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Smart measurement revolution using long range technology and HLW8012 sensor integration Isminarti Isminarti; Nanang Roni Wibowo; Asminar Asminar; Mohamad Ilyas Abas; Muhammad Ali Chandra; Nur Azhary Iriawan Eka Putra; Widya Wisanty; Fauziah Fauziah; Deny Wiria Nugraha; Chaira Saidah Yusrie
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10823

Abstract

Long range (LoRa) has advantages compared to other communication systems such as power line control (PLC), narrowband internet of things (NB-IoT), and Sigfox. The advantage of low power consumption is the implementation and reliability of LoRa communication systems across all segments of smart grid (SG) and home automation applications. Parameters of the reliability of the communication system can also be seen from the maximum distance, signal strength, and network quality in indoor and outdoor conditions as well as different heights and levels of signal collisions when transmitted. This study aims to utilize unlicensed LoRa Shield SX1276 on the 915 MHz frequency spectrum for SG communication networks to monitor messages in the form of current, voltage, and power factor using the HLW8012 sensor. This research produces a complete LoRa communication system monitoring product according to the information needed by the user.
Perbandingan Algoritma Supervised Learning dalam Memprediksi Jalur Seleksi Masuk Mahasiswa di Universitas Negeri Gorontalo Manda Rohandi; Mukhlisulfatih Latief; Mohamad Ilyas Abas
Jurnal Teknik Vol 24 No 1 (2026): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v24i1.750

Abstract

The choice of university admission pathway (SNBT, SNBP, or independent selection/Mandiri) is closely related to students' demographic and administrative characteristics, and understanding this pattern benefits higher education institutions in designing more targeted recruitment strategies. This study aims to compare the performance of four supervised learning algorithms Decision Tree, Random Forest, Naïve Bayes, and k-Nearest Neighbor (k-NN) in predicting the admission pathway of Informatics Engineering students at Universitas Negeri Gorontalo (UNG). Data were obtained from UNG's Integrated Academic Information System (SIAT) for 1,237 students from the Information Technology Education and Information Systems study programs (2018–2024 cohorts), which after data cleaning resulted in 1,232 samples across three pathway classes (SNBT = 682, SNBP = 417, Mandiri = 133). Feature selection using information gain identified five informative features: selection type (national/local), age, cohort year, initial registration semester, and gender. Evaluation was conducted through three scenarios: 80:20 hold-out with Synthetic Minority Oversampling Technique (SMOTE), and 10-fold stratified cross-validation, followed by a Friedman significance test. The 10-fold cross-validation results show mean accuracies of 64.61% for Random Forest, 64.37% for Decision Tree, 63.47% for Naïve Bayes, and 61.03% for k-NN. The Friedman test indicated no statistically significant difference in performance among the four algorithms (χ² = 4.39; p = 0.222). The confusion matrix revealed that all algorithms perfectly classified the Mandiri class due to a definitional dependency with the selection-type feature, while most misclassifications occurred between the SNBP and SNBT classes. Applying SMOTE consistently improved minority-class (SNBP) recall across all four algorithms, but its effect on macro F1-score and overall accuracy varied by algorithm and was not uniformly positive. This study recommends incorporating academic behavioral features to improve the model's discriminative ability between national selection pathways.