BEES: Bulletin of Electrical and Electronics Engineering
BEES: Bulletin of Electrical and Electronics Engineering focused on the energy system and power engineering, which is related to advance and develop technology on a wide-scope of all partial themes, but not limited, such as Control System, Artificial Intelligence, Informatics Engineering, Electronics, Advanced energy material, Automatic power control, Battery technology, Distributed generation, Distribution system, Electric power generation, Electric vehicle, Electrical machine, Energy optimization, Energy conversion, Energy efficiency, Energy exploitation, Energy exploration, Energy management, Energy mitigation, Energy storage, Energy system, Fault diagnostics, Green energy, Green technology, High voltage, Insulation technology, Intelligent power optimization, Monitoring operation, Motor drives, Natural energy source, Power control, Power data transaction, Power economic, Power electronics, Power engineering, Power generation, Power optimization, Power quality, Power system analysis, Power system information, Power system optimization, Protection system, Renewable energy, SCADA, Security operation, Smart grid, Stability system, Storage system, Transmission system, and etc.
Articles
90 Documents
<![CDATA[Integrasi Sensor Elektroanalitik dan Kecerdasan Buatan dalam Pengawasan Pencemaran Industri: Analisis Bibliometrik 1998–2024 ]]>
<![CDATA[Alwi Nofriandi]]>;
<![CDATA[Yulkifli Yulkifli]]>;
<![CDATA[Indang Dewata]]>;
<![CDATA[Yohandri Yohandri]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v6i1.7588
<![CDATA[The integration of electroanalytical sensors with artificial intelligence (AI) technology is increasingly becoming a major focus in the development of real-time and accurate industrial pollution monitoring systems. This study uses a bibliometric approach to analyze publication trends, journal sources, authors, and research topics related to voltammetry, impedimetry, and AI sensors in the context of industrial environmental quality monitoring during the period 1998–2024. The analysis results reveal a significant increase in the number of publications and citations since 2019, with the journals Sensors and Biosensors as the main publication channels. Prominent authors and emerging topics such as the use of artificial neural networks mark rapid progress in this field. However, there is still a need for the development of multifunctional sensors, IoT system integration, and more adaptive AI algorithms. This study emphasizes the urgency of further research with a multidisciplinary approach to support sustainable, efficient, and environmentally friendly industrial pollution monitoring.]]>
<![CDATA[Analisis Sistem Pendukung Keputusan Pemilihan Merek Pasta Gigi Terbaik Menggunakan Metode SERVQUAL dan ORESTE ]]>
<![CDATA[Rahma Dhea Safitri]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v6i1.7713
<![CDATA[This study aims to address the problem of selecting the best toothpaste brand by developing a decision support system using a combination of the SERVQUAL and ORESTE methods. The problem raised is the difficulty consumers face in determining the most suitable product based on service quality. The SERVQUAL method is used to measure performance based on five dimensions of service quality, while ORESTE is used to rank alternatives without explicit weights. The system is implemented in the form of a data-driven evaluation model designed to mimic real-world conditions. The data reflects the perceived and expected values of several toothpaste brands, with the difference (GAP) calculated and processed using the ORESTE method to generate rankings. The results show that the Ciptadent brand received the highest preference with the lowest total ranking (6), followed by Oral-B and Colgate. The integration of these two methods enables a systematic and objective evaluation of overall service quality and can be used to support accurate consumer decision-making.]]>
<![CDATA[Penerapan Support Vector Machine Untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik ]]>
<![CDATA[Nur Aspidayani]]>;
<![CDATA[Ririn Desi Yanti Sinaga]]>;
<![CDATA[Anjarwanto Anjarwanto]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v6i1.7596
