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All Journal Jurnal Riset Kimia PERFORMA : Media Ilmiah Teknik Industri Jurnal Mantik Archipelago Engineering Jurnal i tabaos
Latuny, Wilma
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Transportation

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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 (ALE)
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 O.
i tabaos Vol 5 No 2 (2025)
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.
Classification of regional language diversity in the maluku region using decision trees Tomhisa, Ghyovanno Godlif; Latuny, Wilma; Makaruku, Yoakhina Nicole; Manuhuttu, Jermias Victor; Hawurubun, Hendri
Jurnal Mantik Vol. 9 No. 4 (2026): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v9i4.6935

Abstract

Regional languages are an important part of cultural heritage that reflect the identity, values, and character of a community. In Maluku Province, there is a high degree of linguistic diversity because the region consists of many islands with different community characteristics. However, the passage of time, modernization, and population mobility have led to a decline in the number of speakers in some areas, threatening the extinction of a number of regional languages. This study aims to classify and visualize the diversity of regional languages in Maluku Province using the Decision Tree algorithm. This method was chosen because it is capable of recognizing patterns and relationships between variables, such as region, number of speakers, and language vitality. The research data was obtained from the compilation of the Language Agency and field observations, then processed using Python with the help of the pandas, scikit-learn, matplotlib, and Streamlit libraries to produce an interactive analytical dashboard. The results showed that regional languages on Seram Island, such as Tana, Alune, and Wemale, had higher vitality levels than languages in other regions. The Decision Tree model built was able to classify language status with an accuracy rate of 92%. The resulting visualization provided a clear picture of the actual condition of regional languages in Maluku and could be used as a basis for regional language preservation and development efforts by local governments.
Analysis of hotel visits in Ambon city using the naive bayes algorithm Doren, Henderina; Manuhutu, Jermias Victor; Makaruku, Yoakhina Nicole; Latuny, Wilma; Maimina, Apritiwi
Jurnal Mantik Vol. 9 No. 4 (2026): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v9i4.6936

Abstract

The rapid growth of tourism in Ambon City has increased competition among accommodations, necessitating data-driven performance evaluations. Prospective tourists often struggle with unstructured online reviews, while hotel management requires precise insights for improvement. This study aims to systematically classify hotel performance in Ambon City using the Naïve Bayes Algorithm based on reviews from platforms like Agoda and TripAdvisor. Adopting a descriptive quantitative methodology, the study processes and labels performance data as "Good," "Poor," or "Very Good." Findings demonstrate that the Naïve Bayes model is highly effective, achieving 91% accuracy. Evaluation via a Confusion Matrix confirms the model's reliability in predicting majority categories, proving that ratings and reviews are strong satisfaction predictors. While the model faces minor challenges with the "Poor" minority category due to limited data, the study provides strategic value. It offers management guidance for targeted improvements and helps tourists make informed decisions, ultimately enhancing the competitiveness of Ambon’s hospitality industry
Co-Authors Billy Jhones Camerling Branchiny Imasuly, Geovanny Damanik, Rohni Natalin Doren, Henderina Hawurubun, Hendri Imasuly, Geovanny B. Kelirey, Mayang Sari Lalihatu, Andrew Novanto Latuihamallo, Nevly Lawajo, Sumaria Leatemia, Theovani Maimina, Apritiwi Makaruku, Yoakhina Nicole Manuhuttu, Jermias Victor Manuhutu, Jermias Victor 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 Tomhisa, Ghyovanno Godlif Tupan, Johan M. Victor Oryon Lawalata Yaniah, Samrotul