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Jurnal Media Teknik Elektro dan Komputer
Published by Yayasan Pendidikan Al-Yasiriyah Bersaudara
ISSN : -     EISSN : 30632927     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Metrokom : Media Teknik Elektro dan Komputer diterbitkan oleh Yayasan Pendidikan Al-Yasiriyah Bersaudara. Diterbitkan setiap bulan Januari - Juni, Juli - Desember. Jurnal ini fokus pada Teknik Elektro dan Komputer yang cakupannya diuraikan sebagai berikut : Algoritma Genetika Analisis Numerik Antena dan Propagasi Gelombang Aplikasi Bioinformatika/Biomedis Aplikasi Mobile Aplikasi Web Digital dan Analog Desain sirkuit Distribusi Daya Elektromagnetika Elektronika Daya Elektronika Sensor Sistem Energi Terbarukan Grafika Komputer Ilmu Komputer dan Komputasi Matematis Instrumentasi dan Material Interaksi Manusia Komputer Internet of Things Jaringan dan sistem Telekomunikasi Jaringan Komputer Jaringan Komputer dan Komunikasi Jaringan Sensor Cerdas Jaringan Sensor Nirkabel Keamanan Data dan Dunia Maya Keamanan Informasi Keamanan Jaringan Komputer Keamanan Nirkabel Kecerdasan Buatan Komputasi Awan Komputasi dan Implementasi Algoritma Komputasi Logika Komputasi Mobile Komputasi Paralel/Distribusi Komunikasi dan Jaringan Data Komunikasi dan Jaringan Optik Komunikasi dan Jaringan Seluler Komunikasi Nirkabel dan seluler Komunikasi Satelit Kontrol Daya Kontrol Teori Konversi Daya Kriptografi Kualitas layanan Telekomunikasi Manajemen Energi Manajemen Teknologi Informasi Material kelistrikan Media, Game dan Teknologi Mobile Metode Efisiensi Energi Mikroelektronika dan Nanoteknologi Optimalisasi Diskrit Organisasi dan Arsitektur Komputer Pembangkit Tenaga Listrik Pembelajaran Mesin Pemrograman Linier dan non-linier Pemrosesan Bahasa Alami Pemrosesan Gambar dan Pengenalan Pola Penambangan Data Pengambilan Informasi Pengembangan Layanan Telekomunikasi Pengembangan Perangkat Lunak Penginderaan Jauh Pengkodean jaringan nirkabel Pengolahan Data Pengolahan Sinyal Pengukuran Kelistrikan Perangkat Cerdas Realita Virtual Rekayasa Perangkat Lunak Sistem Basis Data Sistem Kontrol Sistem Operasi Sistem Pakar Sistem pemosisian nirkabel Sistem Pengambilan Keputusan Sistem Proteksi Sistem Robotika Sistem Tertanam Sistem Transportasi Cerdas Standar Komunikasi dan Jaringan Teknik Biomedis Teknologi Multimedia Telepon dan Sinyal Suara Tenaga Listrik dan Energi Teori Algoritma Teori Fuzzy dengan Aplikasi Teori Koding Teori Komputasi Serta berbagai bidang elektro dan komputer relevan yang belum tercantum
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 28 Documents
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Sistem Permohonan Cuti Karyawan untuk Kantor DPRD Provinsi Sumatera Utara Tumanggor, Ahmad AlfanSori; Aulia, Nurul
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 1 (2025): Metrokom : Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i1.115

Abstract

This research focuses on designing a web-based employee leave application system for the Wasbang Room at the North Sumatra Provincial DPRD Office. The goal is to develop a system that improves the efficiency of managing leave requests by simplifying the application process, accelerating data processing, providing transparent access to leave balances, and generating reports more effectively for leadership through centralized database storage. The system design is modeled using Unified Modeling Language (UML), specifically use case diagram, to illustrate system functionality and user interactions. The web-based platform allows access for employees, supervisors, and administrators with role-based access control. System functionality is tested using the black box testing method, focusing on core features such as login, employee data input, leave submission, leave approval, administrative data management, and leave history tracking. Test results confirm that all features operate according to the expected scenarios and that the system provides appropriate validation feedback for invalid inputs. This system has proven to enhance the speed and accuracy of the leave approval process, reduce the risk of manual data loss, and improve the accountability of personnel records. The novelty of this research lies in its integrated design approach, which incorporates organizational authorization flows and traceable leave documentation accessible by administrators. Overall, this study demonstrates the value of web-based information systems in optimizing administrative processes. The proposed solution is functional, user-friendly, and adaptable to other organizations with similar administrative needs. It is expected to support data-driven managerial decision-making and accelerate the digital transformation of personnel systems.
Implementasi Algoritma Mesin Vektor Dukungan untuk Klasifikasi Hari Sibuk di Bengkel Motor Manggala Alfarizi, Abdilah
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.119

