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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 JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA 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) 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) Muria Jurnal Layanan Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Teknik dan Sains (JPTS)
Yudie Irawan
Universitas Muria Kudus
Aerospace Engineering 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 Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Journal : Jurnal Teknologi Informasi dan Pendidikan

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Decision Support System Application for Determining Nutritional Status of Toddlers at Winong Community Health Center Using the KNN Method Arifviando, Muhammad Villa; Irawan, Yudie; Setiawan, R. Rhoedy
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.982

Abstract

Toddler nutrition issues are an important issue in public health because they affect children's physical and cognitive growth and development. The process of assessing nutritional status which is still done manually in health centers, such as in Winong Community Health Center, is often inefficient and prone to errors. This study aims to develop a web-based decision support system that can automatically classify toddler nutritional status based on age, weight, and height data. This system is built using the CRISP-DM approach as a development methodology and the KNN algorithm as a classification method. The benchmark data for nutritional status refers to the standards of the Ministry of Health of the Republic of Indonesia. The test results show that the system is able to classify nutritional status into categories of good nutrition, malnutrition, and excess nutrition with a satisfactory level of accuracy. The system also provides easy access and speed in the decision-making process for health workers. In conclusion, this system is effective in helping health centers monitor toddler nutritional status quickly, accurately, and efficiently based on valid data.
Implementation of the Crisp-Dm Methodology and Naive Bayes Algorithm on A Raw Material Requirement Prediction System to Reduce Food Waste (Case Study: Adamsafee Bakery, Resto, & Cafe) Hakim, Adam Fathul; Irawan, Yudie; Setiawan, R. Rhoedy
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.990

Abstract

Accurate forecasting of raw material requirements is critical for culinary businesses to reduce food waste and optimize costs. In the case of Adamsafee Bakery, Resto, & Cafe, high levels of waste have been caused by reliance on intuition-based forecasting, resulting in both overstocking and understocking. This study develops a web-based predictive system using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes algorithm to classify demand patterns into three categories: high, medium, and low. Historical sales data were transformed into categorical attributes and processed through the Naive Bayes model to generate demand predictions. The system was evaluated by comparing predicted sales with actual outcomes. Results show that the model achieved an accuracy of 98.7% and a mean absolute percentage error (MAPE) of 1.31%, indicating that the forecasts closely aligned with real sales performance. These findings demonstrate the effectiveness of the Naive Bayes algorithm in supporting data-driven decision-making for inventory management. This data-driven approach replaces subjective decision-making, enabling management to optimize inventory, minimize food waste, and enhance operational efficiency and business sustainability, while also offering a baseline for future research using alternative machine learning algorithms.
Co-Authors Aderochman, Itsnain Nur Aditya, Ahsanu 'Amala Adiyono, Soni Andy Prasetyo Utomo Arif Setiawan Arifviando, Muhammad Villa Aris Sugiharto Bagus Deva Pratama, Mohammad Chalim, Noor Diana Laily Fithri Dwi Puspitasari Dwi Puspitasari Eko Darmanto Elsa Violina Damayanti Endang Supriyati Fajar Nugraha Farhan, Faris Ahmad Fitri Budi Suryani, Fitri Budi Hakim, Adam Fathul Hikhmah, Fitria Nurul Ida Siti Marfuah Janah, Susi Nor Kurniawan, Aldhi Ari Kurniawan, Rizky Dwi Lukito, Aji Mimbar Maulana, Bintang Sultan Mochammad Imron Awalludin Mohammad Rosul Mubarrizi, Nor Muhammad Muhamad Sholikhudin Muhammad Arifin Mustafid Mustafid Nanik Susanti Nesicha, Yutia Nia Noor Latifah Noor Latifah Pratomo Setiaji Pratomo Setiaji Priyambodo, Ragil Putri Kurnia Handayani R Rhoedy Setiawan Rahmatika, Alifia Ayu Rahmawati, Yulinda Rizqullah, Iftikhar Romadhon, Zainur Savitri Wanabuliandari Setiawan, Raden Rhoedy Syafiul Muzid Taufiq, Muhammad Bagas Widiyatmoko, Fahmi Agung Wiwit Agus Triyanto Zulfa Himmatul Ulya Zuyyina Syarifa Yahya