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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Journal of Education and Learning (EduLearn) Jurnal RESISTOR (Rekayasa Sistem Komputer) Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Begawe Teknologi Informasi
Arik Aranta
Fakultas Teknik, Universitas Mataram
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )

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PENERAPAN METODE BACKPROPAGATION DAN ICZ-ZCZ PADA PENGENALAN POLA TULISAN TANGAN AKSARA BIMA rizka amalia; Fitri Bimantoro; Arik Aranta
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 4 No 1 (2022): March 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v4i1.172

Abstract

The Bima Script as known as Aksara Bima is one of Bima’s local heritage that needs to be preserved. Based on an online questionnaire of 81 respondent from Bima, there were 66.7% of people who were not familiar with the Bima's Script and 45.7% of people did not even know the existence of the Bima's Script [2]. One of the ways to preserving the Bima script is building a pattern recognition. This research proposes to build a machine learning model that is able to recognize the Handwriting of Bima Script through Zoning feature extraction, Image Centroid Zone (ICZ) and Zone Centroid Zone combined with Backpropagation Neural Network (BPNN) classification. Result of the test using ICZ reached an accuracy up to 87.03% and the result using ZCZ reached and accuracy up to 88.64%, The best performance obtained accuracy up to 89.89% by applying Hidden size = 2, 128 neurons, 0.02 learning rate, error limit 0.001, 1000 epochs, and 9:1 training:testing data.
KLASIFIKASI MOOD MUSIK MENGGUNAKAN K-NEAREST NEIGHBOR DAN MEL FREQUENCY CEPSTRAL COEFFICIENTS Fuad Fadlila Surenggana; Arik Aranta; Fitri Bimantoro
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 4 No 2 (2022): September 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v4i2.191

Abstract

Musik merupakan kombinasi antara nada, vokal dan juga instrumental yang harmoni untuk mengekspresikan sesuatu yang bersifat emosional. Mood musik dapat berpengaruh terhadap emosi manusia. Mood musik dapat meningkatkan gairah dan kesenangan serta dapat mempengaruhi emosi komunikasi. Musik dapat ditemukan dengan mudah melalui Digital Music Library (DML) namun pengelompokan musik pada DML terbatas hanya pada judul, nama penyanyi, album dan genre musik. Perlu adanya variasi dalam mengenali musik untuk menjangkau pengelompokan dan klasifikasi musik yang lebih luas. Penelitian ini betujuan untuk mengklasifikasi mood musik berdasarkan Mel-Frequency Cepstral Coefficients (MFCC) dan menggunakan K-Nearest Neighbor (KNN). Dataset yang digunakan yaitu sebanyak 200 file musik dan terbagi menjadi 4 kelas mood berdasarkan Model Thayer yaitu angry (marah), happy (Bahagia), sad (sedih), dan relax (santai). Ekstraksi fitur menggunakan MFCC akan menghasilkan 13 fitur MFCC, 13 fitur delta dan 13 fitur delta-delta. Pada penelitian mendapatkan akurasi sebesar 85,5% menggunakan KNN dengan nilai k=5 dan menggunakan metode jarak Manhattan.
VERIFIKASI SUARA MAHASISWA SEBAGAI ALTERNATIF PRESENSI KEHADIRAN MENGGUNAKAN EKSTRAKSI FITUR MFCC DAN KLASIFIKASI LVQ Muhammad Afif Ma'ruf; Arik Aranta; Fitri Bimantoro
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 4 No 2 (2022): September 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v4i2.211

Abstract

Attendance is an essential thing in the learning process. In recent years, technology development has been relatively rapid, one of which is in attendance recording. Attendance registration can now be done using QR-Code, palm, face recognition and digital signature. There are shortcomings, such as the lack of flexibility in the attendance process and the problem of the pandemic being limited by distance. The existence of the teacher's online method makes it challenging to communicate. Based on these problems, this study was presented to build a voice verification model using the Mel-Frequency Cepstral Coefficients (MFCC) and Learning Vector Quantization (LVQ) methods as an alternative attendance. This study uses text-dependent recorded data from 35 speakers. In this study, verification was carried out with the condition of using a mask and without a mask. This study obtained an average margin of similarity score of 80% of the average similarity score of 93.96% native speakers and 67.58% fake speakers. The best test results were obtained at a threshold of 0.85 with an accuracy value of 86.2%, precision of 87.86%, and recall of 84% when using a mask, while without a mask the value of accuracy was 90%, precision was 88.97%, and recall was 91.17%
SPEECH TO TEXT BAHASA SASAK MENGGUNAKAN EXTRAKSI FITUR MEL-FREQUENCY CEPSTRAL COEFFICIENTS DAN KLASIFIKASI CONVOLUTIONAL NEURAL NETWORKS Belmiro Razak Setiawan; Arik Aranta; Budi Irmawati
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 5 No 1 (2023): March 2023
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v5i1.235

Abstract

Artificial intelligence technology allows digital signals to be processed by computers. Currently speech to text is available only in Indonesian and English versions. Speech to text is a system that performs commands from human voice input and is then translated into words. The development of speech to text in regional languages ​​is needed because it can be a bridge between culture and technological progress. From 5 research literature, it was found that the mel-frequency cepstral coefficients (MFCC) and convolutional neural networks (CNN) methods are a combination of the commonly used voice signal analysis methods and get accuracy between 70.00% to 99.00%. This study uses the CNN and MFCC methods in the speech to text field to recognize the Sasak language and convert it into text. The result of this research is a real time conversion system from voice to text in Sasak language. The analysis carried out includes determining the best amount of training data, testing the training data on the number of votes based on accuracy, the sensitivity of the algorithm to words that have similar prefixes using the MFCC method as feature extraction and CNN as a classifier for the voice dataset. This study aims to obtain the accuracy of the dataset used and the sensitivity of the algorithm to sentences that have similarities. In this study got 2 results. The first result is the result of training with the accuracy of CNN training is 90% and loss is 0.5%. The second result is the result of an experiment using 3 voice samples for each word on the dateset with 43 correct words, 6 correct words 2, 1 correct word 1 and none of the words incorrect. So it has a percentage of 86% all correct, 12% correct 2, and 2% correct 1, and 0% all wrong.
Co-Authors Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Belmiro Razak Setiawan Budi Irmawati Dewa Ayu Indah Cahya Dewi Fitri Bimantoro Fuad Fadlila Surenggana Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Haidar Rahman Haidra Rahman I Gede Andika I Gede Pasek Suta Wijaya I Gusti Agung Indrawan I Putu Teguh Putrawan Muhammad Afif Ma'ruf Muhammad Daden Kasandi Putra Wesa Muhammad Khaidar Rahman Noor Alamsyah Patriaji Ibrahim Maulana Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra rizka amalia Santi Ika Murpratiwi Santi Ika Murpratiwi Sri Endang Anjarwani