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All Journal Jurnal Teknik Elektro Jurnal technoscientia Sistemasi: Jurnal Sistem Informasi Jurnal Sains Teknologi dan Lingkungan (JSTL) Jurnal Penelitian Pendidikan IPA (JPPIPA) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pepadu JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Bulkis Kanata
Universitas Mataram
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PENERAPAN METODE VERY LOW FREQUENCY (VLF) PADA LOKASI ANOMALI GEOMAGNETIK RENDAH Kanata, Bulkis; Zubaidah, Teti; Susanto, Oki Prio
JURNAL TEKNOLOGI TECHNOSCIENTIA Academia Ista Vol 12 No 02 Februari 2008
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (291.175 KB) | DOI: 10.34151/technoscientia.v0i0.2006

Abstract

The research with title Evaluation of measurement result of geomagnetic anomaly in lombok island, west nusa tenggara and analysis of it’s relation with gravitation anomaly and prediction of local geology (Zubaidah, 2005) in area about 25 x 30 km2 result a isogam map, which known that the minimum geomagnetic anomaly equal to 558,194413 nT located in Seganteng, West Lombok (08035’46,2” LS, 116008’10,9” BT).Very Low Frequency Method is one of Geophysics methods that use magnetic com-ponent of electromagnetics field wich is caused by radio broadcaster use Frequency 15-30 kHz. VLF Method applied to know characteristic around the minimum geomagnetic anomaly in the form result of current density image which able to show conductive or it’s not an measurement area.This VLF Acquisition conducted to know the zona of conductive layer as long as 2500 metres from south to north pass the minimum geomagnetic anomaly. The tilt data processing is done use Moving Average method and Linear filter with use Matlab 6.1 . The Result of data processing show the contour of current density with depth/space = 8 or maximum deep is 160 metres with conductive area is spread near the minimum geomagnetic anomaly which is estimated much water
Pengenalan Citra Sidik Jari Berbasis Transformasi Wavelet dan Jaringan Syaraf Tiruan Suta Wijaya, I Gede Pasek; Kanata, Bulkis
Jurnal Teknik Elektro Vol 4, No 1 (2004): MARET 2004
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (288.953 KB) | DOI: 10.9744/jte.4.1.

Abstract

Image recognition is a mechanism to recognize an image that is not recognized by eyes, using certain method. This research was fingerprint recognition based on wavelet transforms and neural network. The aims of this research are to find the best wavelet and to know what the performance of this method is. Fingerprint recognition algorithms start from extracting an image to find image signature by choosing a little wavelet transforms coefficients that have the biggest magnitude value and neural network was used to select the best match (likeness) to original images in the collection. The test were carried out in three kind of wavelets viz Coiflet 6, Daubechies 8, dan Symlet 8 and 5 types of query images (pure, blur, noise, pencil sketch, and edge) and each query image has 30 samples. Query's success rates were determined by using one percent threshold value times size of databases. The result show that this method has good performance, which the average of success rate over 90% and need a little time query. The Symlet 6 can be considered to be the best wavelet for fingerprint image recognition, with success rate 96.36%. With respect to the elapsed query time, of about 0.11 second, the above method is sufficiently efficient for the database size of 1500 records. Abstract in Bahasa Indonesia : Pengenalan citra merupakan suatu mekanisme untuk mengenali kembali citra yang secara signifikan oleh mata tidak dapat dikenali lagi, namun dengan metode dan teknik tertentu citra tersebut masih dapat dikenali. Penelitian ini merupakan pengenalan citra sidik jari berbasis transformasi wavelet sebagai pengolah awal (pre-processing) dan jaringan syaraf tiruan sebagai elemen pengenal (metrika). Tujuan dari penelitian ini untuk menentukan wavelet yang terbaik untuk pengenalan citra sidik jari dan mengetahui performance dari metode pengenalan ini. Algoritma pengenalan citra sidik jari dimulai dengan mengekstrak citra menjadi ciri-ciri citra dengan cara memilih sejumlah kecil (m) koefisien hasil transformasi wavelet yang memiliki magnitude terbesar dan dilanjutkan dengan menghitung tingkat kemiripan antara ciri-ciri citra query dengan citra pustaka digunakan digunakan metode jaringan syaraf tiruan jenis backpropagation. Pengujian dilakukan pada 3 jenis wavelet, yaitu Coiflet 6, Daubechies 6, dan Symlet 6; dan 5 tipe citra query yaitu asli, blur, berderau, sketsa pencil, dan tepi sisi dengan setiap tipe query memiliki 30 buah sampel. Untuk mengetahui tingkat kesuksesan pengenalan, digunakan nilai ambang 1% x ukuran basis data citra. Hasil penelitian menunjukkan bahwa pengenalan citra sidik jari menggunakan transformasi wavelet dan jaringan syaraf tiruan memberikan hasil yang baik, hal ini ditunjukkan dengan tingkat kesuksesan pengenalan diatas 90% dan waktu pengenalan yang singkat. Dari ketiga jenis wavelet yang diuji ternyata ketiga-tiganya memberikan hasil yang baik. Namun jenis wavelet Symlet 6 merupakan wavelet yang terbaik untuk pengenalan citra sidik jari, dengan tingkat kesuksesan pengenalan 96,36%. Sistem pengenalan ini memerlukan waktu pengenalan relatif kecil, yaitu sekitar 0,11 detik untuk ukuran basis data 1500 rekord. Kata kunci: Citra sidik jari, pengenalan citra, transformasi wavelet, jaringan syaraf tiruan dan citra pustaka dan query.
PENERAPAN METODE VERY LOW FREQUENCY (VLF) PADA LOKASI ANOMALI GEOMAGNETIK RENDAH Kanata, Bulkis; Zubaidah, Teti; Susanto, Oki Prio
JURNAL TEKNOLOGI TECHNOSCIENTIA Academia Ista Vol 12 No 02 Februari 2008
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v0i0.2006

