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All Journal Jurnal E-Komtek Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora Jurnal Kesehatan Akimal Jurnal Teknik Sipil Macca Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Humanities and Social Sciences El-Hikmah Jurnal Ilmu Dakwah dan Komunikasi
luqman
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal E-Komtek

 1 
Comparative Analysis of the Accuracy of Multiple Linear Regression Method and Ridge Regression Method in Predicting Dengue Fever Cases in South Tangerang City Dina Aulia; Herman Bedi Agtriadi; Luqman
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2292

Abstract

One of the main health issues in South Tangerang City is dengue fever (DBD). This study aims to compare the accuracy of Multiple Linear Regression and Ridge Regression methods in predicting the number of DBD cases using weather data such as temperature, humidity, and average rainfall. The data used is monthly data from South Tangerang City. The analysis process includes preprocessing, splitting the dataset into training and testing data, and applying both regression methods. To determine the prediction error rate, model accuracy is evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results indicate that Ridge Regression performs better for datasets with high multicollinearity, yielding a MAPE value of 20.12%, while Multiple Linear Regression is more effective for datasets with low feature correlation, showing a MAPE value of 44.6%. This study provides important insights into selecting predictive techniques based on the characteristics of the analyzed dataset. It is hoped that this research can improve mitigation and planning for DHF cases in South Tangerang City by choosing the appropriate approach.
Literature Study: Prediction of the Type of Company where Students Work Using Naïve Bayes and Neural Network Algorithms Saputra, Angga; Luqman; Herman Bedi Agtriadi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2314

Abstract

Research was conducted to evaluate the effectiveness of various machine learning algorithms, such as Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network (ANN), in predicting and classifying data. Naive Bayes proved to be efficient and accurate in structured data classification, such as predicting alumni's waiting time to get a job (94%) and vocational school students' job readiness (96.95%). On the other hand, neural network methods such as ANN and GRNN are superior in handling non-linear regression problems, such as house price prediction or college students' study period, although there is still room to improve accuracy. Random Forest is more suitable for complex data, while Naive Bayes is more effective for simple data. This research emphasizes the importance of selecting relevant variables, such as gender, major, and GPA, to improve model performance. Therefore, the selection of machine learning methods should be tailored to the type of data and the purpose of the analysis, as each algorithm has its own advantages and disadvantages.
Implementation of Hybrid Recommendations in the Standardized Student Internship Assessment System At ITPLN Octaviasari, Afifah Nurlita; Agtriadi, Herman Bedi; Luqman; Jatnika, Hendra
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2287

Abstract

Student internship assessment is an important aspect of higher education, requiring objective and accurate standards. This research examines implementing a hybrid recommendation system to improve the internship assessment process at ITPLN. The hybrid recommendation method combines content-based and collaborative approaches so that it can provide more relevant and personalized recommendations. Through analysis of previous assessment data and feedback from students and supervisors, this system is designed to assess student performance more comprehensively. The research results show that the use of a hybrid recommendation system can increase accuracy and fairness in assessments, as well as provide additional insight for supervisors in providing evaluations. Thus, this research contributes to the development of better assessment systems in the context of professional education, especially in the fields of engineering and technology. It is hoped that the implementation of this system can become a model for other institutions in optimizing the student internship assessment process.
THE The Application of TOPSIS Method and Djikstra Algorithm in The Development of Potential Tourist Attractions on The Jakarta to Dieng Route Wiwit Rifayanto; Luqman; Herman Bedi; Rizqia Cahyaningtyas
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2289

Abstract

Finding the optimal route for several vehicles in determining destinations according to their travel needs, such as serving customers, finding places for tourism, finding destinations for personal, family, or community touring, or logistical distribution, has become an essential need today. Route selection with considerations of fuel cost savings, tolls, regional levies, dining or rest stops, and fuel refilling stations (gas stations) is also a critical basis for achieving travel objectives. This research was conducted through surveys of alternative routes from Jakarta to Dieng in Central Java, and interviews with motorcycle users for this journey (Motor Touring) conducted by a motorcycle community. The application of the TOPSIS method, which is then compared with the results of the Dijkstra Algorithm in determining a route, can be a solution in route selection, in this case, the route from Jakarta to Dieng (Central Java). Alternative routes with attributes such as route, travel time, road conditions, fuel refilling locations, and rest stops for meals or just drinks become significant factors considered by motorcycle enthusiasts for touring to a region. The selection of the right route recommendation makes this route the primary choice for motor vehicle users, with the busy traffic on this route positively impacting the local residents, who were previously vegetable or fruit farmers, by providing additional income through selling snacks, ready-to-eat meals, coffee, parking, rest areas, toilets, and necessities for the stay in Dieng, such as gloves, hats, and jackets.
Clustering Key Performance Indicators using Convolutional Neural Networks Dimas Arditya Pinandhito; Herman Bedi Agtriadi; Luqman
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2322

Abstract

Performance assessment based on Key Performance Indicators (KPI) is a crucial aspect in making strategic decisions in various industrial fields. Along with the development of artificial intelligence, the Convolutional Neural Network (CNN) method is starting to be applied to increase accuracy in KPI clustering. This research aims to analyze and compare the CNN approach in the KPI clustering process based on literature reviews from various scientific journals. The study results show that CNN is able to increase efficiency in KPI grouping with a better level of accuracy than conventional methods. This study is expected to provide deeper insight into the implementation of CNN in KPI analysis and open opportunities for further development in the future.
Evaluation of the Accuracy of the Naive Bayes Method in the Classification of Key Performance Indicators (KPIs) for Employees: Systematic Literature Review Chaerudin, Muhammad Farhan; Herman Bedi Agtriadi; Luqman
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2323

Abstract

This study aims to evaluate the accuracy of Naive Bayes' method in classifying employee Key Performance Indicators (KPIs) through the Systematic Literature Review (SLR) approach. By collecting and analyzing reputable journals published between 2019 and 2024, this study examines the effectiveness of Naive Bayes in evaluating employee performance. The results of the study show that Naive Bayes is able to achieve a fairly high accuracy, which is between 84% to 90%, in classifying employee KPIs. However, this accuracy can vary depending on the complexity of the data used. Some research suggests that other methods such as Support Vector Machine (SVM) or Decision Tree may be superior in certain situations, especially when the data used is more complex or non-linear. In general, Naive Bayes remains a popular choice due to its ease of implementation and speed in delivering results. This study concludes that the selection of classification methods should be adjusted to the characteristics of the data and the purpose of the analysis to ensure optimal results.
Co-Authors Ahmady, Dedy Amin, Abdul Angga Saputra Chaerudin, Muhammad Farhan Damero, Lian Dimas Arditya Pinandhito Dina Aulia Gunawan H, ST Maryam Heri Kurniawan, Heri Herman Bedi Herman Bedi Agtriadi Herman Bedi Agtriadi, Herman Bedi Hudin, Hafid Ibrahim Jatnika, Hendra Lambang Basri Said Octaviasari, Afifah Nurlita Rizqia Cahyaningtyas Safri, Hayanuddin Suriani Syafrillah, Andi Muhammad Fajrin Mario Syahbudi Wiwit Rifayanto Yanti, Mauliani Harva