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All Journal JPTK: Jurnal Pendidikan Teknologi dan Kejuruan JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi
Safrizal Razali
Universitas Syiah Kuala
Aerospace Engineering Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Pemetaan Potensi Pembangkit Listrik Tenaga Bayu di Perairan Indonesia Berdasarkan Data Satelit ASCAT Safrizal Safrizal; Haimi Ardiansyah; Dailami Dailami
Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Vol 7, No 2 (2021): Oktober
Publisher : universitas teuku umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.829 KB) | DOI: 10.35308/jmkn.v7i2.4137

Abstract

Kebutuhan energi listrik menjadi isu penting yang dapat mendorong daya saing Indonesia di kancah perekonomian dunia. Saat ini, Indonesia masih menggunakan energi yang bersumber dari fosil. Energi fosil adalah energi yang tidak terbarukan sehingga akan habis pada suatu masa. Kemampuan Indonesia dalam menghasilkan energi listrik terbarukan merupakan solusi dari permasalahan tersebut. Salah satu sumber energi listrik terbarukan berasal dari Pembangkit Listrik Tenaga Bayu (PLTB). Penelitian ini bertujuan memetakan sumber energi PLTB di perairan Indonesia dengan menggunakan data dari satelit ASCAT. Penelitian ini dimulai dengan mengumpulkan data harian kecepatan bayu periode 01 Januari 2017 sampai dengan 31 Desember 2018. Data tersebut merupakan data pada ketinggian 10 m, dengan menggunakan model matematis data tersebut kemudian diolah agar didapatkan kecepatan bayu serta power density pada ketinggian 120 m. Langkah selanjutnya adalah pembuatan peta potensi PLTB di perairan Indonesia. Dari peta tersebut, diketahui bahwa perairan Indonesia di Samudera Hindia dan Laut Arafura memiliki potensi yang lebih baik dari pada perairan lainnya. Kecepatan bayu rata-rata pada ketinggian 120 m adalah 9,24 m/s, sedangkan rata-rata power density sebesar 955,64 W/m2. Jumlah turbin yang dapat dibangun di wilayah ZEE Indonesia adalah sebanyak 4.800.292 unit dengan jumlah tersebut maka dapat menghasilkan energi listrik sebesar 10.080 GW.
Vocational Students' Perception of Online Learning during the Covid-19 Pandemic Hilma Erliana; Safrizal Safrizal; Rahmad Nuthihar; Luthfi Luthfi; Wahdaniah Wahdaniah; Ilham Jaya; RN Herman
Jurnal Pendidikan Teknologi dan Kejuruan Vol 27, No 1 (2021): (May)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jptk.v27i1.34283

Abstract

COVID-19 pandemic impacts on vocational education. Lectures that were originally conducted face-to-face learning are diverted to online learning to avoid the spread of the pandemic. Online learning is very difficult to apply for courses conducted in the laboratory. This study discusses vocational students’ responses to the practice of online learning during the COVID-19 pandemic. Data were collected through a questionnaire created on Google form consisting of 20 questions. The questionnaire used a Likert scale to find out the attitudes and students’ perceptions of the implementation of online learning. The number of research respondents was 107 people consisting of 45 respondents from the West Aceh State Community Academy and 62 respondents from Lhokseumawe State Polytechnic, Aceh, Indonesia. The results of this study found that 59.81% of students disagree with online learning. The results also showed a score of 76.95% of the students agree that internet access is the main obstacle in online learning. However, students’ satisfaction with the current online learning system for students shows a score of 67.50%. Opinions related to online learning from 107 respondents showed that 45.42% of them less agree if online learning is still applied when the COVID-19 pandemic ends.
Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan Safrizal .; Fitri Arnia; Rusdha Muharar
JURNAL NASIONAL TEKNIK ELEKTRO Vol 5 No 1: Maret 2016
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.515 KB) | DOI: 10.25077/jnte.v5n1.185.2016

Abstract

Jawi is one variant of Arabic script consists of 35 characters. Some of Jawi characters have the same main shape, but different number of dots in different location. Thus, recognition process of Jawi characters can be done by performing a classification based on the main shape. In recognition process, feature extraction plays an important role. In this research, Freeman Chain Code (FCC) was used as feature extraction and Support Vector Machine (SVM) as classifier. Then we apply the decision rules to classifySVMresult into Jawi characters. FCC is used to represent the boundary of Jawi characters into a chain code. Then the chain code is used bySVMto classify the characters into 19 groups. Feature of location and the number of dots are used by decision rules to classify the groups into Jawi characters. The Jawi characters are handwritten and generated by 10 writers from different backgrounds and ages. The recognition rate of this research was 80.00%.Keywords : Jawi script, handwriting, FCC, SVM, decision rules.Abstrak—Aksara Jawi merupakan salah satu varian dari aksara Arab yang terdiri dari 35 aksara. Dari 35 aksara Jawi  tersebut terdapat beberapa aksara dengan bentuk bagian utama yang sama namun memiliki letak dan jumlah titik yang berbeda. Karena perbedaan tersebut maka proses pengenalan aksara Jawi dapat dilakukan dengan melakukan klasifikasi berdasarkan perbedaan bentuk bagian utama. Pada penelitian ini Freeman Chain Code (FCC) digunakan sebagai ekstraksi fitur dan Support Vector Machine (SVM). FCC digunakan untuk merepresentasikan garis batas (boundary) aksara Jawi kedalam kode rantai. Kode rantai tersebut diklasifikasi dengan menggunakan SVM kedalam 19 kelompok. Fitur letak titik dan jumlah titik digunakan sebagai aturan pengambilan keputusan terhadap 19 kelompok hasil klasifikasi SVM kedalam aksara Jawi. Aksara Jawi yang digunakan merupakan tulisan tangan dari 10 orang penulis dari berbagai latar belakang dan umur. Tingkat keberhasilan klasifikasi penelitian ini mencapai 80,00%.Kata Kunci : aksara Jawi, tulisan tangan, FCC, SVM, aturan pengambilan keputusan
Improved Classification of Handwritten Jawi Script Based on Main Part of Script Body Safrizal Razali; Fitri Arnia; Rusdha Muharrar; Kahlil Muchtar; Akhyar Bintang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4600

Abstract

Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.
Co-Authors Akhyar Bintang Dailami Dailami Fitri Arnia Haimi Ardiansyah Herman Rusli Hilma Erliana Ilham Jaya Kahlil Muchtar Luthfi Luthfi Rahmad Nuthihar Rusdha Muharar Rusdha Muharrar Wahdaniah Wahyudi