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All Journal Jurnal Dedikasi JOIN (Jurnal Online Informatika)
Syafa’ah, Lailis
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Environmental Science Languange, Linguistic, Communication & Media Public Health

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OTOMATISASI ALAT PEMINDAH GAMBAR NEGATIF FILM (LAYOUT) KE SCREEN DENGAN TEKNOLOGI MIKROKONTROLLER DALAM UPAYA PENINGKATAN PRODUKSI PCB SIAP PAKAI DAN EFISIENSI USAHA ., Nurkasan; Irfan, M.; Syafa’ah, Lailis
Jurnal Dedikasi Vol 3 (2006): Mei
Publisher : Direktorat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (784.484 KB) | DOI: 10.22219/dedikasi.v3i0.899

Abstract

Salah satu dalam proses pembuatan PCB adalah screening (pensablonan PCB pada media screen), dimana didalamnya ada proses perpindahan gambar (drawing) ke media screen. Saat ini, dilakukan dengan cara konvensional (manual), oleh karena itu melalui program vucer ini, kami membuat alat otomatisasi pemindah lay out ke media screen. Alat ini mempunyai 2 bagian utama yaitu lampu sebagai pengganti matahari, dan yang kedua bagian sistem kontrol dengan menggunakan mikrokontroller yang berfungsi mengontrol beban yang berupa lampu dengan waktu penyinaran telah ditentukan. Hasil yang didapat dengan mengaplikasikan alat ini dalam proses pemindahan lay out ke media screen adalah waktu yang diperlukan untuk memindah lay out ke media screen yang semula membutuhkan waktu 45 menit, dengan alat ini hnaya memerlukan waktu 8 menit.   Keyword : Screening, Mikrokontroller, Lay Out
Comparison of Machine Learning Classification Methods in Hepatitis C Virus Syafa’ah, Lailis; Zulfatman, Zulfatman; Pakaya, Ilham; Lestandy, Merinda
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.719

Abstract

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
Co-Authors Ilham Pakaya M. Irfan Merinda Lestandy Nur Kasan, Nur Zulfatman Zulfatman