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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal DISPROTEK Jurnal Nasional Komputasi dan Teknologi Informasi EDUMATIC: Jurnal Pendidikan Informatika Jurnal SITECH : Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Pendidikan bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Idealis : Indonesia Journal Information System Jurnal Pengabdian kepada Masyarakat Nusantara Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Information Technology Ampera Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat International Journal of Management Science and Information Technology (IJMSIT) Muria Jurnal Layanan Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Algoritma Jurnal Pengabdian Masyarakat Bhinneka Malcom: Indonesian Journal of Machine Learning and Computer Science Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Jurnal Masyarakat Madani Indonesia Jurnal Pengabdian Teknik dan Sains (JPTS) semanTIK INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian Masyarakat Indonesia
Yudie Irawan
Universitas Muria Kudus
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Journal : bit-tech

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Digital Transformation of Catfish Ponds with AI-based Monitoring System Iftikhar Rizqullah; Yudie Irawan
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2716

Abstract

Digital transformation in the aquaculture sector, particularly in catfish farming, holds significant potential to improve operational efficiency and farm productivity. This study developed an artificial intelligence (AI)-based monitoring system called NusAIra to assist farmers in managing ponds in real-time by monitoring water quality, feed management, and harvest prediction. The system integrates physical sensors with a Decision Tree Regression machine learning algorithm, validated using an 80:20 hold-out split strategy and evaluated through accuracy and Root Mean Square Error (RMSE) metrics. NusAIra was built using Flask and Docker frameworks, employing a POST endpoint with JSON-formatted data for seamless data exchange. Implementation was carried out on three catfish ponds in Jepara Regency from February to April 2025. The predictive model achieved an accuracy of 87% with an RMSE of 0.35. One application example demonstrated that the system reduced the Feed Conversion Ratio (FCR) from 1.9 to 1.6, increased productivity by up to 22%, and lowered average operational costs by 15%. Additionally, NusAIra effectively predicted market prices with stable seasonal patterns, such as the projected catfish price in Boyolali for April reaching IDR 36,442, closely aligning with historical data. These results highlight NusAIra’s role in supporting data-driven decision-making. However, challenges remain, including infrastructure constraints and the low level of digital literacy among traditional fish farmers. Further development will focus on enhancing prediction accuracy, integrating adaptive features, and expanding system reach through cloud computing to support the sustainability and food security of Indonesia’s aquaculture sector.
Classification of Sentiment Tokopedia and Shopee App Reviews on Google Playstore Using Naive Bayes Robait Tajuddin; Yudie Irawan; R. Rhoedy Setiawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3246

Abstract

The rapid growth of e-commerce in Indonesia has led to an increase in user reviews that reflect satisfaction and experiences with applications such as Tokopedia and Shopee. The large volume of reviews makes manual analysis inefficient, thus requiring an automated method to identify user sentiments. This study aims to analyze and classify the sentiment of Tokopedia and Shopee reviews using the Naïve Bayes algorithm. The dataset consists of 10,000 Indonesian-language reviews collected from the Google Play Store. The analysis stages include data cleaning, stopword removal, stemming, and tokenization before classifying the reviews into positive and negative categories. The results show that the Naïve Bayes model performs well in sentiment classification. For Tokopedia data, the model achieved an accuracy of 81.84%, weighted precision of 84.44%, weighted recall of 81.84%, and weighted F1-score of 81.57%. Meanwhile, for Shopee data, the model performed better with an accuracy of 86%, weighted precision of 85.96%, weighted recall of 86%, and weighted F1-score of 83.25%. The word cloud visualization reveals that negative sentiments on Tokopedia are dominated by complaints about products and delivery, while on Shopee, they relate to late orders. Positive sentiments in both platforms highlight transaction convenience and affordable prices. These results demonstrate that Naïve Bayes is effective for sentiment analysis of e-commerce user reviews.
Co-Authors Aditya, Ahsanu 'Amala Amanda Diyas Setiyoadi Amelia Rahmawati Andy Prasetyo Utomo Anteng Widodo Arba Rinata Ardiansyah Ardiansyah Arif Setiawan Arifviando, Muhammad Villa Arina Fawaida Aris Sugiharto Arya Putra Badruzzaman aufa hanif Avin Nuzula Fitranti Bagus Deva Pratama, Mohammad Chalim, Noor Diana Laily Fithri Diana Nur Yasmin Dwi Puspitasari Dwi Puspitasari Eko Darmanto Elsa Violina Damayanti Endang Supriyati Endang Supriyati Endhito Hafiz Meifaza Fadila Ullul Azmie Fajar Nugraha Farhan, Faris Ahmad Fitri Budi Suryani Hakim, Adam Fathul Hikhmah, Fitria Nurul Ida Siti Marfuah Iftikhar Rizqullah Imam Munzagi Ina Kusumawardani Alina Fakhri Indana Fauzul Ula Indriyani, Sofiatul Irsad Nizarudin Itsnain Nur Aderochman Janah, Susi Nor Jhany Feronica Ardina Kalya Agil Prasetya Khilal Arlisna Rahmadani Kurniawan, Aldhi Ari Kurniawan, Rizky Dwi Laili Fitriyani Lukito, Aji Marchela Tri Amanda Mimbar Maulana, Bintang Sultan Mochammad Imron Awalludin Mohammad Rosul Mubarrizi, Nor Muhammad Muhamad Dimas Firmansyah Muhamad Sholikhudin Muhammad Arifin Muhammad Kholid Ridwan Muhammad Rizqi Pradana Mustafid Mustafid Nalendra Cahaya Heraditya Nesicha, Yutia Nia Noor Latifah Nurya Herlina Sari Pratomo Setiaji Priyambodo, Ragil Putri Kurnia Handayani Putri Kurnia Handayani Putri, Najwa Hanindya R Rhoedy Setiawan R. Rhoedy Setyawan Raden Rhoedy Setiawan Rahmatika, Alifia Ayu Rahmawati, Yulinda Robait Tajuddin Romadhon, Zainur Savitri Wanabuliandari Setiawan, Raden Rhoedy Silvia Himmatul Aliyyah Slamet Rahayu Soni Adiyono Syafiul Muzid Syahrul Bagus Andreyan Taufiq, Muhammad Bagas Tsirwatun Nisail Khasanah Vika Aulia Munawaroh Widhiarta, Faris Widiyatmoko, Fahmi Agung Wiwit Agus Triyanto Zulfa Himmatul Ulya Zuyyina Syarifa Yahya