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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Journal of Information Systems Engineering and Business Intelligence Info-Teknik KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jukung (Jurnal Teknik Lingkungan) JTERA (Jurnal Teknologi Rekayasa) Jurnal Komtika (Komputasi dan Informatika) ILKOM Jurnal Ilmiah Astonjadro Jurnal Ilmu Komputer dan Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Buletin Profesi Insinyur Jurnal Sains Komputer dan Teknologi Informasi Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) JUSTE (Journal of Science and Technology) Journal of Advanced Health Informatics Research Jurnal Jalan Jembatan INSTALL: Information System and Technology Journal Jurnal Komtika (Komputasi dan Informatika)
Yuslena Sari, Yuslena
Dosen Program Studi Teknologi Informasi Fakultas Teknik Universitas Lambung Mangkurat, Indonesia
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Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN) Sari, Yuslena; Alkaff, Muhammad; Arif Rahman, Muhammad
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.4605

Abstract

Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.
Modelling and predicting wetland rice production using support vector regression Muhammad Alkaff; Husnul Khatimi; Wenny Puspita; Yuslena Sari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.10145

Abstract

Food security is still one of the main issues faced by Indonesia due to its large population. Rice as a staple food in Indonesia has experienced a decline in production caused by unpredictable climate change. In dealing with climate change, adaptation to fluctuating rice productivity must be made. This study aims to build a prediction model of wetland rice production on climate change in South Kalimantan Province which is one of the national rice granary province and the number one rice producer in Kalimantan Island. This study uses monthly climatic data from Syamsudin Noor Meteorological Station and quarterly wetland rice production data from Central Bureau of Statistics of South Kalimantan. In this research, Support Vector Regression (SVR) method is used to model the effect of climate change on wetland rice production in South Kalimantan. The model is then used to predict the amount of wetland rice production in South Kalimantan. The results showed that the prediction model with the RBF kernel with the parameter of C=1.0, epsilon=0.002 and gamma=0.2 produces good results with the RMSE value of 0.1392.
PSO optimization on backpropagation for fish catch production prediction Yuslena Sari; Eka Setya Wijaya; Andreyan Rizky Baskara; Rico Silas Dwi Kasanda
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14826

Abstract

Global climate change is an issue that is enough to grab the attention of the world community. This is mainly because of the impact it has on human life. The impact that is felt also occurs in waters on the South Kalimantan region. This is of course can disrupt the productivity of fish in the marine waters of South Kalimantan. This study aims to make fish catch production prediction models based on climate change in the South Kalimantan Province because the amount of productivity of marine fish has fluctuated. This study uses climate data as input and fish production as output which is divided into two, namely training and testing data. Then the prediction is conducted using Backpropagation method combined with Particle Swarm Optimization method. The results of the study produced a prediction model with RMSE of 0.0909 with a combination of parameters used, namely, C1: 2, C2: 2, w: 0.7, learning rate: 0.5, Momentum: 0.1, Particles: 5, and epoch: 500. While the model used when predicting testing data produces RMSE of 0.1448.
Vehicle detection using background subtraction and clustering algorithms Puguh Budi Prakoso; Yuslena Sari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.10144

Abstract

Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
Application of neural network method for road crack detection Yuslena Sari; Puguh Budi Prakoso; Andreyan Rizky Baskara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14825

Abstract

The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.
OTOMATISASI TINGKAT KUALITAS KAYU KELAPA MENGGUNAKAN GENETIC ALGORITHM Husnul Khatimi; Yuslena Sari
INFO-TEKNIK Vol 20, No 2 (2019): INFOTEKNIK VOL. 20 NO. 1 DESEMBER 2019
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/infotek.v20i2.7721

