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Analisis Kuat Hantar Arus pada Instalasi Listrik Berdasarkan Standar Persyaratan Umum Instalasi Listrik di Akademi Komunitas Negeri Aceh Barat firnanda, ary; saputra, herdian; ardiansyah, haimi; denk, teuku mizan sya'rani; novriza, ferdiansyah; saputra, ari; simbolon, zulfan khairil
VOCATECH: Vocational Education and Technology Journal Vol 5, No 1 (2023): Oktober
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v5i1.142

AbstractPersyaratan Umum Instalasi Listrik (PUIL) are requirements that must be met in electrical installations. The application of the PUIL standard in electrical installations is very important in order to avoid unwanted things due to errors in electrical installations such as fire. West Aceh State Community Academy, which is a vocational college, really needs electricity to operate practicum equipment. If the electrical installation at the Aceh Barat State Community Academy does not meet PUIL standards, it is very vulnerable that the practicum equipment will be damaged quickly and even a fire occurs because the practicum equipment requires a fairly large amount of electrical power. The purpose of this study was to analyze the suitability of the Kuat Hantar Arus (KHA) in the electrical installation conductors of the West Aceh State Community College based on the 2011 PUIL standards. The research method used was the method of literature study and field observation. The results showed that in general the KHA conducting electrical installations at the West Aceh State Community Academy which was divided into 10 lines was very good where 2 lines were in accordance with the standards and 8 lines were above the 2011 PUIL standards. Keywords:PUIL 2011; Electrical Installation; Strong Conducting Current..__________________________ AbstrakPersyaratan Umum Instalasi Listrik (PUIL) merupakan persyaratan yang harus dipenuhi pada instalasi listrik. Penerapan standar PUIL pada instalasi listrik sangat penting dimana untuk menghindari hal-hal yang tidak diinginkan yang dikarenakan kesalahan pada instalasi listrik seperti kebakaran. Akademi Komunitas Negeri Aceh Barat yang merupakan perguruan tinggi vokasi sangat membutuhkan listrik untuk mengoperasikan alat-alat praktikum. Apabila instalasi listrik di Akademi Komunitas Negeri Aceh Barat tidak memenuhi standar PUIL maka sangat rentan alat-alat praktikum tersebut akan cepat rusak dan bahkan terjadi kebakaran karena alat-alat praktikum tersebut membutuhkan daya listrik yang lumayan besar. Tujuan penelitian ini adalah menganalisis kesesuaian Kuat Hantar Arus (KHA) pada penghantar instalasi listrik Akademi Komunitas Negeri Aceh Barat berdasarkan standar PUIL 2011. Metode penelitian yang digunakan adalah metode studi pustaka dan observasi lapangan. Hasil penelitian menunjukkan bahwa secara umum KHA penghantar instalasi listrik Akademi Komunitas Negeri Aceh Barat yang dibagi 10 jalur sudah sangat baik dimana 2 jalur sudah sesuai standar dan 8 jalur berada di atas standar PUIL 2011. Kabel yang terpasang diatas standar pada beberapa jalur dianggap sangat baik dikarenakan apabila suatu saat nanti dilakukan penambahan beban maka masih mampu dilayani oleh penghantar tersebut. Kata Kunci:PUIL 2011; Instalasi Listrik; Kuat Hantar Arus.

Career understanding, motivation, and students' decision-making to choose vocational high school (SMK) in non-industrial area MS, Hery Wiharja; Firnanda, Ary
Jurnal Pendidikan Teknologi Kejuruan Vol 4 No 2 (2021): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v4i2.20923

This study aims to measure and describe students' decision-making to choose Vocational High School (SMK) in terms of their career understanding and motivation. The subjects of this study were 135 Year 9 students in five SMK throughout Southwest Aceh Regency. The total sampling method was used to choose the subjects from the entire population. Furthermore, the data were analysed using correlation analysis between variables partially and simultaneously with ANOVA (F test). The results of the partial correlation analysis show that the career understanding variable has a significant influence on students' decision-making to choose SMK with rcount = 0.411 at a significance level of 0.05. In addition, the motivation variable also has a significant influence on decision-making to choose SMK with rcount = 0.375. Simultaneously, career understanding and motivation significantly affect choosing SMK with Fcount = 19.850 and Ftable = 3.06 (Fcount > Ftable). This study results indicate that career understanding variables consist of work planning, work exploration and knowledge of the world of work. In contrast, the motivation variables consist of intrinsic and extrinsic motivation, which could be the basis for students' decision making

Analisa Pemanfaatan Daya Generator Set Sebagai Energi Listrik Cadangan Di AKN Aceh Barat Sya'rani Denk, Teuku Mizan; Saputra, Herdian; Firnanda, Ary; Pandria, T. M. Azis; Daili, Cut; Hidayat, Afrilia
VOCATECH: Vocational Education and Technology Journal Vol 6, No 1 (2024): October
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v6i1.191