<![CDATA[Predicting students' on-time graduation is an important indicator in evaluating the quality of higher education institutions, as it is closely related to learning effectiveness and academic success. This study aims to develop a student graduation prediction model using the Support Vector Machine (SVM) algorithm based on academic data. The dataset consists of 50 student records with attributes including Grade Point Average (GPA), completed credit units, failed courses, semester, and graduation status. The research applies several preprocessing stages, including the removal of irrelevant attributes, label encoding, data normalization using StandardScaler, and dataset splitting into training and testing sets with an 80:20 ratio. The SVM model is built using the Radial Basis Function (RBF) kernel to classify student graduation status. Model performance is evaluated using a confusion matrix, accuracy, precision, recall, and F1-score metrics. The experimental results show that the SVM model achieves an accuracy of 90%, precision of 88%, recall of 92%, and an F1-score of 90%. These findings indicate that the SVM algorithm is effective in identifying academic patterns and accurately classifying student graduation status. This study is expected to serve as a foundation for developing decision support systems and early warning systems to assist higher education institutions in identifying students who are at risk of delayed graduation.]]>
<![CDATA[Implementasi Sistem Pemantauan Suhu Air Kolam Ikan Lele Berbasis IoT pada UMKM Budidaya Lele ]]>
<![CDATA[Iwan Fitrianto Rahmad]]>;
<![CDATA[M. Haidil Umam]]>;
<![CDATA[Aditya Rizky Suryanta Sembiring]]>;
<![CDATA[M. Irfan Fadillah]]>;
<![CDATA[Muhammad Wahyu Pratama]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 3 (2026): March 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v6i3.9043
<![CDATA[Catfish cultivation requires good air quality, especially air temperature, because it affects the growth and health of the fish. One problem often faced by farmers is the manual process of monitoring pond water temperature, which is still less effective and efficient. This study aims to develop an automated Internet of Things (IoT)-based air quality monitoring system using ESP8266 and Arduino IoT Cloud. This system uses a DS18B20 sensor to measure temperature and a DHT11 sensor to measure the temperature and humidity of the air around the pond. Data is sent in real time via a WiFi network and displayed on the Arduino IoT Cloud dashboard. Data input is carried out automatically for 15 days at predetermined time intervals. The results show that the system is able to measure air temperature consistently and in real time with a temperature range between 25.5 °C and 31.1 °C, which is still within the tolerance limit for catfish. This indicates that the developed monitoring system can be used to continuously monitor pond environmental conditions. Therefore, this system can assist farmers in maintaining pond conditions remotely and support more accurate decision-making in catfish cultivation management. The main contribution of this research is the development of an IoT-based air quality monitoring system that is easy to implement, efficient, and suitable for application on an MSME scale.]]>
<![CDATA[Monitoring Pertumbuhan Tanaman Hidroponik Berbasis Internet of Things (IoT) ]]>
<![CDATA[Iwan Fitrianto Rahmad]]>;
<![CDATA[Aditya Arielevi]]>;
<![CDATA[Mhei Arni Olivia]]>;
<![CDATA[Amirul Amin PS]]>;
<![CDATA[M Fikry Haikal]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 3 (2026): March 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v6i3.9153
<![CDATA[The development of Internet of Things (IoT) technology encourages the implementation of smart agriculture to support real-time, data-based plant environmental monitoring. This research aims to design and implement a monitoring system for temperature, air humidity, and light intensity on bean sprout growth using a DHT11 sensor and an LDR light sensor module integrated with the Arduino IoT Cloud. The system was developed using the System Development Life Cycle (SDLC) method and tested under two bean sprout planting conditions, namely conditions with light and without light. Environmental data was collected automatically for eight days and displayed in graphs and tables on the cloud dashboard. The test results showed that bean sprout planting without light had higher air humidity and environmental conditions more suitable for bean sprout growth compared to planting with light. The developed IoT system is able to monitor environmental conditions accurately, in real time, and continuously, and supports comparative analysis of plant growth based on differences in light intensity.]]>
<![CDATA[Analisis Pola Asosiasi Penjualan Toko Bangunan Menggunakan Algoritma Apriori Untuk Strategi Penempatan Barang ]]>
<![CDATA[Viany Berliana Jelita Koraag]]>;
