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All Journal Jurnal Informatika dan Teknik Elektro Terapan ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Journal of Applied Computer Science and Technology (JACOST) Jurnal Andalas: Rekayasa dan Penerapan Teknologi JPP (Jurnal Pendidikan dan Pembelajaran)
Baik Budi
Universitas Andalas
Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Other

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Heart Disease Prediction Using KNN, Decision Tree, and Naïve Bayes Baik Budi
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 6 No. 1 (2026): Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v6i1.142

Abstract

Cardiovascular Disease (CVD) remains a leading global cause of mortality, making early and accurate diagnosis critical for effective medical intervention. Machine Learning (ML) algorithms offer promising solutions for automating clinical decision support systems. This study compares three supervised learning algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), and Naive Bayes (NB)—to evaluate their diagnostic efficacy in predicting heart disease. The models were trained and tested using a clinical dataset of 205 instances (100 normal and 105 heart disease cases) with an 80:20 data split. Performance was evaluated based on Accuracy, Precision, Recall, and F1-Score derived from confusion matrices. The experimental results demonstrate that the Decision Tree algorithm achieved the highest aggregate accuracy of 98.54%, exhibiting exceptional clinical reliability with a perfect precision score (zero false positives) and high sensitivity (only three false negatives). The KNN model performed comparably well, achieving 98.05% accuracy and zero false positives. In contrast, the Naive Bayes algorithm underperformed, with 82.93% accuracy and high rates of both Type I and Type II errors. In conclusion, the Decision Tree model emerges as the most robust, precise, and safe algorithmic architecture for clinical implementation in heart disease screening, effectively minimizing both false alarms and missed diagnoses.
Design and Implementation of a Solar-Powered IoT Smart Fish Feeder for Sustainable Freshwater Aquaculture Micko Tomas; Baik Budi; Khadlel Muhammad Romiz
Journal of Applied Computer Science and Technology Vol. 7 No. 1 (2026): Juni 2026
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/njwes527

Abstract

The utilization of solar energy in aquaculture automation still encounters challenges related to energy efficiency, stability, and adaptive control within IoT-based systems. This research presents the design and implementation of a solar-powered IoT Smart Fish Feeder, developed to enable adaptive feeding schedules with optimized power management. The system is composed of a 100 Wp solar panel, a 25 A MPPT charge controller, a 14.8 V lithium battery, a 2P DC MCB (440 V/25–16 A), and an APZEM-017 ModBus DC wattmeter, integrated with a DC–DC Boost Converter to regulate power delivery for the feeder prototype. Experimental tests were conducted to evaluate solar energy performance under real environmental conditions, focusing on parameters such as voltage, current, power output, and energy conversion efficiency. Results demonstrated that the solar panel achieved an average conversion efficiency of 87.2%, the MPPT controller maintained an efficiency of 95%, the battery system reached a charge–discharge efficiency of 90.4%, and the DC–DC converter operated at 92% efficiency, resulting in an overall system efficiency of 68.8%. The system maintained voltage stability within ±2% and was capable of autonomous 24-hour operation without external power. Compared to previous studies that lacked solar–IoT integration and adaptive control, this prototype provides a novel and energy-efficient solution for sustainable aquaculture. The findings confirm that the proposed design enhances renewable energy utilization, operational reliability, and environmental sustainability in innovative aquaculture applications.
Strategi Pengendalian Suplai Air Bersih di PT. Semen Padang dengan Pemanfaatan Level Switch dan Siklus PDCA Refki Budiman; Queen Hesti Ramadhamy; Baik Budi; Jhonny Faizal
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 5 No. 1 (2025): Juni 2025
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v5i1.112

Abstract

Water Plan (WP) is a clean water treatment unit that supplies water for residential areas, offices, and factory operations at PT Semen Padang. One of the main issues faced by WP is the low water level in the canal due to inadequate monitoring by operators, which can lead to operational disruptions and even production shutdowns. This study aims to develop a clean water supply control strategy by implementing an automatic monitoring system using a level switch based on the Plan-Do-Check-Action (PDCA) cycle. The research method begins with problem identification through an analysis of the operator's logbook, followed by determining priorities and root causes using the Strategic Risk Severity Matrix and the Nominal Group Technique (NGT). Improvement implementation is carried out by installing a level switch and an alarm in the operator room to ensure automatic water level monitoring. The evaluation results indicate a significant improvement in water supply quality, a reduction in operational disruption risks, and maintenance cost efficiency, with potential water savings reaching 450,000 m³ per month and a reduction in pump repair costs of Rp. 25,000,000. The implementation of this PDCA-based system has proven effective in enhancing the reliability of the clean water supply and supporting continuous improvement in WP PT Semen Padang. In the future, a similar approach can be applied to optimize water resource management in other industrial sectors.
Leveraging Naive Bayes Classification for Early Detection of Breast Cancer: A Data-Centric Diagnostic Approach Baik Budi; Refki Budiman; Queen Hesti Ramadhamy
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 5 No. 1 (2025): Juni 2025
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v5i1.114

