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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Jurnal IPTEK Jurnal KARISMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Kridatama Sains dan Teknologi bit-Tech Jurnal Pendidikan Teknologi Informasi dan Vokasional JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Informasi dan Sains Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Soft Computing Exploration Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Instal : Jurnal Komputer Formosa Journal of Science and Technology (FJST) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Matematika, Komputasi dan Statistika Blend Sains Jurnal Teknik QISTINA: Jurnal Multidisiplin Indonesia Journal of Education Technology Information Social Sciences and Health Jurnal Indonesia Sosial Teknologi Jurnal Multidisiplin Teknologi dan Arsitektur Journal of Informatics and Data Science (J-IDS) Jurnal Komputer dan Teknologi (JUKOMTEK) Journal of Accounting Law Communication and Technology Journal of Education Transportation and Business
Zulfahmi Indra, Zulfahmi
Uniersitas Negeri Medan
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Melody Transcription from Monophony Audio with Fast Fourier Transform Simanjorang, Rio Givent A; Kana Saputra S; Said Iskandar Al Idrus; Zulfahmi Indra
Journal of Informatics and Data Science Vol. 3 No. 2 (2024): November 2024
Publisher : Universitas Negeri Medan

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

Abstract

Music has been an inseparable part of human life since ancient times. One form of music that is often studied is monophonic music, which consists of a single note played at a time. In the digital era, melody transcription has become an important aspect of music processing, allowing sound to be converted into musical notation. This study focuses on melody transcription from monophonic sound recordings using the Fast Fourier Transform (FFT) method. The research aims to analyze the accuracy of FFT in extracting frequency components from monophonic signals and converting them into musical notation. The research methodology involves collecting monophonic sound recordings from piano and guitar, preprocessing the audio to remove noise and normalize volume, applying FFT to extract frequency features, and mapping these frequencies into musical notation. The evaluation process is conducted using Dynamic Time Warping (DTW) and a confusion matrix to measure accuracy, precision, recall, and F1-score. The results show that the FFT-based transcription system achieves an accuracy rate of 99.24% for piano and 98.86% for guitar. The study also highlights the impact of noise and audio quality on transcription accuracy, as well as the limitations of FFT in detecting closely spaced frequencies. Despite these limitations, FFT proves to be an efficient method for melody transcription in simple monophonic music. Future research could explore hybrid approaches combining FFT with other pitch detection algorithms to improve transcription accuracy.
Optimasi Kinerja Algoritma AES-128 Pada Proses Enkripsi dan Dekripsi File Berbasis Python Wulandari, Siti; Indra, Zulfahmi; Ridho, Muhammad
Jurnal Pendidikan Teknologi Informasi dan Vokasional Vol 6, No 2 (2024): Jurnal Pendidikan Teknologi Informasi dan Vokasional
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jptiv.v6i2.31691

Abstract

Data security is a top priority in the digital era, especially in the face of increasing cyber threats. Advanced Encryption Standard (AES) is one of the most widely used cryptographic algorithms to protect data. The purpose of this study is to implement the AES algorithm in the Python programming language using the Google Colab platform to improve data security in an efficient manner. This implementation explores the use of AES to perform secure and fast data encryption and decryption, and evaluates the algorithm's performance in terms of speed and resource efficiency. The study shows that AES-128 is able to encrypt data in an average time of 0.003663 seconds and decrypt data in 0.000112 seconds. The file size increases by only 17% after encryption, indicating efficiency in resource usage. The Google Colab platform has been shown to improve computational performance, especially in the fast decryption process without compromising data security.Kata kunci: AES, Optimasi, Pyhton
Klasifikasi Akun Buzzer Menggunakan Algoritma K-Nearest Neighbor pada Tagar #STYTanpaDiasporaNol di Media Sosial X Lubis, Afiq Alghazali; Idrus, Said Iskandar Al; Indra, Zulfahmi; S, Kana Saputra; Chairunisah, Chairunisah
Blend Sains Jurnal Teknik Vol. 4 No. 2 (2025): Edisi Oktober
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v4i2.1093