Abstract

This study addresses the problem of unpredictable customer surges at Manggala Motor Workshop, which often lead to long queues, inefficient resource allocation, and reduced service quality. To overcome this problem, the Support Vector Machine (SVM) algorithm was applied to classify workdays into two categories: busy and not busy. The dataset consisted of 400 simulated data points designed to represent real workshop operational conditions by incorporating attributes such as day, weather, promotions, holidays, number of bookings, and number of vehicles. The data acquisition process was carried out through simulation based on average service capacity and external factors that typically influence customer arrivals. Before modeling, preprocessing steps were performed, including one-hot encoding for categorical features and normalization for numerical features. The dataset was then split into 80% training data (320 entries) and 20% test data (80 entries). Using a linear kernel, the SVM model was implemented in Google Colab with the Scikit-learn library. The results showed an accuracy of 96.25%, with high precision and recall scores in both classes. These findings indicate that SVM is effective for binary classification of busy and non-busy days, enabling Manggala Motor Workshop to optimize technician scheduling, manage workloads, and allocate resources more efficiently, thereby improving service quality and customer satisfaction.
Implementasi Algoritma SVM dalam Klasifikasi Komentar Judi Online menggunakan RapidMiner Artika, Priti Rindi; Meilianasari , Khafifah Dwi; Ikhwan, Ali
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.122

Abstract

The spread of negative comments containing elements of online gambling on digital platforms is increasingly affecting users. To address this issue, this study implemented a Support Vector Machine (SVM) algorithm to classify comments into two categories: those containing elements of online gambling and those without. The classification process was carried out using RapidMiner software, which allows data processing without the need for extensive coding. The dataset used was obtained from the Kaggle website and consisted of 8,442 comments. The data underwent preprocessing stages such as tokenization, normalization, and stopword removal. The SVM model was drilled and evaluated using cross-validation and evaluation metrics, with an accuracy of 97.91%, precision of 96.94%, recall of 99.81%, and an F1-score of 98.45%. The results showed that the SVM model achieved an accuracy of 97.91%, with high precision and recall across both classes. This demonstrates that the SVM algorithm is effective and efficient in automatically detecting comments containing elements of online gambling and is suitable for implementation as a content moderation system on digital platforms.
Keamanan Surat Masuk Menggunakan Enkripsi AES-128 dengan Verifikasi Kode QR di Inspektorat Provinsi Sumatera Utara Fahlome, Dodyk; Alya, Dea
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.129

Abstract

Incoming mail security is essential for maintaining information integrity within the North Sumatra Provincial Inspectorate. This study developed a mail metadata security system using the AES-128 algorithm as the encryption mechanism and QR Code as a verification medium. The encryption input consists of mail metadata representing document identity, including sender, receiver, document number, and message description, which are concatenated, converted into 128-bit blocks, and encrypted through ten rounds of AES-128 transformation. The resulting ciphertext is generated from the encrypted metadata output, transformed into a binary bitstream, and encoded into a QR Code to support secure distribution and authenticity validation. System performance stability was evaluated through Black Box testing of functional components, while encryption accuracy was validated through repeated encryption–decryption trials on multiple metadata samples. The results show that the system can generate valid ciphertext, accurately restore metadata, and provide a QR Code–based verification mechanism for government mail management.
Analisis Sentimen Opini Publik tentang Kerusakan Jalan di Sumatera Utara Menggunakan Metode Naive Bayes Berbasis Pengawasan Lemah (Berbasis Leksikon) Dharmawan, Kaka Davi; Hasibuan, Nazwa Aliya Muthmainnah
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.131

