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Tamara Maharani
Akademi Komunitas Negeri Pacitan
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PENGUKURAN PSNR PADA TRANSMISI VIDEO DI KANAL TERAHERTZ MENGGUNAKAN QAM MODULATION Tamara Maharani; Muhammad Agus Zainuddin; Sritrusta Sukaridhoto
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 2 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i2.319

Abstract

In the current era of communication has various challenges that include the intensity of information exchange more often, the amount of information carried and the speed in exchanging information. Communication is not only in the form of text and sound but also in the form of pictures and videos. This study tries to use digital data in the form of video with the aim of providing a view of the PSNR measurement simulation. The method used is modulation of QAM 64, 256, 1024 and 4096 through terahertz channels (0.1-10 THz). Simulation results show that in QAM 64 the PSNR value is 35.2 dB to 36.6 dB. The PSNR value decreases as the M-ary increases. PSNR at 256 QAM ranges from 25.9 to 26.5 dB. PSNR in QAM 1024 is stable at magnitude 16.3 to 16.5. Whereas PSNR in QAM 4096 ranged from 15.0 to 15.25. From this study shows the greater the value of PSNR, the quality of information sent is increasingly similar. In addition, the higher the M-ary, the data carried will also be large so as to speed up the transmission time.Keywords: Terahertz, QAM, PSNR, Video, Simulation Di era saat ini komunikasi memiliki berbagai tantangan yang meliputi intesitas pertukaran informasi yang lebih sering, besarnya informasi yang dibawa dan kecepatan dalam bertukar informasi. Komunikasi tidak hanya berupa text dan suara namun juga berupa gambar dan video. Penelitian ini mencoba menggunakan data digital berupa video dengan tujuan memberikan pandangan tentang simulasi pengukuran PSNR. Metode digunakan yaitu modulasi QAM 64, 256, 1024 dan 4096 melalui kanal terahertz (0.1-10 THz). Hasil simulasi menunjukkan pada QAM 64 nilai PSNR sebesar  35.2 dB hingga 36.6 dB. Nilai PSNR menurun seiring bertambahnya M-ary. PSNR pada QAM 256 di rentang 25.9 hingga 26.5 dB. PSNR pada QAM 1024 stabil di besaran 16.3 sampai 16.5. Sedangkan PSNR pada QAM 4096 di rentang 15.0 hingga 15.25. Dari penelitian ini menunjukkan semakin besar nilai PSNR maka kualitas informasi yang dikirimkan semakin mirip. Selain itu semakin tinggi M-ary maka data yang dibawa pun juga ikut besar sehingga mempercepat waktu transimisi. Kata kunci: Terahertz, QAM,PSNR, Video, Simulasi
IMPLEMENTASI METODE K-MEANS UNTUK PENGELOMPOKAN DATA JAMAAH PADA BIRO UMROH JABAL RAHMAH PACITAN Melinda Yunitasari Yunitasari; Tamara Maharani Maharani; Bagus Hikmahwan Hikmahwan
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 1 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i1.402

Abstract

Currently, religious tourism, especially for Hajj and Umrah, is in great demand by the public because it offers a variety of package facilities according to the economic conditions of the congregation. Diverse congregational data is the basis for this research to find new knowledge as a marketing strategy to find out which packages are most interested in pilgrims in the future.The case study of this research is in the Bureau of Jabal Rahmah Pacitan. This study uses K-Means clustering which is one of the techniques in data mining for unsupervised modeling and method of grouping data by partition. Attributes used in data processing include age, gender, marital status, year of registration, and packages chosen by the congregation. Data processing is assisted using the WEKA application. The results of this study obtained data/cluster A with 83 people or 55% and cluster B with 45 people or 45% of 151 records. So by using the K-Means method, it can be concluded that package A is the most favorite package or the most desirable.Keywords: Clustering, Data Mining, K-Means, WEKA, Umrah. Saat ini perjalanan wisata religi khususnya untuk ibadah haji dan umroh banyak diminati masyarakat karena menawarkan berbagai macam fasilitas paket sesuai kondisi ekonomi jamaah. Data jamaah yang beragam menjadi landasan pada penelitian ini untuk menemukan pengetahuan yang baru sebagai strategi pemasaran guna mengetahui paket yang paling diminati jamaah dimasa yang akan datang.Studi kasus penelitian ini di Biro Jabal Rahmah Pacitan. Penelitian ini menggunakan K-Means clustering yang merupakan salah satu teknik pada data mining untuk pemodelan unsupervised dan metode pengelompokkan data secara partisi. Atribut yang digunakan dalam pengolahan data meliputi usia, jenis kelamin, status pernikahan, tahun daftar, dan paket yang dipilih jamaah. Pengolahan data dibantu menggunakann aplikasi WEKA. Hasil dari penelitian ini diperoleh data/cluster A dengan 83 orang atau 55% dan cluster B dengan 45 orang atau 45% dari 151 record. Sehingga dengan menggunakan metode K-Means dapat disimpulkan bahwa paket A merupakan paket terfavorit atau yang paling diminati. Kata kunci: Clustering, Data Mining, K-Means, WEKA, Umroh.
KLASIFIKASI DATA LULUSAN SMPN 3 TULAKAN MENGGUNAKAN METODE NAÏVE BAYES Lesta Lia Regitaningtyas; Tamara Maharani Maharani; Bagus Hikmawan Hikmawan
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 1 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i1.403

