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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIN (Jurnal Online Informatika) Jurnal Abdi Insani SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi)
Kurniawan, Johanes Dian
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics

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Introduction of Artificial Intelligence to Students Using AIOT-kit Based on ThingSpeak Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Susanto, Bambang; Setiawan, Adi; Nugroho, Didit Budi; Kurniawan, Johanes Dian
SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/spekta.v5i2.9462

Abstract

Background: Schools struggle to engage students in science and technology, highlighting the need for innovative, tech-driven teaching methods to meet 21st-century educational demands. Contribution: An AIOT kit was developed to introduce middle school students to Artificial Intelligence (AI) and the Internet of Things (IoT). The kit measures environmental factors like temperature, humidity, pressure, and light, providing real-time data. Method: Students received training in mathematical and coding fundamentals, programmed the AIOT kit to collect data, and displayed it on the ThingSpeak dashboard. They also designed and assembled the kit, fostering peer-to-peer learning in future activities. Results: Students visualized data effectively and successfully connected the AIOT kit to the dashboard, confirming its functionality. Conclusion: The project enhanced students' understanding of AI and IoT, providing hands-on learning and boosting engagement in science and technology
Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration Kurniawan, Johanes Dian; Parhusip, Hanna Arini; Trihandaru, Suryasatria
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1318

Abstract

This study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an RMSE value of 8.29 and an error ratio of 0.45 for the ARIMA model and an RMSE value of 3.54 and an error ratio of 0.22 for the hybrid ARIMA-LSTM model. Meanwhile, for PM 2.5 concentration, we obtain an RMSE value of 6.61, an error ratio of 0.66 for the ARIMA model, an RMSE value of 2.68, and an error ratio of 0.19 for the hybrid ARIMA-LSTM model. According to this study, the ARIMA model, which is found in autoarima and represents the best model, is (2,0,1) for PM1.0 and (1,0,1) for PM2.5. The hybrid ARIMA-LSTM model outperforms the ARIMA model in terms of prediction accuracy, as evidenced by the RMSE and error ratio values, which are improved by approximately 57.30% and 51.11% for PM1.0 and 59.46% and 71.21% for PM2.5, respectively, since the hybrid ARIMA-LSTM model can accommodate variable-length sequences and capture long-term relationships to become noise-resistant, which makes higher prediction accuracy possible.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model Kurniawan, Johanes Dian; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28490

Abstract

This study introduces the AIOT-Particle, a compact device designed for comprehensive air quality and environmental monitoring in Tegalrejo, Salatiga, Indonesia. Addressing the need for real-time, multi-parameter environmental data, the device simultaneously tracks PM1.0, PM2.5, temperature, humidity, pressure, and altitude, utilizing a built-in data fusion algorithm to ensure accurate and coherent data collection. Air pollution standards classify air quality as "good" (0–50), "moderate" (51–100), "unhealthy" (101-200), "very unhealthy" (201-300), and "hazardous" (>300). The research contribution is the development and validation of the AIOT-Particle using the ARIMA model for precise environmental monitoring. The methods involved deploying the device in Salatiga and applying the ARIMA model to analyze the collected data for accuracy. The results demonstrated promising accuracy: for PM1.0, the RMSE was 8.13 with an MAE of 6.04; for PM2.5, the RMSE was 6.60 with an MAE of 4.49. Environmental data analysis showed an RMSE of 0.74 for temperature (MAE 0.43), 2.11 for humidity (MAE 1.36), 0.25 for pressure (MAE 0.19), and 2.18 for altitude (MAE 1.70). These findings highlight the device's potential to enhance environmental surveillance and public health assessments, advance the understanding of air quality dynamics, and support targeted interventions to mitigate environmental risks. The novelty of this study lies in the integration of multiple environmental parameters into a single monitoring device, validated for accuracy using the ARIMA model.
PENGABDIAN MASYARAKAT UNTUK PEMBELAJARAN CODING ARTIFICIAL INTELLIGENCE KEPADA SISWA SMP KRISTEN WONOSOBO Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Kurniawan, Johanes Dian; Susanto, Bambang; Setiawan, Adi; Nugroho, Didit Budi
Jurnal Abdi Insani Vol 11 No 2 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i2.1536

Abstract

Artificial intelligence and the Internet of Things (AIOT) have been widely used by various activities, especially in the millennial generation. However, scientific technology has not been widely introduced in education. Additionally, schools experience a decline in student enrollment every year, so it is necessary to carry out innovative learning actions that can be introduced to the community through students. Innovation learning is demonstrated by providing coding lessons that students have never done before so that AIOT becomes part of the learning. Therefore, coding as a learning method is  introduced to junior students so they can get to know AIOT early. The method used is making a device called AIOT-kit with training to be able to directly monitor environmental parameters such as temperature and humidity. The Internet of Things was introduced, which uses ThinkSpeak as a dashboard for making observations. This device was made by students so that they could follow the process from making the AIOT-kit hardware and related coding to utilization. It is shown that AIOT-kit is not yet known to students, including how to code in it. AIOT is an urgent need to access developing related technology. This activity is part of the service team's efforts to make a positive contribution to the community and school environment. After carrying out this activity, there was a change in how students could make their own AIOT-kit devices while also coding. The school even received an award from the local government for the innovation activities carried out during that period.
Co-Authors Adi Setiawan Bambang Susanto Didit Budi Nugroho Hanna Arini Parhusip Suryasatriya Trihandaru