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All Journal TEKNIK Prosiding Seminar Nasional Teknik Mesin Mitra Akademia: Jurnal Pengabdian Masyarakat Recent in Engineering Science and Technology
Agus Sukandi
Sekretariat Unit Penelitian dan Pengabdian kepada Masyarakat (UP2M) Politeknik Negeri Jakarta Gedung Direktorat Lt.2, Telp.(021) 7270036 Psw. 236 Fax (021)7270034 Kampus Baru Universitas Indonesia Depok, DEPOK 16425 Email: politeknologi_pnj@yahoo.co
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Rancang Bangun Prototipe Case Powerbank dengan Solarcell sebagai Media Konversi Energi Panas ke Energi Listrik Rizka, Hawa; Gotama, Priya Esa; Manawan, Maykel; Sukandi, Agus
Seminar Nasional Teknik Mesin 2019: Prosiding Seminar Nasional Teknik Mesin 2019
Publisher : Politeknik Negeri Jakarta

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Abstract

Smartphone yang merupakan salah satu contoh kemajuan teknologi di abad ini, yang membuktikkan bahwa kebutuhan manusia semakin tinggi dan menjadi kebutuhan vital, bisa digunakan diperjalanan dengan baterai yang telah diisi dengan pengisi daya sebelumnya. Kinerja baterai smartphone yang terbatas menyebabkan turunnya effisiensi daya baterai sehingga dapat membuat smartphone lebih cepat tereksploitasi baterainya dan membutuhkan pengisi daya yang dapat dibawa kemanapun. Dengan memanfaatkan energi matahari, inovasi powerbank akan membuat pengisian smartphone menjadi lebih mudah dan efisien secara tempat dan waktu. Inovasi yang diperlukan yaitu penambahan solarcell bagi penangkapan sinar matahari lalu dikonversi menjadi energi listrik dan superkapasitor untuk mempercepat pengisian daya bagi smartphone. Metode yang akan digunakan yaitu deskriptif dan eksperimen, yaitu dengan mengadakan survei bagi masyarakat terutama bagi pekerja lapangan seperti driver ojek online sebagai batasan masalah untuk penelitian guna keperluan produk yang dibuat lalu kami akan membuat produk sesuai kebutuhan konsumen dengan hasil yang diinginkan ialah pengisian daya baterai menjadi lebih mudah dan efisien secara waktu dan tempat
Sistem Otomatisasi dan Monitoring Perawatan Berkala AC (Air Conditioner) Berbasis Arduino yang Terintegrasi IoT (Internet of Things) Diori, Gohi; Rianjani, Dhea Amelia; Maulana, Gilang; Tamzil, Tyara Zhafirah; Manawan, Maykel; Sukandi, Agus
Seminar Nasional Teknik Mesin 2019: Prosiding Seminar Nasional Teknik Mesin 2019
Publisher : Politeknik Negeri Jakarta

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Abstract

Air Condtioner (AC) adalah alat pendingin yang populer digunakan saat ini, untuk menciptakan suhu ruangan yang nyaman. Tetapi dalam penggunaannya, AC menimbulkan beberapa permasalahan, contohnya para pengguna sering kali lalai dalam mematikan AC saat sudah tidak dipakai serta jarang melakukan pengecekan dan perawatan terhadap kondisi unit AC. Apabila dibiarkan dapat mempengaruhi peningkatan pada biaya listrik yang dikeluarkan. Oleh karena itu diperlukan suatu kontrol yang secara otomatis dapat mematikan dan menyalakan AC apabila sudah tidak digunakan dan juga mengatur suhu ruangan sesuai SNI serta dapat memonitoring kerusakan pada AC. Alat ini menggunakan sensor PIR yang digunakan untuk mematikan dan menyalakan AC secara otomatis jika ada orang di dalam ruangan. Sensor Suhu ds18b20 digunakan untuk memantau suhu secara akurat di ruangan dan akan mengatur suhu di ruangan serta membandingkannya dengan suhu set di remote AC. Sensor Water level dan sensor Hall Effect berfungsi untuk mendeteksi kerusakan pada AC. Keseluruhan sensor akan dikendalikan oleh Arduino Uno yang akan terintegrasi dengan Internet of Things (IoT) melalui modul wifi ESP8266, yang akan memberikan sinyal peringatan pada pengguna apabila terjadi kerusakan AC, sehingga penggunaan AC dapat efektif dan efisien.
Optimalisasi Rasio Coal Mixing terhadap Kapasitas Supply Udara Pembakaran pada Boiler Berbasis Logika Fuzzy Nurma Putri, Melisa Dian; Nufus, Tatun Hayatun; Sukandi, Agus
Seminar Nasional Teknik Mesin 2019: Prosiding Seminar Nasional Teknik Mesin 2019
Publisher : Politeknik Negeri Jakarta