Abstract

The research with title Evaluation of measurement result of geomagnetic anomaly in lombok island, west nusa tenggara and analysis of it’s relation with gravitation anomaly and prediction of local geology (Zubaidah, 2005) in area about 25 x 30 km2 result a isogam map, which known that the minimum geomagnetic anomaly equal to 558,194413 nT located in Seganteng, West Lombok (08035’46,2” LS, 116008’10,9” BT).Very Low Frequency Method is one of Geophysics methods that use magnetic com-ponent of electromagnetics field wich is caused by radio broadcaster use Frequency 15-30 kHz. VLF Method applied to know characteristic around the minimum geomagnetic anomaly in the form result of current density image which able to show conductive or it’s not an measurement area.This VLF Acquisition conducted to know the zona of conductive layer as long as 2500 metres from south to north pass the minimum geomagnetic anomaly. The tilt data processing is done use Moving Average method and Linear filter with use Matlab 6.1 . The Result of data processing show the contour of current density with depth/space = 8 or maximum deep is 160 metres with conductive area is spread near the minimum geomagnetic anomaly which is estimated much water
EKSTRAKSI CIRI WAJAH MANUSIA MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) UNTUK SISTEM PENGENALAN WAJAH: [Feature Extraction Of Human Face Algorithm Using Principal Component Analysis (PCA) For Face Recognition System] Kanata, Bulkis; Suriakin, Maulana; Wijaya, IGP Suta
DIELEKTRIKA Vol 1 No 1 (2014): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.112 KB)

Abstract

Kemajuan dibidang pemrograman biometri mengalami kemajuan yang pesat, salah satu diantaranya adalah pemprosesan ciri wajah manusia. Pemprosesan ciri wajah manusia dilakukan untuk mendapatkan karakteristik ciri utama dari wajah yang dapat membedakan antara manusia yang satu dengan yang lainnya. Pada penelitian ini dibuat sebuah sistem pengenalan citra wajah manusia menggunakan algoritma Principal Component Analysis (PCA). Proses ekstraksi ciri citra wajah menggunakan algoritma Principal Component Analysis (PCA) menghasilkan vektor eigen yang bersesuaian dengan nilai eigen terbesar. Vektor eigen tersebut selanjutnya digunakan untuk membentuk ruang ciri utama dari citra wajah latih (eigenfaces), dan digunakan untuk memproyeksi citra wajah uji yang akan dikenali. Proses pengenalan dilakukan dengan menggunakan metode euclidean distance, yaitu mencari nilai jarak antara proyeksi citra wajah uji dengan setiap komponen ciri utama citra latih (eigenfaces). Apabila nilai jarak terkecil minimum value (e) lebih kecil dari nilai threshold yang ditentukan maka citra wajah uji dikenali, dan sebaliknya.
MENENTUKAN LUAS OBJEK CITRA DENGAN TEKNIK SEGMENTASI BERDASARKAN WARNA PADA RUANG WARNA HSV: Determining the Image Object Area Using Color-Based Segmentation Technique in HSV Color Space Oni, Muhammad; Kanata, Bulkis; Ratnasari, Dwi
DIELEKTRIKA Vol 8 No 2 (2021): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