<![CDATA[Mutiara Natalia Palit]]>;
<![CDATA[Marcelino Jesdanven Laloan]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v7i1.9999
<![CDATA[Small and medium-scale hardware stores generally still rely on intuition when determining product layout without systematically considering customer purchasing patterns, causing cross-selling opportunities to remain underutilized. This study aims to analyze product association patterns in hardware store transaction data using the Apriori algorithm as the basis for a data-driven product placement strategy. The dataset consists of 90 transaction rows representing 30 unique transactions involving 9 product types. Research stages include data cleaning, one-hot encoding transformation, and application of the Apriori algorithm with a minimum support of 15% and minimum confidence of 40%. The analysis identified 14 frequent itemsets and 10 association rules, all with lift values above 1.0, indicating positive associations. The strongest rules were Wall Paint → Brush with a confidence of 77.78% and lift of 1.46, and Sand → Brick with a confidence of 50.00% and lift of 1.50. These findings provide an empirical foundation for shelf zone arrangement recommendations, product bundling packages, and stock management prioritization in hardware stores. The contribution of this research is to provide a data-driven analytical framework that can be directly adopted by small and medium-scale hardware store managers without requiring complex technological infrastructure, while also extending the application of the Apriori algorithm to the hardware store domain with a specific focus on physical product placement strategies.]]>
<![CDATA[Klasifikasi Sentimen Publik terhadap Isu Toleransi Agama Menggunakan Algoritma Random Forest ]]>
<![CDATA[Flienschy Faith Maxy Tamaka]]>;
<![CDATA[Theresia Sheren Medea]]>;
<![CDATA[Ivana Julia Poli]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v7i1.10023
<![CDATA[Social media, particularly Twitter, has evolved into a dynamic arena for discussions on religious issues in Indonesia. Interfaith tolerance is one of the topics that most frequently elicits a wide range of responses, from support to hate speech. This study designs a five-class sentiment scheme, consisting of Positive/Neutral, Neutral-Abusive, and Negative tweets divided into three intensity levels (Weak, Moderate, and Strong), and classifies them using the Random Forest algorithm. The dataset used is the Indonesian Abusive and Hate Speech Twitter Text available on the Kaggle platform, consisting of 13,169 tweets with dual labels. Sentiment labels were created based on a combination of the HS and HS_Religion columns and hate speech intensity levels: Weak, Moderate, and Strong. Tweets without hate speech and unrelated to religion are considered positive or neutral, while tweets with HS_Religion=1 are classified as negative and grouped into three intensity levels. Prior to modeling, the text undergoes slang normalization, removal of inappropriate words, Nazief-Adriani stemming, and feature extraction using TF-IDF bigrams. Results from 10-fold cross-validation show an accuracy of 66.0%, macro precision of 52.1%, macro recall of 56.1%, and macro F1-Score of 51.3%, comparable to SVM (F1 52.7%) and Naive Bayes (31.8%), with differences between models assessed statistically using the McNemar test.]]>
<![CDATA[Analisis Sentimen Ulasan Pengguna Aplikasi DANA pada Google Play Store Menggunakan TF-IDF dan Naïve Bayes ]]>
<![CDATA[Puspita Wanny]]>;
<![CDATA[Muhammad Iqbal]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v7i1.10083
<![CDATA[The DANA application is a digital wallet service widely used by the public to support various digital financial transaction activities. The high number of users results in numerous reviews on the Google Play Store containing various responses, experiences, and opinions regarding the quality of the DANA application service. However, the large number of review data makes the manual process of identifying and grouping opinions less effective and takes a relatively long time. This study aims to analyze and classify the sentiment of DANA application user reviews on the Google Play Store into positive, negative, and neutral categories and to determine the performance of the algorithm used in the classification process. The solution implemented is sentiment analysis using a text mining approach and Natural Language Processing (NLP) to process user reviews automatically. The research data was obtained through a scraping process and resulted in 3,509 DANA application user reviews. The