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Abstract

Design nilai kalori (HHV) batubara yang dibutuhkan pada PLTU Indramayu adalah 4527 kcal/kg. Sedangkan supply batubara yang tersedia tidak memenuhi nilai tersebut, salah satu solusinya adalah dengan coal mixing. Dan belum ada dasar penentuan untuk rasio coal mixing yang tetap pada PLTU Indramayu. Penelitian ini bertujuan untuk menganalisis rasio coal mixing yang optimal melalui parameter HHV, slagging, fouling index, dan pengaruhnya terhadap kapasitas supply udara pembakaran pada boiler. Metode yang digunakan adalah menganalisis studi kasus untuk memperoleh nilai optimal HHV, slagging, fouling, dan supply kebutuhan udara pembakaran menggunakan metode fuzzy dengan toolbox MatLab. Hasil penelitian menunjukkan bahwa perbandingan rasio coal mixing yang optimal berdasarkan parameter harga murah, HHV cukup, slagging index rendah, dan fouling index low sampai medium adalah 34%-Supplier A, 66%-Supplier F dan 37%-Supplier A, 63%-Supplier F serta kapasitas supply udara pembakaran untuk rasio coal mixing tersebut masih dapat disupply oleh primary air fan dan forced draft fan.
Kontrol Ventilasi Mekanis Berbasis pada Jumlah Estimasi Penghuni menggunakan Sensor Karbon Dioksida Rahman, Haolia; Sukandi, Agus; Nasruddin, Nasruddin; Arnas, Arnas; Lapisa, Remon
TEKNIK Vol 41, No. 3 (2020): December 2020
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v41i3.33416

Abstract

Ventilasi merupakan unsur penting untuk menjaga kualitas udara yang baik di dalam sebuah bangunan. Namun, penggunaan ventilasi yang berlebihan menyebabkan tingginya konsumsi energi dari sistem HVAC. Standar ASHRAE telah memberikan aturan bahwa laju ventilasi tergantung dari banyaknya penghuni dan luas ruangan di dalamnya. Oleh karena itu kuantifikasi populasi penghuni perlu diketahui sebagai acuan sebuah kontrol ventilasi. Pada penelitian ini, jumlah penghuni diestimasi menggunakan metode Bayesian MCMC berdasarkan level CO2 di dalam ruangan. Persamaan kesetimbangan massa CO2 digunakan sebagai model perhitungan Bayesian MCMC. Pengujian metode Bayesian dalam mengestimasi jumlah penghuni diaplikasikan pada sebuah ruangan kantor skala kecil berukuran 96,7 m3 yang dilengkapi dengan sistem ventilasi, sehingga estimasi penghuni dan kontrol ventilasi dapat dilakukan secara bersamaan. Pengujian juga mencakup kontrol ventilasi konvesional menggunakan level CO2 secara langsung tanpa mengkonversinya menjadi jumlah penghuni. Laju ventilasi berdasarkan jumlah penghuni pada ruangan pengujian mengacu pada standar ASHRAE 62.1. Hasil pengujian menunjukan bahwa kontrol ventilasi berbasis pada estimasi jumlah penghuni menggunakan metode Bayesian berhasil dilaksanakan dengan nilai laju ventilasi per penghuni lebih mendekati standar ASHRAE 62.1 dibandingkan dengan ventilasi metode konvensional.
Machine Predictive Maintenance by Using Support Vector Machines Assagaf , Idrus; Sukandi, Agus; Abdillah, Abdul Azis; Arifin, Samsul; Ga, Jonri Lomi
Recent in Engineering Science and Technology Vol. 1 No. 01 (2023): RiESTech Volume 01 No. 01 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 Assagaf, Idrus; Sukandi, Agus; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 1 No. 03 (2023): RiESTech Volume 01 No. 03 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.
Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery Assagaf, Idrus; Abdillah, Abdul Azis; Edistria, Ega; Sukandi, Agus; Prasetya, Sonki; Apriana, Asep; Nugroho; Kamil, Raihan
Recent in Engineering Science and Technology Vol. 3 No. 01 (2025): RiESTech Volume 03 No. 01 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i01.93