AbstractThe quality of electrical energy is a crucial requirement for the Akademi Komunitas Negeri (AKN) Aceh Barat. Electrical outages negatively impact performance, as equipment cannot be utilized during these times. A generator set is a machine that produces electrical energy and serves as a backup when the electrical supply from PLN is interrupted. Currently, AKN Aceh Barat has a generator set with a capacity of 7,800 watts, which is insufficient to meet the academy's power demands. The average monthly energy consumption from April to September 2024 is 5,199.83 kWh. To overcome the lack of power from the generator set, priority loads have been designated based on the importance of different rooms that facilitate essential services, ensuring that teaching and learning activities can proceed effectively. The generator's backup energy primarily supports a load of 6,290 watts, reaching an operational loading percentage of 81%. The electrical energy distribution from the generator to these priority rooms is managed through a new installation known as the Automatic Transfer Switch (ATS) panel, which activates automatically when the electrical supply from PLN is disrupted.Keywords:Power; Electrical; Distribution; Generator; EfficiencyAbstrakKualitas energi listrik menjadi kebutuhan utama bagi Akademi Komunitas Negeri Aceh Barat, pemadaman energi listrik berdampak buruk terhadap kinerja yang disebabkan peralatan tidak dapat digunakan. Generator set adalah mesin generator yang mampu menghasilkan energi listrik yang digunakan sebagai energi cadangan bila suplai energi listrik dari PLN terputus. pada AKN Aceh Barat generator set yang tersedia memiliki daya sebesar 7.800 watt dan tidak mampu memenuhi kebutuhan daya yang diperlukan oleh AKN Aceh Barat, rata-rata pemakaian beban AKN Aceh Barat dari bulan april sampai bulan September tahun 2024 mencapai 5.199,83 kWh per bulanya. Untuk mengatasi kekurangan daya dari generator set sebagai energi cadangan maka dipilih beban prioritas berdasarkan ruang prioritas yang berfungsi untuk pelayanan sehingga kegiatan belajar mengajar berjalan dengan baik. besar beban prioritas yang distribusi oleh daya generator sebagai energi cadangan sebesar 6.290 watt dengan persentase pembebanan mencapai 81%. Distribusi energi listrik dari genset ke ruang prioritas sebagai energi cadangan terhubung dengan instalasi baru yaitu panel ATS yang hidup secara automatis bila suplai energi listrik dari PLN terputus.Kata kunci:Daya; Distribusi; Listrik; Generator; Efisiensi

Enhancing the Accuracy of Diabetes Prediction Using Feedforward Neural Networks: Strategies for Improved Recall and Generalization Setiawan, Herry; Firnanda, Ary; Khair, Ummul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3888

This study explores the development and evaluation of a neural network model for predicting diabetes based on clinical data. The model was built using the Keras API with TensorFlow backend. Key steps included data preprocessing, such as feature scaling with `StandardScaler` and splitting the data into training and testing sets. The neural network architecture consisted of an input layer, two hidden layers with ReLU activation functions, and an output layer with a sigmoid activation function, optimized using the Adam optimizer and binary crossentropy loss function. The model was trained over 50 epochs with a batch size of 10, incorporating a validation split of 20% to monitor performance on unseen data during training. The results demonstrated a high accuracy of approximately 97% on the test set, indicating the model's efficacy in predicting diabetes. Further analysis using a confusion matrix revealed a high count of true positives and true negatives, alongside minimal false positives and false negatives, confirming the model's robustness. These findings suggest that neural networks can be effectively employed for diabetes prediction, offering significant potential for integration into clinical decision support systems. However, further validation with larger and more diverse datasets, alongside considerations for data imbalance and model interpretability, is recommended to ensure generalizability and practical application in real-world healthcare settings.

Implementation of The Internet of Things on Monitoring and Control Tools Hydroponic System Ilham, Dirja Nur; Setiawan, Herry; Harahap, Muhammad Khoiruddin; Firnanda, Ary; Budiansyah, Arie
PERFECT: Journal of Smart Algorithms Vol. 1 No. 1 (2024): PERFECT: Journal of Smart Algorithms, Article Research January 2024
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v1i1.6

This Hydroponics is a way of growing crops without using soil. Soil that is actually a place for plants to grow can be supported with sand, husk charcoal and rockwool. In a hydroponic system, nutrients and the pH value of water are absolutely necessary for plant development, every plant that requires different levels of nutrients and pH values of the air, if the nutrient levels are lacking then the plant will not grow as well as if the nutrient content is more then the plant will be poisoned. nutrients, water nutrients will continue to decrease along with the development of the plant itself. Therefore, a measuring tool for nutrient levels in hydroponic plant irrigation systems with android monitoring is made so that humans can monitor hydroponic plants. The tool used by Arduino nano as a microprocessor that is integrated into the TDS sensor works to measure nutrient levels and the pH sensor works to measure the pH value of the air, ESP8266 wifi module on NodeMCU as a gateway that aims to be a liaison from Arduino nano to the web server, Using websites and android applications that act as remote controls for nutrient pumps, pH up, pH down and hydroponic water pumps.

Enhancing the Accuracy of Diabetes Prediction Using Feedforward Neural Networks: Strategies for Improved Recall and Generalization Setiawan, Herry; Firnanda, Ary; Khair, Ummul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3888

This study explores the development and evaluation of a neural network model for predicting diabetes based on clinical data. The model was built using the Keras API with TensorFlow backend. Key steps included data preprocessing, such as feature scaling with `StandardScaler` and splitting the data into training and testing sets. The neural network architecture consisted of an input layer, two hidden layers with ReLU activation functions, and an output layer with a sigmoid activation function, optimized using the Adam optimizer and binary crossentropy loss function. The model was trained over 50 epochs with a batch size of 10, incorporating a validation split of 20% to monitor performance on unseen data during training. The results demonstrated a high accuracy of approximately 97% on the test set, indicating the model's efficacy in predicting diabetes. Further analysis using a confusion matrix revealed a high count of true positives and true negatives, alongside minimal false positives and false negatives, confirming the model's robustness. These findings suggest that neural networks can be effectively employed for diabetes prediction, offering significant potential for integration into clinical decision support systems. However, further validation with larger and more diverse datasets, alongside considerations for data imbalance and model interpretability, is recommended to ensure generalizability and practical application in real-world healthcare settings.

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