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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Widiyasari, Diyah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Design and Implementation of an Organic and Inorganic Waste Detection System Using Capacitive, Inductive, and LDR Sensors with Rule-Based Classification Widiyasari, Diyah; Mukhtar, Husneni; Cahyadi, Willy Anugrah; Wijaya, Adhi Dharma Surya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.133

Abstract

The continuous increase in daily waste accumulation has become a major issue in many areas, primarily due to the mixing of various waste types and the lack of effective household waste management. This complicates waste processing and contributes to environmental degradation. This study aims to design and implement a practical tool for detecting organic and inorganic waste types, specifically for use by household waste collection personnel. The developed system utilizes three sensors, capacitive, inductive, and light-dependent resistors (LDR), to acquire characteristic data from different types of waste. The device is designed in the shape of a pistol to enhance mobility and ease of use by waste collection officers. For the waste-type classification system, several machine learning methods were employed, namely Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Based on the experimental results, AdaBoost was selected as the primary model for the waste classification system because of its superior performance in terms of cross-validation accuracy and the balance of evaluation metrics, such as precision, recall, and F1-score. Consequently, AdaBoost predictions were adopted to establish a rule-based classification logic by extracting threshold values from the most influential sensor features. This study utilized AdaBoost analysis as the foundation for rule formulation, ensuring that classification decisions were based on reliable and tested data patterns. Based on testing with several samples, the device can classify organic and inorganic waste types with an accuracy rate of 91.67%. Additionally, the tool can estimate the composition of mixed waste with an error rate of 5.06%. The presence of this device has been proven to accelerate and simplify the waste-sorting process, thereby increasing the efficiency of household waste management.
Gait Variability and Phase Segmentation in Obese and Normal Individuals Using Multi-Location IMUs and Hidden Markov Models Supervised Marginal Setiyadi, Suto; Muktar, Husneni; Cahyadi, Willy Anugrah; Widiyasari, Diyah; Ramadhani, Mohamad; Tang, Nigel Bryan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.269

Abstract

Obesity is known to disrupt motor control and biomechanics; however, detailed gait alterations in individuals with obesity remain underexplored, particularly in dynamic and real-world walking conditions. This study aims to quantitatively characterize gait differences between individuals with obesity and those of normal weight by analyzing postural and temporal gait parameters. The investigation focuses on pitch, roll, and cadence dynamics using body-worn inertial sensors, with phase transition modeling via Hidden Markov Models. This work proposes a novel framework that integrates multi-location Inertial Measurement Unit (IMU) sensors and a Hidden Markov Model–Supervised Marginal (HMM-SM) approach to detect and classify gait phases with high accuracy, offering practical value for clinical gait assessment and personalized rehabilitation. IMU sensors were placed on the waist, thigh, calf, and heel to record gait data from participants in both obese and normal-weight groups. Gait segmentation and phase modeling were conducted using 4-, 5-, and 8-state HMMs. Quantitative analysis revealed significantly greater postural variability in the obese group during slow walking, with standard deviations in roll and pitch reaching 20.68° and 9.23°, respectively—much higher than the normal-weight group (0.60° and 0.26°). Hidden state transitions from 5-state pitch HMMs showed a very strong effect size for the obese group (Cramér’s V = 0.72) compared to a moderate effect for the normal-weight group (V = 0.33). Similar patterns were observed for roll and cadence. In terms of segmentation accuracy, the 4- and 5-state HMMs outperformed the 8-state model, achieving accuracy levels above 99%, while the 8-state model reached only ~93%. The findings demonstrate that obesity significantly alters gait dynamics, particularly in postural stability and gait phase transitions. The proposed IMU-based HMM-SM framework effectively captures these changes, offering a reliable tool for gait analysis in clinical and biomechanical applications.
Co-Authors Husneni Mukhtar Muktar, Husneni Ramadhani, Mohamad Suto Setiyadi Tang, Nigel Bryan Wijaya, Adhi Dharma Surya Willy Anugrah Cahyadi