Jurnal Nasional Teknik Elektro dan Teknologi Informasi

Prototype of Internet of Things-Based Automatic Hydroponic System

Isyara Khairani — 2025-01-20

The increase in food needs, including vegetables and fruits, corresponds with population growth. However, agricultural land is increasingly declining due to land conversion. This decline can threaten national food security. Utilizing hydroponic systems for plant cultivation is one of the efforts to adapt to land reduction, land degradations, and adverse impacts of global climate change. Unfortunately, hydroponic cultivation requires constant monitoring of plant nutrition. This research aimed to create an automatic hydroponic system that controlled the process of regulating nutrients to save growers time and energy. Through Internet of things (IoT) technology, automatic hydroponic cultivation can monitor plant life, temperature, humidity, water level in reservoirs, total dissolved solids (TDS), and pH of nutrient solutions. In addition, it can visually monitor plants through Android applications. The hydroponic system used for planting was the nutrient film technique, and the plant cultivated was lettuce. The system consisted of TDS sensors to measure TDS, analog pH sensors to measure the pH, the HC-SR04 ultrasonic sensors to measure the water level in the reservoir, DHT11 sensors, ESP32 microcontrollers, and ESP32-CAM to monitor plant growth remotely. Based on system testing results, the average of TDS increased from 600 ppm in the first week to 900 ppm in the fifth week, the average pH was 6.19, and the average water level in the reservoir was 20.89 cm. All test result parameters are at the designed values.

Digitized Cursive Handwriting for Determining FMS in Early School-Age Children

Nurul Zainal Fanani — 2025-01-20

Assessing fine motor skills (FMS) in early school-age children is crucial for insights into their school readiness. In many countries, including Indonesia, teachers assess FMS by observing handwriting, often with the aid of an educational psychologist. However, this approach can be subjective and prone to observer bias. This study aimed to classify children’s FMS based on their cursive writing abilities using a digitizer to capture data. The system recorded data in real-time as children wrote in cursive, capturing the stylus’s relative position on the digitizer board (including x, y, and z positions), and pressure values, which served as features in the classification process. The study involved 40 1st and 2nd-grade students from various elementary schools. The data recording process generated substantial raw datasets. The random forest algorithm, renowned for its effectiveness in analyzing large datasets, was employed for classification. The results demonstrated this method’s efficacy in identifying FMS, achieving an accuracy rate of approximately 97.3%. This study concludes that integrating a digitizer with the random forest classification method provides a reliable and objective approach to assessing FMS in children, reducing observer bias, and ensuring precise results. In the long term, this approach can significantly enhance the accuracy of FMS assessments, enabling better-targeted interventions and support for children in need.

Trust Perception and Information Use for Informational Website: Structural Equation Modelling Approach

Umi Proboyekti — 2025-02-26

Trust is described in various contexts, such as e-commerce, e-government, reviews, and online health information. Credibility and information quality are fundamental to building trust in those contexts. This study aimed to develop trust perception (TP) and information use (IU) indicators in an information evaluation context. Indicators were developed through three processes: searching, grouping, and construction. Relevant indicators were grouped based on similarities to construct statements, which were validated for face and content validity by three experts. The validated TP and IU were then tested using the partial least squares structural equation modeling (PLS)-SEM. The data used for measurement obtained from 110 participants comprising 55 Indonesian academic librarians and 55 university students. Participants responded to indicator statements after evaluating information from four prepared informational websites. This study yielded five TP indicators and a single IU indicator, where TP significantly predicted IU. The five indicators described TP as make-sense information relevant to needs, provided by trusted authors and providers, and accompanied by accessible author information, provider information, and reference sources. IU was described as the information used for its credibility. The measurement demonstrated distinct participant behaviors. Differences in needs influenced assessments, while author and provider trustworthiness showed no bias toward participant type. Trust perception significantly predicted IU, with moderate model fit and varying predictive strengths across the websites. Tested as reliable, valid, and a significant predictor of IU, TP serves as a tool for examining factors that potentially influence trust in online information.

Comparison of KNN and SVM Algorithms Performance Using SMOTE to Classify Diabetes

