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PEMANFAATAN SERABUT KELAPA DAN TRAY TELUR SEBAGAI MATERIAL KOMPOSIT PEREDAM SUARA PADA MOBIL Ulfiyah, Laily; Rohmah, Faizatur; wilujeng, Auliana diah
TURBO [Tulisan Riset Berbasis Online] Vol 12, No 2 (2023): TURBO: Jurnal Program Studi Teknik Mesin
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/trb.v12i2.2568

Kebisingan merupakan suatu masalah yang tengah dihadapi masyarakat Indonesia. Kebisingan dapat direduksi dengan menggunakan material yang dapat meredam dan menyerap bunyi. Material peredam akustik yang banyak digunakan masyarakat umumnya menggunakan glasswool dan rockwool, namun karena harganya mahal maka dibuat beberapa alternatif lain dengan memanfatkan bahan dari alam, yaitu berbahan dasar serabut kelapa dan tray telur. Pembuatan komposit berbahan dasar dari serabut kelapa, dan tray telur dengan matriks resin dan katalis. Pembuatan komposit berbahan dasar serabut kelapa dan tray telur dengan matriks resin dan katalis. Paduan bahan serabut kelapa dan tray tekur dengan komposisi 1:1, 2:1 dan 1:2. Pembuatan komposit menggunakan cetakan triplek dengan tebal 1 cm, lebar 20 cm, dan panjang 20 cm. Komposit yang dibuat diuji nilai koefisien absorpsi suara (α) menggunakan sound level meter dan uji coba dengan balok impedansi. Berdasarkan hasil pengujian koefisien suara (α) pada penelitian ini, ketiga material telah memenuhi nilai α ≥ 0,15 dB. Material B dengan komposisi 2:1 merupakan material yang optimal untuk dijadikan bahan untuk peredam suara pada kabin mobil dengan nilai α 0,4 dB.

Implementasi Alat Pemisah Gabah Padi Menggunakan Sistem Cyclone sebagai Upaya Meningkatkan Efektivitas Pekerjaan Buruh Tani di Kelurahan Karang Dalam Ulfiyah, Laily; Wilujeng, Auliana Diah; Fatah, Misbakhul; Febriana, Ike Dayi; Fikri, Mohammad Anas; Hadiwijaya, Lukman; Jakfar, Amin; Rohmah, Faizatur; Annafiyah; Hamid, Abdul; Ulfah, Nadiyah; Wijaya, Septian Dwi; Dewi, Ratna Ayu Pawestri Kusuma
Sewagati Vol 8 No 1 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i1.766

Padi merupakan tanaman pangan utama di Indonesia yang kaya karbohidrat sehingga menjadi makanan pokok masyarakat Indonesia. Dan untuk meningkatkan produksi padi, tidak hanya dengan memperluas areal pertanaman dan peningkatan teknik budidaya saja, tetapi perlu diperhatikan dalam penanganan panen khususnya dipemisahan padi yang harus tepat. Berdasarkan profil Kabupaten Sampang tahun 2017, produk unggulan daerah dalam sektor pertanian adalah padi. Oleh karna itu produksi padi harus lebih ditingkatkan agar dapat memenuhi kebutuhan sektor pangan masyarakat. Salah satu upaya meningkatkan produksi padi adalah dengan mengoptimalkan proses pemisahan gabah padi. Dalam artikel ini dibuat alat pemisah gabah padi dengan memanfaatkan sistem cyclone sehingga padi isi yang lebih berat akan terpisah dengan padi kosong yang lebih ringan. Dengan memanfaatkan gaya sentrifugal padi isi yang lebih berat akan terlempar keluar menuju penampungan padi isi. Sedangkan padi kosong yang ringan akan turun keluar ke penampungan padi kosong. Alat ini berukuran 80 x 40 x 80 cm. Dengan kapasitas 100 kg/jam, alat ini mampu meningkatkan produktivitas pekerjaan panen padi. Alat ini dihibahkan pada Kelompok Tani Bandar Kumala di Desa Karang Dalam, Kecamatan Sampang.

