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Jurnal E-Komtek
Published by Politeknik Piksi Ganesha Indonesia
ISSN : 25803719     EISSN : 26223066     DOI : https://doi.org/10.37339/e-komtek.v4i2.269
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal E-Komtek (Elektro-Komputer-Teknik) is a Journal that contains scientific articles in the form of research results, analytical studies, application of theory, and discussion of various problems relating to Electrical, Computer, and Automotive Mechanical Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 276 Documents
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Comparison of DES, AES, IDEA RC4 and Blowfish Aglorithms in Data Encryption and Decryption Stefanus Eko Prasetyo; Gautama Wijaya; Felix
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.v9i1.2259

Abstract

The advancement of information technology has had a significant impact, one of which is as a medium for transmitting information from one place to another, making information access easier for many people. However, the ease of access to communication media also poses challenges for information security, as information becomes more vulnerable to being accessed, stolen, or manipulated by irresponsible parties. To protect the confidentiality of information, specific methods are needed, one of which is cryptography. In cryptography, there are various algorithms, including DES, AES, IDEA, Blowfish, Twofish, and RC4. This research aims to compare the performance of several cryptographic algorithms in the data encryption and decryption processes, focusing on processing speed and the size of the encrypted file. The results of the research show differences in processing time and file size of encrypted and decrypted data for each algorithm. Keywords: Aglorithms, Decryption, Encryption.
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.
Forecasting Water Pollution in Cengklik Reservoir Using Triple Exponential Smoothing Method Nooriza Modistira Sakti; Hakim, Dimara Kusuma; Elindra Ambar Pambudi; Maulida Ayu Fitriani
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.v9i1.2414

Abstract

Water quality is a crucial element for the sustainability of ecosystems and human life, yet it is often threatened by pollution resulting from human activities. Cengklik Reservoir in Boyolali Regency has shown increasing levels of pollution influenced by domestic waste, agricultural fertilizers, and residual fish feed from Floating Net Cages (KJA). This study aims to predict water pollution levels to support more effective management efforts by applying the Triple Exponential Smoothing (TES) method to pollution index data from 2016 to 2023. The forecasting results reveal a clear seasonal pattern, with a Mean Absolute Percentage Error (MAPE) of 34.36%, indicating a moderately good level of accuracy. These findings suggest that TES is capable of identifying general pollution patterns, although further approaches are needed to fully capture the dynamics of water pollution. As a follow-up, the study recommends optimizing the number and placement of KJA units, improving waste management, and implementing community education programs to preserve water quality and ensure the sustainability of the reservoir ecosystem.
Pengembangan Media Pembelajaran Sistem Video Animasi Materi Sistem Pengisian Baterai Sepeda Motor Bagi Mahasiswa Otomotif UM Purworejo Dwi Jatmoko; Agus Haryadi; Arif Susanto; Mohammad Reza Listiana
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.v9i1.2423

Abstract

This study aims to 1) produce a product. 2) determine the feasibility of the product. This research method includes development research. Based on assessing the feasibility of the media, the results of the questionnaire were obtained from two experts, namely media experts and material experts as well as the results of responses to small group trials and the results of responses to the data analysis test. The validation results from media experts showed the percentage of feasibility of the differential system learning media, namely 85%. The figure of 85% is included in the "good" classification. Analysis of the validation results carried out on material experts obtained 85%. From the data validation criteria, 85% is included in the "good" classification. Small group trials involving 5 students with an assessment percentage of 90%. Based on the analysis of the results of the small group trials above, the score is included in the "Very Good" classification. Large group trials with an assessment percentage of 91%.
Design and Construction of a 5 Kg Capacity Cracker Dough Mixer Yadi Hikmah Setiana; Susilawati; Muhamad Kamaludin; Rian Dwi Aji Saputro; Nurizzi Rifqi Ferdian; Ari Setian
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.v9i1.2472

Abstract

This study developed a design for a dough mixer machine for cracker production with a capacity of 5 kg, intended to meet the needs of small to medium-scale production. The machine design incorporates stainless steel for parts that come into direct contact with food and hollow steel for the main frame to ensure both strength and hygiene. The single-phase motor used is tailored to the needs of small and medium enterprises to achieve optimal energy efficiency. The mixing blades are designed with a dual-blade system to enhance dough homogeneity in a short amount of time. The control panel, which includes a simple on/off switch and an emergency stop button, is ergonomically designed for ease of use by non-technical operators. The machine cleaning process is also simplified with a mixing container that can be opened or tilted, supporting cleanliness and speeding up the workflow. This design emphasizes efficiency, safety, and user comfort. The contribution of this research is expected to advance food machinery technology, particularly in cracker processing, and serve as a reference for the development of similar machines in the future.

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