Abstract

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.
PENGENALAN PEMBUATAN SABUN CUCI MINYAK JELANTAH PADA WARGA KAMPUNG KEBON DUREN-DEPOK Nuriskasari, Isnanda; Ekayuliana, Arifia; Sukandi, Agus; Abadi, Cecep Slamet
Mitra Akademia: Jurnal Pengabdian Masyarakat Vol 4 No 2 (2021): Mitra Akademia: Jurnal Pengabdian Masyarakat
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/mapnj.v4i2.4280

Abstract

Environmental pollution due to waste cooking oil is a problem that is currently being faced by residents of RW 04 Kampung Kebon Duren-Cilodong-Depok, West Java. Community service carried out by the lecturer team of the Energy Conversion Engineering Study Program of the Politeknik Negeri Jakarta (PNJ) was to introduce and to train in making laundry soap made from used cooking oil. It was also as an effort to empower Kampung Kebon Duren-Cilodong, Depok in dealing with the COVID-19 pandemic. The implementation of community service begins with the presentation of material about used cooking oil and the purpose of using used cooking oil, preparing tools and materials, procedures for making soap from used cooking oil, and packaging soap. This activity, in general, consists of 1) pre-activity, 2) activity planning, 3) pre-implementation of training activities, 4) counseling and mentoring, and 5) activity evaluation. This training program creates entrepreneurship opportunities and educates residents on how to process used cooking oil which is household waste to be converted into a product that is worth selling so that it is not simply thrown into the environment because it can pollute the environment.  Keywords, Used Cooking Oil, Soap, Environment, Training, Community Service   Pencemaran lingkungan akibat limbah minyak jelantah merupakan permasalahan yang saat ini sedang dihadapi oleh Warga RW 04 Kampung Kebon Duren-Cilodong-Depok.Pengabdian masyarakat yang dilakukan oleh tim dosen program studi Teknik Konversi Energi PNJ adalah pelatihan pembuatan sabun cuci berbahan dasar minyak jelantah sebagai upaya pemberdayaan Kampung Kebon Duren-Cilodong, Depok. Pelaksanaan pengabdian masyarakat diawali dengan pemaparan materi tentang bahaya minyak jelantah bagi kesehatan dan lingkungan serta tujuan pemanfaatan minyak jelantah, persiapan alat dan bahan, prosedur pembuatan sabun cuci dari minyak jelantah dan pengemasan sabun. Kegiatan ini, secara garis besar terdiri atas: 1) pra kegiatan, 2) perencanaan kegiatan, 3) pre-pelaksanaan kegiatan pelatihan, 4) penyuluhan dan pendampingan, serta 5) evaluasi kegiatan. Program pelatihan ini menciptakan peluang berwirausaha dan mengedukasi warga terkait cara untuk mengolah minyak jelantah yang merupakan limbah rumah tangga untuk diubah menjadi produk bernilai jual sehingga tidak dibuang begitu saja ke lingkungan karena dapat mencemari lingkungan.   Kata-kata kunci: Minyak Jelantah, Sabun Cuci, Lingkungan, Pelatihan, Pengabdian Masyarakat
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Assagaf, Idrus; Sukandi, Agus; Jannus, Parulian; Prasetya, Sonki; Apriana, Asep; Edistria, Ega; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 3 No. 04 (2025): RiESTech Volume 03 No. 04 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Co-Authors Abadi, Cecep Slamet Abdillah, Abdul Azis Arnas Arnas, Arnas Asep Apriana, Asep Assagaf, Idrus Diori, Gohi Edistria, Ega Ekayuliana, Arifia Ga, Jonri Lomi Gotama, Priya Esa Isnanda Nuriskasari Jannus, Parulian Kamil, Raihan Manawan, Maykel Manawan, Maykel Maulana, Gilang Nasruddin Nasruddin Nugroho Nurma Putri, Melisa Dian Rahman, Haolia Remon Lapisa Rianjani, Dhea Amelia Rizka, Hawa Samsul Arifin Sonki Prasetya Tamzil, Tyara Zhafirah Tatun Hayatun Nufus