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Electrician : Jurnal Rekayasa dan Teknologi Elektro
Published by Universitas Lampung
ISSN : -     EISSN : 25493442     DOI : https://doi.org/10.23960/elc
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Engineering
Focus and Scope Publication of scientific research results in the field of electrical engineering which covers: ~ Power system analysis ~ Electrical energy conversion ~ High voltage technology ~ Electronics ~ Control system ~ Telecommunication system ~ Computer and interfacing ~ Information engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 16 Documents
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Search results for , issue "vol. 20 no. 3 (2026)" : 16 Documents clear
Perbandingan Implementasi Decision Tree C4.5 dan Single Layer Perceptron (SLP) untuk Klasifikasi Penyakit Stroke Indah Marsya Fitadea; Sulistiani; Anita Desiani; Yuli Andriani; Endang Sri Kresnawati; Siti Rusdiana Puspa Dewi
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.2767

Abstract

Stroke adalah suatu kondisi terjadi akibat aliran darah ke otak terhambat akibat penyumbatan pembuluh darah arteri, penumpukan darah, atau pecahnya pembuluh darah. Penyebabnya bisa berupa kelainan pada sifat darah atau lemahnya dinding pembuluh darah. Stroke menempati urutan kedua sebagai penyebab kematian paling besar dan urutan ketiga penyebab kecacatan di seluruh dunia. Untuk mencegah dampak yang lebih parah, deteksi dini sangat penting karena banyak faktor kecil yang sering kali tidak disadari menjadi penyebab munculnya penyakit stroke. Deteksi dini dilakukan dengan cara memanfaatkan teknologi informasi seperti machine learning. Machine learning yang digunakan ialah Decision Tree Classifer 4.5 (C4.5) dan Single Layer Perceptron (SLP). Hasil penelitian yang dilakukan dengan metode percentage split memperlihatkan bahwa algoritma C4.5 menghasilkan nilai akurasi sebesar 71%, sementara pada algoritma SLP akurasi meningkat sebesar 5%, yaitu 76%. Selain itu, nilai presisi dan recall pada kelas 0 (negatif) masing-masing adalah 71% dan 70% sedangkan pada kelas 1 (positif) menghasilkan nilai sebesar 74% dan 72%. Kemudian, presisi dan recall pada algoritma SLP pada kelas 0 (negatif) menghasilkan nilai sebesar 76% dan 71% sedangkan pada kelas 1 (positif) menghasilkan nilai sebesar 76% dan 81%. Dari kedua algoritma, dapat disimpulkan bahwa algoritma SLP memberikan hasil prediksi yang lebih akurat dan efektif untuk klasifikasi penyakit stroke.
Rancang Bangun Alat Deteksi Stres Berdasarkan Detak Jantung, Suhu Tubuh, Dan Konduktivitas Kulit Menggunakan Metode Logika Fuzzy Achmad Fauzan Arief Fauzan; Sri Purwiyanti; Umi Murdika; Herlinawati
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.2935

Abstract

Stres merupakan respons fisiologis dan psikologis manusia terhadap tekanan atau ancaman yang dapat berdampak negatif jika tidak ditangani dengan baik. Deteksi dini terhadap kondisi stres sangat penting untuk mencegah dampak jangka panjang, seperti gangguan mental dan penyakit kronis. Penelitian ini bertujuan untuk merancang dan membangun alat deteksi tingkat stres berdasarkan parameter fisiologis yaitu detak jantung, suhu tubuh, dan konduktivitas kulit dengan memanfaatkan metode logika fuzzy mamdani. Sistem dirancang menggunakan sensor Pulse Heart, sensor suhu DS18B20, dan sensor GSR (Galvanic Skin Response) yang terintegrasi dengan mikrokontroler Arduino Mega 2560. Data yang diperoleh dari sensor kemudian diproses melalui tahapan fuzzifikasi, evaluasi 64 rule base, dan defuzzifikasi menggunakan metode centroid untuk mengkategorikan tingkat stres ke dalam empat kondisi yaitu rileks, tenang, cemas, dan stres. Hasil pengujian pada 25 responden menunjukkan bahwa alat mampu mendeteksi tingkat stres secara real-time dengan tingkat akurasi 60% terhadap hasil kuisioner standar DASS-42. Alat ini diharapkan dapat menjadi solusi awal dalam pemantauan tingkat stres secara mandiri, praktis, dan objektif. Kata Kunci: Stres, Detak Jantung, Suhu Tubuh, GSR, Logika Fuzzy.
Material Selection for Battery Cell Casings Based on LCA and Steady‑State Thermal Performance (Comparative Analysis) Fauzi Ibrahim; Teuku Marjuni; Rina Febrina; Agus Apriyanto; Novia Utami Putri; Retno Wahyudi; Alexander Sembiring; Adam Wisnu Murti
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.2968

