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All Journal TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Jurnal E-Komtek JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Herman Bedi Agtriadi
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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PELATIHAN MS. Office Word dan Excel BAGI PERANGKAT DESA & MASYARAKAT DESA CIARUTEUN ILIR BOGOR Max Teja Ajie; Efy Yosrita; Darma Rusjdi; Meilia Nur Indah Susanti; Indrianto Indrianto; Rizqia Cahyaningtyas; Dewi Arianti Wulandari; Herman Bedi Agtriadi
Terang Vol 1 No 1 (2018): TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.852 KB) | DOI: 10.33322/terang.v1i1.209

Abstract

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Implementation of WYSIWYG in the Development of ITCC ITPLN Letter Management Information System Herman Bedi Agtriadi; Jatnika, Hendra; Yessy Fitriani; Zakiya Viantika Sihabudin4; Muhammad Nur Khanib; David Gabriel Sembiring; Ocha Nia Martcya Situmorang
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 5 No 2 (2021)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v5i2.2326

Abstract

Information Technology Certification Center (ITCC) is a laboratory focused on Certification activities in Information and Technology. Currently, ITCC uses a manual system with Microsoft Excel for creating letter numbers, which is time-consuming and prone to data loss due to unintegrated storage. To solve this, the author developed a letter archive application using the Rapid Application Development (RAD) model, which accelerates the development process by producing incremental software versions. This application improves the efficiency of numbering and securely storing mail archives while providing a WYSIWYG editor for easy document editing
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.
A Herman Bedi Agtriadi; M Habibi; Zakiyah Misfazilah
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.v9i1.2501

Abstract

Breast cancer is the most common cancer globally with a malignant category that poses a serious and frightening threat to women. According to data from Globocan. In Indonesia alone in 2022 the number of new cases of breast cancer reached 66,271 cases, thus contributing (30,1.6%) of the total cancer cases in Indonesia. Of the cases with more than 22 thousand deaths, breast cancer is the second most deadly cancer. 70% of breast cancer cases are detected already at an advanced stage, where this case can occur due to delays in medical personnel who have not been able to detect breast cancer manually. This requires technology to help doctors and radiologists to evaluate Magnetic Resonance Imaging (MRI) images automatically. One of the deep learning methods useful for MRI image analysis is Convolutional Neural Network (CNN) using VGG19 and AlexNet architecture which has been proven in the classification process. This study uses data from Kaggle with a total of 1400 data. Through the use of the Convolutional Neural Network method, this study obtained a fairly optimal accuracy on the VGG19 architecture of 99% and on the AlexNet Architecture of 97%.
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.
Peran Kompetensi Instruktur dalam Menyampaikan Materi Pelatihan kepada siswa di Perguruan ANNIDA Al Islamy : Kajian Profesional, Kepribadian, dan Sosial Herman Bedi Agtriadi; Dewi Arianti Wulandari; Indrianto; Meilia Nur Indah Susanti; Abdurrasyid; Rahmad Evan
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 6 No. 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2820

Abstract

This community service aimed to improve training quality for senior high school students by enhancing instructors’ professional, personal, and social competencies. Activities included training, observation, and evaluation using a Likert-scale questionnaire distributed to 24 participants. Results showed that 50% of respondents rated instructors’ readiness and organization as Very Good, and 37.5% as Good. Professional competence, such as topic explanation, received Very Good ratings from 45.8%, while social competencies like communication and tolerance were rated Very Good by 37.5% and Good by 50%. The average score across all indicators was above 4.25 (on a scale of 1–5). These findings indicate a positive impact on training effectiveness and student engagement. This program contributes to creating more interactive and meaningful training sessions, aligning with the learning needs of today’s students.
Co-Authors Abdurrasyid Angga Saputra Chaerudin, Muhammad Farhan Darma Rusjdi David Gabriel Sembiring Dewi Arianti Wulandari Dimas Arditya Pinandhito Dina Aulia Efy Yosrita Hendra Jatnika Indrianto Indrianto Indrianto luqman M Habibi Max Teja Ajie Meilia Nur Indah Susanti Muhammad Nur Khanib Ocha Nia Martcya Situmorang Rahmad Evan Rizqia Cahyaningtyas Yessy Fitriani Zakiya Viantika Sihabudin4 Zakiyah Misfazilah