Garuda - Garba Rujukan Digital

Rancang Bangun Alat Pembersih dan Pengering Gabah Berbasis Arduino Uno Pratama, Sifa Ardika; Prasetyo, Aries Alfian; Hajah, M. Sohibul
Techno Bahari Vol 10 No 2 (2023)
Publisher : Politeknik Negeri Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52234/tb.v10i2.285

The planning of this grain cleaning and drying machine is one of the efforts to apply appropriate technology. To assist the community in processing crop yields with a grain drying machine that is heated using a heater using a dht22 sensor. As a temperature and humidity sensor with a control system using an arduino uno as the brain of this tool . One of the reasons is that the grain drying process takes a long time because it still depends on sunny weather and requires a relatively large area. Because of that, the idea emerged to make a Design and Development of an Arduino Uno-Based Grain Cleaning and Dryer Tool. What I want to achieve from this final project is to dry grain without waiting for sunny weather first and of course it is more efficient in terms of energy and land for farmers and the community and produces good quality rice.

Metode K-Means Berbasis Ordered Weighted Averaging (OWA) pada Data Potensi Desa untuk Penentuan Status Desa Prasetyo, Aries Alfian; Suprapedi, Suprapedi; Narulita, Siska; Praharsena, Bayu
Jurnal Bingkai Ekonomi (JBE) Vol 7 No 2 (2022): Jurnal Bingkai Ekonomi (JBE)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) - Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jbe.v7i2.224

Indeks Pembangunan Desa yang dibangun dari Pendataan Potensi Desa (Podes) tahun 2014. untuk menilai tingkat perkembangan desa, dibagi menjadi 3 klasifikasi yaitu Desa Mandiri, Berkembang, dan Tertinggal, memiliki 5 dimensi. Penulis bertujuan untuk menilai tingkat perkembangan desa melalui status desa berdasar data IPD, penentuan status desa menggunakan teknik clustering dengan metode K-Means berbasis Ordered Weighted Averaging (OWA), OWA dapat mengurangi kompleksitas data dengan memadukan nilai multi attribut ke nilai agregat berupa nilai tunggal dengan menggunakan expert judgement untuk menentukan nilai orness (α). Hasil dari penelitian ini pengelompokan data IPD tahun 2014 kedalam 3 status desa dengan algoritma K-Means berbasis OWA menunjukkan metode clustering K-Means dengan OWA memiliki Index Davies 1,42 lebih baik daripada metode K-Means dengan euclideance yang memiliki nilai 1,65. Hasil akhir penelitian diperoleh jumlah desa untuk setiap cluster, yaitu cluster Desa Tertinggal sebanyak 98, cluster Desa Berkembang sebanyak 32, dan cluster Desa Mandiri sebanyak 100 desa.

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.

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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