Abstract

This study evaluates eight materials for 18650 cylindrical battery casings by coupling a cradle‑to‑gate Life Cycle Assessment (LCA, A1–A3) with a steady‑state radial heat‑transfer model that integrates thin‑wall conduction and external convection. Candidates include Ni‑plated low‑carbon steel, stainless steel 304, aluminum 6061, high‑recycled aluminum (70% secondary content), polypropylene, polycarbonate, GFRP, and CFRP. Minimum wall thickness is set by a 0.5 MPa design pressure with a safety factor of 2 (≥0.15 mm for metals; 0.7 mm for polymers/composites). Simulations at a 5 W heat load for h = 50 W/m²K (forced) and h = 10 W/m²K (natural) show that external convection dominates total thermal resistance; under forced convection, ΔT spans 26.7–29.9 K with <1% variation among metals. From the LCA, per‑casing carbon footprints range from ≈6.4 gCO₂e (Ni‑plated steel) to ≈74 gCO₂e (CFRP). Pareto and MCDA place Ni‑plated steel and high‑recycled aluminum on the optimal frontier when ΔT, mass, and GWP are considered together. The key insight is that improving the convective coefficient (via airflow/interface design) is far more effective at lowering temperature than substituting casing materials, while material circularity (recycled content) delivers larger climate benefits than marginal conductivity gains.
Optimalisasi Faktor Daya dan Reduksi Rugi Daya Menggunakan Capacitor Bank Berbasis MATLAB Simulink Fatahilal Rasyid Agusta; Yenni Afrida; Fitriono Fitriono; Dasweptia D; Hamimi Hamimi; Jeckson son; Yudi Eka Putra
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3092

Abstract

Abstract. Operation of electrical equipment with a low power factor results in increased current and reactive power, reducing efficiency and causing losses in the PLN distribution network. This study examines the capacitor bank requirements based on load measurements and power factor (cosφ) for 52 three-phase low-voltage customers of PT PLN (Persero) ULP Karang. MATLAB simulations were used to determine the required capacity of the capacitor bank to increase the power factor above 0.85. The simulation results show that after installing capacitor banks, the average power factor increased to 0.99. Active power increased by 30.4% (from 76,638 watts to 99,970 watts), while reactive power decreased by 63% (from 65,768 VAR to 24,521 VAR). These improvements are expected to enhance the operational efficiency of electrical equipment with inductive loads and reduce power losses in the PLN network.
Analisis Kinerja Sistem PLTS Bifasial 595 Wp Terhadap Variasi Sudut dan Jarak Penempatan Reflektor Cermin Datar Putri Iriyani Purnama Sari; I Made Ari Nrartha; Agung Budi Muljono; I Made Ginarsa; Sultan Sultan; I Nyoman Wahyu Satiawan
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3096