Asri Mulyani — 2025-02-26

Diabetes seringkali tidak terdeteksi atau didiagnosis terlambat. Hal ini dapat menyebabkan berbagai komplikasi serius, seperti kerusakan organ, stroke, dan penyakit jantung. International Diabetes Federation (IDF) menyebutkan bahwa 10,5% dari populasi orang dewasa berusia 20 hingga 79 tahun didiagnosis menderita diabetes dan hampir setengahnya tidak menyadari kondisi tersebut, sehingga angka penderita diabetes terus meningkat secara signifikan, mencapai empat kali lipat dibandingkan dengan periode sebelumnya. Deteksi diabetes secara dini merupakan langkah penting bagi penderita untuk mencegah munculnya komplikasi, salah satunya dengan memanfaatkan teknologi kecerdasan buatan, yaitu penambangan data. Oleh sebab itu, diperlukan pengetahuan mengenai algoritma yang efektif digunakan untuk melakukan deteksi penyakit diabetes. Penelitian ini bertujuan untuk membandingkan dua algoritma, yakni k-nearest neighbor (KNN) dan support vector machine (SVM), dalam klasifikasi penyakit diabetes menggunakan synthetic minority oversampling technique (SMOTE). Penelitian ini menerapkan metode machine learning life cycle untuk mengukur kinerja kedua algoritma. Hasil penelitian menunjukkan bahwa kedua algoritma memiliki kinerja yang baik dalam mendeteksi diabetes, tetapi terdapat perbedaan kinerja yang signifikan antara keduanya. Algoritma SVM dengan kernel radial basis function (RBF) mencapai akurasi sebesar 81,67%, presisi 85,91%, recall 79,01%, dan F1-score 82,32%. Di sisi lain, algoritma KNN dengan nilai k = 3 yang ditemukan melalui cross-validation mencapai akurasi sebesar 83,33%, presisi 85,00%, recall 83,95%, dan F1-score 84,47%. Berdasarkan evaluasi confusion matrix, KNN menunjukkan kinerja yang lebih unggul dibandingkan SVM dalam hal akurasi dan metrik evaluasi lainnya. Hasil ini menunjukkan bahwa KNN lebih efektif dalam mendeteksi diabetes pada dataset yang digunakan dalam penelitian ini.

Optical Flow Performance in the SUAV Flight Speed Estimation Using Farneback Method

Aziz Fathurrahman — 2025-02-27

This paper evaluates the performance of the Farneback optical flow method for estimating the flight speed of a small unmanned aerial vehicle (SUAV) in a simulated 3D World MATLAB-Unreal Engine environment. Optical flow offers a promising solution for velocity estimation, which is crucial for autonomous navigation. A downward-facing monocular camera model was simulated on an SUAV during steady state, straight flight at 100 m altitude and 25 m/s airspeed. Three simulated flight scenes—forest, city block, and water—representing poor, moderate, and rich textures were used to assess the method’s performance. Results demonstrate that using the median estimate of the optical flow field yields accurate velocity estimations in moderate to rich texture scenes. Over the city block and forest scenes, mean velocity estimation accuracy was 0.6 m/s (σ = 0.2 m/s) and 0.3 m/s (σ = 0.4 m/s), respectively. The impact of camera tilt angle and altitude variations on estimation accuracy was also investigated. Both factors introduced bias, with accuracy decreasing to 1.7 m/s (σ = 0.2 m/s) and 1.9 m/s (σ = 0.2 m/s) for +10° and -10° camera tilt, respectively. Similarly, altitude differences of +10m and -10m resulted in reduced accuracy of 1.9 m/s (σ = 0.2 m/s) and 4.3 m/s (σ = 0.1 m/s), respectively. This study demonstrates the potential of the Farneback method for determining flight speed under steady, straight flight conditions with acceptable accuracy.

Perbandingan Model U-Net dan ELU-Net untuk Segmentasi Semantik Citra Medis Kanker Pankreas

Algi Fari Ramdhani — 2025-02-26

Analisis citra medis untuk melakukan segmentasi semantik menggunakan teknologi deep learning masif dikembangkan saat ini. Salah satu pengembangannya adalah arsitektur U-Net, yang dapat menghasilkan akurasi yang baik. Pengembangan dilanjutkan menghasilkan ELU-Net dengan fokus membuat model makin efisien. ELU-Net menghasilkan akurasi yang cukup baik, tetapi perlu adanya kajian lebih lanjut mengenai perbandingan kedua model. Kedua model akan dibandingkan berdasarkan akurasi, penggunaan penyimpanan, dan waktu proses dalam melakukan segmentasi semantik citra kanker pankreas. Citra kanker pankreas yang digunakan pada penelitian ini berasal dari sebuah challenge PAIP 2023 yang berisi sebuah citra dengan pewarnaan haematoxylin dan eosin (H&E). Eksperimen dilakukan dengan mengubah jumlah filter dan kedalaman model untuk kedua arsitektur. Evaluasi dilakukan terhadap dataset citra kanker pankreas yang berjumlah 57 citra. Dari serangkaian percobaan yang dilakukan, diperoleh hasil bahwa U-Net memiliki akurasi terbaik sebesar 92,8%, sedikit lebih unggul dibandingkan ELU-Net yang mencapai 89,7%. Namun, ELU-Net lebih efisien dari segi penggunaan penyimpanan (8,1 MB untuk ELU-Net dan 93,31 MB untuk U-Net) dan waktu proses (4,0 s untuk ELU-Net dan 5,3 s untuk U-Net). Akurasi yang diperoleh ELU-Net memang lebih kecil daripada U-Net, tetapi dari penggunaan penyimpanan dan waktu proses, ELU-Net jauh unggul dengan selisih 85,21 MB dan 1,3 detik. Dari hasil tersebut, model ELU-Net tidak lebih baik daripada U-Net, terutama dari segi akurasi. Namun, dengan perbandingan ELU-Net terhadap U-Net pada ukuran penyimpanan sebesar 1:11,51 dan waktu proses 1:1,325, selisih akurasi 3,1% merupakan trade-off yang cukup logis.

