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All Journal ComEngApp : Computer Engineering and Applications Journal International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Belitung Nursing Journal Jurnal Keperawatan Silampari Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Global Health research
Kurnianingsih, -
Department Of Electrical Engineering, Politeknik Negeri Semarang
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Public Health

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Assistive Robotic Technology: A Review Anton Satria Prabuwono; Khalid Hammed S. Allehaibi; Kurnianingsih Kurnianingsih
Computer Engineering and Applications Journal Vol 6 No 2 (2017)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.089 KB) | DOI: 10.18495/comengapp.v6i2.203

Abstract

Older people with chronic conditions even lead to some disabilities face many challenges in performing daily life. Assistive robot is considered as a tool to provide companionship and assist daily life of older people and disabled people. This paper presents a review of assistive robotic technology, particularly for older people and disabled people. The result of this review constitutes a step towards the development of assistive robots capable of helping some problems of older people and disabled people. Hence, they may remain in at home and live independently.
PELATIHAN PEMBUATAN TUTORIAL BAHAN AJAR MULTIMEDIA DENGAN VIRTUAL REALITY DI SMP ALAM AR-RIDHO KOTA SEMARANG Muhammad Irwan Yanwari; Kurnianingsih Kurnianingsih; Tri Raharjo Yudantoro; Mardiyono Mardiyono; Nurseno Bayu Aji; Kuwat Santoso; Wiktasari Wiktasari; Muttabik Fathul Lathief; Prayitno Prayitno
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2021): Volume 2 Nomor 3 Tahun 2021
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v2i3.2632

Abstract

Virtual reality (VR), realitas maya, atau realitas virtual adalah teknologi multimedia yang ditujukan agar pengguna dapat merasakan berada pada lingkungan digital yang seolah-olah nyata. Lingkungan yang disimulasikan oleh komputer disebut pula dengan nama computer-simulated environment, yaitu tiruan dari suatu lingkungan sebenarnya atau benar-benar suatu lingkungan yang hanya ada dalam imajinasi. Sekolah Alam Ar-Ridho merupakan sekolah yang berbasis pada explorasi alam sebagai bahan pendidikan dengan konsep penelitian dasar. Pada sekolah ini, siswa dididik memanfaatkan alam sebagai media penelitian dan penggalian ide. Tujuan dari pengabdian masyarakat ini adalah membuat konten virtual reality yang dapat digunakan oleh sekolah Alam Ar-Ridho untuk membantu dalam pengembangan proses belajar mengajar pada sekolah tersebut. Adapun metode yang dilakukan pada pengabdian ini terdiri dari enam tahapan, yaitu 1) Observasi lapangan, 2) Perancangan konten VR, 3) Pembuatan konten VR, 4) Pengujian, 5) Pelatihan SDM, dan 6) Pemeliharaan peralatan. Diharapkan dengan adanya fasilitas Virtual Reality, siswa dapat bereksplorasi lebih jauh dengan mempelajari tanaman yang tidak tersedia di lingkungan sekolah.
Human-In-The-Loop (HITL) application design for early detection of pregnancy danger signs Melyana Nurul Widyawati; Ery Hadiyani Puji Astuti; Kurnianingsih Kurnianingsih
Belitung Nursing Journal Vol. 8 No. 2 (2022): March - April
Publisher : Belitung Raya Foundation, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.1984