Abstract

Solar energy is a renewable energy source with enormous potential and can be harnessed through photovoltaic-based solar panels. Currently, bifacial solar panels are an increasingly developed technology because they are capable of absorbing solar radiation from both sides, resulting in higher electrical power output compared to monofacial solar panels. Therefore, a reflector is required to direct solar energy toward both sides of the panel. This study aims to obtain the optimal power output of a 595 Wp bifacial solar panel equipped with a flat mirror reflector. The placement of the flat mirror reflector was analyzed based on variations in the tilt angle and the distance between the reflector and the bifacial solar panel. An experimental method was employed with nine testing variations, consisting of three reflector angles (45°, 55°, and 65°) and three reflector distances (50 cm, 100 cm, and 150 cm). The results show that the combination of a 45° reflector angle and a 150 cm reflector distance produced a power output of 301.35 W, which is significantly higher than the output without a reflector, which was only 116.99 W under nearly identical irradiance conditions of approximately 920 W/m². Based on these results, it can be concluded that appropriate selection of reflector angle and distance plays a crucial role in enhancing and optimizing the performance of bifacial solar panels.
Evaluasi Teknik Seleksi Fitur terhadap Performa Model Machine Learning pada Dataset Penyakit Sirosis Agus Wantoro; Ferly Ardhy; Dwi Yana Ayu Andini; Aviv Fitria Yulia; Dwi Feriyanto
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3102

Abstract

Cirrhosis is a chronic disease of the liver with high levels of morbidity and mortality, so accurate and efficient early detection efforts are needed. The use of Machine Learning (ML) in the health sector is one solution to help the process of classifying and diagnosing diseases based on patient clinical data. However, using all the features in a dataset without selection can cause a decrease in model performance due to the presence of irrelevant or redundant features. Therefore, this study aims to evaluate the effect of feature selection techniques on the performance of ML models such as SVM, Tree, kNN and Random Forest on the cirrhosis dataset. The stages used in this research include the application of several ML models as classification models, as well as the application of various feature selection techniques such as Information Gain (IG), Gain Ratio (GR), and Gini Decrease (GD) and performance evaluation. Model evaluation is carried out by comparing classification results before and after feature selection using accuracy, precision and recall metrics. The research results show that the application of feature selection can significantly improve the performance of the cirrhosis classification model, while reducing the complexity of the model. In addition, the effectiveness of feature selection techniques varies depending on the ML algorithm used, so there is no one feature selection method that is always superior for all models. This research proves that feature selection is an important stage in ML-based medical data processing and can increase the accuracy and efficiency of cirrhosis diagnosis
Desain Tata Kelola Teknologi Informasi Berbasis COBIT 2019 pada Fakultas Keguruan dan Ilmu Pendidikan Universitas Lampung. Eko Indra Pangestu
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3138

Abstract

Abstrak. Teknologi Informasi (TI) merupakan aset strategis bagi FKIP Universitas Lampung (Unila) dalam mendukung layanan akademik dan administrasi digital. Namun, pengelolaan 141 sistem informasi di institusi ini menghadapi tantangan serius berupa serangan siber mingguan dan keterbatasan sumber daya operasional. Penelitian ini bertujuan merancang desain tata kelola TI yang komprehensif menggunakan kerangka kerja COBIT 2019 melalui analisis sebelas faktor desain (design factors). Metodologi yang digunakan meliputi observasi, wawancara, dan pengisian kuesioner menggunakan Design Factor Toolkit. Hasil penelitian menunjukkan bahwa FKIP Unila memprioritaskan strategi inovasi dan pelayanan pelanggan dengan pendekatan implementasi Agile (46%) serta strategi adopsi teknologi sebagai First Mover (53%). Berdasarkan pemetaan Governance and Management Objectives (GAMO), ditemukan dua domain dengan tingkat kepentingan tertinggi (skor >80), yaitu BAI02 (Managed Requirements Definition) dan BAI03 (Managed Solutions Identification and Build). Desain ini menjadi landasan strategis bagi fakultas untuk melakukan audit prioritas dan perbaikan tata kelola guna memitigasi risiko TI serta mewujudkan Good University Governance. Abstract. Information Technology (IT) is a strategic asset for the Faculty of Teacher Training and Education, University of Lampung (Unila) in supporting digital academic and administrative services. However, the management of 141 information systems at this institution faces serious challenges in the form of weekly cyber attacks and limited operational resources. This study aims to design a comprehensive IT governance design using the COBIT 2019 framework through an analysis of eleven design factors. The methodology used includes observation, interviews, and questionnaires using the Design Factor Toolkit. The results show that the Faculty of Teacher Training and Education, Unila, prioritizes innovation and customer service strategies with an Agile implementation approach (46%) and a technology adoption strategy as a First Mover (53%). Based on the Governance and Management Objectives (GAMO) mapping, two domains with the highest level of importance (score >80) were found, namely BAI02 (Managed Requirements Definition) and BAI03 (Managed Solutions Identification and Build). This design serves as a strategic foundation for the faculty to conduct priority audits and governance improvements to mitigate IT risks and realize Good University Governance.
Alokasi Optimal Pembangkit Tersebar Pada Sistem Distribusi Tenaga Listrik Untuk Meminimisasi Rugi-Rugi Daya Rudy Gianto; M. Iqbal Arsyad
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3141

