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All Journal JMM (Jurnal Masyarakat Mandiri) Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Proceeding of Community Service and Engagement
Diyah Widiyasari
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Other

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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.
PENERAPAN SISTEM PENYIRAMAN OTOMATIS PADA PROSES PENYEMAIAN BIBIT PADI KERING BERBASIS PANEL SURYA DI DESA BODEH Wahmisari Priharti; Diyah Widiyasari; Suto Setiyadi
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 4 (2025): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i4.32962

Abstract

Abstrak: Sistem penyemaian bibit padi kering merupakan metode alternatif yang diprakarsai oleh kelompok tani Ponco Tani di Desa Bodeh. Meskipun metode ini terbukti efektif untuk mempercepat proses penanaman dan memperbanyak hasil tanaman padi, terdapat kelemahan yaitu kebutuhan akan sistem penyiraman yang efisien dan terjadwal. Hal ini sulit dipenuhi menggunakan sumber listrik konvensional karena seringnya terjadi pemadaman yang menghambat proses penyiraman bibit.Untuk mengatasi permasalahan tersebut, kegiatan pengabdian masyarakat ini dilaksanakan untuk mengimplementasikan suatu sistem penyiraman otomatis berbasis panel surya. Pada kegiatan ini, 11 petani anggota Ponco Tani ikut dilibatkan sebagai pengguna dan diberikan sosialisasi serta pelatihan terkait cara penggunaan serta pemeliharaan sistem. Umpan balik yang diperoleh dari sosialisasi menunjukkan tingkat pemahaman sebesar 97% yang mengindikasikan bahwa sistem yang diberikan dapat dipahami serta dijaga secara mandiri oleh petani. Harapannya, sistem ini mampu menggantikan ketergantungan pada sumber listrik konvensional dan dapat terus digunakan selama 15-20 tahun ke depan untuk meningkatkan produktivitas petani bibit padi kering di Desa Bodeh.Abstract: The dry rice seedling system is an alternative method initiated by the Ponco Tani farmer group in Bodeh Village. Although this method has proven effective in accelerating the planting process and increasing rice crop yields, it has a drawback the need for an efficient and scheduled irrigation system. This need is difficult to meet using conventional electricity sources due to frequent power outages, which hinder the seedling watering process. To address this issue, this community service activity was carried out to implement an automatic irrigation system powered by solar panels. In this program, 11 farmers who are members of Ponco Tani were involved as users and received socialization and training regarding the usage and maintenance of the system. Feedback obtained from the sessions indicated a 97% level of understanding, showing that the system can be understood and maintained independently by the farmers. It is hoped that this system will replace the dependence on conventional electricity sources and continue to be used for the next 15–20 years to improve the productivity of dry rice seedling farmers in Bodeh Village.
Deteksi Keadaan Tanah Kebun Teh Untuk Optimalisasi Produksi Di Perkebunan Rakyat CPCL Sebagai Upaya Untuk Mendukung Produksi Berkelanjutan Diyah Widiyasari; Vinsensius Sigit Widhi Prabowo; Aloysius Adya Pramudita; Erna Sri Sugesti
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 5 No. 1 (2025): The Proceeding of Community Service and Engagement (COSECANT) Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v5i1.9377

Abstract

Kelompok tani Batu Belang Medal merupakan bagian dari perkebunan rakyat yang memiliki potensi budidaya teh yang baik, namun masih menghadapi berbagai kendala adaptasi teknologi. Keterbatasan akses terhadap teknologi, rendahnya tingkat adopsi inovasi, dan metode pemetaan lahan yang masih manual menjadi hambatan utama dalam pengelolaan kebun teh. Petani cenderung melakukan perlakuan seragam pada seluruh lahan tanpa mempertimbangkan perbedaan kondisi tanah dan tanaman, yang berdampak pada pemborosan sumber daya serta rendahnya efisiensi produksi. Penelitian ini mengusulkan penggunaan sistem berbasis kamera multispektral, radar, drone, dan GPS untuk memetakan kondisi tanah meliputi kadar air air dan tingkat kesuburan tanah untuk mengatasi permasalahan produktivitas. Hasil uji menunjukkan bahwa 91% titik memiliki komposisi tanah dominan 60%, dengan kadar air terendah sebesar 22%. Sebanyak 75% titik sampel mengalami defisiensi unsur hara utama (N, P, K, Mg), dan hanya 5% yang kekurangan N dan K saja. Teknologi ini terbukti mampu mengidentifikasi kebutuhan spesifik lahan, sehingga dapat membantu petani rakyat dalam pengambilan keputusan berbasis data untuk mendukung peningkatan produksi secara berkelanjutan.
Non-Contact Heart Rate Detection Using FMCW Radar Based on 1-D Convolutional Neural Networks Diyah Widiyasari; Istiqomah Istiqomah; Fiky Yosef Suratman; Suto Setiyadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1547

Abstract

Non-contact heart rate (HR) estimation using frequency-modulated continuous-wave (FMCW) radar has emerged as a promising solution for unobtrusive, continuous vital-sign monitoring. However, accurately extracting HR from radar signals remains challenging because of low-amplitude cardiac-induced chest vibrations, environmental clutter, motion artifacts, and system noise. Traditional signal processing techniques, such as bandpass filtering combined with fast Fourier transform (FFT) analysis, are commonly employed to estimate HR in the frequency domain. Nevertheless, these approaches are highly sensitive to noise and often struggle to robustly capture weak cardiac components, leading to unstable or inaccurate estimates. To address these limitations, this study proposes a non-contact HR estimation framework based on FMCW radar combined with a one-dimensional convolutional neural network (1D-CNN). A systematic radar signal preprocessing pipeline is developed, including range-bin selection, phase extraction, noise suppression, filtering, and structured data labeling, to construct learning-ready input features. The 1D-CNN model is designed to automatically learn discriminative temporal patterns associated with cardiac activity directly from preprocessed radar signals. The proposed method is evaluated using two datasets: a publicly available dataset and an independently acquired dataset collected under controlled conditions. Performance is benchmarked against conventional bandpass filtering- and FFT-based HR estimation methods. The experimental results demonstrate that the proposed 1D-CNN framework achieves more accurate and stable HR predictions. On the public dataset, MAE decreases from 17.96 to 6.09 BPM, RMSE from 21.28 to 7.34 BPM, and MedAE from 17.66 to 5.43 BPM. The independent dataset yields consistent gains, with MAE decreases from 14.05 to 5.45 BPM, RMSE from 18.05 to 6.84 BPM, and MedAE from 10.74 to 4.57 BPM. These results indicate that the proposed 1D-CNN framework can effectively estimate HR from radar signals and demonstrate its capability to operate across datasets acquired with different radar frequencies
Co-Authors Aloysius Adya Pramudita Erna Sri Sugesti Fiky Yosef Suratman Husneni Mukhtar Istiqomah Istiqomah Muktar, Husneni Ramadhani, Mohamad Suto Setiyadi Suto Setiyadi Suto Setiyadi Tang, Nigel Bryan Vinsensius Sigit Widhi Prabowo Wahmisari Priharti Wijaya, Adhi Dharma Surya Willy Anugrah Cahyadi