Judul: Agricultural technology journal article summarization with Bi-LSTM and key feature extraction
Abstrak: Teknologi pertanian adalah bidang yang terus berkembang dan penting untuk mendukung ketahanan pangan dunia. Dalam era informasi saat ini, jumlah artikel jurnal tentang teknologi pertanian semakin meningkat dan menyulitkan para peneliti untuk memilih artikel yang relevan. Oleh karena itu, perlu dikembangkan metode peringkasan artikel jurnal teknologi pertanian yang dapat menghasilkan ringkasan secara otomatis, efektif dan efisien. Tujuan dari penelitian ini adalah untuk membuat model peringkasan teks ekstraktif dengan melakukan rekayasa fitur untuk mengekstrak informasi semantik dari teks asli itu sendiri. Penelitian ini menggunakan metode pemrosesan bahasa alami (NLP), yaitu Bi-directional Long Short-Term Memory (Bi-LSTM) untuk mendapatkan kalimat yang paling relevan untuk disajikan sebagai ringkasan dikombinasikan dengan Latent Semantic Analysis (LSA) sebagai metode pelabelan ringkasan. Data yang digunakan dalam penelitian ini adalah artikel jurnal teknologi pertanian yang berasal dari basis data Scopus. Metode pembobotan kalimat berhasil mengekstraksi ciri-ciri kunci dalam teks, mempertahankan aspek-informasi penting dan meningkatkan kemampuan sistem dalam merangkum konten dengan presisi semantik. Hasil evaluasi menunjukkan bahwa model yang dikembangkan mencapai tingkat akurasi 97,84% dan loss sebesar 0,0029. Melalui analisis word cloud terhadap hasil ringkasan model, disimpulkan bahwa penelitian pada paper-paper teknologi pertanian tersebut fokus pada mengeksplorasi inovasi dan pengembangan teknologi pertanian. Fokus utama mencakup pemahaman area tanam, hubungan dengan petani, aplikasi teknologi pada tanah dan pengairan, pengembangan sistem, dan penanganan masalah kekeringan di lapangan., Agricultural technology is a continually evolving field that plays a crucial role in supporting global food security. In the current information era, the growing number of journal articles on agricultural technology poses a challenge for researchers in selecting relevant ones. Therefore, there is a need to develop a method for summarizing agricultural technology journal articles that can generate summaries automatically, effectively, and efficiently. This research aims to create an extractive text summarization model by engineering features to extract semantic information from the original text. This study utilizes Natural Language Processing (NLP) methods, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) for extracting the most relevant sentences to serve as summaries, combined with Latent Semantic Analysis (LSA) as a summarization labeling method. The data used in this research are agricultural technology journal articles retrieved from the Scopus database. The sentence weighting method successfully extracts key features in the text, preserving important informational aspects and enhancing the system's ability to summarize content with semantic precision. The evaluation results indicate that the developed model achieves an accuracy rate of 97.84% and a loss of 0,0029. Through word cloud analysis of the model's summary results, it is concluded that research in agricultural technology papers focuses on exploring innovation and the development of agricultural technology. The primary focus includes understanding crop areas, farmer relationships, technology applications in soil and irrigation, system development, and addressing field drought issues.
Keyword: bi-lstm, deep learning, LSA, NLP, text-summarization

Judul: Analysis of User Experience Related to the use of the Large Language Model (LLM) for the Agricultural Community
Abstrak: Salah satu cara dalam mengatasi tantangan petani dalam mencari informasi secara efisien di internet adalah penggunaan chatbot yang dapat diimplementasi menggunakan Large Language Model (LLM) seperti ChatGPT. Pengalaman yang diberikan LLM dapat memengaruhi pengalaman pengguna yang berkaitan dengan kenyamanan dan kepuasan. Cara untuk menilai sejauh mana peran LLM dalam memberikan pengalaman pengguna yang baik adalah dengan melakukan penelitian menggunakan kuesioner dan wawancara untuk menilai informasi yang diberikan LLM dari aspek kepercayaan, pemahaman, dan gaya komunikasi. Kuesioner dibuat berdasarkan wawancara kepada petani dan penyuluh pertanian serta jawaban dari ChatGPT dan dosen pertanian yang dibagikan kepada 11 petani di Kecamatan Dramaga. Sebanyak 6 responden di antaranya diwawancara untuk menjelaskan hasil kuantitatif. Hasilnya, jawaban dari dosen lebih unggul dalam segala aspek. Meskipun demikian, rata-rata respon positif setiap jawaban LLM bernilai cukup tinggi. Akan tetapi, masih ada beberapa kekurangan informasi dari LLM seperti panjang informasi dan penggunaan kata-kata yang dianggap masih asing oleh responden., One way to overcome farmers' challenges in finding information efficiently on the internet is using chatbot that can be implemented using Large Language Model (LLM) such as ChatGPT. The experience provided by the LLM can influence the user experience in terms of comfort and satisfaction. The way to assess the extent of the LLM's role in providing a good user experience is to conduct research using questionnaires and interviews to assess the information provided by the LLM in terms of trust, understanding, and communication style. The questionnaire was made based on interviews with farmers and agricultural extension workers, also responses from ChatGPT and agricultural lecturers, and was distributed to 11 farmers in Dramaga District. A total of 6 respondents were interviewed to explain the quantitative results. The results showed that the responses from the lecturers were superior in all aspects. Nevertheless, the average positive response of each LLM response was quite high. However, there were still some shortcomings in the information provided by the LLM, such as the length of information and the use of unfamiliar words for respondents.
Keyword: LLM, Pertanian, Pengalaman Pengguna, ChatGPT

Judul: RISIKO PAPARAN KADMIUM DARI COKELAT BATANG PADA KONSUMEN DI INDONESIA
Abstrak: Cadmium is a common contaminant found in cocoa derivative products such as chocolate bars because it is carried away from the cocoa beans. The popularity of chocolate bars among Indonesian consumers may pose a health risk due to cadmium (Cd) content in chocolate bars. The present study aimed to obtain the cadmium (Cd) content in chocolate bars and its dietary exposure to the Indonesia population. Samples of 10 different varieties of chocolate bars were collected from markets in Indonesia and analyzed for their Cd content using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The results show that Cd content in chocolate bars ranged between 0.00012 mg/kg to 0.12 mg/kg with the highest content was found in one variant of sweet dark chocolate bar. None of samples contained Cd above the maximum limits of Cd (0.5 mg/kg in chocolate bar products) as regulated by NADFC. The highest average consumption of chocolate bars was found in adolescence (13-18 years), followed by toddler (6-59 months) and children (5-12 years). The highest P95 exposure to Cd from chocolate bars was found in toddler (6-59 months), followed by children (5-12 years), 0.28733 μg/kg bw/day and 0.17846 μg/kg bw/day. The P95th exposures to Cd from chocolate bars in all age groups did not exceed its Provisional Tolerably Monthly Intake (PTMI), indicating that the exposure to Cd through chocolate bar products do not pose any potential risk to human health
Keyword: