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All Journal MEDISAINS International Journal of Advances in Intelligent Informatics Jurnal Rekayasa Mesin ZOO INDONESIA JOIV : International Journal on Informatics Visualization Journal of Applied Information, Communication and Technology (JAICT) Home Economics Journal Journal of Maternal and Child Health Journal of Telenursing (JOTING) Indonesian Journal of Global Health research MAHESA : Malahayati Health Student Journal Abditeknika - Jurnal Pengabdian Kepada Masyarakat Society: Jurnal Pengabdian Masyarakat Journal Of Human And Education (JAHE) Innovative: Journal Of Social Science Research Berita Biologi
Kurnianingsih Kurnianingsih, Kurnianingsih
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Advanced Instance Segmentation of Aeroponics Tissue Culture-Based Seeds Potatoes Based on Improved YOLOv8l-small Avisyah, Gisnaya Faridatul; Kurnianingsih, Kurnianingsih; Hidayat, Sidiq Syamsul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3085

Abstract

To improve agricultural production, this study develops an advanced instance segmentation system for aeroponic tissue culture-based potato seedlings. We present an IoT system that integrates multiple sensors for humidity, temperature, pH, and turbidity to enable real-time monitoring. Additionally, we adapt the YOLOv8l-small computer vision model, an optimized version of YOLOv8, designed explicitly for efficient potato leaf disease detection and segmentation, even in resource-constrained IoT environments. YOLOv8 is a significant advancement in the YOLO series, for instance, segmentation, combining better accuracy, efficiency, and flexibility. YOLOv8 outperforms previous methods in generating precise segmentation masks while maintaining real-time performance. These innovations make YOLOv8 a robust choice for a variety of computer vision tasks, including instance segmentation, in both research and practical applications. When tested on a custom dataset of potato leaf pictures, the suggested model produced mask mAP50 of 0.842 and mAP50-95 of 0.566, with a model size of 36.1 MB and an inference duration of 9.3 ms. These outcomes are similar to those of the original YOLOv8l model, which had a slower inference time of 11.0 ms and a much larger model size of 92.3 MB, albeit at the expense of a somewhat higher mAP50 of 0.843. The study concludes that the proposed model provides similar accuracy with greater computational efficiency, making it ideal for IoT-based agricultural systems. Future research will explore additional aspects, while practical experiments aim to reduce labor costs.
LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency Enriko, I Ketut Agung; Gustiyana, Fikri Nizar; Kurnianingsih, Kurnianingsih; Puspita Sari, Erika Lety Istikhomah
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: 10.62527/joiv.7.4.1198

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. 
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing. 
CNN-LSTM for Heartbeat Sound Classification Aji, Nurseno Bayu; Kurnianingsih, Kurnianingsih; Masuyama, Naoki; Nojima, Yusuke
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).
A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka; Kurnianingsih, Kurnianingsih; Hashimoto, Takako
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.
Co-Authors Abu Hasan Adi Wibowo alfiah alfiah Alifiansyah, Muhammad Fikry Amalia, Dhanty Nurul Amin Suharjono Anindya Wirasatriya Anis Roihatin Apandi, Apandi Aquarista, Nita Ari Suwondo Arselatifa, Elviga Asmaul Husna Avisyah, Gisnaya Faridatul Azka Khoirunnisa Chin, Wei Hong Darmawan Darmawan Dhanio, Yeyen Wulandari Diana, Tri Rettagung Donny Kristanto Mulyantoro edy susanto Fahriah, Sirli Fatahul Arifin, Fatahul fatimah Fatimah Fitriyani, Rizki Putri Gustiyana, Fikri Nizar Haerul, Haerul Hajrianti, Siti Hashimoto, Takako Henra, Mustika Hesti Kurniasih I Ketut Agung Enriko Ika Rahmawati Istiqomah, Nursita Kubota, Naoyuki Kuntarjo, Samuel Beta Kusuma, Yanti Yandri Lutfan Lazuardi Maharadatunkamsi Maharadatunkamsi, Maharadatunkamsi Mardiyono Mardiyono Masuyama, Naoki Melyana Nurul Widyawati Miyar, Miyar Muhammad Anif Mulyadi Mulyadi Muryasari, Ika Nana Supriatna Nojima, Yusuke NOVA MUJIONO Nur Ghaniaviyanto Ramadhan Nurhaman, Ujang Nurhaswinda Nurseno Bayu Aji, Nurseno Bayu Oktaviani, Nur Hilda Prayitno Prayitno Prihandini, Riena Priyanti, Esteria Priyatna, Yayat Puspita Sari, Erika Lety Istikhomah Putri Hana Pebriana Putri, Winda Astria Rachmatiyah, Rina Rakasiwi, Rizky Khaerul Maulana Runjati Santosa, Naufal Adli Santoso, Pramono Hery Sarino . Sauri, Sopian Septiani, Camilla Sidiq Syamsul Hidayat, Sidiq Syamsul Sofyani, Umar Sri Sumarni Sudiyono Sudiyono Suparno Suparno Susmiyati, Susmiyati Tatag Bagus Putra Prakarsa Tri Raharjo Yudantoro Triastuti, Unggul Yuyun Trilaksono, Wahyu Triyono, Liliek Veryal, Veryal Wahyu Sulistiyo Wahyudin, Mohamad Walin Walin, Walin Wikanta, Hadi Wiktasari Wiktasari, Wiktasari Yanwari, M. Irwan Yanwari, Muhammad Irwan Yusuf Dewantoro Herlambang