Abstract

Coconut plantation in South Borneo had a total area of 30,513 ha with 26,633 tons of production result in the year of 2016. But South Borneo is still limited in the utilization of fruit part and leaf, whereas the coconut wood then was often used for construction material. The level of needs for coconut wood material in the industrial world were greatly increased. Indonesia is one of the exporter of coconut wood material into other countries. To determine good quality woods for best quality materials, control for the full process was necessary in order for the product to be ready to use. The visual determination of quality level (grading) for coconut wood need to be automated, with the result that could be used for determination of suitable material for furniture as well as building construction and deacrese the dependency for manual grader. This research produced the proposed enhancement methode for quality image recognition in a visual manner for coconut wood, Genetic Algorithm, that could obtain the necessary accuracy for the quality determination of coconut wood. The benefits of this research was to support coconut plantation on South Borneo in producing coconut wood material as one of the material industry commodities.
KLASIFIKASI KUALITAS KAYU KELAPA MENGGUNAKAN ARSITEKTUR CNN Nurul Fathanah Mustamin; Yuslena Sari; Husnul Khatimi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 8, No 1 (2021)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v8i1.370

Abstract

The increase in the export volume of coconut logs, which are materials that can efficiently substitute for conventional wood, demands that the quality of coconut wood classified quickly. However, due to the limitations of a grader as a human being, it is necessary to have assistance from machines or technology that can classify coconut wood quickly. Techniques that used for rapid classification can use computer visualization. Convolutional Neural Network (CNN) with the right architecture makes this method able to recognize and detect objects well, which influenced by computerized factors, large datasets, and techniques to train deeper networks. This study uses five types of CNN architecture, AlexNet, GoogLeNet, ResNet101, ResNet18, and ResNet50. The research results obtained for the classification of the quality of coconut wood using images show that the GoogLeNet architecture has the best classification performance among other architectures. GoogLeNet gets result with an average accuracy of 84.89% in each layer, followed by RestNet101 architecture with an average accuracy of 78.41%, RestNet50 with an average accuracy of 77.18%, RestNet18 with an average accuracy of 72.94% and the lowest accuracy performance among other architectures obtained by AlexNet with an average accuracy of 65.84%.Keywords: Classification, Coconut Wood, Computer Visualization Techniques, CNN Meningkatnya volume ekspor kayu kelapa yang merupakan bahan pengganti kayu konvensional secara efisien menuntut klasifikasi kualitas kayu kelapa dengan cepat. Namun karena keterbatasan seorang grader sebagai manusia maka diperlukan bantuan mesin atau teknologi yang dapat mengklasifikasikan kayu kelapa dengan cepat. Teknik yang dapat digunakan untuk klasifikasi cepat dapat menggunakan teknik visualisasi komputer. Convolutional Neural Network (CNN) dengan arsitektur yang tepat menjadikan metode ini mampu mengenali dan mendeteksi objek dengan baik, yang sebagian besar dipengaruhi oleh faktor komputerisasi, dataset yang besar, dan teknik untuk melatih jaringan yang lebih dalam. Penelitian ini menggunakan lima jenis arsitektur CNN yaitu, AlexNet, GoogLeNet, ResNet101, ResNet18, dan ResNet50. Hasil penelitian yang diperoleh untuk klasifikasi kualitas kayu kelapa menggunakan citra menunjukkan bahwa arsitektur GoogLeNet memiliki performansi klasifikasi terbaik diantara arsitektur lainnya. GoogLeNet mendapatkan hasil dengan rata-rata akurasi 84,89% pada setiap lapisan, disusul arsitektur RestNet101 dengan akurasi rata-rata 78,41%, RestNet50 dengan akurasi rata-rata 77,18%, RestNet18 dengan akurasi rata-rata 72,94% dan kinerja akurasi terendah di antara arsitektur lainnya diperoleh AlexNet dengan akurasi rata-rata 65,84%.Kata kunci: Klasifikasi, Kayu Kelapa, Teknik Visualisasi Komputer, CNN
PROTOTYPE MONITORING SUHU BERBASIS MIKROKONTROLER PADA COOL BOX IKAN MENGGUNAKAN SENSOR DS18B20 DENGAN METODE FUZZY Eka Setya Wijaya; Muti`a Maulida; Yuslena Sari; Andreyan Rizky Baskara; Akhmad Rivaldy
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 3 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i3.343

