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Electrical Engineering Department Faculty of Engineering State University of Gorontalo Jenderal Sudirman Street No.6, Gorontalo City, Gorontalo Province, Indonesia
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Jambura Journal of Electrical and Electronics Engineering
Published by Universitas Negeri Gorontalo
ISSN : 26547813     EISSN : 27150887     DOI : 10.37905/jjeee
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Jambura Journal of Electrical and Electronics Engineering (JJEEE) is a peer-reviewed journal published by Electrical Engineering Department Faculty of Engineering, State University of Gorontalo. JJEEE provides open access to the principle that research published in this journal is freely available to the public to support the exchange of knowledge globally. JJEEE published two issue articles per year namely January and July. JJEEE provides a place for academics, researchers, and practitioners to publish scientific articles. Each text sent to the JJEEE editor is reviewed by peer review. Starting from Vol. 1 No. 1 (January 2019), all manuscripts sent to the JJEEE editor are accepted in Bahasa Indonesia or English. The scope of the articles listed in this journal relates to various topics, including: Control System, Optimization, Information System, Decision Support System, Computer Science, Artificial Intelligence, Power System, High Voltage, Informatics Engineering, Electronics, Renewable Energy. This journal is available in online and highly respects the ethics of publication and avoids all types of plagiarism.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 208 Documents
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Classification of Chili Plant Diseases Through GLCM Feature Selection and the K Parameter in the K-Nearest Neighbor Fi’Nawu, Ratna A.; Salihi, Irvan Abraham; Lamasigi, Zulfrianto Yusrin; Idris, Irma Surya Kumala
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34661

Abstract

Chili pepper (Capsicum annuum L.) is a strategic horticultural commodity in Indonesia with high economic value. However, chili plants are often infected by diseases such as Anthracnose, Fusarium Wilt, Fruit Fly, and Thrips, which can lead to significant yield losses. Early and accurate identification of these diseases is crucial for effective control measures. This study aims to classify chili plant diseases based on leaf images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the K-Nearest Neighbor (K-NN) algorithm for classification. A total of 736 leaf images were used, divided into four disease classes. The pre-processing stages included resizing the images to 300×300 pixels, rotation augmentation (0°, 45°, 75°, 90°), and conversion to grayscale. Textural features were extracted using GLCM at four angles, and K-NN was applied with K values of 5, 7, and 9. The highest classification accuracy of 88.19% was achieved at a GLCM angle of 0° and K=5, with an overall average accuracy across all angles of 85.06%. These findings not only reinforce previous findings on the effectiveness of GLCM and K-NN but also contribute by identifying the optimal parameter configuration (angle 0° and K=5) for the specific chili disease dataset. The results have the potential to be applied as a foundation for developing an automated plant disease detection system in the field.
Integration of Temperature and Weather Sensors with LORA Technology for Real-Time Monitoring Systems in Basin Cooling Tower Rinaldo, Muhammad Agus; Ariandi, Muhammad; Paramytha, Nina; Dasmen, Rahmat Novrianda
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34492

Abstract

— The cooling system of geothermal power plants (PLTP) critically depends on cooling towers, particularly the basin as the reservoir for cooled water to be recirculated into the system. Real-time monitoring of basin water temperature and weather conditions is essential for maintaining operational efficiency and prolonging equipment lifespan. Manual observation and wired systems are limited in terms of range and response time, prompting the need for a more advanced monitoring solution. This article describes the development and implementation of a LoRa-based monitoring  system that integrates temperature and weather sensors in the cooling tower basin of a geothermal power plant. The research was conducted through literature reviews, direct observation, consultations with experts, and comprehensive field testing of both hardware and software under various operational scenarios. The designed system consistently transmits temperature and weather sensor data with a success rate exceeding 95%, and achieves an effective transmission range of 400–500 meters in open geothermal areas. Implementation of this system results in increased data acquisition speed, improved energy efficiency, and enhanced monitoring accuracy, supporting faster technical responses and better risk management. Evaluation results confirm that the LoRa-based solution functions optimally and is highly applicable for deployment in challenging and remote geothermal industrial environments.
Optimizing of IndoBERT Embedding with Ditto Whitening for Measuring Research Title Similarity Ishak, Rezqiwati; Bengnga, Amiruddin
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.35554