data then went through preprocessing stages including cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. Then, sentiment labeling and word weighting were carried out using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The data was then divided into 80% training data and 20% testing data. Classification was then performed using the Multinomial Naïve Bayes algorithm. Model performance was evaluated using a Confusion Matrix with Accuracy, Precision, Recall, and F1-Score metrics. The results showed that the Naïve Bayes model produced an Accuracy value of 80.48%, Precision of 76.75%, Recall of 80.48%, and F1-Score of 78.13%. These results indicate that the combination of the TF-IDF method and the Naïve Bayes algorithm is capable of classifying the sentiment of DANA app user reviews with quite good performance and can be used to help obtain an overview of user perceptions of the DANA app based on reviews provided on the Google Play Store]]>
<![CDATA[Optimasi Load Balancing Trafik Jaringan LTE Melalui Implementasi Antena Pro Sectoral 1800 Mhz & 2100 Mhz ]]>
<![CDATA[Almurozy Mursidan]]>;
<![CDATA[Basmallah Ramadhani Aisyah Putri]]>;
<![CDATA[Indra Sari Kusuma Wardhana]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v7i1.10473
<![CDATA[This study aims to analyze the effectiveness of implementing a sectoral pro antenna on LTE 1800 MHz and 2100 MHz frequencies in improving LTE network performance at the Kota Wisata site, Bogor, West Java. A quantitative comparative method was employed by comparing network performance before and after the implementation of the pro antenna. Data were collected from the operator’s network monitoring system during the pre-implementation period (Mei 11–12, 2026) and the post-implementation period (Mei 18–19, 2026). The analyzed parameters included Main Site Payload, Cluster Payload, and RSRP Coverage. The results indicate that the implementation of the sectoral pro antenna significantly improved LTE network performance. The Main Site Payload increased from 335.42 GB to 726.13 GB, representing an improvement of 116.48%, while the Cluster Payload increased from 6,308.82 GB to 6,431.93 GB, representing an increase of 1.95%. In addition, network coverage quality improved, as indicated by the increase in the percentage of RSRP values greater than -100 dBm from 99.09% to 99.85%, as well as an overall RSRP coverage improvement of 2.11%. These results demonstrate that the implementation of the sectoral pro antenna on LTE 1800 MHz and 2100 MHz frequencies is effective in distributing traffic more evenly, reducing the potential for network congestion, improving signal coverage quality, and enhancing overall LTE network performance.]]>
<![CDATA[Prototype Pengendali Lampu Pada Rumah Pintar dengan Tinkercad ]]>
<![CDATA[Nober Yohanis]]>;
<![CDATA[Azahari Azahari]]>;
<![CDATA[Ahmad Fajri]]>
<![CDATA[ BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bees.v7i1.10080
<![CDATA[The development of automation technology has encouraged the implementation of smart home concepts aimed at improving comfort, efficiency, and convenience in controlling electronic devices, particularly lighting systems. This study aims to design and implement an automatic lamp control prototype through simulation using Tinkercad by utilizing Arduino Uno, an LDR sensor as a light intensity detector, PIR sensors as motion detectors, and a relay as the lamp controller. The method used in this research is the Prototyping Process Model, which is carried out iteratively through the stages of communication, planning, design, prototype construction, evaluation, and system refinement. The results show that the system operates according to the designed logic, where the lamp turns on only under dark conditions when motion is detected, and remains off under bright conditions even when activity is present. The use of multiple PIR sensors has proven effective in expanding motion detection coverage, thereby increasing system responsiveness, while the LDR sensor plays an important role in improving energy efficiency by preventing unnecessary electricity consumption. Simulation using Tinkercad has also proven effective in simplifying the design and testing process before real-world implementation. The contribution of this research is the development of a smart home lighting control prototype that has been validated through Tinkercad simulation as an initial testing platform, thereby reducing hardware development costs and risks while providing a reference for the future development of Arduino-based smart home automation systems.]]>