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

Abstract

The color image segmentation technique is one of many approaches used to extract information from an image. In this study, the calculation of the image object area was carried out using the color image segmentation technique in HSV color space. There are three objects that the area has calculated those are triangle, square, and circle. The object area calculated by multiply the image background original area with the proportion difference between the total number of object pixels and the sum of background pixels. The threshold value for the HSV filter was determined using two different approaches, which are the trial-and-error method and the multi-otsu thresholding method. The result of this study shows that the object area calculation system works pretty well. Using the trial-and-error method, the difference percentage mean of all objects is 1,35%, meanwhile, using the multi-otsu thresholding method is 1,31%. Keywords: Color segmentation, hsv, multi-otsu, object area.
Analisis Pergeseran Garis Pantai Di Wilayah Pesisir Kabupaten Lombok Utara Dengan Metode Weighted Normalized Difference Water Index (Wndwi) Dan Deteksi Tepi Canny Pada Citra Landsat 8 Utomo, Rizqi D. C.; Kanata, Bulkis; Zainuddin, Abdullah
DIELEKTRIKA Vol 9 No 2 (2022): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v9i2.315

Abstract

Perubahan garis pantai dapat terjadi setiap waktu. Perubahan yang terjadi dapat memengaruhi kondisi fisik pantai sampai kondisi masyarakat di sekitarnya. Perubahan tersebut dapat dengan mudah diketahui dengan bantuan produk penginderaan jauh sehingga menekan kekurangan akibat keterbatasan waktu, ruang, dan biaya dalam penelitian. Pada penelitian ini dilakukan analisis garis pantai menggunakan metode klasifikasi Weighted Normalized Difference Water Index (WNDWI) dan deteksi tepi Canny terhadap citra Landsat 8 dengan lokasi penelitian di wilayah pesisir Kabupaten Lombok Utara (KLU) dalam kurun waktu 5 tahun (2016-2021). Hasil yang diperoleh menunjukkan secara umum garis pantai wilayah pesisir KLU mengalami pergeseran sebesar 10,385 m dengan laju pergeseran sebesar 2,077m/tahun. Sehingga berdasarkan PERMEN-KP RI nomor 21 tahun 2008, bahwa pergeseran yang terjadi tidak berada dalam level ancaman yang dikategorikan. Meskipun perubahan fisik pesisir akibat akresi maupun abrasi tetap terjadi.
Perubahan Fungsi Lahan Untuk Status Rawan Bencana Dengan Remote Sensing Di Daerah Mandalika: Changes in Land Function for Disaster Prone Status Using Remote Sensing in the Mandalika Area Yadnya, Made Sutha; Kanata, Bulkis; Zainuddin, Abdullah; Paniran, Paniran; Ramadhani, Cipta; Rosmaliati, Rosmaliati
Jurnal Pepadu Vol 5 No 4 (2024): Jurnal PEPADU
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/pepadu.v5i4.5952

Abstract

Masalah utama Desa Tangguh Bencana (Destana)  di Desa Penyanggaya Sirkuit Madalika adalah bahaya tanah longsor akibat kemiringan tanah yang terjal,  masyarakat harus tahu kalau potensi tanah longsor dan menghidari pembuatan pemukiman akibat hujan atau gempa yang membuat retakan tanah. Situasi yang berbahaya tanah longsor dengan perubahan fungsi lahan dari perbukitan menjadi tanah urug. Universitas Mataram memiliki obsevatorium di Rembitan bagian dari Pusat Unggulan Iptek (PUI) Geomagnetik mengukur magnet bumi dengan satuan magnet bumi nTesla (Nano Tesla). Hasil pengukuran terjadi anomali (penurunan nilai magnet bumi. Ini merupakan precursor akan terjadinya gempa. Desa Sade dan Rembitan merupakan satu kawasan yang menjadi satu kesatuan yang harus dijaga dan memberikan pengetahuan akan bahaya banjir dan tanah longsor akibat cuaca ekstrim. Proses menggunaka remote sensing dengan foto udara. Hal hasil telah didapatkan bebarapa titik rawan bencana.
KLASIFIKASI GENUS TANAMAN ANGGREK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN MENGGUNAKAN ARSITEKTUR VGG 16 Zulkipli; Akbar, Lalu A. Syamsul Irfan; Kanata, Bulkis
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 4 (2024): EDISI 22
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i4.4982

Abstract

Indonesia is one of the countries that has very diverse orchid genetic resources. Orchid species are found in several islands in Indonesia such as Java, Sumatra, Kalimantan, Sulawesi, Nusa Tenggara, Bali, Maluku and Papua and have types of orchids with varying flower characters. Orchids are one of the most popular ornamental plants and have a beautiful charm. The beauty and value of orchids are in their flowers. However, some orchids have the same color and appearance even though they belong to different species. In fact, many people think that one orchid species with other species that have similar shapes are the same species. Therefore, a system is needed that can make it easier to recognize orchid species. The method used is the Convolutional Neural Network with VGG 16 Architecture to classify orchid flower types, where the dataset used is divided into 3 scenarios, namely scenario 1 using orchid petal images, scenario 2 using orchid leaf tree images, and scenario 3 using combined images of orchid flower petals and leaves. From the three scenarios, the results of the model with high accuracy in scenario 3 were 99.47%. This shows that the model built is able to predict well.
Perkiraan Suhu Menggunakan Algoritma Recurrent Neural Network Long Short Term Memory Zahidin, Ilham; Kanata, Bulkis; Akbar, Lalu A. Syamsul Irfan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6242