Abstract

Measuring the semantic similarity of research titles is a crucial component in maintaining academic originality and preventing topic duplication in higher education. However, IndoBERT embeddings, as a pretrained Indonesian language model, are known to suffer from anisotropy, causing many titles to exhibit high similarity scores despite being semantically distinct. This study aims to optimize the quality of IndoBERT embeddings through Ditto Whitening and to evaluate its impact on research title similarity measurement. The dataset comprises 7.785 undergraduate thesis titles collected from six disciplinary domains and processed using mean pooling and L2 normalization before and after whitening. An intrinsic evaluation was conducted by assessing embedding isotropy, cosine similarity distribution, global bias toward the mean vector, and hubness phenomena, supported by embedding space visualizations using t-SNE, UMAP, and cosine similarity heatmaps. Experimental results demonstrate substantial improvements in embedding quality, indicated by a reduction in Cosine Pair Mean from 0.559 to −0.000145, a decrease in MeanCos-to-Mean from 0.748 to 0.0068, and a reduction in Hubness Skew from 1.60 to 0.68. The isotropy of the embeddings also increased markedly, reflecting a more uniform vector distribution. These findings confirm that Ditto Whitening effectively improves the isotropy of IndoBERT embeddings and directly enhances the accuracy of research title similarity detection and academic document retrieval systems, thereby supporting topic management and research quality assurance in higher education.
Designing Wemos and GPS-based Wireless Communication Gloves to Prevent Group Members from Getting Lost Pratama, Achmad Alfath; Paramytha, Nina; Fitriani, Endah; Dasmen, Rahmat Novrianda
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34491

Abstract

Group travel in densely populated areas presents risks of losing members, especially during peak periods such as weekends. This study aims to develop a wireless communication glove device as an aid for inter-member detection to prevent loss during travel. The method involves designing a device based on the Wemos microcontroller integrated with GPS sensors for position tracking, MAX30100 heartbeat sensor for health monitoring, an OLED I2C display as visual interface, and a buzzer as alarm. The system functions by identifying unique IDs between devices within a certain radius and triggers an alarm when connection is lost. Testing results indicate the heartbeat sensor accuracy with an average deviation of less than 3 bpm compared to manual measurements. Maximum connection distance without physical obstacles ranges from 24 to 26 meters, decreasing to 18 meters with wall obstructions. The master device detects connection loss within approximately 20 seconds, indicated by an audio alarm and display changes on the OLED screen. The estimated operational duration is 5.5 hours at 2.4 Watts power consumption. Overall, this device effectively prevents member loss with simple health monitoring and reliable connection detection features.
The Influence of Population Size on the Computational Time of Genetic Algorithms in Course Scheduling Salman, Rudi; Sinuraya, Arwadi; Irfandi, Irfandi; Eswanto, Eswanto; Rahman, Sayuti; Herdianto, Herdianto; Hutajulu, Olnes Yosefa; Halawa, Agung Y S
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33508