Solar Purchase Volume Prediction Using The K-Nearest Neighbor Algorithm Based On Backward Elimination Prasetyo, Aries Alfian; Pramono, Yudi; Ulfiyah, Laily; Fattah, Misbakhul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4964

The profit earned by a Public Filling Station or gas station comes from the purchase of fuel per period and sales in accordance with the volume amount. So the exact purchase volume will determine the turnover of each month. But the profit or turnover is often irregular, there are several causes such as the price of fuel that tends to change, the volume of orders that are not in accordance with consumer demand. With these prediction methods, the expected turnover is increased with more efficient purchases. This research was conducted to study about k-NN Algorithm and then apply k-NN Algorithm in data prediction. The data used are secondary data in the form of data on the number of purchases of BBM in volume liter in the period January 2012 - December 2024. The k values used are k = 1, K = 4, k = 5 and k = 7. Before calculation with k = 1 is done, determined the data of training and data testing, in this research determined as much 70% training data and 30% for data testing. Then the initial cluster determination of the training data based on the interval class. While the cluster in the data testing is determined based on testing with K = 4, k = 5 and k = 7. From the process of analysis and evaluation of the research predicted the volume of fuel purchases using the data ransed dataset that processed data into multivariate data, the process of analysis using K-NN method using 2,3,4 and 5 periods produce the smallest K located in the period to 3, so that the 3rd period will be predicted by K-NN based backward elimination. With the aim of finding the best method to predict the volume of fuel purchases, generate predictions with backward elimination, that the attribute weights in the period xt - 3 and in the period xt 1 selected as the reference in the prediction process, since the weight is 1. K = 13 is the K best way to perform the Analyzing process with K-NN for the prediction of fuel purchase volume, with K = 4 value of 45556,788. So in the analysis and prediction of oil fuel purchasing volume data, for the type of diesel, K is best K = 13 with K-NN analysis method with backward elimination process. The above results show that xt3 or week 3 and week xt1 to 1 in the last period of 2024 can be used as a reference in the purchase in the next year that is 2025.

Analisis Pengaruh Jenis SAE (Viskositas) Oli Bekas Terhadap Waktu Konsumsi dan Temperatur pada Alat Kompor Oli Bekas Al Fahrurrozi, Mochammad Arya; Abdi, Ferly Isnomo; Warju, Warju; Riandadari, Dyah; Ulfiyah, Laily
Jurnal Rekayasa Mesin Vol 9 No 02 (2024): JRM Agustus 2024
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jrm.v9i02.62763

Solar Purchase Volume Prediction Using The K-Nearest Neighbor Algorithm Based On Backward Elimination Prasetyo, Aries Alfian; Pramono, Yudi; Ulfiyah, Laily; Fattah, Misbakhul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4964

The profit earned by a Public Filling Station or gas station comes from the purchase of fuel per period and sales in accordance with the volume amount. So the exact purchase volume will determine the turnover of each month. But the profit or turnover is often irregular, there are several causes such as the price of fuel that tends to change, the volume of orders that are not in accordance with consumer demand. With these prediction methods, the expected turnover is increased with more efficient purchases. This research was conducted to study about k-NN Algorithm and then apply k-NN Algorithm in data prediction. The data used are secondary data in the form of data on the number of purchases of BBM in volume liter in the period January 2012 - December 2024. The k values used are k = 1, K = 4, k = 5 and k = 7. Before calculation with k = 1 is done, determined the data of training and data testing, in this research determined as much 70% training data and 30% for data testing. Then the initial cluster determination of the training data based on the interval class. While the cluster in the data testing is determined based on testing with K = 4, k = 5 and k = 7. From the process of analysis and evaluation of the research predicted the volume of fuel purchases using the data ransed dataset that processed data into multivariate data, the process of analysis using K-NN method using 2,3,4 and 5 periods produce the smallest K located in the period to 3, so that the 3rd period will be predicted by K-NN based backward elimination. With the aim of finding the best method to predict the volume of fuel purchases, generate predictions with backward elimination, that the attribute weights in the period xt - 3 and in the period xt 1 selected as the reference in the prediction process, since the weight is 1. K = 13 is the K best way to perform the Analyzing process with K-NN for the prediction of fuel purchase volume, with K = 4 value of 45556,788. So in the analysis and prediction of oil fuel purchasing volume data, for the type of diesel, K is best K = 13 with K-NN analysis method with backward elimination process. The above results show that xt3 or week 3 and week xt1 to 1 in the last period of 2024 can be used as a reference in the purchase in the next year that is 2025.

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