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All Journal Media Statistika Mechatronics, Electrical Power, and Vehicular Technology Jurnal INKOM Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pilar Nusa Mandiri Jurnal Linguistik Komputasional Journal of Applied Data Sciences Jurnal Pengabdian Ibnu Sina
Pardede, Hilman Ferdinandus
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Social Sciences

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Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps Prasetyo, Onky; Supianto, Ahmad Afif; Anam, Syaiful; Pardede, Hilman Ferdinandus; Zilvan, Vicky; Kusumo, R. Budiarianto Suryo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022915579

Abstract

Digitalisasi proses pembelajaran memungkinkan untuk dihasilkannya rekaman terhadap setiap aktivitas siswa selama belajar. Rekaman yang dihasilkan tersebut dapat digunakan untuk mengelompokkan siswa berdasarkan pola dari proses belajar yang dilakukan. Hasil pengelompokkan yang peroleh dapat digunakan untuk melakukan penyesuaian komponen pembelajaran ataupun metode pembelajaran bagi siswa. Salah satu metode pengelompokan yang sering digunakan adalah Self-Organizing Maps (SOM), SOM merupakan metode jaringan syaraf tiruan dengan tujuan untuk mempertahankan topologi data ketika data input multidimensi diubah menjadi data output dengan dimensi yang lebih rendah. Neuron SOM pada dimensi input diperbaharui sepanjang proses pelatihan, sedangkan neuron pada dimensi output tidak mendapatkan pembaruan sama sekali, hal ini menyebabkan struktur neuron yang digunakan pada tahapan inisialisasi akan tetap sama hingga akhir proses pengelompokan. Pada penelitian ini menggunakan metode Adaptive Moving Self-Organizing Maps (AMSOM) yang menggunakan struktur neuron lebih fleksibel, dengan dimungkinkannya terjadi perpindahan, penambahan dan penghapusan dari neuron menggunakan data 12 assignments dari media pembelajaran MONSAKUN. Hasil penelitian menunjukkan terdapat perbedaan yang signifikan secara statistik antara nilai quantization error dan nilai topographic error dari algoritme AMSOM dengan algoritme SOM. Metode AMSOM menghasilkan rata-rata nilai quantization error 27 kali lebih kecil dan rata-rata nilai topographic error 54 kali lebih kecil dibandingkan dengan metode SOM.AbstractThe digitization of the learning process makes it possible to produce recordings of each student's activity during learning. The resulting record can be used to group students based on the pattern of the learning process. The grouping results can be used to make adjustments to the learning components or learning methods for students. One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. The SOM neurons in the input dimension are updated throughout the training process, while the neurons in the output dimension do not get updated at all, this causes the neuron structure used in the initialization stage to remain the same until the end of the grouping process. In this study, the Adaptive Moving Self-Organizing Maps (AMSOM) method uses a more flexible neuron structure, allowing for the transfer, addition and deletion of neurons using 12 assignments of data from MONSAKUN learning media. The results showed that there was a statistically significant difference between the quantization error and the topographic error of the AMSOM algorithm and the SOM algorithm. The AMSOM method produces an average quantization error 27 times smaller and an average topographic error 54 times smaller than the SOM method.
MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING Apriadi, Sena Aditia; Pardede, Hilman Ferdinandus
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.3028

Abstract

Employee performance directly impacts organizational efficiency, yet traditional HR analytics often lack predictive precision. This study bridges HR theory and machine learning by evaluating tree-based algorithms for employee data analysis. Using a dataset of 15,227 employee records, we tested the Bagged Decision Tree algorithm, focusing on variables such as talent, career values, and aspirations. The Bagged Decision Tree achieved 98.65% accuracy, with talent and career values as key predictors. Excluding aspiration values reduced accuracy slightly to 98.57%, while excluding career values lowered it significantly to 92.13%. These findings highlight the robustness of the Bagged Decision Tree in HR analytics and emphasize the importance of variable selection, particularly career values and talent, in predicting performance outcomes. Future work should further explore real-world implementation challenges.
Co-Authors Ade Ramdan Ahmad Afif Supianto Ambarwati, Rini Novi Amri, M Faizal Angga Ardiansyah Apriadi, Sena Aditia Asih, Anggun Yuli Dewi, Margareta Aprilia Kusuma Dwiza Riana Ferda Ernawan Fhadilla, Nahdatul Hapsari, Riskhia Hasanah, Riyan Latifahul Heryana, Ana Irwan Susanto Isnandar Slamet, Isnandar Izzuddin, Mochammad Kadar, Jimmy Abdel Krisnandi, Dwi Kusumo, Budiarianto Suryo Kusumo, R. Budiarianto Suryo Na'am, Jufriadif Nugraha, Fitra Septia Pramanik, Subrata Prasetyo, Onky pratiwi, hasih Sri Subanti Sugiyanto - Suryawati, Endang Syaiful Anam Tibyani Tibyani, Tibyani Vicky Zilvan, Vicky Wijayanti, Monica Dini Winita Sulandari Yudho Yudhanto, Yudho Yuliani, Asri Rizki Yuwana, Raden Sandra Zilfan, Vicky Zukhronah, Etik