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All Journal Prosiding Semnastek Jurnal Ilmiah FIFO Jurnal ULTIMA InfoSys TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Herman Bedi Agtriadi, Herman Bedi
Institut Teknologi PLN
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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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.
Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) Model Using GIS and Machine Learning Febryansyah Hans Arieyantho, Febry; Luqman; Agtriadi, Herman Bedi
ULTIMA InfoSys Vol 16 No 2 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i2.4543

Abstract

The adoption of electric vehicles in Indonesia is a strategic initiative supporting the national “Go Green” agenda and the Net Zero Emission target by 2060. As electric vehicle usage continues to grow, especially in West Java, well-distributed Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) locations are increasingly important. Inefficient placement may lead to operational issues, reduced user convenience, and financial losses. This research develops such a model by integrating Geographic Information System (GIS) techniques with machine learning algorithms, specifically Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). Data preparation includes collecting spatial datasets such as road networks, housing, apartments, parking areas, public facilities from OSM, administrative boundaries from Geofabric, and existing SPKLU points from OpenData West Java. Proximity analysis is used to measure distances to key features, enabling classification of potential locations into Shared-Residential, Enroute, and Destination categories. These outputs are combined with socio-economic variables, including population density, income levels, vehicle ownership, household characteristics, education levels, and age distribution processed using Kernel Density Estimation (KDE). Results show that MLP performs best with 92.8% accuracy. The most influential variable is the productive-age population, minority population, unemployment, and total population. Overall, demographic factors play a dominant role in determining optimal SPKLU locations.
Clustering for Motivating New Student Admissions in Study Program Selection: Systematic Literature Review Afif Dwi Kurniawan; Luqman; Agtriadi, Herman Bedi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025): (In Press)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2510

Abstract

This research aims to evaluate clustering in new student admissions in determining effective strategies, to help prospective students in choosing study programs that match the interests and potential of prospective new students. Clustering as a machine learning technique to group data that has similarities, is increasingly used in the field of education to support the decision-making process. This Systematic Literature Review (SLR) examines the application of clustering methods in new student admissions, especially in recommending the right study program. By analyzing 10 studies in applying clustering methods that are often used, to determine the main factors that influence the selection of courses, as well as their impact on student satisfaction in choosing courses and optimal academic results. The results of this study provide insight into strategies for the admissions team in optimizing marketing, so that there is a more effective alignment between student profiles and study program characteristics.
Pemanfaatan LLM dalam Proses Automasi pada Pengenalan Template Kontrak Kusnandar, Adam Ramdani; Herman Bedi Agtriadi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025): (In Press)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2511

Abstract

This research investigates the use of various text similarity methods in automating the recognition of varied contract templates. Determining the correct template is a crucial step before the automation process proceeds to the clause-by-clause evaluation stage. This recognition process involves dynamically comparing clause text between drafts and templates without data labeling, relying on available text. Testing was conducted using traditional methods (Jaccard similarity, TF-IDF, BM25) and natural language processing methods (BERT, LaBSE, LLM). The research methodology involves acquiring contract samples from various sources, creating templates, and testing template recognition. The testing output is evaluated based on its effectiveness in capturing semantic equivalence and contextual understanding. Research results show that LLM is highly robust in recognizing templates by only learning from the first few sample clauses. These findings indicate that template recognition automation through LLM will provide the best precision and accuracy compared to traditional methods and other natural language processing methods. Thus, this research can serve as a foundation for developing a template-based contract review automation system that is more robust against contract variations.
Co-Authors Abdurrasyid Abdurrasyid, Abdurrasyid Afandi, Adam Afif Dwi Kurniawan Angga Saputra Chaerudin, Muhammad Farhan Darma Rusjdi David Gabriel Sembiring Dewi Arianti Wulandari Dimas Arditya Pinandhito Dina Aulia Djunaidi, Karina Dwina Kuswardani Efy Yosrita Febryansyah Hans Arieyantho, Febry Haris, Abdul Hendra Jatnika Indrianto Indrianto Indrianto Kusnandar, Adam Ramdani Linda Alifiana luqman Luqman, Luqman M Habibi Max Teja Ajie Meilia Nur Indah Susanti Muhammad Nur Khanib Ocha Nia Martcya Situmorang Octaviasari, Afifah Nurlita Purwanto, Yudhi Rahmad Evan Rizqia Cahyaningtyas Susanti, Meilia Yessy Fitriani Zakiya Viantika Sihabudin4 Zakiyah Misfazilah