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All Journal TEKNIK INFORMATIKA JUITA : Jurnal Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal INFOTEL JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Sudianto
Telkom University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Design and Evaluation of a CMS-Integrated Academic Chatbot Using Gemini AI Bunga Laelatul Muna; Muhammad Lulu Latif Usman; Sudianto Sudianto; Bachtiar Herdianto
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1318

Abstract

Efficient academic services are a crucial component, and their implementation is the responsibility of higher education institutions. Telkom University Purwokerto (TUP) faces challenges in providing responsive academic services, especially in conventional online services. This research proposes the development and integration of an artificial intelligence-based academic chatbot, ‘Akif’, utilizing the Gemini 1.5 Flash model, which is linked to the institution’s Content Management System (CMS). This integration enables the retrieval of real-time information and automatic updates to the model. The tuning and evaluation process was conducted using the BLEU metric, with a value of 0.88 being reached, indicating a fairly good level of agreement between the generated answers and the reference. Although the results are promising, the system still faces limitations, particularly the risk of hallucination, which is a common challenge with generative models. Additionally, the use of BLEU as an initial evaluation metric overlooks aspects of semantic depth and user satisfaction. This research contributes a modular integration framework between generative AI and institutional systems, and highlights its potential and limitations in academic service automation.
Implementation of Rain-Fed Reservoirs as an Agricultural Irrigation Solution in Kedungbenda Purbalingga Village: Penerapan Embung Tadah Hujan sebagai Solusi Irigasi Pertanian di Desa Kedungbenda Purbalingga Sudianto; Reni Dyah Wahyuningrum; Ajeng Dyah Kurniawati
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 8 No. 1 (2024): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

Kedungbenda Village, Purbalingga Regency, has great agricultural potential. Agriculture in Kedungbenda Village is the livelihood of most of the village population. However, the "Harja Tani" farmer group faces serious obstacles in its limited planting cycle due to a lack of access to water. Previous attempts to overcome this problem, such as drawing water from nearby rivers and drilling wells, were unsuccessful due to the long distances caused by significant elevation differences between the river and agricultural land and high operational costs. This activity aims to implement innovative rain-fed reservoirs as an agricultural irrigation solution. The method is to create rain-fed reservoirs for water storage and agricultural irrigation. The output target of this activity is a rain-fed reservoir, which can be used to store water and be used for agricultural irrigation. Based on the activity results, the rain-fed reservoir is suitable for the partners' needs because it can be used as water storage during the dry season. Apart from that, farmer groups hope to increase agricultural productivity and improve local communities' welfare. Thus, this activity supports economic growth and sustainability of the agricultural sector in Kedungbenda village.
An Adaptive Random Forest for Data Stream Sentiment Classification under Concept Drift Brian Farrel Arkana; Sudianto Sudianto; Nenen Isnaeni
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1153

Abstract

Data labeling plays a crucial role in determining the performance of machine learning models, especially in data stream environments where concept drift frequently occurs. The primary objective of this study is to analyze the effectiveness of adaptive learning models in managing dynamic data distribution changes and to evaluate the influence of different labeling strategies on sentiment classification performance using user reviews from the OVO mobile application. The research contributes to understanding how labeling approaches interact with adaptive modeling under real-time data stream conditions. Two labeling methods were employed: score-based labeling derived from user ratings and content-based labeling generated automatically using the IndoRoBERTa language model. These labeled data streams were evaluated using two classifiers: a conventional Random Forest model and an Adaptive Random Forest model designed to handle evolving data distributions. The evaluation was conducted through streaming experiments that continuously fed new review data to simulate real-world drift scenarios. The results reveal that in the score-based labeling scenario, the conventional Random Forest model’s accuracy gradually declined, reaching a final accuracy of 31%, while the Adaptive Random Forest achieved 80%, reflecting a 49% performance gap. In the content-based labeling scenario, both models improved over time, with final accuracies of 57% for Random Forest and 76% for the adaptive model, resulting in a 19% difference. These findings indicate that Adaptive Random Forest is more robust in adapting to distributional and temporal changes in data streams regardless of the labeling strategy used. This study implies that combining adaptive learning with semantically rich labeling approaches can substantially enhance model reliability in real-time sentiment analysis tasks. Future research may further explore hybrid adaptive mechanisms to improve the resilience of data stream classification models across various domains.
Co-Authors Abdul Aziz Ajeng Dyah Kurniawati Akbar Maulana Yusuf Al Fachri, Moh Aminullah Aldean, Muhammad Yusril Alif Aryaputra Alvina Tahta Indal Karim Andini Mulia Kusumaningrum Asti Dwi Sripamuji Ayuningtyas, Puji Bachtiar Herdianto Bagas Prihatnowo Brian Farrel Arkana Bunga Laelatul Muna Cahyo Prihantoro Chandra Ayunda Apta Soemedhy Dedy Agung Prabowo Dwi Puspa Martiyaningsih Egidya Zalsabila Hapsari Warih Utami Hasna Nur Hanifah Hermanto, Agyl Restu Hikari Adriansyah Hilmawan, Muhammad David Imada Riski Ramadhanti Jonathan Indra Chelidivano Juan Arton Arton Masheli Julian Saputra Khusnul Khatimah Lasama, Jerry M Yoka Fathoni Mahda Laina Arnumukti Martin Parhusip Muhammad Hilmi Kafabi Muhammad Rahaji Jhaerol Naden, Yoga Naufal, Adzan Bari Nawang Anggita Winanti Nenen Isnaeni Nicolaus Euclides Wahyu Nugroho Nora Trivetisia Nursatio Nugroho Puspa Wahyuningtias Rafi Wika Ananda Rumpoko Ramadhani, Rafian Reni Dyah Wahyuningrum Reni Dyah Wahyuningrum Risa Riski Amalia Rizki Mugi Setya Adi Sandhy Fernandes Satria Nur Saputro Satria, Muhammad Adhan Hady Siti Khomsah, Siti Siti Maghfiroh Surya Haganta Brema Ginting Tangguh Widodo Tavany Amalia Rizky Tiara Khumaira Putri Tri Ginanjar Laksana Tri Wulandari Utami Uli Ahda Raihan Ummi Athiyah Usman, Muhammad Lulu Latif Widi Afandi Yanuar Sabikhi Yehezkiel Nicholas Adanson Zarkasih Akhmad