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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

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

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

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.

Improving Tesseract OCR Accuracy Using SymSpell Algorithm on Passport Data Had, Iqbaluddin Syam; Maulana Baihaqi, Wiga; Putriana Nuramanah Kinding, Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14395

Optical Character Recognition (OCR) is a technology used to recognize text from images or digital documents, such as passports. One popular OCR tool is Tesseract as it offers high accuracy. However, OCR accuracy is often affected by various factors, including image noise and/or non-text elements. This article discusses the application of the SymSpell algorithm for post processing to improve OCR accuracy on standard Indonesian passports. OCR will be focused on the Visual Inspection Zone, specifically the Place of Birth and Issuing Office values. Unlike the Machine Readable Zone which is composed of individual codes and a clear background, the Visual Inspection Zone often experiences OCR errors due to holograms blocking the text and spaced layouts. SymSpell is an edit distance based spelling correction algorithm designed to process data quickly and efficiently, even on very huge datasets. In this study, SymSpell is used to detect and correct errors in OCR results that are compared to a corpus word list. Experimental results with 10 tested scans and passport photos showed that the integration of SymSpell with the Research and Development methodology was able to improve the OCR accuracy rate by 21,43% for certain Place of Birth and Issuing Office data from the Visual Inspection Zone. With this approach, OCR systems can provide more reliable results for practical applications.

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

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.

A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients Munandar, Arif; Maulana Baihaqi, Wiga; Nurhopipah, Ade
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1632.344-352

Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.

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