Abstract

Air temperature is a critical variable in weather conditions that affects various aspects of human life, including health, agriculture, and the economy. In Indonesia, particularly in Mataram City, which is situated in a tropical region, significant temperature changes can impact sectors such as tourism, agriculture, and daily activities. Accurate temperature forecasting can aid the public, industries, and the government in making more informed decisions, both for short-term and long-term planning. However, weather in tropical regions like Mataram tends to be difficult to predict accurately due to its dynamic nature and the influence of multiple atmospheric factors. Conventional weather prediction methods often fail to capture the complex patterns in historical temperature data, necessitating more advanced methods to improve forecast accuracy. Recurrent Neural Networks (RNNs), particularly the Long Short-Term Memory (LSTM) variant, have proven to be highly effective tools for modeling complex time series data. This algorithm can retain long-term information and recognize patterns in data that change over time, making it well-suited for temperature prediction challenges. In this study, the RNN-LSTM algorithm is applied to forecast temperatures in Mataram City, aiming to improve forecast accuracy and produce results useful for various purposes. The temperature prediction model using the LSTM algorithm involves several steps: data collection, data normalization, splitting data into test and training sets, building the LSTM model by determining the number of epochs, layers, and batch size, and finally, evaluating the model with RMSE. Two parameters, epoch and batch size, influence the LSTM model’s forecasting results in this study. Epochs used in this study are 5, 10, 20, 30, 40, 50, and 100, with a fixed batch size of 32. The LSTM algorithm employs the RMSProp optimizer. The temperature prediction model using the LSTM method achieved the best average accuracy with a batch size of 32 and 50 epochs, yielding an RMSE value of 0.13 and a prediction accuracy of 99.96% in forecasting Mataram City’s temperature for the year 2023.
The Klasifikasi Suara Paru-Paru Menggunakan Mel Frequency Cepstral Coefficient dan Convolutional Neural Network Halim, Sayyidis Syariful; Kanata, Bulkis; Akbar, Syamsul Irfan
Jurnal Bumigora Information Technology (BITe) Vol 6 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4487

Abstract

Background: Challenges in diagnosing respiratory disorders are often caused by the lack of technological tools capable of accurately recognizing lung sound patterns, thereby reducing the potential for subjective misdiagnosis by medical personnel. Objective: This study aims to develop a lung sound classification model that is able to detect respiratory disorders early and accurately. Methods: The method used includes a combination of data augmentation techniques and Mel Frequency Cepstral Coefficient (MFCC) feature extraction to improve the performance of Convolutional Neural Network (CNN) in classifying lung sounds. A total of 1,350 lung audio recordings were categorized into nine classes, including normal and abnormal sounds. The augmentation techniques applied include the addition of white noise, pitch scaling, time stretching, and random gain to enrich the variety of training data. Result: The results show that the E-CNN2D model is able to achieve an accuracy of up to 95%, surpassing the previous model, which had an accuracy range of 83-93%. Conclusion: With these results, this study has the potential to be a fast and accurate diagnostic tool solution so that it can support medical personnel in reducing the risk of subjective misdiagnosis in respiratory disorders.
Co-Authors A. Sjamsjiar Rachman Akbar, L. Ahmad Syamsul Irfan Akbar, Lalu A. Syamsul Irfan Akbar, Lalu Muhamad Roviq Akbar, Muhammad Fajrin Akbar, Syamsul Irfan Akhdan, M. Rafif Anis Arrafi, Muhammad Aryoso, Galih Dimas Budi Irmawati Cipta Ramadhani Derajat, Dirga Dama Dwi Ratnasari Fitratunnisa, Nur Giri Wahyu Wiriasto Halim, Sayyidis Syariful I Gede Pasek Suta Wijaya Ida Ayu Putu Sri Widnyani IGP Suta Wijaya Indira Puteri Kinasih Kafrawi, Achmad Evin Made Sutha Yadnya Majdi, Shafwatul Misbahuddin Oni, Muhammad Paniran Paniran Ramdani, Cipta Ramdani, M. Rosmaliati Rosmaliati , Rosmaliati Rosmaliati, Rosmaliati Rosyida, Amrina Setiawan, Didin Suharyadi, Farhan Ahmad Suriakin, Maulana Susanto, Oki Prio Syafaruddin Ch Teti Zubaidah Utomo, Rizqi D. C. Zahidin, Ilham Zainuddin, Abdullah Zamroni, Sulthon