Abstract

Often fish supply in to the consumers need long relatives distance and much time. Meanwhile the consumers always want fresh fish. The problem occurred was broken stock of fresh fish in every shipping and happened almost all the time. It made fish became stale and made supplier got lose. The study was took place in Batulicin Simpang Empat in a harbor (Pelelangan Pembongkaran Ikan)  until now the handling of fish catches generally used ice block in the usual cool box. Using ice block is one of easiest method. Although, using ice block only could maintain low temperature in short time whereas the ideal temperature of cool box should be 10  Celsius measured by usual temperature’s device room. In shipping process, the driver unpack the cool box to checked temperaturemanually. This matter waste time and cost because the driver should provide extra ice block to maintain low temperature of cool box. In order to resolve the problem, monitoring could be done automatically by using a microcontroller chip in the cool box with DS18B20 censor and fuzzy methodbase microcontroller. The purpose was supplier could be helped to monitoring the stabilities of cool box temperature used website. This system is made by computerization. It would make supplier job easier because driver able to check fish condition without unpack the cool box and made time, energy and cost more efficient.Keywords: Temperature Censor, Fish cool box microcontroller, Fuzzy method, and websiteSeringkali dalam penyuplaian ikan kepada konsumen membutuhkan jarak dan waktu yang relatif panjang, sedangkan konsumen selalu mengharapkan ikan segar. Permasalahan besar yang dihadapi dalam produksi ikan segar, yaitu persediaan yang rusak (broken stock) dalam jumlah yang banyak, sehingga jumlah ikan yang membusuk menjadi berlebihan dan akhirnya mengalami kerugian. Di Batulicin Simpang Empat, tempat dilakukanya penelitian, tepatnya di pelabuhan PPI (Pelelangan Pembongkaran Ikan), penanganan hasil tangkapan ikan saat ini masih menggunakan pendinginan es batu di dalam cool box biasa. Penggunaan es merupakan salah satu cara yang paling mudah dilakukan. Akan tetapi, pendinginan dengan menggunakan es batu  hanya dapat mempertahankan suhu rendah dalam waktu yang singkat sedangkan suhu ideal dari dalam cool box adalah 10 C yang diukur dengan menggunakan alat suhu ruangan biasa. Jadi, saat perjalanan driver akan membongkar muatan dan mengecek apakah suhu turun atau tidak secara manual. Hal ini sangat memakan waktu dan biaya karena supplier selalu membawa cadangan es untuk berjaga-jaga jika suhu turun didalam cool box tersebut. Untuk mengatasi permasalahan di atas, monitoring dapat dilakukan secara otomatis dengan memanfaatkan sebuah chip mikrokontroler pada cool box dengan sensor DS18B20 bersama metode fuzzy berbasis mikrokontroler. Tujuannya agar supplier dapat terbantu untuk memonitoring kestabilan suhu cool box dengan menggunakan website. Karena sistem dibuat terkomputerisasi maka akan memudahkan kinerja dari supplier atau driver sehingga driver dapat memonitor kondisi ikan di dalam cool box tanpa harus membongkar muatan dan hal ini dapan mengefisiensikan waktu, tenaga, dan biaya karena suhu cool box tetap terjaga dengan baik  dan mudah.Kata kunci: Sensor Suhu, Mikrokontroler Cool Box Ikan,  Metode Fuzzy, dan website
SISTEM PENERIMAAN PEGAWAI SALES KPR PADA BANK MENGGUNAKAN METODE MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS Andreyan Rizky Baskara; Yuslena Sari; Muhammad Adetya Ashari
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 8, No 1 (2021)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v8i1.372