A Multilevel and Hierarchical Approach for Multilabel Classification Model in SDGs Research

Berliana Sugiarti Putri — 2025-02-26

The progress of research lines, marked by the increasing number of research publications, makes it increasingly difficult to identify the implementation of research publications, especially related to SDGs. Currently, the categorization of research publications into SDGs level has not been carried out. Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS needs this to monitor lecturers in implementing SDGs. This study aims to implement and evaluate problem transformation methods and machine learning classification algorithms with a multilevel and hierarchical approach to categorize research publications into SDGs levels. The problem transformation methods used are Binary Relevance, Label Powerset (LP), and Classifier Chains. In addition, machine learning classification algorithms used are Logistic Regression and Support Vector Machine (SVM). The inputs used are titles, abstracts, and titles and abstracts. The best filter model that classifies data into SDGs-NonSDGs is model with title and SVM with an accuracy of 0.8634. The best level model that classifies data to SDGs level is model with title, LP method, and SVM with a multilevel approach. The level model classifies data into 4 pillars, goals, targets, and indicators of SDGs with an accuracy of 0.8067, 0.7501, 0.6792, and 0.6194. In comparison to other inputs with more comprehensive information, the results show that title input has best accuracy. This results from the simultaneous use of English and Indonesian. Thus, future research can modify model with input of only one language to optimize TF-IDF process so that the meanings of words from each other languages are not considered different important words.

Mesin Pengisian Cairan Otomatis Menggunakan Arduino dan LabVIEW

Syafriyadi Nor — 2025-02-26

Mesin pengisian cairan otomatis merupakan elemen krusial dalam meningkatkan efisiensi dan produktivitas industri modern, khususnya pada sektor manufaktur dan pengemasan. Namun, implementasi teknologi ini sering kali menghadapi kendala seperti biaya yang tinggi, kebutuhan akan perangkat yang kompleks, serta keterbatasan pemahaman teknis. Penelitian ini bertujuan untuk merancang sistem praktikum edukatif yang sederhana, terjangkau, dan mudah diimplementasikan guna membantu mahasiswa memahami konsep dasar sekaligus aplikasi nyata mesin pengisian cairan otomatis. Sistem ini mengintegrasikan perangkat lunak LabVIEW untuk pemrosesan visual dan mikrokontroler Arduino Nano dengan protokol komunikasi Modbus RTU, yang menyimulasikan standar komunikasi industri. Program LabVIEW mengendalikan proses pergerakan conveyor belt, pengisian (filling), dan penutupan (capping) menggunakan ladder logic, serta mencatat jumlah botol yang diisi. Mikrokontroler Arduino berfungsi untuk mengelola kendali tombol on-off conveyor belt melalui LabVIEW serta keypad untuk menentukan takaran volume dan jumlah botol. Komunikasi antara LabVIEW dan Arduino dilakukan secara serial menggunakan protokol Modbus RTU, memberikan pengalaman langsung kepada mahasiswa dalam mengonfigurasi komunikasi industri. Uji eksperimental dilakukan dengan berbagai skenario operasional untuk mengevaluasi kinerja sistem. Hasil pengujian menunjukkan bahwa sistem mampu mengisi botol dengan akurat pada rentang volume cairan 250–1.000 ml, dengan kecepatan pengisian hingga 10 ml/s dan kapasitas maksimal lima botol per siklus. Sistem berjalan stabil tanpa gangguan selama pengujian. Penelitian ini berkontribusi pada pengembangan praktikum instrumentasi sistem kendali; menghadirkan solusi pembelajaran yang efektif, interaktif, dan terjangkau. Penggunaan Modbus RTU terbukti menjadi pendekatan komunikasi yang andal untuk mendukung implementasi mesin pengisian cairan otomatis sekaligus meningkatkan pemahaman mahasiswa tentang aplikasi teknologi industri.

The Analysis of Facial Areas to Identify CHD Risks Based on Facial Textures

Budi Sunarko — 2025-02-27

Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. Previous studies have reported a classification accuracy of only 72.73%, which is inadequate. This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. The research involved constructing a machine learning model that extracted texture features from six facial regions of interest (ROIs) using the gray level co-occurrence matrix (GLCM) and employed an artificial neural network (ANN) algorithm. The datasets were full-face images of CHD patients (positive) and healthy people (negative). The face parts identified were the right crow’s feet, right canthus, nose bridge, forehead, left canthus, and left crow’s feet. A total of 132 (72 positive and 60 negative CHD) datasets were divided into 80% (n = 106) training data and 20% (n = 26) testing data. The developed model achieved a notable accuracy of 76.9%. The findings revealed that two facial regions—canthus and forehead—demonstrated excellent accuracy of 80.97% and 90%, respectively. Meanwhile, the crow’s feet and nose bridge regions showed good accuracies at 73.50% and 65%, respectively. Based on the results, this research has proven to be able to become a model for early CHD screening with good accuracy and faster execution.