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All Journal Ulul Albab: Jurnal Studi Islam El-HARAKAH : Jurnal Budaya Islam Jurnal Akuntabilitas Manajemen Pendidikan Jurnal Penelitian Humaniora KARSA: Jurnal Sosial dan Budaya Keislaman (Journal of Social and Islamic Culture) Jurnal Ilmiah Pendidikan Pancasila dan Kewarganegaraan ETNOSIA : Jurnal Etnografi Indonesia Jurnal Metamorfosa Rang Teknik Journal Owner : Riset dan Jurnal Akuntansi Jurnal Ilmu Dakwah Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton SeBaSa: Jurnal Pendidikan Bahasa dan Sastra Indonesia Stilistika: Jurnal Pendidikan Bahasa dan Sastra Jurnal Teknologi Dan Sistem Informasi Bisnis Akbis: Media Riset Akuntansi dan Bisnis Best Journal (Biology Education, Sains and Technology) Al-Misbah : Jurnal Islamic Studies Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Khalifa: Journal of Islamic Education LITERATUR: Jurnal Bahasa, Sastra dan Pengajaran Journal Of World Science Jurnal Ekonomi dan Bisnis Digital (MINISTAL) METAL : Jurnal Sistem Mekanik dan Termal Triangulasi: Jurnal Pendidikan Kebahasaan, Kesastraan, dan Pembelajaran Journal of Hospitality Accommodation Management Islam Realitas: Journal of Islamic and Social Studies Journal of Universal Community Empowerment Provision BAHASTRA GEMBIRA (Pengabdian Kepada Masyarakat) Jornal Of Tourism Sciences (Toursci) SASI Jurnal Polimesin Profesi Pendidikan Dasar Tabasa: Jurnal Bahasa, Sastra Indonesia, dan Pengajarannya International Journal of Elementary School International Journal of Islamic Teaching and Learning PUSAKA: Journal of Educational Review Proceedings of International Conference on Islamic Economic Finance and Social Finance Jurnal Pendidikan Islam JIMEKA
Islahuddin, Islahuddin
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Journal : Jurnal Polimesin

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Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods Maimuzar, Maimuzar; Hendra, Hendra; Khan, Syarif; Leni, Desmarita; Islahuddin, Islahuddin
Jurnal Polimesin Vol 22, No 5 (2024): October
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i5.5381

Abstract

In machine learning, predicting the mechanical properties of stainless steel, such as Yield Strength (YS), Ultimate Tensile Strength (UTS), and Elongation (EL), requires many input variables, such as chemical composition, type of heat treatment, heating duration, and cooling method. However, the complexity and number of these variables can increase processing time and reduce model accuracy. This study aims to explore the impact of selecting the most influential input variables to improve prediction accuracy. We compared two feature selection techniques: Recursive Feature Elimination (RFE), which systematically removes less important features, and Information Gain (IG), which measures the contribution of each variable to the target prediction. Both techniques were implemented using the random forest algorithm, chosen for its robustness in handling large datasets and its ability to capture complex interactions between variables. Parameter optimization was performed using a grid search. The analysis showed that the RFE-based model outperformed both the IG-based model and the model without feature selection. In predicting YS, RFE identified 13 out of 21 influential variables, achieving a Mean Absolute Error (MAE) of 9.91, Root Mean Square Error (RMSE) of 14.20, and R-squared value of 0.89. For UTS, RFE identified 8 out of 21 variables, with an MAE of 12.89, RMSE of 16.97, and R-squared of 0.97. In predicting EL, RFE identified 14 out of 21 variables, with an MAE of 3.82, RMSE of 6.10, and an R-squared value of 0.85. The high R-squared values (0.85) across all properties indicate the model’s strong predictive capabilities, making it suitable for practical applications in predicting the mechanical properties of stainless steel.
Investigation for adhesion enhancement of hydroxyapatite coatings on Ti-12Cr alloy using the dipcoating method for orthopedic implant Ardhy, Sanny; Islahuddin, Islahuddin; Putra, Meiki Eru
Jurnal Polimesin Vol 23, No 6 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i6.7642

