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All Journal Journal of Welding Technology Jurnal Pengabdian Masyarakat Bangsa Journal Of Artificial Intelligence And Software Engineering
Kuswanto, Teguh Junian
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Sosialisasi Penggunaan Alat Pelindung Diri bagi Pekerja Galangan Kapal Tradisional di Weru sebagai Upaya Peningkatan Keselamatan dan Kesehatan Kerja Agustina, Nur Aini Amalia Dinda; Kuswanto, Teguh Junian; Firdaus, Muhammad Iqbal; Rokhim, Imam Nur; Putra, Ekananda Sulistyo; Rochmad, Alif Nur
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 9 (2025): November
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i9.3452

Abstract

Pengrajin kapal di galangan Desa Weru, Lamongan memiliki risiko pekerjaan dengan berbagai macam kemungkinan kecelakaan kerja. Salah satu cara yang bisa dilakukan oleh pengrajin untuk meindungi dirinya dari potensi bahaya tersebtu Adalah dengan memakai Alat Pelindung Diri (APD). Tujuan pengabdian Masyarakat ini Adalah memaksimalkan pemahaman pengrajin terkait urgensi penggunaan APD. Lokasi penelitian berada di galangan kapal tradisional Desa Weru, Kecamatan Paciran, Kabupaten Lamongan, Jawa Timur. Penelitian ini bertujuan untuk mendeskripsikan dan menganalisis proses serta dampak dari kegiatan penyuluhan yang berfokus pada peningkatan produktivitas dan strategi pemasaran tepat guna bagi nelayan di Desa Weru, Lamongan. Data dikumpulkan menggunakan metode kualitatif deskriptif melalui kombinasi observasi, wawancara, kuesioner. Hasil post test menunjukkan peningkatan signifikan hingga 90% peserta mampu menjelaskan manfaat APD dan risiko yang dapat timbul bila alat tersebut tidak digunakan. Rekomedasi APD yang perlu mereka gunakan yaitu helm, sarung tangan, masker, kacamata maupun sepatu keselamatan. Setelah tingkat kesadaran pekerja terhadap pentingnya penggunaan APD mulai meningkat, tantangan berikutnya yang dihadapi adalah rasa ketidaknyamanan dalam mengenakan APD selama bekerja. Sehingga, diperlukan proses penyesuaian secara bertahap disertai dengan pendekatan yang berkesinambungan agar para pengrajin dapat beradaptasi dengan kebiasaan baru dalam bekerja menggunakan APD secara lengkap
Current progress on wire-arc and welding-based additive manufacturing with its potential application: a review Dioktyanto, Mudzakkir; Abdullah, Hidayat; Dewi, Yulia Puspa; Rokhim, Imam Nur; Kuswanto, Teguh Junian
Journal of Welding Technology Vol 7, No 2 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jowt.v7i2.8418

Abstract

Wire-Arc Additive Manufacturing (WAAM) is one of the leading metal additive manufacturing procedures for producing medium to large-scale components due to its high deposition rate, low material cost, and compatibility with common structural alloys. This paper review provides a focused and critical assessment of welding-based Additive Manufacturing (AM) technologies, particularly WAAM processes utilizing Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), and Plasma Arc Welding (PAW). The scope covers the fundamental process principles, thermal–metallurgical behaviour, mechanical performance, and deposition control methods. The specific contribution of this review is: (i) explaining key process–structure–property relationships documented in recent studies, (ii) identifying core technological barriers—such as thermal distortion, porosity, residual stresses, and anisotropic microstructures—that limit industrial deployment, and (iii) outlining strategic future research directions that important for improving process stability and weld results. Key findings indicate that heat input management governs bead morphology, cooling rate, phase formation, and residual stress accumulation across multi-layer builds. Advances such as adaptive arc modes, interpass temperature control, closed-loop sensing, and hybrid subtractive–additive workflows have shown significant reductions in geometric deviation and defect formation. Nevertheless, reproducibility, dimensional accuracy, and mechanical property predictability remain persistent challenges. Overall, the review shows that integrating real-time monitoring, predictive simulation, alloy design tailored for WAAM, and intelligent control systems represents the most impactful pathway toward achieving certified and industrial-grade components.
Tomato Classification Based On Ripeness With Rgb And Hsv Feature Extraction Using Naïve Bayes Algorithm Kuswanto, Teguh Junian; Fahlevi, Muhammad Fauzan; Firdaza, Akhira Maulidio
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7230

Abstract

Tomato is a vegetable commodity that is also categorized as a fruit and is easy to cultivate in various regions. Differences in the level of tomato ripeness often become a challenge in the accurate classification process. Although many studies have been conducted related to the shape, disease, and varieties of tomatoes, classification based on the level of ripeness is still rarely done. This study aims to develop a classification of tomatoes using the level of tomato ripeness based on the color extracted through the RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) channels, using the Naïve Bayes algorithm. This research was conducted by collecting 150 tomato images that had similar shapes but with varying levels of ripeness, with a total of 135 training data and 15 test data. The research stages include the extraction of tomato color image features in RGB and HSV features, data simplification, separation between training data and testing data with a ratio of 90:10, and the application of the Naïve Bayes algorithm for the classification process. The results of the study showed that the RGB and HSV feature extraction methods combined with the Naïve Bayes algorithm were able to classify tomato ripeness levels with an accuracy of 80%. RGB and HSV color attributes together contributed to the classification accuracy, by producing a significant effect on certain ripeness categories
Co-Authors Abdullah, Hidayat Agustina, Nur Aini Amalia Dinda Dewi, Yulia Puspa Dioktyanto, Mudzakkir Fahlevi, Muhammad Fauzan Firdaza, Akhira Maulidio Muhammad Iqbal Firdaus, Muhammad Iqbal Putra, Ekananda Sulistyo Rochmad, Alif Nur Rokhim, Imam Nur