Abstract

Road infrastructure is a vital aspect of regional development that often receives public attention in online media, especially in North Sumatra. Manual monitoring of public opinion on this issue is inefficient due to the large volume of data and the imbalance of sentiment, which is dominated by complaints. This study aims to develop an automatic sentiment analysis model using a Weak Supervision approach that combines the Lexicon-Based method for automatic labelling and the Multinomial Naive Bayes algorithm to classify public opinion into three distinct categories: positive, negative, and neutral. Data was collected through web scraping techniques from various online news portals. To overcome data class imbalance, this study applied the Synthetic Minority Over-sampling Technique (SMOTE) to the training data. Test results on the test data showed that the model was able to achieve an accuracy of 70.93%. The model performed very well in detecting negative sentiment with a Precision value of 0.86, and was able to recognize positive sentiment with a Recall of 0.70 thanks to the application of SMOTE. Based on these results, the Naïve Bayes model can be used effectively to classify public sentiment towards road damage. In addition, these findings serve as strategic references and recommendations for stakeholders, such as the Inspectorate, to formulate relevant and data-driven policies in infrastructure improvement and regional development efforts.
Analisis Sentimen Pengguna Aplikasi MyASN di Twitter Menggunakan Algoritma Support Vector Machine Zalfa, Amirah Nafiah; Cahyani, Anisa Ninda
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.132

Abstract

This study aims to analyze user sentiment toward the MyASN application based on public discussions on X. A total of 1059 tweets were collected through a web scraping process using the Tweet-Harvest tool on Google Colab, with data obtained from tweets containing keywords and hashtags related to MyASN and BKN. The collected data were preprocessed through case folding, tokenizing, normalization, stopword removal, and stemming, then represented using the TF-IDF weighting scheme. Sentiment labels were assigned into three categories: positive, negative, and neutral. The classification process employed the Support Vector Machine (SVM) algorithm, with data divided into 80% training data and 20% testing data. The experimental results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 98.1% with a precision value of 0.983, a recall of 0.981, and an F1-score of 0.982. Evaluation based on sentiment class shows that in the negative class, SVM produced a precision of 1.000, a recall of 0.977, and an F1-score of 0.989. In the neutral class, it achieved a precision of 0.929, a recall of 1.000, and an F1-score of 0.963, while in the positive class, SVM achieved a precision of 0.885, a recall of 1.000, and an F1-score of 0.939. These results show that the indicates that the implemented SVM model demonstrates strong reliability in handling text-based sentiment classification, particularly in datasets with imbalanced sentiment distributions. Overall, the results demonstrate that SVM is effective in capturing user sentiment patterns and can provide meaningful insights for evaluating and improving the MyASN application service.
Klasifikasi Kualitas Benih Sawit Menggunakan Metode Naïve Bayes di PPKS Marihat Batubara, Qisti Azraladiba; Rifki, Mhd. Ikhsan
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.133

Abstract

Oil palm is one of the important plantation commodities in Indonesia, so seed quality is a major factor in production success. The main problem in the field is that seed quality determination is still done manually, which takes a long time and is prone to human error. Therefore, this study aims to minimize human error and support decision-making in determining planting priorities for superior seeds through the classification of oil palm seed quality using the Naïve Bayes algorithm. The model was built based on three main parameters, namely moisture content, storage room humidity, and seed storage duration. The results were labeled as low, medium, and high quality categories. Testing results using an 80% of data training (130 data) and 20% of data testing (32 data) model splitting, that the Naïve Bayes model produced an accuracy of 91% from 162 dataset. The classification results showed that 38 data points fell into the low quality category, 55 into the medium category, and 56 into the high category. The research results should be more oriented towards statements regarding the ability of Naïve Bayes to classify palm oil seed types, so that it can be used as a model recommendation in palm oil determination.
Klasifikasi Prestasi Siswa MAN 2 Labuhanbatu Melalui Komponen Indeks Prestasi Belajar Menggunakan Klaster K-Means Rasyid, Abdul; Rifki, Mhd Ikhsan
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.135

Abstract

Optimal utilization of academic data is an important requirement in supporting data-based learning decision making. One approach that can be used is Educational Data Mining (EDM) through clustering techniques to map students' academic abilities. This study aims to apply the K-Means Clustering algorithm in grouping students based on exam score patterns in one subject at MAN 2 Labuhanbatu Utara. The data used consists of daily scores, midterm scores, and final exam scores of 11th grade students, which were processed through pre-processing, data normalization, and clustering analysis stages. The determination of the optimal number of clusters was carried out using the Elbow method with the Within Cluster Sum of Squares (WCSS) indicator. The results showed that the three-cluster configuration was the most representative grouping structure, which could be interpreted as groups of students with high, medium, and low academic performance, respectively. The differences in centroid values between clusters indicate significant and structured variations in academic achievement. These findings prove that the K-Means algorithm is effective for mapping student learning groups objectively without requiring initial labels. The clustering results are expected to serve as a basis for teachers and schools in designing more adaptive learning strategies tailored to students' ability characteristics.

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