Abstract

Background: Pregnancy period is a period for mothers to empower themselves to be safe and comfortable. Pregnant women must acquire pregnancy-related information, such as warning signs of pregnancy, to avoid severe complications and even death during pregnancy and childbirth. Therefore, developing an application for pregnant women would be very helpful. Objective: This study aimed to apply Human-In-The-Loop design with an android application to detect pregnancy risk early and avoid maternal morbidity and mortality. Methods: We collected data from the cohort of 5324 pregnant women at the community health centers in the West Lombok District from 2020 to February 2021. The data included age, parity, height, inter-pregnancy interval, hemoglobin levels, upper arm circumference, previous diseases, and bleeding history. We developed a Human-In-The-Loop mobile application and employed the decision tree for identifying pregnancy danger signs. The midwife (human-in-the-loop) reviewed and clarified the data to generate the final detection and made a recommendation. Results: The ordinal regression model revealed that older patients who have more parity, lower height, the distance of children <2 years, hemoglobin <11 g/dl, upper arm circumference (UPC) <23.5 cm, have positive HBsAg, have HIV disease, have a history of diabetes mellitus (DM), have a history of hypertension, positive protein urine, and have other diseases are more likely to have a high maternal risk. The decision tree outperformed and obtained a high accuracy of 92% ± 0.0351 compared to the nine individual classifiers (Nearest Neighbors, Random Forest, Neural Net, AdaBoost, Gaussian Naïve Bayes, Bagging, Extra Tree, Gradient Boosting, and Stacking). Conclusion: The Human-In-The-Loop mobile app developed in this study can be used by healthcare professionals, especially midwives and nurses, to detect danger indications early in pregnancy, accurately diagnose the high risk of pregnancy, and provide treatment and care recommendations during pregnancy and childbirth.
PENERAPAN SISTEM APLIKASI PROMOSI DAN PENJUALAN ON LINE BERBASIS ANDROID PADA UKM BATIK BLEKOK DI KELURAHAN MANGUNHARJO KECAMATAN TEMBALANG KOTA SEMARANG Tri Raharjo Yudantoro; Mardiyono Mardiyono; Kurnianingsih Kurnianingsih; Kuwat Santoso; Irwan Yanwari; Wiktasari Wiktasari; Nurseno Bayu Aji; Angga Wahyu Wibowo; Afandi Nur Aziz Thohari
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2021): Volume 2 Nomor 3 Tahun 2021
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v2i3.2960

Abstract

Kegiatan diseminasi produk teknologi kepada masyarakat yang diajukan pada proposal ini bertujuan untuk memberikan alternatif solusi pemecahan masalah yang ada pada UKM Batik Blekok di Kelurahan Mangunharjo Kecamatan Tembalang Kota Semarang, yaitu dengan penerapan teknologi informasi (IT) untuk digitalisasi sistem pemasaran dan penjualannya. Permasalah utama yang ada pada UKM Batik Blekok ini adalah minimnya order, ketidakpastian jumlah order, minimnya customer, dan masih terbatasnya wilayah pemasaran dan penjualannya. Alternatif solusi yang akan diterapkan di UKM tersebut adalah penggunaan aplikasi mobile untuk promosi dan penjualan berbasis android yang terintegrasi ke media sosial seperti facebook, dan instagram. Kegiatan ini direncanakan dalam waktu 5 (lima) bulan yang terdiri dari 5 tahapan meliputi: bulan pertama untuk kegiatan observasi lapangan, diskusi dengan Mitra, dan analisis situasi untuk menetapkan permasalahan yang dihadapi Mitra. Pada bulan  pertama ini pula mulai dilakukan proses desain aplikasi dan database aplikasi online berbasis android. Bulan kedua digunakan untuk pembuatan aplikasi Android dan database. Bulan ketiga digunakan untuk pelatihan SDM agar terampil dalam mengoperasikan sistem aplikasi berbasis android dan sekaligus dilakukan uji coba (trial and error) penerapan dan koreksi sistem aplikasi Android. Bulan keempat dilakukan publikasi pada media massa dan jurnal, sedangkan pada bulan kelima  dilakukan pembuatan laporan akhir. Sistem aplikasi promosi dan penjualan online berbasis android ini akan diterapkan di UKM Batik Blekok sehingga diharapkan dapat membantu UKM tersebut dalam mengatasi permasalahan yang selama ini dialaminya. Sistem aplikasi promosi dan penjualan online berbasis android ini akan diterapkan di UKM Batik Blekok sehingga diharapkan dapat membantu UKM tersebut dalam mengatasi permasalahan yang selama ini dialaminya. Pada akhirnya outcome yang diharapkan dari kegiatan ini adalah dapat memperluas wilayah pemasaran, meningkatkan jumlah pelanggan, meningkatkan volume penjualan dan meningkatkan omset dan keuntungan UKM Batik Blekok. Metode yang akan diterapkan untuk menyelesaikan permasalahan yang dihadapi oleh kedua mitra ini meliputi 5 tahap/macam kegiatan yaitu:  observasi lapangan, penyediaan infrastruktur internet dan peralatan pendukung, pelatihan SDM, penerapan Sistem, aplikasi promosi dan penjualan online dan pengoperasian Sistem Aplikasi promosi dan penjualan online.
Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system Adnan Rachmat Anom Besari; Azhar Aulia Saputra; Wei Hong Chin; Kurnianingsih Kurnianingsih; Naoyuki Kubota
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.901