Abstract

Course scheduling is a complex problem in higher education because it must satisfy multiple constraints involving courses, instructors, rooms, and time slots. This study examines the impact of population size variation on the computational efficiency of a Genetic Algorithm (GA) applied to a medium-scale instance consisting of 35 courses, 15 instructors, 12 rooms, and 20 time slots. Simulations were conducted in MATLAB using population sizes ranging from 20 to 1000, while all other GA parameters were held constant to isolate the effect of population size. Solution quality was evaluated using a conflict-based fitness function, and all configurations yielded valid timetables with zero hard-constraint violations. Experimental results reveal a consistent non-linear relationship between population size and computation time. Statistical findings in Table 1—including mean values, standard deviations, and 95% confidence intervals—show that both very small and very large populations produce higher and more variable execution times. In contrast, population sizes of 300–400 achieve the lowest and most stable computation times, indicated by the smallest mean values and narrow confidence intervals. For the instance and configuration used in this study, this range serves as an effective starting point for population size tuning. Overall, the findings highlight the importance of empirical parameter selection to balance computational efficiency and solution quality in academic timetabling systems.
Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm Hasan, Maryam; Panna, Sudirman S.; Haba, Abd. Rahmat Karim; Alhamad, Apriyanto
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33145

Abstract

The Province of Gorontalo is highly vulnerable to flood disasters due to its geographical conditions, high rainfall, and uncontrolled land-use changes. This study aims to apply the K-Means Clustering algorithm to classify regions based on flood impact levels to support disaster mitigation and decision-making processes by the National Search and Rescue Agency (BNPP) Gorontalo. The dataset comprises 405 disaster incident records obtained from related institutions, including the number of affected, injured, deceased, and missing individuals. The analysis process involves data collection, preprocessing, distance calculation using the Euclidean Distance method, and the formation of two clusters based on impact levels. The iteration process stopped at the second iteration, indicating that a stable (convergent) condition had been achieved. The results revealed that Cluster 1 (C1) includes areas significantly affected by floods such as Imana, Iloheluma, and Tudi villages, while Cluster 2 (C2) represents unaffected areas like Wapalo, Ilomata, Motihelumo, and others. The implementation of the K-Means algorithm proved effective in identifying disaster-prone regions objectively and data-driven, thus supporting more efficient disaster response planning.
Optocoupler Sensor Optimization for an Arduino Nano–Based Ocean Current Velocity Measurement System Masud Baihaqi, Ahmad Syah; Solichan, Achmad; Muntasiroh, Laily
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.32897

Abstract

This study aims to optimize the use of an optocoupler sensor in an Arduino Nano–based ocean current velocity measurement device that is simple, cost-effective, and sufficiently accurate. The measurement system employs a propeller mechanism driven by ocean currents, where the optocoupler sensor detects rotational pulses and the Arduino Nano processes the data to calculate current velocity. The research methodology includes hardware design, software development, and performance evaluation. A comparative testing method was conducted by comparing the measurement results of the developed device with those obtained from a standard ocean current measurement instrument as a reference. Initial test results indicated measurement deviations with an average error of 13.54%, influenced by technical factors such as sensor orientation relative to flow direction, water depth, current turbulence, and data acquisition delay.
Analysis of Opinion Classification on Marriage Based on Support Vector Machine and Multi-Layer Perceptron Isma, Nur; Syahyaningsih, Lutfiah Tri; Surianto, Dewi Fatmarani; Fadilah, Nur
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.29616

Abstract

Marriage is an important aspect of social life that is influenced by cultural changes and public opinion, especially in the digital age. Public opinion on marriage is now widely disseminated through social media, both from traditional and modern perspectives. This study aims to classify public opinions on marriage using the Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms. The data used consists of 1,003 comments collected from social media. The study was conducted using two different approaches: stemming and data augmentation, which involves increasing the training data by modifying the original data to improve model performance. The results show that in the first approach, SVM achieved an accuracy of 77%, while MLP improved from 75% to 76% without stemming. In the second approach, data augmentation without stemming provided a significant improvement in accuracy, with SVM reaching 93% and MLP reaching 96%. Wordcloud visualization also highlights the importance of removing stopwords to reduce noise in the data. These findings indicate that data augmentation is an effective strategy for improving the performance of opinion classification models. This research contributes to the field of social sentiment analysis using machine learning approaches and is expected to serve as a reference for formulating policies aimed at improving marriage quality and family stability in Indonesia.

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