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All Journal JOIV : International Journal on Informatics Visualization Jurnal INSYPRO (Information System and Processing) ILKOM Jurnal Ilmiah Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Energi dan Lingkungan (Enerlink) Jurnal Bidan Cerdas Journal of Applied Data Sciences Jurnal Ilmiah Kesehatan
Ansar Suyuti
Universitas Hasanuddin, Gowa, South Sulawesi 92171, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Nursing Public Health

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The Intelligent kWh Export-Import Utilizing Classification Models for Efficiency in Hybrid PLTS Baso, Muchlis; Manjang, Salama; Suyuti, Ansar
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.244

Abstract

Electricity demand is integral to the stability of the community's economic condition, where currently electricity is predominantly sourced from fossil fuels, posing limitations. One effort to maintain this stability is through the utilization of renewable energy, particularly solar energy. The abundance of solar energy in Indonesia presents an opportunity to maximize its potential. This study develops an intelligent kWh export-import system based on the Internet of Things (IoT) and integrated with machine learning. Users can access real-time conditions via mobile based on three parameters: "current," "power," and "voltage." Machine learning is employed to classify conditions as "efficient" or "less efficient" by analyzing and comparing five different models: AdaBoost Classifier, DecisionTree Classifier, support vector machine (SVM), naïve Bayes classifier, and extra tree classifier. Model evaluation using accuracy percentage and F1-score indicates that the AdaBoost classifier exhibits high accuracy and F1-score values of 94.5% and 0.937, respectively.
Perakitan Sistem WECG IoT dan Diseminasi Unjuk Kinerja untuk Memperkuat Sistem Layanan Kesehatan Puskesmas Kitta, Ikhlas; Palantei, Elyas; Suyuti, Ansar; Manjang, Salama; Samman, Faizal Arya; Nappu, Muhammad Bahtiar; Arief, Ardiaty; Said, Sri Mawar; ., Gassing; Gunadin, Indar Chaerah; ., Hasniaty; Adil, Andi Eri Andika; Palantei, Idris
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 8 No 1 (2025): Community Empowerment through Higher Education Community Service Programs
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v8i1.555

Abstract

This community service program is focused on a number of issues such as the implementation of the wireless ECG device design and the dissemination and demonstration of its technical operations in a such manner to all participants, e.g. the activists and supporters of the sub-village community healthcare center (PUSTU) located in Malimongeng Village, Salomekko District, Bone Regency. Several models of the latest WECG devices and the latest innovations have been developed and produced independently on the UNHAS campus, both hardware and software components. This Electrocardiogram (ECG) functions to monitor the condition of heart health and blood pressure in each patient who has been fitted with an ECG device. Each wireless ECG device is connected by an internet network and integrated at once in 1 Website that displays ECG signal graphics so that it can be monitored and controlled in real time from a distance. The application of ECG is very necessary for nurses or medical teams in a health institution to facilitate the control of the condition of patients in the room without having to walk back and forth to check the condition of patients being treated. The implementation of this community service is divided into 2 stages, namely the dissemination stage of basic knowledge of ECG technology and the technical training stage for operating ECG devices to support the patient condition control system at the health center. The results of recording the level of knowledge and understanding of the Malimongeng Village community who participated in community service activities carried out by a team of researchers and inventors from the Department of Electrical Engineering, Faculty of Engineering, Hasanuddin University were documented using the Googleform questionnaire application which had been designed earlier before the implementation of the activity in such a way as to measure the level of success of the implementation of the 2024 PKM program. The questionnaire survey was distributed and filled out by PKM participants online, both before and after the provision of material. After the implementation of the dissemination agenda and demonstration of the operation of the IoT WECG technology device, there was an increase in the knowledge and general understanding of the Malimongeng Village community who participated in the LBE Excellent Collaboration community service program in 2024 in both aspects of the category, namely general knowledge of health science and technical matters related to the development and production of IoT WECG devices along with their direct application in the midst of community life. The influence of participation in the series of PKM activities on both aspects of the target of counseling/socialization resulted in an increase in the capacity of knowledge in the community, namely around 30 percent and 25 percent respectively. Aspects of Technology Benefits and Activities, Based on the recommendations and input of all PKM activity participants, the following issues are highly expected by the Malimongeng Village community, including: aspects of the quality and diversity of cutting-edge science should be formulated to be more interesting and increase the insight of the village community, aspects of the availability of more sophisticated health devices should and should be equipped as soon as possible with more sophisticated and modern health infrastructure and facilities in the village and aspects of sustainability of research and development and production to overcome the problem of the availability of health facilities in the village and nationally.
Comparison Architecture of Convolutional Neural Network for Fertility Level of Paddy Soil Detection Natsir, Muh. Syahlan; Suyuti, Ansar; Nurtanio, Ingrid; Palantei, Elyas
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3342

