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Budi Susanto
Duta Wacana Christian University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PENENTUAN NOT ANGKA LAGU DARI SUARA MENGGUNAKAN DISCRETE FOURIER TRANSFORM Puji Resmiati; Budi Susanto; Lukas Chrisantyo
Jurnal Informatika Vol 9, No 1 (2013): Jurnal Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5827.465 KB) | DOI: 10.21460/inf.2013.91.140

Abstract

In the field of music, beam notation is the standard for musical notation, albeit difficult to understand. It is easier for people to read numbered musical notation rather than beam notation. A song that already has a notation is easy to be arranged, but many songs are made spontaneously without notation. Therefore, a computation system is developed to automatically generate the numbered musical notation of a song. The system is using the Discrete Fourier Transform algorithm to convert the audio signal from time domain to frequency domain, without implementing noise filtering beforehand. Tones perceived from the digital sound will be converted into numbered musical notation. We have tested our system in two mode based on the input, i.e. human voice and music keyboard device. In general, our system can detect about 53% precisely for human voice, and 66% precisely for music keyboard device.
IMPLEMENTASI ALGORITMA WINNOWING UNTUK MENDETEKSI KEMIRIPAN PADA DOKUKEN TEKS Obed Kharisman; Budi Susanto; Sri Suwarno
Jurnal Informatika Vol 9, No 1 (2013): Jurnal Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (8362.684 KB) | DOI: 10.21460/inf.2013.91.141

Abstract

Plagiarism is one of the main problems which the academic world must cope with recently. Many student papers and assignments had been found containing lines or sentences directly copied or sourced without sufficient acknowledgments to their original authors. Based on this fact, this research is conducted by developing software capable of detecting similarity between text documents, using “Winnowing algorithm” which is a document fingerprinting algorithm. The goal of this research is to measure its effectiveness in comparing test documents and reporting their similarity by percentage.
The Application of Agglomerative Clustering in Customer Credit Receipt of Fashion and Shoe Retail Michael Abadi Santoso; Budi Susanto; Gloria Virginia
International Journal of Industrial Research and Applied Engineering Vol 3, No 1 (2018)
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.593 KB) | DOI: 10.9744/jirae.3.1.37-44

Abstract

Agglomerative Clustering is one of data mining methods to get a cluster in form of trees. In order to achieve these objectives, we used two agglomerative methods such as Single Linkage and Complete Linkage. Searching for nearest items to be clustered into one cluster also needs a similarity distance to be measured. We used Euclidean Distance and Cosine Similarity for measuring similarity distance between two points. The factors that promote high levels of accuracy depend on the pre-proceeding stage for clustering process and also affect the results obtained. Therefore, we conducted research through several stages: pre-processing such as ETL, normalization, and pivoting. The ETL process consisted of removing outliers using IQR method, data-cleaning and data-filtering processes. For normalization, we used Min-Max and Altman Z-Score methods to get the best normal value. The results of this research demonstrate that the highest accuracy occurs when using the Complete Linkage with Min-Max and the Euclidean method with the average purity of 0.4. The significant difference is observed when using the Z-Score and Cosine Similarity methods; the average purity is around 0.11. Besides, we found that the system also could not predict the customers’ preferences in buying goods for the next period. Another result in the research is that transactional data in a company are not good enough to be clusterized.
Co-Authors Gloria Virginia Lukas Chrisantyo Michael Abadi Santoso Obed Kharisman Puji Resmiati Sri Suwarno