Abstract

Computer vision-based cyber-physical-social systems (CPSS) are predicted to be the future of independent hand rehabilitation. However, there is a link between hand function and cognition in the elderly that this technology has not adequately supported. To investigate this issue, this paper proposes a multiscopic CPSS framework by developing hand–object interaction (HOI) based on visual attention. First, we use egocentric vision to extract features from hand posture at the microscopic level. With 94.87% testing accuracy, we use three layers of graph neural network (GNN) based on hand skeletal features to categorize 16 grasp postures. Second, we use a mesoscopic active perception ability to validate the HOI with eye tracking in the task-specific reach-to-grasp cycle. With 90.75% testing accuracy, the distance between the fingertips and the center of an object is used as input to a multi-layer gated recurrent unit based on recurrent neural network architecture. Third, we incorporate visual attention into the cognitive ability for classifying multiple objects at the macroscopic level. In two scenarios with four activities, we use GNN with three convolutional layers to categorize some objects. The outcome demonstrates that the system can successfully separate objects based on related activities. Further research and development are expected to support the CPSS application in independent rehabilitation.
A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Nur Ghaniaviyanto Ramadhan; Azka Khoirunnisa; Kurnianingsih Kurnianingsih; Takako Hashimoto
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1171

Abstract

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
CNN-LSTM for Heartbeat Sound Classification Nurseno Bayu Aji; Kurnianingsih Kurnianingsih; Naoki Masuyama; Yusuke Nojima
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2115

Abstract

Cardiovascular disorders are among the primary causes of death. Regularly monitoring the heart is of paramount importance in preventing fatalities arising from heart diseases. Heart disease monitoring encompasses various approaches, including the analysis of heartbeat sounds. The auditory patterns of a heartbeat can serve as indicators of heart health. This study aims to build a new model for categorizing heartbeat sounds based on associated ailments. The Phonocardiogram (PCG) method digitizes and records heartbeat sounds. By converting heartbeat sounds into digital data, researchers are empowered to develop a deep learning model capable of discerning heart defects based on distinct cardiac rhythms. This study proposes the utilization of Mel-frequency cepstral coefficients for feature extraction, leveraging their application in voice data analysis. These extracted features are subsequently employed in a multi-step classification process. The classification process merges a convolutional neural network (CNN) with a long short-term memory network (LSTM), forming a comprehensive deep learning architecture. This architecture is further enhanced through optimization utilizing the Adagrad optimizer. To examine the effectiveness of the proposed method, its classification performance is evaluated using the "Heartbeat Sounds" dataset sourced from Kaggle. Experimental results underscore the effectiveness of the proposed method by comparing it with simple CNN, CNN with vanilla LSTM, and traditional machine learning methods (MLP, SVM, Random Forest, and k-NN).
LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency I Ketut Agung Enriko; Fikri Nizar Gustiyana; Kurnianingsih Kurnianingsih; Erika Lety Istikhomah Puspita Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01198