Abstract

Titanium and its alloys are extensively applied in biomedical due to their light weight, corrosion resistance, and biocompatibility. However, conventional alloys such as Ti-6Al-4V possess a high elastic modulus (~110 GPa), much greater than that of natural bone (10-30 GPa), leading to stress shielding and delayed bone healing. To overcome this limitation, β-type titanium alloys with lower modulus have been developed, including Ti-12Cr, which is intended for spinal fixation implants. Previous studies have reported frequent surface cracking in HA layers, potentially reducing implant durability. In this study, bio-HA derived from scales of ikan kakap putih (Lates calcarifer), an abundant fishery by-product, was applied as a coating suspension. The natural collagen present in the scales was expected to enhance coating adhesion. HA layers were deposited on Ti-12Cr substrates using dip coating with dipping times of 20, 24, 34, and 40 seconds. The results show that HA derived from scales of ikan kakap putih exhibits good coating adhesion strength. This improvement in adhesion helps minimize cracking in the HA layer. The highest adhesion was achieved at a dipping time of 20 s, with only 2% of the coated area peeling off. In addition, the dip-coating process produced thin and uniform HA layers, with surface coverage reaching 98.14% at a dipping time of 40 s. The improved adhesion of the HA coating is expected to enhance osseointegration and reduce implant inflammation effects in biomedical applications.
Co-Authors Ab., Ahmad Abdulrahman Jehtae Abidin, Saenal Adam, Yasmin Maharani Adri Efferi Aflah, Kuntarno Noor Agus Iswanto Ainy, Noer Sarifah Akhir, Husnul Akram, Miftachul Akramadina, Akramadina Andayani, Noviar Anwari Masatip Aprilia, Surita Ardiyanto, Muhammad Riza Arif Rahman Aritonang, Firdaus Aryanie, Irne Asep Maulana Rohimat Asep Supena. Asriani, Fauziah Azhar, Ghina Atikah Budi Setiawan Budi Waluyo Budianto, Muh. Annas BZ, Fazli Syam Chema, Adareena Choiriyah, Arifatul Dahri, Harapandi Daulay, Maswan Dedi Pramono Djoko Sulaksono Dwiprabowo, Risky Eka Nurhidayat Eppang, Buntu Marannu Fachry Abda El Rahman Fahira, Afra Fahira, Suri Rahma Faiz, Muhammad Farid Said, Farid Faujiah, Evi Fazwa, Monika Febriyanto, Budi Fibriani, Cut Dara Fujastawan, I Ngurah Gede Verar Gumgum Gumelar Fajar Rakhman Gustini, Sri Ha, Halimoh Hadi, Nestiyanto Hadiyan Wijaksana, Haris Hamsu Hanafi, Hamsu Hamzah, Reodhy Hasbullah Hasbullah Hendra Hendra Hidayat, Kun Ichsan, Yazida Ilma Fahmi Aziza, Ilma Fahmi jelita, jelita Khan, Syarif Ku-Ares Tawandorloh Kundharu Saddhono Lestari, Winda Dwi Lilik Wahyuni Lixian , Xiao Lukman Asha Luqyana, Luqyana LUTHFIRALDA SJAHFIRDI Machmuri, Andri Maimuzar Maimuzar Marziah, Fikar Meiki Eru Putra Menjamin, Sumaiyah Moch Lukluil Maknun, Moch Lukluil Moch. Lukluil Maknun Mohamad Syarif Sumantri Mohammad, Nhelbourne Mubarock, Wildan Fauzi Muhamad Parhan Muhammad Arfin Muhammad Salim Muhammad Husaini Muhammad Idris muhammad rizky, muhammad Nasution, Ali Napiah Nelwati, Sasmi Novitasari, Meisya Nugraheny, Devita Cahyani Nur Mahmudah Nur Qorimah, Esti Nurul Hasfi Oki Dermawan Pramadhanti, Dhelinta Fitri Prima Veronika Pudjiastuti, Sri Rahayu Putri, Moulisa Friana Qadriani, Nur Lailatul Ramadhani, Fauziah Aulia Ratna Yunita Setiyani, Ratna Yunita Roch Aris Hidayat, Roch Aris Safrizal Safrizal Saharuddin Saharuddin Saimima, Johan Robert Sanny Ardhy Saputra, Dudu Suhandi Sari, Dewi Arnita Sayid Habiburrahman, Sayid Sinaga, Rouli Retta Trifena Sohnui, Suhailee Som, Ahmad Puad Mat Sulistyawati, Anggraeni Susanti Susanti Syahidin Syahidin, Syahidin Syahriyah, Ummi Ulfatus Syarif, Nur Atika Syukriy Abdullah Taufiq Mathar Tawandorloh, Ku-Ares Touku Umar TYA RESTA FITRIANA Vioreza, Niken Waeno, Mahamadaree Wulandari Pratiwi, Raden Yonanda, Devi Afriyuni Yuliati, Yuyu Yuniyanti, Indah Zahro, Amalia Zulkarnaini Zuraida Zuraida