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Integration Hydroponic Aquaculture Systems for Optimizing Catfish Growth Management with Arduino Lawrence Adi Supriyono; A Andhika; Amalia Shifa Aldila; Prasetyo Hartanto
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 2 (June 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i2.912

Modern agriculture faces significant challenges such as limited land and declining soil fertility. The integration of hydroponic systems with aquaculture, particularly catfish farming, offers an innovative solution to enhance agricultural efficiency and productivity. This study explores the integration of hydroponic systems with aquaculture for the simultaneous cultivation of plants and catfish in a controlled environment. The research utilizes Arduino technology for monitoring temperature, pH, and water turbidity, which are essential for the health of fish and plants. The research method used is Research and Development (R&D), involving the following steps: (1) Identifying potential problems, (2) Data collection, (3) Product design, (4) Design validation, (5) Design revision, and (6) Product testing. The results indicate that integrating hydroponic systems with Arduino technology improves the efficiency of monitoring and managing the cultivation environment, positively impacting plant and catfish growth. The implementation of this system shows an increase in plant nutrient content and better fish health. And then, this research significantly contributes to the development of sustainable agriculture and global food security through the adoption of innovative technology.

The Effectiveness of Adaptive Learning Systems Integrated with LMS in Higher Education andhika, Andhika; Aldila, Amalia Shifa; Supriyono, Lawrence Adi; Previana, Cantika Nur; Habibie, Dedi Rahman
Jurnal KomtekInfo Vol. 11 No. 2 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i2.505

Higher education has been paying close attention to adaptive learning systems (ALS) coupled with learning management systems (LMS) because of their potential to improve student outcomes and personalise learning experiences. The purpose of this study is to assess how well ALS combined with LMS can raise student engagement, academic achievement, and general satisfaction in higher education environments. Using a combination of quantitative data from academic performance measurements and qualitative input from focus groups and student questionnaires, a mixed-methods approach was used. A mid-sized university hosted the study over two semesters, with 500 undergraduate students enrolled in a range of subjects. A control group utilising a conventional LMS and an experimental group using an LMS linked with ALS were each given a set of participants. The quantitative analysis revealed a statistically significant improvement in academic performance for students in the experimental group (p < 0.05). Additionally, student engagement, measured through LMS activity logs and interaction frequencies, was notably higher in the experimental group. Qualitative feedback indicated that students appreciated the personalised learning paths and timely feedback provided by the ALS, reporting increased motivation and satisfaction with their learning experience. The integration of adaptive learning systems within LMS platforms demonstrates a positive impact on student academic performance, engagement, and satisfaction in higher education. These findings suggest that educational institutions should consider adopting an ALS-integrated LMS to support personalised learning and improve educational outcomes. Further research is recommended to explore the long-term effects and scalability of such systems across diverse educational contexts.

Integration Hydroponic Aquaculture Systems for Optimizing Catfish Growth Management with Arduino Supriyono, Lawrence Adi; Andhika, A; Aldila, Amalia Shifa; Hartanto, Prasetyo
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 2 (June 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i2.912

Modern agriculture faces significant challenges such as limited land and declining soil fertility. The integration of hydroponic systems with aquaculture, particularly catfish farming, offers an innovative solution to enhance agricultural efficiency and productivity. This study explores the integration of hydroponic systems with aquaculture for the simultaneous cultivation of plants and catfish in a controlled environment. The research utilizes Arduino technology for monitoring temperature, pH, and water turbidity, which are essential for the health of fish and plants. The research method used is Research and Development (R&D), involving the following steps: (1) Identifying potential problems, (2) Data collection, (3) Product design, (4) Design validation, (5) Design revision, and (6) Product testing. The results indicate that integrating hydroponic systems with Arduino technology improves the efficiency of monitoring and managing the cultivation environment, positively impacting plant and catfish growth. The implementation of this system shows an increase in plant nutrient content and better fish health. And then, this research significantly contributes to the development of sustainable agriculture and global food security through the adoption of innovative technology.

