm has a 5% MAPE. The decision recommendation using the C4.5 decision tree algorithm has an accuracy of 83%. Both algorithms have a fairly good percentage of accuracy, so they can be applied to the development of this system. The development of this system is used to determine the condition of the machine in real-time so that the response from the maintenance party can be carried out immediately., Sistem maintenance mesin merupakan aspek penting dalam menjaga produktivitas mesin dan produktivitas produksi. Saat ini, PT Corinthian Industries Indonesia menggunakan preventive maintenance sebagai sistem maintenance mesinnya. Sistem tersebut masih menyebabkan kerusakan mendadak karena terkadang maintenance mesin tidak dilakukan pada waktu yang tepat sesuai dengan kondisinya. Contohnya pada mesin Hot-press yang beberapa kali mengalami breakdown dalam waktu satu bulan. Tujuan dari projek ini adalah untuk mengembangkan sistem perawatan yang lebih baik yaitu sistem predictive maintenance dengan algoritma Prophet dan decision tree C4.5 berbasis aplikasi mobile. Sistem tersebut memulai perawatan sebagai respon terhadap kondisi mesin tertentu atau penurunan kinerja mesin. Suhu dan getaran adalah beberapa parameter yang bisa diukur sebagai penanda kondisi mesin. Projek ini mengembangkan sistem perawatan prediktif dengan fitur perhitungan OEE real-time, deteksi kondisi mesin dari sensor suhu dan getaran, dan saran keputusan. Deteksi kondisi mesin menggunakan algoritma Prophet memiliki MAPE 5%. Rekomendasi keputusan menggunakan algoritma decision tree C4.5 memiliki akurasi 83%. Kedua algoritma tersebut memiliki persentase akurasi yang cukup baik sehingga bisa diterapkan pada pengembangan sistem ini. Pengembangan sistem ini digunakan untuk mengetahui kondisi mesin secara real-time sehingga respon dari pihak maintenance dapat segera dilakukan.
Keyword: predictive maintenance, prophet algorithm, decision tree algorithm

Judul: Analisis dan Perancangan Sistem Predictive Maintenance Menggunakan Association Rule Mining pada Mesin Moulding PT X.
Abstrak: One of the challenges in the implementation of Total Productive Maintenance (TPM) in the manufacturing industry is a slow managerial decision-making to respond the condition in the factory. This research investigates the answers of these challenges by analyzing and modeling the equipment condition and the response of actions required in a wooden door manufacturing industry. TPM implementation in this company has deployed the Overall Equipment Effectiveness (OEE) measurement as an indicator of the equipment utilization and condition. Through an analysis and modeling of the OEE value obtained from the factory, the formulation of Association Rule Mining (ARM) aims to find a rule that shows the well computed relationship between measurable indicators of OEE with the response of action required to take in certain condition of machine utilization. Results obtained from ARM accelerate the decision to establish an appropriate TPM management strategy based on the rules. The generated dynamic rules form and facilitate the process of decision-making by related stakeholders. Furthermore, relying on these rules the action taken by the company induced to a higher reliable and increasing the effectiveness of response and efficiency of time and costs.
Keyword: Overall Equipment Effectiveness, Association Rule Mining, Data Mining, Total Productive Maintenance

Judul: Light Microscope Study of ConnectiveTissue in The Spleen of Common Rousette Bat (Rousettus amplexicaudatus)
Abstrak: The spleen is the largest lymphoid organ. As a secondary lymphoid organ, in addition to its immune function, the spleen also has functions as blood filtration, storage, and hematopoiesis. The spleen has different morphologies between species that support different physiological functions, one of which is the difference in the structure of the capsule and trabeculae. Capsule and trabeculae of the spleen generally consist of connective tissue and smooth muscle. The thickness of smooth muscle composed together with connective tissue in the capsule and trabecula determines the function of the spleen that is divided spleen into three storage, defense, and intermediate. Bats are the reservoir of various zoonotic diseases. Bats are thought to have unique immune systems compared to other mammals but the characteristics of its immune organs such as the spleen have never been studied before. This study was conducted to examine the characteristics of capsulae and trabecula of the Common Rousette (Rousettus amplexicaudatus).
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