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All Journal Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Applied Data Sciences
Maulana Baihaqi, Wiga
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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KOMPARASI METODE PEMBELAJARAN MESIN UNTUK IMPLEMENTASI PENGAMBILAN KEPUTUSAN DALAM MENENTUKAN PROMOSI JABATAN KARYAWAN Romadloni, Pristian; Adhi Kusuma, Bagus; Maulana Baihaqi, Wiga
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 6 No. 2 (2022): JATI Vol. 6 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v6i2.5238

Abstract

Di era industri 4.0, perusahaan multinasional dituntut untuk beradaptasi dengan kemajuan teknologi dan perlu untuk bergerak cepat. Jenjang karier karyawan yang seimbang dengan beban kerja dan kebutuhan karyawan di lapangan, merupakan salah satu kunci pertahanan perusahaan. Selama ini tidak sedikit divisi pengelola sumber daya manusia di perusahaan yang menetapkan jenjang karier karyawan, dilakukan penilaian secara konvesional, yaitu dengan melihat nilai-nilai karyawan dari database perusahaan dan menimbang beberapa kriteria yang ada. Hal tersebut kemudian memakan waktu yang cukup lama. Sedangkan permintaan untuk menentukan promosi jabatan akan ada di setiap bulan. Kemudian dari masalah tersebut, dengan menggunakan data penelitian yang di peroleh dari perusahaan PT. Telkom Akses yang mana kriteria untuk promosi jabatan berbeda dengan perusahaan lain. Pada penelitian ini kami ingin memberikan solusi yaitu membuat perbandingan atau komparasi metode pembelajaran mesin untuk implementasi pengambilan keputusan dalam menentukan promosi jabatan karyawan, dengan menggunakan media aplikasi Rapidminer versi 9.10 dan dua metode, Decision Tree dan Naïve Bayes. Besar harapan kami dapat menjadi referensi khasanah ilmu pengetahuan baru di bidang Pengelolaan Sumber Daya Manusia. Pada penelitian ini, didapatkan hasil akurasi yang tertinggi yaitu, pada metode Naïve Bayes dengan nilai akurasi 92.29%, nilai presisi 97.05% dan nilai recall 89.86%.
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
Perbandingan Random Forest dan K-Nearest Neighbors untuk Klasifikasi Body Mass Index Menggunakan SMOTE-ENN untuk Mengatasi Ketidakseimbangan Data pada Analisis Kesehatan Naufal Yogi Aptana; Ikhsan, Ali Nur; Maulana Baihaqi, Wiga; Ajeng Widiawati, Chyntia Raras
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2553

Abstract

This study aims to compare the Random Forest and K-Nearest Neighbors (KNN) algorithms in Body Mass Index (BMI) classification using the SMOTE-ENN method to address data imbalance. The dataset consists of 2111 entries with demographic and health attributes of individuals. Data imbalance poses a significant challenge that may affect the accuracy of machine learning models. The SMOTE-ENN combination was employed to improve data distribution, enabling models to recognize patterns in minority classes better. Key evaluation factors included both algorithms' accuracy, precision, recall, and F1-score. Results indicate that the Random Forest algorithm achieved higher performance with 100% accuracy than KNN with 96% after applying SMOTE-ENN. These findings highlight the unique contribution of SMOTE-ENN in handling imbalanced data, enhancing classification model quality, and significantly impacting machine learning applications in healthcare.
Co-Authors Adhi Kusuma, Bagus Ajeng Widiawati, Chyntia Raras Fandy Setyo Utomo Ikhsan, Ali Nur Naufal Yogi Aptana Romadloni, Pristian Rujianto Eko Saputro Sarmini Sarmini