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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Journal of Regional Public Administration The Indonesian Journal of Computer Science Jurnal Serina Abdimas AQUANIPA - Jurnal Ilmu Kelautan dan Perikanan
Denny
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : The Indonesian Journal of Computer Science

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MUSIC SIMILARITY RANKING: STUDI KASUS FAIRPHONIC Kharisma, Rayhan; Denny
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4298

Abstract

Fairphonic Pte Ltd is a technology company operating in the music sector. Fairphonic provides services to detect content on social media that is suspected of copyright infringement. Fairphonic utilizes audio features for the detection process. The current algorithm used by Fairphonic requires pairwise comparison, and the content to be compared is collected through a scraping process immediately after the process is run. Fairphonic has hundreds of thousands of music data in their database. Fairphonic desires a more scalable algorithm to compare an input music piece with the entire Fairphonic music catalog. This research uses features such as Harmonic Pitch Class Profile (HPCP), Chroma, and Rhythm Pattern. The study compares previously researched algorithms, namely binary similarity matrix, Euclidean distance, and similarity matrix profile. The results show that the combination of HPCP with the binary similarity matrix yields the highest Mean Average Precision of 0.989. Speed testing by performing comparisons 10 times shows that the combination of Chroma and the similarity matrix profile is 72% faster compared to the combination of HPCP with the binary similarity matrix. The author recommends the Chroma and similarity matrix profile algorithm for music similarity ranking due to its faster process.
Optimizing E-Commerce in Indonesia: Ensemble Learning for Predicting Potential Buyers Insani, Faiz Nur Fitrah; Denny
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3690

Abstract

In the competitive Indonesian e-commerce sector, data-driven decisionmaking is crucial for success. This study addresses the challenge faced by a leading e-commerce company, where despite a 134% increase in promotional expenses, active user transactions remained low. Focusing on predicting potential buyers to optimize promotional spending, the research evaluates various ensemble learning methods, including Random Forest, XGBoost, and LightGBM algorithms. Through extensive testing, all three models demonstrated high precision in identifying potential buyers. Remarkably, XGBoost achieved an exceptional precision score of 89.5%. Further enhancement through a soft voting strategy combining XGBoost and LightGBM resulted in the highest precision rate of 89.8%, suggesting a promising approach for targeted marketing and improved promotional strategies in the e-commerce industry
Prediksi Penempatan Pegawai Menggunakan Algoritma Supervised Machine Learning dan Rules Based Experts: Studi Kasus di Institusi Pendidikan Romdhoni, Bayu Suciono; Denny
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4120

Abstract

Education plays a crucial role in shaping the future of a nation. To maintain the quality of education, effective human resource management is essential in educational institutions. This study addresses the challenges of employee’s placement under the Educational Institution. According data from December 2021 to May 2024, only 2,452 out of 41,722 employees were reassignment, which is significantly below the target set by regulation. This study evaluates several supervised machine learning algorithms, including Gaussian Naive Bayes, Decision Tree, Support Vector Machine, and Random Forest. Random Forest emerges as the most suitable algorithm due to its superior accuracy, precision, recall, and F1 Score. Following the evaluation of the chosen algorithm, the deployment phase includes comprehensive data preprocessing steps, such as handling missing values, data normalization, and categorical feature encoding. This system integrates with Google API for geospatial data, ensuring accurate and efficient decision-making.
Co-Authors Adi Susilo Arifin, Ari Dolly Priatna Erfin Fathoni, Abdurrahmat Hartiningtias, Diny Hendryawan, Shofwan Insani, Faiz Nur Fitrah Jeferry C Kharisma, Rayhan Monk, Kathryn A Romdhoni, Bayu Suciono Verawati Yohanes D.B.R. Minggo