IMPLEMENTASI PENERAPAN MATA KULIAH TERHADAP SISWA/I SMA KRISTOFORUS II, JAKARTA Novita, Wanda; Juniarto, Antonius; Susanto, Chandra; Andhika, Andhika; Aldila, Amalia Shifa; Budiyanto, Sarah Khairunnisa; Andini, Siwi Putri; Toar, Yandri Ardolof
E-Amal: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2: Mei-Agustus 2024
Publisher : LP2M STP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47492/eamal.v4i2.3257

Pendidikan merupakan hak tiap tiap warga negara, dari pendidikan lah perubahan pola pikir dan perilaku dapat tercipta, melalui pendidikan juga membuka cakrawala baru bagi tiap insan manusia di suatu negara. Tugas pokok dan fungsi seorang pendidik salah satunya dengan melaksanakan unsur pendidikan, diluar 3 unsur tri-dharma lainnya. Tiap- tiap mata kuliah yang diajarkanpun seyogyanya memiliki implementasi yang sesuai dengan keadaan yang terjadi saat ini di dunia pekerjaan. Untuk itu tim pengabdian Universitas Jakarta Internasional menyelenggarakan pengabdian kepada masyarakat terhadap siswa/i SMA Kristoforus II, Jakarta sebagai bentuk tanggungjawab sosial seorang pendidik terhadap lingkungan sebagai bekal untuk para siswa/i menghadapi hari-hari setelah kelulusan mereka dari sekolah menengah. Metode dalam pelaksanaan pengabdian ini adalah ceramah , dan juga metode partisipatori, dimana menekankan keterlibatan siswa/i dalam melaksanakan kegiatan ini

Membangun Benteng Emosi Anak: Psikoedukasi untuk Orang Tua dalam Mengenali dan Merespons Tantangan Emosi dan Perilaku Siswa. Susanto, Chandra; Budiyanto, Sarah Khairunnisa; Toar, Yandri Ardolof B.L.; Andini, Siwi Putri; Juniarto, Antonius; Supriyono, Lawrence Adi; Scantya, Miranti Andhita; Aldila, Amalia Shifa
Gotong Royong: Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat Vol. 4 No. 2 (2025): Gotong Royong (JP3KM) Juni 2025
Publisher : Yayasan Mata Pena Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51849/jp3km.v4i2.71

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan pemahaman orang tua siswa SMAK 2 Penabur Jakarta mengenai identifikasi masalah emosi dan perilaku pada anak remaja melalui program psikoedukasi. Latar belakang kegiatan ini didasari oleh pentingnya peran orang tua dalam mendeteksi dini dan memberikan respons yang tepat terhadap tantangan kesehatan mental anak, serta potensi tekanan akademik dan sosial di lingkungan sekolah dengan standar tinggi. Metode pelaksanaan kegiatan meliputi ceramah interaktif, diskusi kelompok, dan studi kasus yang disampaikan dalam satu sesi pertemuan. Evaluasi kegiatan dilakukan melalui perbandingan skor pengetahuan peserta sebelum (pre-test) dan sesudah (post-test) mengikuti psikoedukasi, serta analisis umpan balik kualitatif. Hasil analisis menunjukkan adanya peningkatan yang signifikan dalam rata-rata skor pengetahuan orang tua setelah mengikuti kegiatan psikoedukasi (p < 0.05). Umpan balik dari peserta juga mengindikasikan peningkatan kesadaran dan keyakinan diri dalam menghadapi masalah emosi dan perilaku anak. Simpulan dari kegiatan ini adalah bahwa program psikoedukasi efektif dalam meningkatkan pengetahuan dan pemahaman orang tua siswa SMAK 2 Penabur Jakarta terkait identifikasi masalah emosi dan perilaku pada remaja, yang diharapkan dapat berkontribusi pada deteksi dini, respons yang lebih tepat, dan peningkatan kesejahteraan emosional siswa

Predictive Modeling of Covid-19 Spread with Machine Learning: A Focus on Decision Tree Accuracy Aldila, Amalia Shifa; Supriyono, Lawrence Adi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Virus Sars CoV-2 merupakan penyebab utama wabah Covid-19 yang pertama kali terdeteksi di Wuhan, Tiongkok, pada Desember 2019 dan dengan cepat menyebar ke seluruh dunia. Penelitian ini bertujuan untuk memprediksi jumlah kasus terkonfirmasi dan tingkat keparahan wabah dalam rentang 23 Januari hingga 10 Juni 2020. Data yang digunakan adalah dataset terbuka dari Kaggle berjudul "Global Forecasting Covid-19 Week 5”. Untuk menghasilkan prediksi yang optimal, penelitian ini menguji berbagai algoritma pembelajaran mesin dan pembelajaran mendalam, yaitu Random Forest, XGBoost, Polynomial Regression, Decision Tree, ANN, dan LSTM. Kinerja model dinilai melalui skor dan Root Mean Square Error (RMSE). Hasil terbaik dicapai oleh model Decision Tree dengan skor sebesar 0,97 dan RMSE 52,57, menunjukkan akurasi tinggi dalam prediksi kasus Covid-19. Penelitian ini mengindikasikan bahwa model Decision Tree unggul dalam prediksi Covid-19 dibandingkan algoritma lain dan menawarkan potensi signifikan untuk pengembangan strategi mitigasi yang lebih efektif di masa mendatang.