Abstract

This paper presents a new method to determine the optimal DG (Distributed Generation) location and capacity in radial distribution networks to minimize power losses. The proposed method is based on the power loss formula as a function of node currents or injection currents. To obtain the optimal DG location and capacity, only a single load flow solution (i.e. basecase load flow solution) is required. Furthermore, the calculation of bus impedance matrix or bus admittance matrix is also not necessary in the procedure to obtain the optimal DG allocation. The proposed method has been tested and validated using two IEEE test systems, namely the 12-bus and 33-bus systems. The test results show that significant power loss reduction can be achieved through the application of the method proposed in this paper.
Evaluasi Kinerja Sistem Monitoring TDS dan Sirkulasi Larutan Nutrisi Berbasis IoT pada Hidroponik Tower Bertenaga Photovoltaic Linda Faridah; Rian Nurdiansyah; Farraditta Nugraha; Prama Permana
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3154

Abstract

Penelitian ini bertujuan merancang dan mengimplementasikan sistem monitoring dan kontrol nutrisi pada hidroponik tower berbasis Internet of Things (IoT) dengan catu daya photovoltaic. Sistem dikembangkan untuk mengatasi pengelolaan nutrisi yang masih dilakukan secara manual serta ketergantungan terhadap listrik konvensional. Metode yang digunakan adalah system design and development yang meliputi perancangan perangkat keras dan perangkat lunak, implementasi, serta pengujian kinerja. Sistem menggunakan mikrokontroler ESP32 yang terintegrasi dengan sensor Total Dissolved Solids (TDS) dan sensor suhu untuk memantau kondisi larutan secara real-time. Data dikirim ke dashboard monitoring berbasis IoT. Sistem kontrol otomatis menggunakan metode ambang batas, di mana pompa nutrisi aktif ketika TDS < 550 ppm dan nonaktif ketika nilai telah mencapai rentang yang ditentukan. Sistem juga didukung panel surya dan baterai sehingga dapat beroperasi secara mandiri. Hasil pengujian menunjukkan nilai TDS berada pada rentang 450–850 ppm dan suhu 26–30°C. Sistem mampu melakukan monitoring dan kontrol nutrisi secara real-time dengan respons yang stabil. Dengan demikian, sistem yang dikembangkan efektif dalam meningkatkan efisiensi pengelolaan nutrisi dan mendukung pertanian cerdas yang berkelanjutan
Application of MobileNetV2 for Coffee Bean Ripeness Classification to Support Smart Agriculture Angga Bayu Santoso; Okma Arnilia; Sahrial Ihsani Ishak; I Gusti Nyoman Agung Bisma Tatwa; Riki Rikardo
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3155

Abstract

This study addresses the problem of manual classification of coffee bean ripeness, which is often inconsistent and inefficient, potentially affecting product quality and market value. To overcome this issue, the study proposes an automated classification approach using a deep learning model based on MobileNetV2. The objective of this study is to develop an accurate and efficient model for classifying coffee beans into four ripeness levels: Dark, Green, Light, and Medium, while ensuring its applicability in real-world conditions with limited resources. The dataset consists of 1,200 images collected from coffee plantations in Indonesia, processed through resizing, normalization, and data augmentation to improve generalization. The model was trained using transfer learning and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieves an accuracy of 98.33% on the testing dataset, with a precision of 0.985, recall of 0.983, and F1-score of 0.983. When implemented in a web-based application, it achieves 97.8% accuracy with an average response time of 1.8 seconds and is capable of handling up to 100 concurrent users with stable performance. These findings indicate that the proposed model is effective and reliable for supporting automated coffee bean classification and enhancing efficiency in post-harvest processing.

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