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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Sinkron : Jurnal dan Penelitian Teknik Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Minfo Polgan (JMP)
Wirayasa, I Ketut Adi
Universitas Pradita
Computer Science & IT Control & Systems Engineering Library & Information Science Mathematics Social Sciences

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Analisis Employee Satisfaction Menggunakan Teknik Clustering Dan Classification Machine Learning I Ketut Adi Wirayasa; Handri Santoso
Progresif: Jurnal Ilmiah Komputer Vol 18, No 1: Februari 2022
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.261 KB) | DOI: 10.35889/progresif.v18i1.766

Abstract

Abstrak. Kepuasan kerja pekerja sangat berhubungan dengan pekerjaan maupun kondisi dirinya ditempat kerja. Tingkat kepuasan kerja pekerja dapat di analisis dan menjadi bahan evaluasi perusahaan dalam menjalankan bisnis untuk mencapai target yang diinginkan. Kombinasi teknik clustering dan classification merupakan algoritma machine learning yang dapat membantu bagian Sumber Daya Manusia dalam menganalisis dan prediksi tingkat kepuasan kerja pekerja di perusahaan. Teknik clustering yang digunakan dalam penelitian ini adalah KMeans dan teknik classification menggunakan algoritma classificafier dari library Pycaret. Hasil analisis dari penggunaan teknik clustering dan classification dari ke-5 model classifier yang dipilih, 3 model yaitu LightGBM, Catboost dan XGBoost menunjukkan performa yang konsiten dan menghasilkan tingkat accuracy prediksi diatas 98% dengan jumlah cluster ideal 2, ncomponent 27, waktu proses rata-rata setiap model kurang dari 2 menit setiap tahapan proses dan menggunakan K-means clustering.Kata kunci: Kepuasan pekerja; Klaster; Klasifikasi; Pembelajaran mesin Abstract. Job satisfaction of workers is closely related to their work and conditions at work. The level of job satisfaction of workers can be analyzed and become an evaluation material for companies in running a business to achieve the desired target. The combination of clustering and classification techniques is a machine learning algorithm that can assist the Human Resources department with analyzing and predicting the level of job satisfaction of workers in the company. The clustering technique used in this research is K-Means in the classification technique using a binary classification algorithm from the Pycaret library. The results analysis of the clustering and classification techniques from the five selected classifier models, three models namely LightGBM, Catboost, and XGBoost shown consistent performance and the prediction accuracy levels above 98% with the ideal number of clusters 2, n-components 27, the average of processing time each model is less than 2 minutes each stage process and using K-means clustering.Keywords: Employee Satisfaction; Clustering; Classification; Machine Learning
Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent I Ketut Adi Wirayasa; Arko Djajadi; H andri Santoso; Eko Indrajit
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.69366

Abstract

Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%,  and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.
Use of ChatGPT as a Decision Support Tool in Human Resource Management Muhammad Subhan Iswahyudi; Nofirman Nofirman; I Ketut Adi Wirayasa; Suharni Suharni; Ita Soegiarto
Jurnal Minfo Polgan Vol. 12 No. 1 (2023): Artikel Penelitian 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i1.12869

Abstract

Human Resource Management (HRM) is an important aspect of the success of an organization or company. Effective and efficient HR management can help organizations better achieve their goals and ensure optimal performance from each team member. However, HR management is also faced with various complex challenges, including the management of large teams, diverse needs and expectations of team members, and rapid changes in the business and technological environment. This research aims to investigate the effectiveness and potential use of ChatGPT as a decision-support tool in HR management. This research is a literature review that adopts a qualitative method approach, which means it will analyze and interpret data by relying on information and text from various sources. The study results show that the use of ChatGPT as a decision-support tool in Human Resource Management offers great potential in improving the efficiency, effectiveness, and transparency of HR processes. Artificial intelligence models such as ChatGPT can act as virtual assistants that provide text-based responses, assisting in recruitment, employee development, performance management, and employee support processes. ChatGPT can gather valuable data and insights about employees and the work environment, and help HR managers make more informed decisions.
Performance Comparison of Ultrasonic Sensor Accuracy in Measuring Distance Sze, Edward; Hindarto, Djarot; Wirayasa, I Ketut Adi; Haryono, Haryono
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11883

Abstract

Digital technology is now very sophisticated. Its use is widely applied in all areas of human life. Starting from waking up, human activities and others always use technology. In carrying out their activities, modern humans now almost all use vehicles as a mode of transportation. Today's vehicles use a variety of sensors as a sixth sense. The results of detection using sensors on the vehicle are usually displayed on the dashboard of the vehicle. Modern humans currently use sensors to complete their needs. Besides that, the internet of things technology is growing rapidly in its role and development to support the needs of modern humans. Micro-controller technology is also experiencing rapid and massive development. One of the most common and most popularly used microcontrollers is Arduino. In many streets in Indonesia, people still use vehicles not equipped with many sensors. One of them is a simple parking sensor that many old vehicles don't have. Parking sensor problems are needed at the time of parking so that the vehicle that will be parked does not hit other objects or vehicles. There are many types of ultrasonic sensors. The purpose of this research is to make a prototype ultrasonic sensor that is applied to vehicles and compare some of the most accurate ultrasonic sensors in measuring the distance between the vehicle and the object being measured.
Co-Authors Arko Djajadi Eko Indrajit H andri Santoso Handri Santoso Haryono Haryono Hindarto, Djarot Ita Soegiarto Muhammad Subhan Iswahyudi Nofirman, Nofirman Suharni Sze, Edward