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Pemanfaatan Minyak Jelantah Menjadi Lilin Aromaterapi sebagai Upaya Pengelolaan Limbah dan Pemberdayaan Ekonomi Masyarakat
Zuhro, Nurul Shofiatin
; Azhari, Muhammad Asshiddiqi
; Rovidhoh, Ana
; Petrik, Shela
; Izzatadiini, Arini
; Cahyono, Moch. Rizki Nur Hidayat
; Rachmatullah, Alya Zahirah
; Labibah, Sajiya
; Azizzah, Sabrina Ghina
; Lestari, Cica Aisyah
; Agustin, Syifa Frida
Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia
Vol 4
, No 2
(2025)
Used cooking oil is a household waste that may pollute the environment and harm human health if disposed of or reused improperly. This community service program aimed to provide a waste management solution while enhancing the community’s economic skills through training on producing aromatherapy candles from used cooking oil. The implementation method included awareness sessions on the hazards of used cooking oil, technical training using a learning by doing approach, and assistance in productio...
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Leveraging SAMME for Improved Multi-Class Cirrhosis Diagnosis in Clinical Settings
Sulistyawati, Arum Kurnia
; Meliala, Dyan Avando
; Soejono, Ajie Wibowo
; Sari, Dini
; Hiswati, Marselina Endah
; Diqi, Mohammad
Jurnal Informatika dan Rekayasa Perangkat Lunak
Vol 7
, No 1
(2025)
This study explores the use of the SAMME algorithm to develop a predictive model for identifying various stages of cirrhosis. The dataset includes 418 records with 20 attributes, targeting the classification of cirrhosis stages: C (censored), CL (censored due to liver transplantation), and D (death). The model achieved an overall accuracy of 94%, demonstrating high precision and recall for classes C and D. However, the precision for class CL was lower, indicating a tendency to over-predict this...
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Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms
Jurnal Informatika dan Rekayasa Perangkat Lunak
Vol 5
, No 2
(2023)
This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribu...
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PENGEMBANGAN MEDIA MOONSTAR (MONOPOLI SUPER PINTAR) PADA MATA PELAJARAN IPS MATERI PERKEMBANGAN TEKNOLOGI PRODUKSI, KOMUNIKASI, DAN TRANSPORTASI UNTUK SISWA KELAS IV SEKOLAH DASAR
Widya Wacana: Jurnal Ilmiah
Vol 13
, No 2
(2018)
Abstract              This study aims to describe the media validity level of MOONSTAR (super smart monopoly) and to know learning using media MOONSTAR (super smart monopoly) aided speaking stick teaching model is more effective than conventional learning. This research includes the type of R & D research and development (research and development) with the research procedure using the ADDIE model. The data collection in this research is done at Analysis, Design, Development, I...
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