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All Journal Jurnal SOLMA Jurnal Komtika (Komputasi dan Informatika) Infotek : Jurnal Informatika dan Teknologi ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal PRINTER : Jurnal Pengembangan Rekayasa Informatika dan Komputer Jurnal Teknologi Informasi untuk Masyarakat (Jurnal Teknokrat) Jurnal Komtika (Komputasi dan Informatika) Dialog Legal: Jurnal Syariah, Jurisprudensi dan Tata Negara
Fathurrahman, Imam
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Other

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Penguatan Program Kampung Iklim Melalui Edukasi Digital, Pengelolaan Sampah Organik, Dan Ketahanan Pangan Berkelanjutan Di Desa Timbanuh Imam Fathurrahman; Muhammad Djamaluddin; M. Nurul Wathani; Ida Wahidah
Jurnal Teknologi Informasi untuk Masyarakat Vol. 4 No. 1 (2026): Jurnal Teknologi Informasi untuk Masyarakat (Teknokrat)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jt.v4i1.35353

Abstract

Timbanuh Village has considerable potential to support the implementation of the Climate Village Program (ProKlim). However, the community still faces several challenges, including suboptimal household organic waste management, limited utilization of organic waste, and high dependence on chemical pesticides that may negatively affect environmental quality. This community service program aimed to improve community knowledge, skills, and participation in environmental management and food security through digital education and environmentally friendly practices. The program was implemented using a participatory approach involving socialization, mentoring, hands-on activities, and the development of digital educational media that can be accessed sustainably by the community. The activities included socialization on organic waste management, assistance in eco-enzyme production, development of digital educational media, tree planting, and training on the preparation of botanical pesticides. The results demonstrated improvements in community knowledge and skills in managing organic waste, utilizing eco-enzymes, applying botanical pesticides, and increasing access to environmental information through digital media. This program contributed to enhancing community capacity to implement sustainable environmental management practices and supported community-based climate change mitigation and adaptation efforts in Timbanuh Village
Implementasi Retrieval-Augmented Generation dan Semantic Search pada Chatbot Artificial Intelligence Berbasis Web untuk Optimalisasi Layanan Akademik Muhammad Saiful; L M Samsu; Imam Fathurrahman; Amri Muliawan Nur
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35253

Abstract

Academic information services in higher education institutions still face various obstacles, such as delays in information delivery, limited access to services, and high administrative burdens due to repetitive student inquiries. This study aims to implement Retrieval-Augmented Generation (RAG) and Semantic Search technology in a web-based Artificial Intelligence chatbot to optimize academic services at the Faculty of Engineering, Hamzanwadi University. The research method used is Design Science Research (DSR), which includes data collection, system requirements analysis, design, implementation, testing, and system evaluation. The chatbot's knowledge base is built from academic documents such as academic guidelines, service SOPs, academic calendars, scholarship information, and other administrative documents. The system was developed using an integration of LangChain, Azure OpenAI Service, Azure AI Search, FastAPI, Next.js, and Supabase. Semantic Search techniques are used to perform vector embedding-based searches, while RAG is utilized to generate contextual answers based on relevant documents. Test results show that all key system features performed well with a 100% success rate in unit testing. A user satisfaction evaluation using the Customer Satisfaction Index (CSI) method with 54 respondents yielded a score of 89.34%, categorized as "Very Satisfied," with a Mean Satisfaction Score above 4.37 on a maximum scale of 5.0. The AI ​​chatbot successfully addressed traditional academic information service issues by providing 24/7 service, reducing the workload of campus staff, and ensuring information consistency through RAG technology.
Pengembangan Model Deep Learning Long Short-Term Memory untuk Generasi Musik Berbasis Data MIDI Muhammad Djamaluddin; Imam Fathurrahman; M. Nurul Wathani
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33165

Abstract

This study develops an automatic music generation model based on Long Short-Term Memory (LSTM) by utilizing MIDI data as a symbolic representation of classical piano music sequences. The approach is computational and experimental, with a workflow that includes extracting and converting MIDI files using music21, constructing note and chord tokens, forming input–output sequences, designing a three-layer LSTM architecture, and generating music in an autoregressive manner. The model is trained for 100 epochs with a batch size of 64 and evaluated using loss, accuracy, top-3 accuracy, and perplexity metrics to assess its predictive capability on unseen data. The experimental results show a consistent decrease in validation loss, with a final value of approximately 2.93 and a validation accuracy of 0.33, while the top-3 accuracy reaches 0.53, indicating that more than half of the correct predictions fall within the top three candidates. A perplexity value around 18 suggests that the model has a reasonably adequate sequence prediction ability for symbolic music data. Qualitatively, the model is able to generate simple melodies whose patterns remain coherent with the note distribution in the dataset, although some parts of the compositions still exhibit repetition and limited variation. An important contribution of this study is the provision of a systematic methodological documentation of the LSTM-based music generation pipeline, which can serve as a practical reference for future development and research in deep learning–based music generation.
Co-Authors Ahmad, Ramli Aris Sudianto Azizi, Wahyu Bahtiar, Hariman Baiq Andriska Candra Permana Baiq Ayu Indah Wardaningsih Baiq Vinaru Widarina Eka Puspita Fathurrahman Fathurrahman Hadian Mandala Putra Hamzan Ahmadi Harianto Harianto Harianto Harianto Haris, M.Sabirin Ida Wahidah Indra Gunawan Indra Gunawan Indra Gunawan Ira Ayu Sekar Dadu Jumawal, Jumawal Kertawijaya, Lalu L M Samsu Lalu Kerta Wijaya LM Samsu M. Nurul Wathani M. Samsu, L. M.Yazid Mahpuz, Mr. Mahpuz Maulidi, Dedi Moh Farid Wajdi Moh. Farid Wajdi Mu'Tashim, Aan Muh. Fathin Kautsar Muh. Kamarur Rijali Hilali Muhammad Djamaluddin Muhammad Sadali Muhammad Wasil Muhammad Wasil Muhammad, Saiful Muliawan Nur, Amri Muzarrofah, Liana Rozita Nia Budiarti Nur, Amri Muliawan Nurhidayati Nurhidayati nurhidayati, nur ida Pranata, Aditya Puspita Sari Dewi, Baiq ROHANA ROHANA Sabirin Haris, M. Sadali, Muhamad Sadali, Muhammad Samsu, L M Sholihah, Hikmatun Siti Quthrotunnada Suhartini Suhartini Suhartini Suhartini, Suhartini Taufik Akbar Tirmidzi, Tirmidzi Usma Wardani, Iwan Veithzal Rivai Zainal Wajizatul Amnia wajizatul Wajizatul Amnia Wijaya, Lalu Kerta Yahya, Yahya Yusril Hadi Zaenul Amri Zulkipli