Abstract

Alumni of junior high school students have a fairly diverse distribution of data. With a case study at SMP Negeri 3 Tulakan, the basis for this research is to predict the distribution of junior high school graduates in the following year. The data mining process is assisted by the WEKA application. The classification used is the nave Bayes classification using the test training set mode and cross validation folds 10. The results of this study from the test training set mode got Correctly Classified 83.2787% and Incorrectly Classified 16,7213% while in cross validation it got Correctly Classified 81.3115% and Incorrectly Classified 18.6885%. The percentage of accuracy value shows the effectiveness of the Admissions dataset applied to the Naïve Bayes Classification method which reaches 80%. The results of this study indicate that the data classification using Naïve Bayes has an accuracy level that is close to accurate.Keywords: data mining, Naive Bayes classification, student data, WEKA applicationAlumni siswa SMP memiliki persebaran data yang cukup beragam. Dengan studi kasus di SMP Negeri 3 Tulakan, menjadikan landasan pada penelitian ini untuk memprediksi persebaran lulusan Sekolah Menengah Pertama pada tahun selanjutnya. Proses data mining dibantu oleh aplikasi WEKA. Adapun klasifikasi yang digunakan adalah klasifikasi naïve bayes dengan menggunakan mode test training set dan cross validation folds 10. Hasil dari penelitian ini dari mode test training set mendapat sebesar Correctly Classified 83.2787% dan Incorrectly Classified 16.7213% sedangkan pada cross validation mendapat sebesar Correctly Classified 81.3115% dan Incorrectly Classified 18.6885%. Nilai persentase akurasi menunjukkan efektifitas dataset Admissions yang di terpkan pada metode Naive Bayes Classification yang mencapai 80%. Dari hasil penelitian ini menunjukkan bahwa klasifikasi data menggunakan Naive Bayes memiliki tingkat akurasi yang mendekati akurat.Kata kunci: data mining, klasifikasi naïve bayes, data siswa, aplikasi WEKA.
Metode Peningkatan Akurasi pada Sensor TDS Berbasis Arduino untuk Nutrisi Air Menggunakan Regresi Linier Dhodit Rengga Tisna; Berlian Juliartha Martin Putra; Tamara Maharani; Hasnira Hasnira
JURNAL INTEGRASI Vol 14 No 1 (2022): Jurnal Integrasi - April 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v14i1.3906

Abstract

Water quality has an important role in the field of aquaculture. One of the factors that determine water quality is the level of TDS (Total Disolved Solid). Therefore, a TDS meter that has precise accuracy is needed to be able to accurately measure the quality of various types of water. In this study developed a prototype capable of measuring TDS levels in water. This prototype consists of a TDS sensor device, Arduino UNO and an LCD to display the results of the measured water quality readings. So that the accuracy read by the prototype is able to match the commercial TDS meter, the researchers used a linear regression algorithm to be included in the Arduino TDS program. The results of the experiment show that the accuracy of the TDS prototype which was originally 77% increased to 98.3%, is almost close to precision with commercial TDS meters in general.
Co-Authors Bagus Hikmawan Hikmawan Dhodit Rengga Tisna Dhodit Rengga Tisna Dyiono Galang Putra Kedua Hasnira Hasnira Lesta Lia Regitaningtyas Melinda Yunitasari Yunitasari Muhammad Agus Zainuddin Niecola Jody Setiawan Putra, Berlian Juliartha Martin Rizky Adinda Rahma Sritrusta Sukaridhoto