Abstract

The recruitment of new employees, especially for housing loan sales employees (KPR) is often carried out by banks because the performance evaluation of KPR sales employees is carried out regularly. The large number of prospective employees and the variety of criteria determined for selecting new employees lead to a lengthy decision-making process. Decision Support System (DSS) is basically a comprehensive computer system that can help make decisions and solve problems. The Multi Objective Optimization method on The Basis of Ratio Analysis (MOORA) as a decision-making method is used to build a decision support system. The decision support system is implemented as a web-based application using ATOM software which is integrated with the MySQL database. The Black Box testing method is used to test the system. The results showed that the MOORA method is very suitable to be applied in the decision making of KPR sales employee recruitmentKeywords: Decision Support System, KPR Sales Employee, MOORA Perekrutan pegawai baru khususnya untuk pegawai Sales Kredit Pemilikan Rumah (KPR) sering dilakukan oleh bank karena evaluasi kinerja pegawai sales KPR dilakukan secara berkala. Banyaknya calon pegawai dan beragamnya kriteria-kriteria yang ditentukan untuk menseleksi pegawai baru menyebabkan lamanya proses pengambilan keputusan. Sistem Pendukung Keputusan (SPK) pada dasarnya adalah sistem komputer komprehensif yang dapat membantu membuat keputusan dan menyelesaikan masalah. Metode Multi Objective Optimization on The Basis of Ratio Analysis (MOORA) sebagai salah satu metode pengambilan keputusan digunakan untuk membangun sistem pendukung keputusan. Sistem pendukung keputusan diimplementasikan sebagai aplikasi berbasis web menggunakan perangkat lunak ATOM yang terintegrasi dengan database MySQL. Metode pengujian Black Box digunakan untuk menguji sistem. Hasil penelitian menunjukkan bahwa metode MOORA sangat cocok diterapkan dalam pengambilan keputusan perekrutan pegawai sales KPRKata kunci: MOORA, Pegawai Sales KPR , Sistem Penunjang Keputusan
Zakat Classification with Naïve Bayes Method in BAZNAS Yuslena Sari; Muhammad Alkaff; Eka Setya Wijaya; Gusti Nizar Syafi'i
TECHNO: JURNAL PENELITIAN Vol 10, No 1 (2021): Techno Jurnal Penelitian
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/tjp.v10i1.2750

Abstract

The National Amil Zakat Agency (BAZNAS) of the Banjar Regency, is the regional Zakat Management Agency of the Banjar Regency. BAZNAS Banjar Regency distributes the required alms according to the target to mustahik that is under the criteria or following the provisions of the Shari'a. However, BAZNAS often experiences difficulties in determining mustahik (people who are entitled to receive zakat) due to limited distribution funds and excessive data on Fakir and miskin people who are the main priority. The existence of a system that can determine two groups of recipients of the Fakir and miskin zakat based on data from the underprivileged population can help the distribution of zakat to these 2 groups. In this case, using the Naive Bayes method is very suitable in the classification of the BAZNAS mustahik determination so that it can be used to determine the prospective recipient of zakat. Based on the results of tests conducted on the Naïve Bayes classification with the Confusion Matrix calculation, the accuracy value reached 92.30%.
Co-Authors Adla, Musyfia Ahmad Rivaldy Ahmad Shalludin Akhmad Rivaldy Andreyan Rizky Baskara Ariefah Khairina Islahati ARIF RAHMAN, MUHAMMAD Bambang Lareno, Bambang Bayu Nugraha Chastine Fatichah Dany Primanita Kartikasari Darmawan, Puja Dewi Astria Faroek Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Endi Gunawan Fardian, Muhammad Reza Ferry Pratama Finki Dona Marleny Firmansyah, Muhammad Ilham Gusti Nizar Syafi'i Halimah Halimah Hani Pratiwi Husnul Khatimi Irfan Prasetia Johan Wahyudi Johan Wahyudi Mambang Maulidia Arafah Maulidiya, Erika Maya Amalia Meila Izzana, Meila Muhammad Adetya Ashari Muhammad Alkaff Muhammad Andri Firdaus Muhammad Arif Rahman Muhammad Baballe Ahmad Muhammad Faidhorrahman Muhammad Syauqi Al Fath Mutia Maulida Muti`a Maulida Nanik Suciati Noor Razikin Novitasari Novitasari Novitasari Novitasari Nurul Fathanah Mustamin Nurul Qamaria Prakoso, Puguh Budi Prastya, Septyan Eka Pratiwi, Hani Puguh Budi Prakoso Puguh Budi Prakoso Purwono, Purwono Rahman, Muhammad Arif Rakhmadhany Primananda, Rakhmadhany Rama Aria Megantara Ricardus A P, Ricardus A Ricardus Anggi Pramunendar Rico Silas Dwi Kasanda Royani, Noorhanida Rudy Ansari Rusiana, Novi Subhan Panji Cipta Syarifah Soraya Wenny Puspita Wijaya, Eka Setya