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All Journal Jurnal Bakti Nusa Jurnal Rekayasa elektrika Indonesian Journal of Applied Science and Technology
Bulkis Kanata
Universitas Mataram
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Other

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PERANCANGAN SISTEM UNTUK MENGETAHUI KUALITAS BIJI KOPI BERDASARKAN WARNA DENGAN K-NEAREST NEIGHBOR I Made Ary Swantika; Bulkis Kanata; I Made Budi Suksmadana
Jurnal Bakti Nusa Vol. 1 No. 2. Oktober 2020: JURNAL BAKTI NUSA
Publisher : Jurusan Teknik Elektro Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/baktinusa.v1i2. Oktober 2020.14

Abstract

BAHASA Kopi sebagai salah satu hasil perkebunan memiliki peran yang sangat penting salah satunya sebagai sumber devisa untuk negara sehingga dibutuhkan kualitas biji kopi yang baik. Selama ini pembagian kualitas biji kopi masih dilakukan secara manual dikalangan masyarakat yang mana akan menghabiskan waktu yang lama dan jumlah tenaga kerja yang banyak juga sehingga besar kemungkinan akan mendapatkan hasil yang kurang baik karena faktor kurang cermat, kelelahan, dan juga persepsi masing-masing orang yang berbeda-beda. Pada tugas akhir ini dirancang sebuah sistem untuk mengetahui kualitas biji kopi berdasarkan warna dengan K-Nearest Neighbor sehingga dapat dibedakan kualitas biji kopi menjadi 3 kelas (berdasarkan kebiasaan para petani) dan menjadi 6 kelas (berdasarkan sistem nilai cacat) dengan menggunakan parameter ciri statistik orde satu dan orde dua serta pengklasifikasian menggunakan K-Nearest Neighbor. Berdasarkan sistem yang telah dibuat, sistem dapat mengenali kualitas biji kopi untuk pembagian menjadi 3 kelas dengan akurasi terbesar menggunakan ciri statistik orde satu yaitu pada parameter means dan kombinasi means dan skewness dan kombinasi means, skewness dan entrophy dengan tingkat keberhasilan 100% walaupun nilai k yang diberikan berbeda-beda. ENGLISH Coffee as a result of the plantation has a very important role to one as a source of income to the State so that it takes good quality coffee beans . During this time the division of quality coffee beans are still done manually among people which will spend a long time and the amount of labor that many so likely will get poor results as well as factors less careful , fatigue , and also the perception of each person different . In this final project designed a system to determine the quality of coffee beans by color with K - Nearest Neighbor can be distinguished quality coffee beans into 3 classes ( based on the habits of the farmers ) and into 6 classes ( based on the value system defects ) using parameters characteristic of statistic orde one and the second order and classification using the K - Nearest Neighbor . Under the system that has been created , the system can recognize the quality of the coffee beans to the division into three classes with an accuracy of greatest use of characteristic statistical order one, namely the parameter means and combinations of means and skewness and a combination of means , skewness and entrophy with a success rate of 100 % although the value of k given vary.
PELATIHAN PERENCANAAN DAN PEMASANGAN INSTALASI LISTRIK YANG AMAN BERDASARKAN PUIL 2011 (SNI 0225:2011) UNTUK BANGUNAN BAGI MASYARAKAT DESA JELANTIK, KECAMATAN JONGGAT, KABUPATEN LOMBOK TENGAH Sultan Sultan; Sudi Mariyanto Al Sasongko; I Made Ari Nrartha; Bulkis Kanata; I Made Ginarsa; Agung Budi Mulyono
Jurnal Bakti Nusa Vol. 4 No. 1 (2023): JURNAL BAKTI NUSA
Publisher : Jurusan Teknik Elektro Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/baktinusa.v4i1.90

