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All Journal Jurnal Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information System Jurnal Informatika Upgris Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Nasional Komputasi dan Teknologi Informasi EVOLUSI : Jurnal Sains dan Manajemen Jurnal Teknologi Informasi dan Pendidikan Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Jurnal Teknik Informatika (JUTIF) Jurnal Nuansa Akademik: Jurnal Pembangunan Masyarakat Jurnal Pengabdian kepada Masyarakat EXPLORER Jurnal Minfo Polgan (JMP) Journal International of Lingua and Technology Journal of Artificial Intelligence and Digital Business
Lutfi Syafirullah
Politeknik Negeri Cilacap
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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Public Health Social Sciences Transportation Other

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Penerapan Metode Simple Additive Weighting (SAW) dan Tabel Keputusan pada Sistem Pendukung Keputusan Menentukan Tingkat Punishment Siswa Bermasalah Purwanto, Riyadi; Novia Prasetyanti, Dwi; Hafsarah Maharrani, Ratih; Syafirullah, Lutfi
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.780

Abstract

RETRACTED Following a rigorous, carefully concerns and considered review of the article published in Infotekmesin to article entitled “Penerapan Metode Simple Additive Weighting (SAW) dan Tabel Keputusan pada Sistem Pendukung Keputusan Menentukan Tingkat Punishment Siswa Bermasalah ” Vol 12, No 2, pp.115-121, July 2021, DOI: https://doi.org/10.35970/infotekmesin.v12i2.780 This paper has been retracted at the request of the author of this article because by mistake the same article has been published in another journal publisher. The article contained redundant material, the paper published in Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science - iCAST-ES, ISBN 978-989-758-615-6; ISSN 2975-8246, pages 1194-1202. DOI: 10.5220/0010962300003260, The document and its content has been removed from Infotekmesin, and reasonable effort should be made to remove all references to this article.
Performance Evaluation and Optimization of an IoT-Based Fish Smoking Monitoring System for Ensuring Product Quality Syafirullah, Lutfi; Mahardika, Fajar; Purwanto, Riyadi; Prasetyanti, Dwi Novia
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15736

Abstract

Fish smoking is a widely used preservation method; however, the quality of smoked fish is highly dependent on the stability of temperature, humidity, and smoking duration. Manual control of these parameters has limitations and may reduce product quality. Existing studies on fish smoking monitoring systems primarily focus on temperature control without providing quantitative evaluation of how multi-parameter process stability affects product quality and shelf life. This study aims to design and implement an Internet of Things (IoT)-based monitoring system for fish smoking equipment to ensure the quality of smoked fish. The research method used is Research and Development (R&D), which includes needs analysis, system design, development, testing, and evaluation stages. The system integrates temperature and humidity sensors, a microcontroller, and an IoT platform for real-time monitoring. The test results show that the system is capable of monitoring the smoking chamber temperature within a range of 60–80 °C with an average error of ±1.5 °C compared to a standard measuring instrument, and maintaining an optimal temperature of 70 °C during the smoking process. Quality testing of the smoked fish indicates uniform doneness, a golden-brown color, firm texture, and an average moisture content reduction of 35%. Shelf-life testing shows that the smoked fish can last up to 7–10 days at room temperature and up to 21 days under cold storage without significant changes in aroma and texture. Unlike previous works, this study provides quantitative evidence that improved stability of multiple smoking parameters through IoT-based monitoring significantly enhances product quality consistency and extends the shelf life of smoked fish.
IoT-Based Smart Detector with SVM and XGBoost for Real-Time Child Growth Monitoring and Stunting Risk Prediction Mahardika, Fajar; Syafirullah, Lutfi; Nugroho, Adlan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5394

Abstract

Stunting is a major public health issue, particularly in developing countries, causing long-term physical and cognitive impairments in children that reduce their quality of life and future productivity. To address this challenge, this study aims to develop an IoT-based smart detection system for child growth monitoring, enabling quicker and more accurate detection of stunting risks. The proposed system combines both hardware and intelligent software components to measure key growth indicators—height, weight, and BMI—using digital sensors and microcontrollers, transmitting the collected data to a cloud platform for real-time analysis. Machine learning algorithms, such as Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), are employed to predict stunting risk. Experimental results show that the XGBoost model outperforms SVM, achieving an accuracy of 80%, precision of 82%, recall of 78%, and F1-score of 79.9%, compared to SVM’s accuracy of 70%, precision of 68%, recall of 65%, and F1-score of 66.4%. This research provides a scalable technological framework for real-time stunting monitoring and early intervention, with the potential for implementation in resource-limited settings. By supporting national stunting reduction initiatives, the system enhances public health innovation and child welfare.
Co-Authors Abdul Rohman Supriyono Annas Setiawan Prabowo Annisa Romadloni Arif Fahrizal Azzahra, Shabila Nurfajri Betti Widianingsih Cahya Vikasari, Cahya Devi Taufiq Nurrohman Dodi Satriawan Dodi Satriawan, Dodi Dwi Novia Prasetyanti Evila Purwanti Sri Rahayu, Theresia Fadillah Fadillah Fadlilah, Ilma Guilin, Xie Hani Hidayati Hani Hidayati Hastuti, Hety Dwi Hera Susanti Hidayat Muhammad Nur Ika Dewi Rozaurrohmah Isa Bahroni, Isa Jiao, Deng Joko Dwi Mulyanto Listyaningrum, Rostika Mahardika, Fajar Maharrani, Ratih Hafsarah Mardiyana Mardiyana Muhammad Nur Faiz Murni Handayani Novia Prasetyanti, Dwi Nugroho, Adlan Nurlinda Ayu Triwuri Nurrohman, Devi Taufiq Oman Somantri Oto Prasadi Prabowo, Annas Setiawan Pramita, Ayu Prasetyanti, Dwi Novia Prihantara, Andesita Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Riyadi Purwanto Riyadi Purwanto Rosita Dwityaningsih Rostika Listyaningrum Roy Aries Permana Tarigan Sari, Eka Dyah Puspita Seppy Ayu Rachmawati Sodikin, Jenal Supriyono, Abdul Rohman Ulikaryani, Ulikaryani Vadlya Ma’arif Vicky Prasetia Widianingsih, Betti Yuanyuan Wang, Yuanyuan