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Perbandingan Pemakaian Variasi Pegas CVT Terhadap Torsi, Daya dan Konsumsi Bahan Bakar Honda Beat Pgm-Fi Tahun 2018 2018年本田比姆菲(本田Beat - fi - fi)在扭矩、电力和燃料消耗上使用CVT变量的比值

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4919

Jusnita Jusnita, Putri Ayu Lestari

{"title":"Perbandingan Pemakaian Variasi Pegas CVT Terhadap Torsi, Daya dan Konsumsi Bahan Bakar Honda Beat Pgm-Fi Tahun 2018","authors":"Jusnita Jusnita, Putri Ayu Lestari","doi":"10.37859/jst.v10i1.4919","DOIUrl":"https://doi.org/10.37859/jst.v10i1.4919","url":null,"abstract":"Modifikasi sistem CVT dengan merubah pegas dari sistem penggerak dengan tujuan agar kinerja mesin lebih bertenaga dan tarikannya lebih ringan. Hasil penelitian pegas CVT standar, torsi maksimum 29,51 NM diputaran 7500 Rpm, torsi minimum 8,80 NM di Rpm 1750 dan daya maksimum 9,8 HP di Rpm 4043, Rpm 1750 hingga 2250 Rpm sudah mencapai peak power. Bahan bakar pertalite 200 ml, di run selama 1 menit di Rpm 6000 tersisa 180 ml. Tinggi pegas 94,3 mm, diameter luar 57,2 mm, diameter dalam 46,1 mm, jumlah lilitan 6 dan diameter pegas 5, 4 mm. Pegas variasi Rpm 1000 torsi maksimum 20,25 NM di Rpm 2913. Daya maksimum 9,9 HP di Rpm 3903, Rpm 1750 hingga 2250 Rpm sudah mencapai peak power. Tinggi pegas 117, 4 mm, diameter luar 58, 5 mm, diameter dalam 50,2 mm, jumlah lilitan 6 dan diameter pegas 5, 8 mm. Pegas dengan variasi 1500 Rpm, torsi maksimum 26,83 NM pada Rpm 2560, torsi menurun di Rpm 2750 sebesar 26,46 NM. Daya maksimum 10,2 HP di Rpm 2949. Putaran 2000 Rpm hingga 2750 Rpm sudah mencapai peak power. Tinggi pegas 119, 2 mm, diameter luar 59,4 mm, diameter dalam 51,2 mm, jumlah lilitan 6 dan diameter pegas 5,6 mm. Kesimpulan pegas CVT 1500 Rpm lebih efisien dibandingkan dengan standar karena tinggi pegas antara variasi kedua inilah yang menyebabkan perbedaan antara torsi dan daya dipengaruhi oleh diameter menyebabkan rata-rata torsi dan daya yang dihasilkan lebih besar, sedangkan konsumsi bahan bakar tidak saling mempengaruhi satu sama lain \u0000 ","PeriodicalId":17743,"journal":{"name":"Jurnal Surya Teknika","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77356589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Peningkatan Kualitas Sablon Kaos dengan Menggunakan Metode Seven tools of Quality: Studi Kasus di Workshop sablon Thinkthinggs.co 通过使用七工具改善t恤的质量:在Sablon thinkthingings .co的案例研究

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.5014

M. Tajul Arifin, Nina Aini Mahbubah, Mohammad Jufriyanto

引用次数: 0

Usulan Perbaikan Sistem Kerja Membatik dengan Pendekatan Ergonomi Makro untuk Mengurangi Keluhan Musculoskeletal Disorders 建议用Ergonomi宏观方法改进系统工作，以减少肌肉萎缩症的投诉

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4970

Alex Kisanjani, Wahyu Ismail Kurnia, Dimaz Harits

引用次数: 0

Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN) Pemodelan Machine Learning untuk Memprediksi拉伸强度铝

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4843

D. Leni, Helga Yermadona, Ade Usra Berli, R. Sumiati, Haris Haris

{"title":"Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)","authors":"D. Leni, Helga Yermadona, Ade Usra Berli, R. Sumiati, Haris Haris","doi":"10.37859/jst.v10i1.4843","DOIUrl":"https://doi.org/10.37859/jst.v10i1.4843","url":null,"abstract":"This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.","PeriodicalId":17743,"journal":{"name":"Jurnal Surya Teknika","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78988408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penyusunan Material di Warehouse PT XYZ

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.5095

Nur Farida Ruhfi, Elsya Paskaria Loyda Tarigan

引用次数: 0

Evaluasi Job Safety Analysis Guna Meminimalisir Potensi Kecelakaan Kerja Karyawan PT AAA 评估工作安全分析，以尽量减少PT AAA员工的潜在工作事故