Energy Efficiency Analysis of LED, CFL, and Incandescent Bulbs with Smart Energy Management (SEM) Technology Supriyono, Lawrence Adi; Fitrianto, Yuli; Setiawan, Dwi; Putranto, Kartiko Eko; Aldila, Amalia Shifa
MEANS (Media Informasi Analisa dan Sistem) Volume 9 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

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This study focuses on the comparative energy efficiency analysis of LED, CFL, and incandescent bulbs to assess the potential savings offered by LED technology. With growing global concerns about energy consumption and carbon reduction, it is essential to eval_uate lighting options based on energy efficiency. Incandescent bulbs, though widely used, are inefficient as they emit more energy as heat than light. Compact Fluorescent Lamps (CFLs) are more energy-efficient but have a shorter lifespan compared to LEDs, making them less environmentally friendly. In this research, the real-time power consumption of each bulb type is measured using ACS712 and ZMPT101B sensors, connected to an Arduino Uno R3 microcontroller. The ACS712 sensor is used to monitor electrical current, while the ZMPT101B sensor detects voltage, enabling accurate energy consumption calculations. All bulbs are tested under identical conditions, providing clear, comparative data on their energy efficiencies. The analysis demonstrates that LED bulbs not only offer lower energy consumption but also result in long-term environmental and economic benefits. These findings suggest significant savings in electricity costs, supporting the shift towards energy-efficient LED lighting.

The Impact of Aurora Supercomputer in Scientific Area and the Development Analysis Okafor, Ngozi Lilian; Gunawan, Stella Putri; Zhafirah, Rafdah; Sujatmiko, Devlin Wen; Aldila, Amalia Shifa; Scantya, Miranti Andhita; Supriyono, Lawrence Adi; Putranto, Kartiko Eko
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

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Supercomputers are crucial in solving complex scientific and industrial computing due to its tremendous computational power in enabling large- scale simulations, scientific research, and advancement in various scientific fields. This study is conducted on Aurora supercomputer, a powerful exa-scale supercomputer designed for intricate computing tasks, such as climate modeling, intense simulations, and AI and machine learning. By making use of the literature review approach, we analyze the capabilities and impact of Aurora on the scientific environment. Our research suggests that Aurora is capable of significantly enhancing performance on processing data, surpassing supercomputers such as Frontier and Fugaku. Furthermore, we discuss Aurora's impact on driving groundbreaking research across multiple scientific domains and its real-world applications such as drug discovery driven by AI and machine learning. The result highlights that Aurora marked a remarkable milestone in revolutionizing computational research and further research can show the true power of the Aurora supercomputer

Predictive Modeling of Covid-19 Spread with Machine Learning: A Focus on Decision Tree Accuracy Aldila, Amalia Shifa; Supriyono, Lawrence Adi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Virus Sars CoV-2 merupakan penyebab utama wabah Covid-19 yang pertama kali terdeteksi di Wuhan, Tiongkok, pada Desember 2019 dan dengan cepat menyebar ke seluruh dunia. Penelitian ini bertujuan untuk memprediksi jumlah kasus terkonfirmasi dan tingkat keparahan wabah dalam rentang 23 Januari hingga 10 Juni 2020. Data yang digunakan adalah dataset terbuka dari Kaggle berjudul "Global Forecasting Covid-19 Week 5”. Untuk menghasilkan prediksi yang optimal, penelitian ini menguji berbagai algoritma pembelajaran mesin dan pembelajaran mendalam, yaitu Random Forest, XGBoost, Polynomial Regression, Decision Tree, ANN, dan LSTM. Kinerja model dinilai melalui skor dan Root Mean Square Error (RMSE). Hasil terbaik dicapai oleh model Decision Tree dengan skor sebesar 0,97 dan RMSE 52,57, menunjukkan akurasi tinggi dalam prediksi kasus Covid-19. Penelitian ini mengindikasikan bahwa model Decision Tree unggul dalam prediksi Covid-19 dibandingkan algoritma lain dan menawarkan potensi signifikan untuk pengembangan strategi mitigasi yang lebih efektif di masa mendatang.

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