Abstract

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems. 
Design and Develop An Early Detection System Application to Monitor Kidney Health in Pregnant Women Dhanty Nurul Amalia; Melyana Nurul Widyawati; Kurnianingsih Kurnianingsih
Indonesian Journal of Global Health Research Vol 6 No 5 (2024): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v6i5.3377

Abstract

Pregnancy is a physiological process that can become pathological if not well monitored. Kidney disease will increase the risk during pregnancy, namely preeclampsia, fetal growth restriction, and loss of maternal kidney function. Chronic kidney disease in pregnant women often goes undiagnosed. Kidney disease problems detected will worsen if not examined at the early signs and symptoms or delaying treatment for kidney disease. This study proves the effectiveness and accuracy of early detection systems for kidney health in pregnant women. In the design of this application, exploratory data analysis (EDA) and data visualization techniques are used, which will provide deeper insight into the distribution, trends and relationships between variables in the data which includes data on pregnant women, perceived symptoms and laboratory examination. From the results of the design of this early detection system application, it shows perfect performance of the model on the overall dataset with precision, recall, and F1-score scores all reaching 1.00 or 100% accuracy. The developed classification model shows outstanding performance. This success can be attributed to the selection of relevant features, effective data preprocessing, and the selection of the appropriate classification model.
Big data analytics for relative humidity time series forecasting based on the LSTM network and ELM Kurnianingsih Kurnianingsih; Anindya Wirasatriya; Lutfan Lazuardi; Adi Wibowo; I Ketut Agung Enriko; Wei Hong Chin; Naoyuki Kubota
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.905

Abstract

Accurate and reliable relative humidity forecasting is important when evaluating the impacts of climate change on humans and ecosystems. However, the complex interactions among geophysical parameters are challenging and may result in inaccurate weather forecasting. This study combines long short-term memory (LSTM) and extreme learning machines (ELM) to create a hybrid model-based forecasting technique to predict relative humidity to improve the accuracy of forecasts. Detailed experiments with univariate and multivariate problems were conducted, and the results show that LSTM-ELM and ELM-LSTM have the lowest MAE and RMSE results compared to stand-alone LSTM and ELM for the univariate problem. In addition, LSTM-ELM and ELM-LSTM result in lower computation time than stand-alone LSTM. The experiment results demonstrate that the proposed hybrid models outperform the comparative methods in relative humidity forecasting. We employed the recursive feature elimination (RFE) method and showed that dewpoint temperature, temperature, and wind speed are the factors that most affect relative humidity. A higher dewpoint temperature indicates more air moisture, equating to high relative humidity. Humidity levels also rise as the temperature rises.
Co-Authors Adi Wibowo Adnan Rachmat Anom Besari Afandi Nur Aziz Thohari Angga Wahyu Wibowo Anindya Wirasatriya Anton Satria Prabuwono Azhar Aulia Saputra Azka Khoirunnisa Dhanty Nurul Amalia Erika Lety Istikhomah Puspita Sari Ery Hadiyani Puji Astuti Fikri Nizar Gustiyana I Ketut Agung Enriko I Ketut Agung Enriko Ika Muryasari Irwan Yanwari Khalid Hammed S. Allehaibi Kuwat Santoso Lutfan Lazuardi Mardiyono Mardiyono Melyana Nurul Widyawati Melyana Nurul Widyawati Muhammad Irwan Yanwari Muttabik Fathul Lathief Naoki Masuyama Naoyuki Kubota Nisa Annisa Arfiyanti Nita Aquarista Nur Ghaniaviyanto Ramadhan Nurseno Bayu Aji Nurseno Bayu Aji Prayitno Takako Hashimoto Tri Raharjo Yudantoro Wei Hong Chin Wiktasari Wiktasari Yusuke Nojima