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.5019

Syahrul Akbar, N. Mahbubah

引用次数: 0

Re-Desain Masker Yang Ergonomis untuk Anak Usia 6-11 Tahun dengan Pendekatan Antropometri 人体测量技术为6-11岁的儿童重新设计人体工程学

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4978

Sri Zetli, E. Tarigan, Nofriani Fajrah

{"title":"Re-Desain Masker Yang Ergonomis untuk Anak Usia 6-11 Tahun dengan Pendekatan Antropometri","authors":"Sri Zetli, E. Tarigan, Nofriani Fajrah","doi":"10.37859/jst.v10i1.4978","DOIUrl":"https://doi.org/10.37859/jst.v10i1.4978","url":null,"abstract":"Despite the significance of mask use, there is still a lack of understanding among Indonesians, with youngsters adhering to the wearing of masks at a rate of 51.6%. The causes include heat, irritability, pain, difficulty breathing, social rejection, and improper mask size and form. One of the ergonomics-related issues is the shape and size of the mask, so one solution is to use anthropometric data while designing the mask. In this study, groups of kids between the ages of 6 and 11 were recruited to redesign masks based on their anthropometric measurements. According to the findings, the distances between the nose's tip and the lower jaw joint (T), the tip of the nose and the rear of the ear bone (U), the nose's distance from the chin (C), and the distance between the eyes and the chin were utilized as anthropometric measurements in the construction of the mask. (B). The mask's size is determined. The mask's length without the strap is 11.16 cm, its length with the strap is 14.53 cm, the central mask's width is 7.02 cm, and the mask's total width is 13.70 cm. The size of the mask from the study and the size of the mask currently on the market differ after measurements were taken into account for anthropometric data","PeriodicalId":17743,"journal":{"name":"Jurnal Surya Teknika","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87400282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rancang Bangun Alat Press Ban Sistem Pneumatik untuk Sepeda Motor

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4940

Indra Hasan, Denur Denur, Legisnal Hakim

引用次数: 0

Proses Kominusi Batubara Berdasarkan Kinetic Energy Impact

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.4991

Rinaldi Rinaldi, Lukman Hakim Nasution, Arden Simeru, Adi Isra, Syafril Syafar

引用次数: 0

Analisis Persediaan Bahan Baku Roti Menggunakan Inventori Probabilistik Back Order dan Lost Sales

Jurnal Surya Teknika Pub Date : 2023-06-26 DOI: 10.37859/jst.v10i1.5021

Okti Wardani Okti, Fitriani Surayya Lubis, Suherman, Muhammad Nur, R. Taslim

{"title":"Analisis Persediaan Bahan Baku Roti Menggunakan Inventori Probabilistik Back Order dan Lost Sales","authors":"Okti Wardani Okti, Fitriani Surayya Lubis, Suherman, Muhammad Nur, R. Taslim","doi":"10.37859/jst.v10i1.5021","DOIUrl":"https://doi.org/10.37859/jst.v10i1.5021","url":null,"abstract":"UMKM Richard Bakery merupakan UMKM yang memproduksi roti donat original, donat gula, dan lain-lain. UMKM Richard Bakery memiliki kelemahan dalam penyediaan bahan baku. Diantaranya terjadi keterlambatan dalam memenuhi permintaan bahan baku yang diakibatkan oleh ketidakpastian permintaan pasar terhadap produk. Sehingga dalam menyediakan stok bahan baku menjadi terhambat dalam melakukan pendistribusian dan mengalami kesulitan dalam memenuhi permintaan pasar yang tidak pasti terhadap produk roti donat. Metode Inventori Probabilistik dengan Kebijakan Backorder dan Lost sales digunakan untuk mengoptimalkan tingkat pelayanan dan meminimalkan biaya persediaan. Dalam peramalan pemakaian tepung, metode Moving Average dengan periode n = 10 terpilih sebagai metode terbaik dengan nilai MAPE 10,657%. Hasil peramalan menunjukkan permintaan total tepung sebesar 2.919,41 Kg pada tahun 2022. Sedangkan dalam peramalan pemakaian telur, metode Moving Average dengan periode n = 8 terpilih sebagai metode terbaik dengan nilai MAPE 10,2%. Hasil peramalan menunjukkan permintaan total telur sebesar 13.693,41 butir pada tahun 2022. Setelah melakukan peramalan, dilakukan perhitungan biaya bahan baku menggunakan metode inventory probabilistik sesuai dengan situasi perusahaan. Hasil analisis menunjukkan bahwa model persediaan sistem P back order memberikan biaya persediaan yang paling optimum, yaitu Rp 43.930.885 untuk tepung dengan tingkat pelayanan 99,96%, dan Rp 28.289.892 untuk telur dengan tingkat pelayanan 85,57%.","PeriodicalId":17743,"journal":{"name":"Jurnal Surya Teknika","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90061718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0