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All Journal EPIGRAM (e-journal) Recent in Engineering Science and Technology
Agus Sukandi
Politeknik Negeri Jakarta
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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KESALAHAN BERBAHASA INDONESIA PADA TUGAS AKHIR MAHASISWA PROGRAM STUDI TEKNIK MESIN POLITEKNIK NEGERI JAKARTA Asep Yana; Ratna Khoirunnisa; Agus Sukandi
EPIGRAM (e-journal) Vol 19 No 1 (2022): Epigram Volume 19 Nomor 1 Tahun 2022
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/epi.v19i1.4189

Abstract

Penelitian ini berjudul, “Analisis Kesalahan Berbahasa Pada Tugas Akhir Mahasiswa Program Studi D-III Teknik Mesin Tahun 2020”. Latar belakang penelitian ini ialah karena masih bnayaknya kesalahan penulisan dalam bahasa Indonesia. Kesalahan berbahasa terjadi kettika adanya penyimpangan kaidah kebahasaan, baik yang tidak sesuai dengan aturan Pedoman Umum Ejaan Bahasa Indonesia, khusus dalam tulisan ilmiah. Kesalahan berbahasa tersebut dikategorikan menjadi kesalahan dan kekeliruan kebahasaan. Kesalahan berbahasa merupakan pemakaian bahasa di luar aturan yang berlaku dalam kaidah atau aturan bahasa Indonesia. Tujuan penelitian ini untuk dapat menelaah berbagai kesalahan kebahasaan mulai dari kata, frasa klausa, kalimat, tanda baca, diksi, dan kaidah lainnya. Akan tetapi dalam penelitian kali ini dibatasi permasalahannya yakni kesalahan kata hingga kalimat, kemudian tanda baca, dan kesalahan imbuhan. Adapun metode penelitian yang digunakan dalam penulisan ini ialah metode kualitatif deskriptif dengan dokumentasi penelitian berupa Tugas Akhir yang disusun oleh mahasiswa di tahun 2020. Tugas akhir yang diteliti sebanyak empat, dipilih secara random. Hasil penelitian menunjukan bahwa kesalahan terbanyak terdapat pada penulisan kata yang tidak baku, tidak sesuai dnegan KBBI. Kesalahan kebanyak kedua terjadi pada tanda baca, dan kesalahan ketiga banyak terjadi pada penulisan imbuhan. Kesimpulan penelitian diperoleh bahwa dari beberapa kesalahan yang terjadi mahasiswa tidak meliahat rujukan kamus untuk menuliskan istilah-istilah yang tidak umum, selain itu buku Panduan Umum Ejaan Bahasa Indonesia juga tidak menjadi rujukan, sehingga banyak terjadi kesalahan.
Machine Predictive Maintenance by Using Support Vector Machines Idrus Assagaf; Agus Sukandi; Abdul Azis Abdillah; Samsul Arifin; Jonri Lomi Ga
Recent in Engineering Science and Technology Vol. 1 No. 1 (2023): RiESTech Vol. 1 No. 1 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.6

Abstract

Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses.  Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.
Machine Failure Detection using Deep Learning Idrus Assagaf; Agus Sukandi; Abdul Azis Abdillah
Recent in Engineering Science and Technology Vol. 1 No. 3 (2023): RiESTech Vol. 1 No. 3 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i03.21

Abstract

This article focuses on the application of deep learning methods for failure prediction. Failure prediction plays a crucial role in various industries to prevent unexpected equipment failures, minimize downtime, and improve maintenance strategies. Deep learning techniques, known for their ability to capture complex patterns and dependencies in data, are explored in this study. The research employs Multi-Layer Perceptron as deep learning architectures. This model is trained on AI4I 2020 Predictive Maintenance data to develop accurate failure prediction models. Data preprocessing involves cleaning, feature engineering, and normalization to ensure the quality and suitability of the data for deep learning models. The dataset is split into training and testing sets for model development and evaluation. Performance evaluation metrics such as accuracy, ROC, and AUC are utilized to assess the models' effectiveness in predicting failures. The experimental results demonstrate the effectiveness of deep learning methods in failure prediction. The models showcase high accuracy and outperform SVM approaches, particularly in capturing intricate patterns and temporal dependencies within the data. The utilization of Multi-Layer Perceptron architecture further enhances the models' ability to capture long-term dependencies. However, challenges such as the availability of diverse and high-quality data, the selection of appropriate architecture and hyperparameters, and the interpretability of deep learning models remain significant considerations. Interpretability remains a challenge due to the inherent complexity and black-box nature of deep learning models. In conclusion, deep learning method offer significant potential for accurate failure prediction. Their ability to capture complex patterns and temporal dependencies makes them well-suited for analyzing operational and sensor data. Future research should focus on addressing challenges related to data quality, interpretability, and model optimization to further enhance the application of deep learning in failure prediction.
Co-Authors Abdul Azis Abdillah Abdul Azis Abdillah Asep Yana Assagaf, Idrus Jonri Lomi Ga Ratna Khoirunnisa Samsul Arifin