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INDONESIA
Teknologika
Published by Sekolah Tinggi Teknologi Wastukancana Purwakarta
ISSN : 16932978     EISSN : 27154645     DOI : https://doi.org/10.51132/teknologika.v12iNo%202
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mechanical Engineering Other
Jurnal Teknologika adalah wadah informasi berupa hasil penelitian, studi kepustakaan maupun tulisan ilmiah yang terkait bidang ilmu Teknik Informatika, Teknik dan Manajemen Industri, Teknik Mesin dan Teknik Tekstil. Terbit dua kali setahun pada bulan Mei dan November. Artikel Jurnal Teknologika dalam versi cetak telah di-online-kan menggunakan Open Journal System (OJS)
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 282 Documents
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Perbandingan Metode Random Forest dan LightGBM untuk Prediksi Harga Berlian: Pendekatan Probabilistik dan Statistik Daud Aldo Santoso; Yosefina Finsensia Riti
Jurnal Teknologika Vol. 16 No. 1 (2026): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v16i1.685

Abstract

Figuring out diamond prices in the market is actually pretty tricky because the prices go up and down a lot and depend heavily on what the diamond looks like physically. The main problem here is that people often struggle to guess the right price manually, which can easily lead to losing money if they guess wrong. Through this study, we really want to test and directly compare two smart Machine Learning tools, which are the Random Forest and LightGBM algorithms, to see which one is actually better and more reliable at forecasting these prices without just blindly guessing. For how we did it, we used a huge dataset containing about 53,940 past diamond sales as study material. This data came complete with nine main features like carat weight, cut quality, color pigment, and physical size. Before doing the math with the computer, we quickly threw away any weird or impossible data rows to keep it clean. Then, we changed all the text descriptions into numbers so the program could read them easily. Next, we split the whole dataset up so that 80 percent was used to teach the program, and the remaining 20 percent was kept purely for testing how smart their guesses really were. The test results clearly showed that Random Forest did a much better job overall. It hit a super high R² accuracy score of 0.9835, and its guesses were only off by an average of $38.89 (MAE) and an RMSE of $77.24. This easily beat the LightGBM program, which only got an R² score of 0.9830, an MAE of $41.28, and an RMSE of $78.27. In short, this research proves that the team-work or ensemble system in Random Forest is way more reliable, stable, and accurate for predicting diamond prices in the market.
indonesia Ahmad Dikin; Jatira; Ghany Heryana
Jurnal Teknologika Vol. 16 No. 1 (2026): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v16i1.692

Abstract

The development of electric vehicles is becoming a trend in the development of global automotive, this is supported by the development of energy storage technology and energy conversion devices that allow for an increase in overall energy efficiency in current electric motor vehicles. Therefore, the study aims to determine the effect of welding strength with 1 mm thick ST40 sphc steel plate material, this material must be tested first for the manufacture of battery casing, one of which is important in making this battery casing by welding, the welding used is SMAW (Shielded Metal Arc Welding), with current variations of 20A, 30A, and 40A. Therefore, it aims to find out which current is good in the welding process itself. With data processing of the two-way ANOVA method. The process of testing the effect of current variations on visual conditions using 40A is better than the others, Tensile Strength ?????????????????????????>???????????????????????? 12.71>5.143, Yield Strength ?????????????????????????>???????????????????????? 7.24>5.143, and elongation ?????????????????????????<???????????????????????? 3.40<5.143.

Page 29 of 29 | Total Record : 282

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