Abstract

Peningkatan pengguna media sosial X pada tahun 2024 sebesar 639 ribu mengakibatkan penyebaran informasi yang sangat masif, menjadikan buzzer berperan dalam mengarahkan opini publik dan memicu konflik sosial, seperti yang terlihat pada tren #STYTanpaDiasporaNol usai gugurnya tim nasional Indonesia di ASEAN Championship 2024. Penelitian ini bertujuan untuk membangun model machine learning dalam klasifikasi akun buzzer menggunakan algoritma K-Nearest Neighbor (KNN). Data yang akan digunakan dalam penelitian ini berasal dari kumpulan tweet dari sosial media X dalam tagar #STYTanpaDiasporaNol. Penelitian ini memiliki prosedur penelitian, di antaranya pengumpulan data, pra-pemrosesan data (cleaning, labelling, feature engineering dan standardization), splitting data, pemrosesan data, dan evaluasi model. Hasil penelitian ini mendapatkan model dengan akurasi terbaik yaitu varian model perbandingan split data 80:20 dan K = 5 dengan nilai akurasi sebesar 89% serta nilai precision dan recall sebesar 89% lalu nilai F1-score sebesar 88%. Model sangat baik dalam memprediksi kelas mayoritas namun kesulitan dalam memprediksi kelas minoritas. Kemudian dilakukan eksperimen resampling data dengan tujuan membuat keseimbangan data. Hasil didapatkan bahwa varian pada split data 70:30 dengan K = 9 diperoleh akurasi sebesar 91% dengan precision, recall dan accuracy juga sebesar 91%. Model eksperimen ini cukup baik mendeteksi kelas mayoritas maupun kelas minoritas.
Implementasi ROT13 sebagai Algoritma Enkripsi Sederhana dalam Python untuk Keamanan Komunikasi Digital Sitompul, Dicky Sambora; Lubis, Muhammad Ghafur Rahman; Indra, Zulfahmi
Journal of Accounting Law Communication and Technology Vol 2, No 1 (2025): Januari 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jalakotek.v2i1.4030

Abstract

Penelitian ini mengkaji implementasi algoritma enkripsi Caesar Cipher dengan pergeseran 13 huruf, atau ROT13, menggunakan Python. ROT13 berfungsi sebagai mekanisme enkripsi sederhana yang memutar setiap huruf dalam teks input sebanyak 13 posisi dalam alfabet, sehingga menghasilkan teks yang tidak dapat dengan mudah dipahami. Penelitian ini merinci langkah-langkah implementasi, disertai dengan kode dan contoh hasil enkripsi serta dekripsi. Analisis menunjukkan bahwa ROT13 merupakan metode yang efektif dan mudah diterapkan untuk meningkatkan keamanan komunikasi digital dalam konteks yang tidak terlalu sensitif.
Co-Authors Abdi Setiawan Adidtya Perdana, Adidtya Adwitia, Keysa Shifa Alsya Adelia Putri Ananda Hatmi, Reza Anastasya Carity S, Disty Angga Warjaya ARNAH RITONGA, ARNAH Arnita Arnita Asra, Naufal Aqiilah Barus, Angelica Chairunisah Chairunisah, Chairunisah Dede Yusuf Wagiman DIdi Febrian Evaliana Sembiring, Khatrin Fahra Pebiana Putri Farhan Ramadhan, Haikal Fauzan, M Rosyid Halawa, Sovantri Putra Paskah Hamidah Nasution Harahap, Muhammad Abarorya Hasibuan, Najwa Latifah Hermawan Syahputra Hidayat, Muhammad Ferdiansyah Hijka Listia Hutagalung, Fhadillah Br Ida Ayu Putu Sri Widnyani Inna Muthmainnah Insan Taufik Kana Saputra S Kartika, Dinda Khairani, Nerli Khusnul Arifin . Lorinez, Yohana Lubis, Afiq Alghazali Lubis, Muhammad Ghafur Rahman Luge, Miclyael M. Reza Pratama Harahap MANSUR AS Manullang, Sudianto Muhammad Andika Muslim Muhammad Rheza Palevi Muhammad Ridho Nasution, Adzkia Nur Nasution, Hamidah . Neltriana Syafira Niska, Debi Yandra Nouri, Maulana Al Pandiangan, Gus Rosauli Paramitha Purba, Desni Pratama, Ega Purba, Jogi Putri, Repi Meilani Putri, Rezkya Nadilla Rahmah, Nadya Ramayani Siagian Rinjani Cyra Nabila Risna Simorangkir Rizky Ananda Sabina Wardaniah Said . Iskandar Saketang, Tia Risky Yasmin Saragih, Vinny Ramayani Savana HSB, Muhammad Alby Selfi Audy Priscilia Siagian, Angel Agasari Simamora, Elmanani Simanjorang, Rio Givent A Simanulang, Mika Monica Fransiska Simanungkalit, Ada Novisari D. Sinaga, Marlina Setia Sipahutar, Nuriana siti wulandari Sitompul, Dicky Sambora Sri Mulyana subanar subanar Sultan Lazuardiansyah Syahfitri, Ardilla Syarida Aini, Desti Syifa Cendikia, Yolanda Venina Agustine Wahabi Hasibuan, Rahman Yulita Molliq Rangkuti Zai, Samuel Anaya Putra