Article Per Year (5 Year)
p-Index From 2020 - 2025
2.461
P-Index
This Author published in this journals
All Journal Jurnal Riset Kimia PERFORMA : Media Ilmiah Teknik Industri Archipelago Engineering Jurnal i tabaos
Latuny, Wilma
Unknown Affiliation
Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Corrosion Analysis on Stainless Steel (SS304) Using A Coating Method Based On Silica from Natural Sand of Hukurila Village Silahooy, Stevi; Branchiny Imasuly, Geovanny; Latuny, Wilma; Nggolaon, Delpina
Jurnal Riset Kimia Vol. 16 No. 2 (2025): September
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jrk.v16i2.808

Abstract

Corrosion analysis was performed on stainless steel (SS304) coated with silica extracted from silica sand in Hukurila Village using the coprecipitation method. The XRF results showed an increase in silica content from 58.118% to 81.247%, indicating high purity. XRD testing revealed that the silica was amorphous, while SEM analysis showed that the silica powder particles were irregular in shape and size, and tended to undergo agglomeration. The silica was then applied as a coating on SS304 using Nippon Paint with silica-to-paint weight ratios of 95:5, 90:10, and 80:20. The samples were tested in a 3.5% NaCl solution for 7 days using polarization methods on a potentiostat to measure corrosion resistance. The results showed that the 80:20 weight ratio provided the highest improvement in corrosion resistance. This enhancement is attributed to the more compact and uniform coating structure formed at higher silica loading, which effectively minimizes micro-pores, strengthens the barrier effect, and suppresses localized pitting corrosion.
Machine Learning-Based Hierarchical Clustering for Priority CCUS Zones in Indonesia Imasuly, Geovanny B.; Latuny, Wilma; Rikumahu, Marcia V.
ALE Proceeding Vol 7 (2025): Archipelago Engineering
Publisher : Fakultas Teknik Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ale.7.2025.65-75

Abstract

Indonesia's upstream oil and gas industry is facing significant challenges due to climate change, alongside the global shift toward clean energy transition to reduce CO₂ emissions and achieve the Net Zero Emission target by 2060. One of the key strategies in this effort is the implementation of Carbon Capture, Utilization, and Storage (CCUS).The adoption of the CCUS program is an integral part of SKK Migas' strategic plan to reach a production target of 1 million barrels of oil per day (BOPD) and 12 billion standard cubic feet of natural gas per day (BSCFD) by 2030. This study applies machine learning-based hierarchical clustering to analyze and classify CCUS project zones in Indonesia, utilizing data from the IEA CCUS Projects Database 2024. The methodology includes data collection, pre-processing, and clustering using a hierarchical algorithm to group projects with similar characteristics in CCUS implementation. The clustering process, interpreted through a dendrogram, considers key factors such as Announced Capacity (Mt CO₂/yr) and Estimated Capacity by IEA (Mt CO₂/yr). The Silhouette Coefficient after applying hierarchical clustering is 0.746, indicating well-defined cluster separation. The findings of this study provide valuable insights into the relationships among CCUS projects in Indonesia, categorizing them into priority zones. Additionally, this research supports strategic decision-making regarding CCUS project development, contributing to the achievement of the Net Zero Emission target and long-term energy security.
IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM PENGAMBILAN KEPUTUSAN UNTUK MEMBELI PRODUK BAGEA Leatemia, Theovani; Latuny, Wilma; Lawalata, Victor
i tabaos Vol 5 No 2 (2025): In Progress
Publisher : Fakultas Teknik Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/i-tabaos.2025.5.2.61-69

Abstract

Produk bagea merupakan salah satu industri mikro yang berkontribusi dalam peningkatan taraf hidup penduduk di Pulau Saparua. Sektor industri yang bergerak di bidang pangan ini mampu menghidupi banyak keluarga dengan tingkat pendapatan yang relatif tinggi. Peneliti ingin melakukan preferensi terhadap produk bagea berdasarkan atribut yaitu Merek, Kualitas, Kemasan, Fitur dan Desain untuk mengetahui faktor-faktor yang mempengaruhi keputusan membeli atau tidak, serta menghitung tingkat akurasi menggunakan algoritma SVM berbantuan machine learning WEKA pada dataset penilaian responden berdasarkan atribut. Hasil akhir penelitian ini, tingkat akurasi keputusan membeli bagea diperoleh dengan menggunakan uji persentase split 50% dengan variabel yang mempengaruhi keputusan membeli yaitu merek, kualitas, dan fitur.
Co-Authors Billy Jhones Camerling Branchiny Imasuly, Geovanny Damanik, Rohni Natalin Imasuly, Geovanny B. Kelirey, Mayang Sari Lalihatu, Andrew Novanto Latuihamallo, Nevly Lawajo, Sumaria Lawalata, Victor Leatemia, Theovani Nggolaon, Delpina Paillin, Daniel B. Paillin, Daniel Bunga Rikumahu, Marcia V. Rumakat, Putri Anugrah Rumakey, Abdul Samat Sangadji, Rini Silahooy, Stevi Soleman, Aminah Suhartini, Atika Tupan, Johan M. Victor Oryon Lawalata Yaniah, Samrotul