Abstract

This study proposes to detect the fertility of paddy soil based on texture, the power of Hydrogen (pH), and the amount of production. Fertile paddy soil provides essential nutrients and supports optimal plant growth. Therefore, monitoring and analyzing soil fertility is crucial in agricultural land management, which significantly increases rice yields. Paddy soil is categorized into three parts: very fertile soil, fertile soil, and reasonably fertile soil. This research proposes a new approach to detecting soil fertility levels based on factors that influence soil fertility using the Convolutional Neural Network (CNN) algorithm. There are 558 paddy soil datasets of 178 very fertile datasets, 135 fertile datasets, and 245 quite fertile datasets. In this research, we conducted trials using the CNN, Resnet, Enet, and VGG19 models. According to the test results, the CNN model using the Adam optimizer and a learning rate of 0.001 achieves the highest training accuracy of 0.9687 and validation accuracy of 0.8333. This suggests that this model can accurately identify the fertility of paddy soil, making it easier to calculate the fertility of paddy soil through its use. Future research can expand this study by integrating additional soil parameters, such as nitrogen, phosphorus, potassium levels, and organic matter content, to improve classification accuracy further. Additionally, employing multimodal data sources, such as remote sensing and hyperspectral imaging, could enhance the model's robustness in various environmental conditions. Further optimization of deep learning architectures and Artificial Intelligence (AI) techniques can also provide better interpretability and usability for agricultural stakeholders.
Socialization of the Application of Internet of Things (IoT) Technology for Temperature and Humidity Control in Oyster Mushroom Cultivation for Women Farmers Groups at the Takalar Mushroom House Salam, Andi Ejah Umraeni; Subir, Ade Nur Fatimah; Suyuti, Ansar; Manjang, Salama; ., Yusran; Akil, Yusri Syam; Said, Sri Mawar; Kitta, Ikhlas; A, Hasniaty; Arief, Ardiaty; Dewi, Dianti Utami; B, Ian Adrian
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 8 No 2 (2025): Collaboration for Accelerated Community Achievement
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v8i2.631

Abstract

This community service activity was conducted by the Department of Electrical Engineering, Hasanuddin University, in collaboration with the Women Farmers Group (Kelompok Wanita Tani/KWT) Rumah Jamur Takalar in Takalar Regency. The program was initiated to address low efficiency and unstable temperature and humidity conditions in oyster mushroom (Pleurotus ostreatus) cultivation houses, which were previously managed manually. The main objective was to improve the knowledge and technical skills of mushroom farmers in understanding and applying Internet of Things (IoT) technology for automatic environmental monitoring and control. The activity was grounded in the concept of IoT-based smart farming, integrating temperature and humidity sensors, a microcontroller, and the Blynk application for real-time environmental supervision. The implementation stages included device design, system socialization and demonstration, and evaluation through pre- and post-activity questionnaires. A total of 15 KWT participants and 4 vocational students were actively involved in the training sessions. The results showed a significant enhancement in participants’ understanding, with the average knowledge score increasing from 2.0 (low awareness) to 4.0 (good understanding), indicating a 100% improvement after training. Participants also demonstrated high enthusiasm during the activities, actively engaging in discussions, operating the Blynk application, and recognizing the advantages of automated systems in maintaining stable temperature and humidity levels in mushroom cultivation houses. The impact of this program was reflected not only in improved knowledge but also in greater awareness and interest among participants in adopting IoT technology for their farming practices. Overall, the activity effectively introduced simple yet relevant technological innovations for small-scale farmers.
Composition Model of Organic Waste Raw Materials Image-Based To Obtain Charcoal Briquette Energy Potential Saptadi, Norbertus Tri Suswanto; Suyuti, Ansar; Ilham, Amil Ahmad; Nurtanio, Ingrid
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.7.3.1682

Abstract

Indonesia needs new renewable energy as an alternative to fuel oil. The existence of organic waste is an opportunity to replace oil because it is renewable and contains relatively less air-polluting sulfur. Previous research that has been widely carried out still utilizes coconut shell raw materials, which are increasingly limited in number, so other alternative raw materials are needed. A model is needed to make a formulation that can optimize the composition of organic waste raw materials as a basic ingredient for making briquettes. The research objective was to determine the best raw material composition based on digital image analysis in processing organic waste into briquettes. An artificial intelligence approach with a Convolutional Neural Network (CNN) architecture can predict an effective object detection model. The image analysis results have shown an effective model in the raw material composition of 60% coconut, 20% wood, and 20% adhesive to produce quality biomass briquettes. Briquettes with a higher percentage of coconut will perform better in composition tests than mixed briquettes. The energy obtained from burning briquettes is useful for meeting household fuel needs and meeting micro, small, and medium business industries.
Co-Authors ., Dewiani ., Yusran A. Ejah Umraeni Salam Adil, Andi Eri Andika Aditia Silvia Fazila Adnan - Akil, Yusri Syam Amil Ahmad Ilham Andani Achmad Andani Achmad Andi Ejah Umraeni Anshar, Muh Ardiaty Arief Ardiaty Arief . Arief, Ardiaty Azran Budi Arief B, Ian Adrian Baso, Muchlis Dewi, Dianti Utami Dewiani - Dewiani . Dewiani Dewiani Ejah Umraeni Salam Elyas Palantei Faisal Arya Samman Faizal A. Samman Faizal A. Samman . Faizal Arya Samman Fauzan, Arief Fighi S. Permadi Fighi S. Permadi . Fitriyanti Mayasari Gassing . Gassing Gassing Gunadin, Indar Chaerah Hasniaty Ida Rachmaniar S Ida Rachmaniar Sahali Ikhlas Kitta Ikhlas Kitta Imran Amin Ingrid Nurtanio Intan Sari Areni Irwan Setiawan K Kasmiati Merna Baharuddin Muh Anshar Muh. Anshar . Muh. Syahlan Natsir Nadjamuddin Harun Nappu, Muhammad Bachtiar Nappu, Muhammad Bahtiar Palantei, Elyas Palantei, Idris Rhiza S. Sadjad Rhiza S. Sadjad . S Sriwidyastuti Salam, Andi Ejah Umraeni Salama Manjang Sri Mawar Said Sri Ramadany Sri Ramadany Subir, Ade Nur Fatimah Sudirman Palaloi Sudirman Sudirman Syafruddin Syarif Wardi - Werna Nontji Wulan Wulan Yusran . Yusran Yusran Zaenab Muslimin Zahir Zainuddin