Abstract

This study aims to develop a breast cancer detection model using two distinct approaches: the Naive Bayes algorithm for classification and the K-Means algorithm for clustering. The methodology involves the collection of diagnostic clinical feature data, data preprocessing for normalization, and the separate training and evaluation of each model. Naive Bayes is employed to classify breast cancer as malignant or benign based on training and testing datasets, while K-Means is applied to unlabeled data as an additional analytical method. The performance of the Naive Bayes classifier is assessed using a confusion matrix, whereas the clustering results from K-Means are evaluated based on cluster validity metrics. The results indicate that Naive Bayes achieves a high level of accuracy (93%) in breast cancer classification, while K-Means offers additional insights through data pattern clustering. Together, these approaches demonstrate potential to effectively support the medical diagnostic process.
Simulation Study of 2.4 GHz Rectangular Microstrip Patch Antenna for Sensing Sugar Content Detection Queen Hesti Ramadhamy -; Annisa Maulidya; Baik Budi; Refki Budiman
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 5 No. 1 (2025): Juni 2025
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v5i1.115

Abstract

This study presents a simulation-based analysis of 2.4 GHz rectangular microstrip patch antenna for sensing sugar content in aqueous solutions. The antenna was designed and simulated using software with performance evaluated based on return loss and resonance frequency shifts in response to changes in the dielectric properties of the sugar solution. The primary objective was to assess the sensitivity of the rectangular microstrip antenna to variations in sugar concentration. The result show that the antenna exhibits measurable resonance frequency shifts as the sugar content in the solution increases, indicating the potential of microstrip antennas as effective, non-invasive sensors for liquid concentration monitoring. These findings contribute to the development of microwave-based sensing technologies, offering insights into the application of microstrip patch antennas for sugar detection and other similar application.
Comparison of Clutter Reduction Methods for Buried Object Detection in Heterogeneous Soil QUEEN HESTI RAMADHAMY; ZUMAR AHMAD; BAIK BUDI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 2: Published April 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i2.209

Abstract

A GPR was conducted in this study using a Vector Network Analyzer (VNA) connected to two antennas to detect buried metals. The target was located 17 cm below the ground surface. The detection process was carried out to obtain radar images, namely A-scan and B-scan. Clutter reduction was performed using three methods: weighting, averaging, and singular value decomposition (SVD). This study reviews the performance of clutter-reduction methods under heterogeneous soil conditions. Qualitatively, all methods clarified the target and reduced clutter in the radar image. Quantitatively, the Signal-to-Clutter Ratio (SCR) is calculated after clutter reduction. In terms of results, the weighting method increased the SCR to 28.81 dB, while the averaging method increased it to 25.31 dB. Meanwhile, the SVD method only provided a small increase to 4.45 dB.
Development of numerical algorithm for power system computation as project-based learning in numerical methods course Syafii Syafii; Baik Budi
JPP (Jurnal Pendidikan dan Pembelajaran) Vol. 32 No. 2 (2025)
Publisher : Lembaga Pengembangan Pendidikan dan Pembelajaran, Universitas Negeri Malang in Collaboration with Asosiasi Pendidik dan Pengembang Pendidikan Indonesia (APPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This article presents the application of a Project-Based Learning (PjBL) approach in the Numerical Methods course, focusing on developing a numerical algorithm for power system computation. The study addresses the challenges of efficiently calculating the bus admittance matrix (Ybus), bus numbering sequences, and handling double-line connections, all crucial for accurate power flow analysis. The objective is to create an efficient algorithm that can be applied to a 7-bus power system, enhancing students' practical skills in numerical methods. The results demonstrate that the algorithm successfully computes the Ybus matrix, resolves bus numbering issues, and handles double-line connections. This approach not only improves computational efficiency but also deepens students' theoretical knowledge and practical problem-solving abilities. The study highlights the effectiveness of PjBL in teaching numerical methods, offering valuable insights for its application in other engineering fields to enhance both learning and real-world problem-solving skills.
COMPARISON OF GAUSSIAN NAIVE BAYES AND RANDOM FOREST FOR ANEMIA CLASSIFICATION USING HEMATOLOGICAL PARAMETERS baik budi; Refki Budiman; Queen Hesti Ramadhamy
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.11019

Abstract

Anemia is a global health problem affecting approximately 1.92 billion people, or 24% of the population, according to the WHO. Accurate early detection is crucial for data-driven healthcare. This study evaluates two machine learning algorithms, Gaussian Naive Bayes (GNB) and Random Forest (RF). The classification is based on four key hematological parameters: Hemoglobin (Hb), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Mean Corpuscular Hemoglobin Concentration (MCHC). GNB relies on Bayes' Theorem with Gaussian distribution assumptions, whereas RF is a decision-tree-based ensemble method capable of capturing non-linear patterns without specific distributional assumptions. Evaluated using 5-fold cross-validation and standard metrics (accuracy, precision, recall, F1-score), results showed that RF outperformed GNB. RF achieved 94.2% accuracy (CV 94.9% ± 1.1%), compared to GNB's 90.5% (CV 90.1% ± 1.3%). RF feature importance confirmed Hb as the dominant predictor (score 0.562), aligning with its strong correlation to anemia (r = −0.80). Although not surpassing larger-scale studies, these results remain highly competitive. Ultimately, this research provides evidence to support the development of automated, data-driven clinical decision support systems for anemia detection.
Co-Authors ANNISA MAULIDYA Jhonny Faizal Khadlel Muhammad Romiz Micko Tomas Ramadhamy, Queen Hesti Refki Budiman Refki Budiman Syafii . ZUMAR AHMAD