Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Teknik Mesin Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Islamic Economics Lariba
Syarif, Muhyiddin
Unknown Affiliation
Religion Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Rancang Simulasi Hidrolik Press Syarif, Muhyiddin; Abdillah, Amin; Septian, Vionanda; Sudarmawan, R. Grenny
Seminar Nasional Teknik Mesin 2019: Prosiding Seminar Nasional Teknik Mesin 2019
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hidrolik banyak dimanfaatkan pada industri manufaktur karena dibandingkan dengan power sistem yang lain mempunyai rasio tenaga yang tinggi. Salah satu pemanfaatannya adalah mesin hidrolik press. karena penggunaanya yang tidak mudah maka dibutuhkan pelatihan untuk mendapatkan pengetahuan dasar dan tipe pemeliharaan yang harus dilakukan. Project ini dilakukan di divisi Learning Center PT. X yang bergerak di bidang otomotif. Pada saat ini, divisi Learnig Center belum mempunyai alat simulasi yang membantu karyawan untuk mempelajari mengenai press hidrolik. Rancangan ini menggunakan rangkaian hidrolik dan elektrik didalamnya. Metodelogi yang digunakan yaitu merancang berdasarkan parameter dari data gaya maksimal yang dibutuhkan untuk melakukan kompresi kaleng alumunium secara aksial. Dilakukan uji tekan dengan standar pengujian ASTM E 9-89a. Didapatkan gaya terbesar berdasarkan pengujian tersebut yaitu 1450 [N], dari data uji coba tersebut dan spesifikasi aktuator yang digunakan, tekanan yang dibutuhkan adalah 28,63 [bar].
The influence of social media on the decision to boycott Israel-affiliated products among the Muslim community in Jabodetabek Syarif, Muhyiddin; Herman, Sebastian
Journal of Islamic Economics Lariba Vol. 10 No. 2 (2024)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jielariba.vol10.iss2.art10

Abstract

IntroductionThe recent escalation of the conflict between Israel and Palestine triggered a global solidarity movement, including Boycott campaigns against products affiliated with Israel.ObjectivesThis study aims to analyze the determinants of digitalization's influence on online boycott product buying behavior among Muslim communities in Jabodetabek (Jakarta–Bogor–Depok–Tangerang—Bekasi), the Jakarta Metropolitan Area, or Greater Jakarta. MethodThis study's hypothesis is that social media, religiosity, subjective norms, behavioral control, and attitude have a positive and significant influence on boycotting behavior. It used an online survey with a randomly selected sample of 100 respondents. To test the hypothesis, it used structural equation modeling (SEM) with partial least squares (PLS).ResultsThe results show that social media and religiosity have a positive and significant influence on boycotting attitudes, while subjective norms and behavioral control do not have a significant influence on boycotting attitudes. Boycotting attitude also has a positive and significant influence on buying behavior. The R-square value of the model is 0.763, which means that the independent variables can explain 76.3% of the variability of buying behavior. ImplicationsThis study also provides implications for marketers and policymakers to understand the factors influencing the attitude and behavior of boycotting products affiliated with Israel among Muslim communities in Jabodetabek.Originality/NoveltyThis study contributes to the literature on consumer behavior, especially in the context of boycotting products related to political and religious issues.
Prediksi Nilai Unburned Carbon Batubara yang Dihasilkan PLTU Menggunakan Algoritma Linear Regression, Random Forest, dan LightGBM Regression Syarif, Muhyiddin; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3313

Abstract

This study focuses on predicting unburned carbon levels in coal-fired power plants to enhance operational efficiency. Accurate prediction of unburned carbon is crucial as it directly affects fuel combustion efficiency and environmental sustainability. The research compares three machine learning algorithms: Linear Regression, Random Forest, and LightGBM Regression, using performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results show that LightGBM Regression performs the best, with MAE of 0.31, MAPE of 1.29, and RMSE of 0.38, outperforming the other two models. This model can be further optimized to improve prediction accuracy, contributing to more efficient and environmentally friendly power plant operations. The application of machine learning in this study supports data-driven decision-making in the energy sector.
Prediksi Nilai Unburned Carbon Batubara yang Dihasilkan PLTU Menggunakan Algoritma Linear Regression, Random Forest, dan LightGBM Regression Syarif, Muhyiddin; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3313

Abstract

This study focuses on predicting unburned carbon levels in coal-fired power plants to enhance operational efficiency. Accurate prediction of unburned carbon is crucial as it directly affects fuel combustion efficiency and environmental sustainability. The research compares three machine learning algorithms: Linear Regression, Random Forest, and LightGBM Regression, using performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results show that LightGBM Regression performs the best, with MAE of 0.31, MAPE of 1.29, and RMSE of 0.38, outperforming the other two models. This model can be further optimized to improve prediction accuracy, contributing to more efficient and environmentally friendly power plant operations. The application of machine learning in this study supports data-driven decision-making in the energy sector.
Co-Authors Abdillah, Amin Afiyati Herman, Sebastian Septian, Vionanda Sudarmawan, R. Grenny