Abstract

Desa Jelantik merupakan salah satu desa dari 13 desa yang ada dikecamatan Jonggat, Kabupaten Lombok Tengah  berpenduduk 10.393 Jiwa. Masyarakat desa Jelantik  berpendidikan masih rendah yaitu sebagian besar hanya tamat SD, SMP dan SMA, dan mereka tidak memiliki keterampilan untuk mendapatkan pebekerjaan, sehingga desa Jelantik dijadikan mitra untuk dilaksanakan pelatihan.  Perekonomian masyarakat Desa Jelantik masih tergolong rendah, dengan mata pencaharian mereka adalah sebagai petani dan buruh tani. Melalui kegiatan pengabdian kepada masyarakat, Akademisi dari Univesitas Mataram memberikan pelatihan bidang kelistrikan kepada masyarakat Desa Jelantik khususnya pemuda  putus sekolah, sehingga dengan bekal keterampilan yang diperoleh dari pelatihan tersebut mereka memperoleh kesempatan atau peluang untuk mendapatkan pekerjaan dan penghasilan terutama untuk diri sendiri. Kegiatan pelatihan perencanaan dan pemasangan instalasi listrik untuk bangunan yang telah  dilaksanakan di Desa Jelantik, Kecamatan Jonggat, Kabupaten Lombok Tengah, hampir 100%  peserta  merasakan manfaat dan menginginkan pelatihan kembali yang dilaksanakan secara periodik. Demikian pula setelah mengikuti pelatihan, sebanyak 77% peserta menyatakan instalasi rumah mereka tidak aman (tidak memenuhi standar menurut SNI-PUIL 2011).
Pencocokan Citra Sidik Jari Menggunakan Korelasi Silang Ternormalisasi Bulkis Kanata
Jurnal Rekayasa Elektrika Vol 11, No 4 (2015)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1484.08 KB) | DOI: 10.17529/jre.v11i4.2405

Abstract

Fingerprint image matching is an important procedure in fingerprint recognition. Robust fingerprint image matching under a variety of different image capture conditions is difficult to achieve, because of changes in finger pressure, variation of the angle, etc. Fingerprint matching is very important for the development of fingerprint system recognition that is sensitive to finger pressure. This paper proposes a fingerprint matching algorithm that enables the so-called fingerprint template (extracted specific part (region of interest (ROI)) of a person’s fingerprints to be matched to the different fingerprint of the same person or different people taken on different time, angle and a different finger pressure using normalized cross-correlation (NCC). This algorithm was implemented in MATLAB. The results showed that the maximum NCC value for ROI of the source fingerprints and targets that was greater than 0.62 indicates a strong correlation or similarity. 
Pencocokan Citra Sidik Jari Menggunakan Korelasi Silang Ternormalisasi Bulkis Kanata
Jurnal Rekayasa Elektrika Vol 11, No 4 (2015)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v11i4.2405

Abstract

Fingerprint image matching is an important procedure in fingerprint recognition. Robust fingerprint image matching under a variety of different image capture conditions is difficult to achieve, because of changes in finger pressure, variation of the angle, etc. Fingerprint matching is very important for the development of fingerprint system recognition that is sensitive to finger pressure. This paper proposes a fingerprint matching algorithm that enables the so-called fingerprint template (extracted specific part (region of interest (ROI)) of a person’s fingerprints to be matched to the different fingerprint of the same person or different people taken on different time, angle and a different finger pressure using normalized cross-correlation (NCC). This algorithm was implemented in MATLAB. The results showed that the maximum NCC value for ROI of the source fingerprints and targets that was greater than 0.62 indicates a strong correlation or similarity. 
Perbandingan Kinerja CPU dengan GPU dan Tanpa GPU dalam Pemrosesan Gambar Menggunakan Metode Convolutional Neural Network Izfan Yunus; Bulkis Kanata; Suthami Ariessaputra
Indonesian Journal of Applied Science and Technology Vol. 2 No. 4 (2021): Edisi Desember 2021
Publisher : Indonesian

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Machine learning is a branch of artificial intelligence that focuses on learning itself (self-learning) or without having to be repeatedly programmed by humans. One of the new scientific fields in machine learning that has developed due to the development of GPU (Graphic Processing Unit) technology is deep learning. Currently, the use of the deep learning method can be one of the methods often used in research. One of them is the classification using the Convolutional Neural Network method. In this final project, the researcher has tested the most optimal classification performance with the fastest execution time by comparing the use of CPU (Central Processing Unit) with GPU or without GPU in considering the image in the Matlab software. The research was conducted in three stages, namely (1) the system design process (2) the execution program (3) parameter observation. According to the research results, testing using the GPU improves the accuracy and precision of the training and scanning process. However, GPU usage can take a longer training time when compared to a process without GPU. The scanning process without the GPU runs faster than when the GPU is on.
Co-Authors Agung Budi Mulyono Ariessaputra, Suthami I Made Ari Nrartha I Made Ary Swantika I Made Budi Suksmadana I Made Ginarsa Izfan Yunus Sudi Mariyanto Al Sasongko Sultan Sultan Teti Zubaidah