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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Industri JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Community Development Journal: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
Syafaah, Lailis
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Public Health

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Penerapan Sistem Informasi Manajemen untuk Pembuatan Jadwal Induk Produksi dengan Metode Linier Programming Syafaah, Lailis
Jurnal Teknik Industri Vol 4, No 2 (2003)
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2825.343 KB) | DOI: 10.22219/JTIUMM.Vol4.No2.11-19

Abstract

Generally, factories always want to give bidden to consumer, therefore ne€d the best production of plain and schedule by programming linier method in order that can be eff€ctive and efficient. Reality main problem flom PT. Serasa Puma Cipta Sidoarjo was pile up of sort ploduct and little others product to next times and to threaten to the first skipsi about difficulty of factories to make QS program for actuality production ofexcellent schedule. Therefore how to actuality management of information system in ordsr that process prepamtion data in tll€ factories was not difficultly for the actuality. The meaning for based on and experienc€ last time, the factories to do that program was difficultly to program operation so production of excellent schedule realize difiiculty. To that answer need actually Management of information system for production of excellent schedule to every group product to every period.
Buck-boost Converter using GA-based MPPT for Solar Energy Optimization Syafaah, Lailis; Faruq, Amrul; Noor Cahyadi, Basri; Hidayat, Khusnul; Setyawan, Novendra; Lestandy, Merinda; Zulfatman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1658

Abstract

Energy optimization in the Solar Power Plant system needs to have more attention. Indonesia is a tropical country that has two seasons, where the weather and cloud movements are frequently unpredictable, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) was used. However, the current MPPT switching system was still not optimal with an efficiency of around 90%. In this study, the installation of MPPT was carried out in order to optimize the power in solar photovoltaic (PV) system due to the fluctuations of solar irradiation at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this study, the modeling of solar PV system was made using MATLAB software, where the design of the solar PV system consisted of a PV module with capacity 240W, DC to DC converter, battery and MPPT. Genetic Algorithm (GA)-based MPPT had been tested and compared to Particle Swarm Optimization (PSO)-based MPPT and conventional MPPT, where the GA-based MPPT worked well in finding the maximum power point in the solar photovoltaic system. It was found that GA-based MPPT produced a maximum power point close to PV power with an efficiency of 92%, while the effciciency of PSO-based MPPT and conventional MPPT were 85% and 79% respectively. In selecting the method for designing MPPT, a method with a wide range of sample data is required. This is due to the fluctuation of solar irradiance received by the solar PV.
Smart Early Detection of Rheumatoid Arthritis Tool on Nails with a Certainty Factor Technology Approach Based on Image Processing Octavio, Abi Mufid; Syafaah, Lailis; Vhirdausia, Nuri; Wijaya, Frenischa Yincenia; Hery Soegiharto, Achmad Fauzan; Faruq, Amrul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3252

Abstract

This study developed the Smart Early Detection Rheumatoid Arthritis (SEDRA) tool, designed to diagnose RA at an early stage by analyzing nail conditions. Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects joints, commonly in older individuals. Left untreated, RA can lead to severe complications such as pain, fatigue, paralysis, and even death. Early detection is essential to mitigate these effects. The research utilized advanced image processing techniques, MATLAB, Python, and a certainty factor approach. The experimental method involved capturing nail images, which were then processed in MATLAB to identify abnormalities associated with RA. Key nail indicators, including yellowing, brittleness, bloody splinters, textured surfaces, and jagged or perforated patterns, were validated using certainty factor technology to ensure diagnostic accuracy. The findings indicate that SEDRA effectively identifies RA through these nail features, providing accurate and timely diagnostic results. The results showed that this tool can detect Rheumatoid Arthritis disease through yellowing, brittle nails, bloody splinters, textured nails, and jagged or perforated nails. SEDRA was created to meet the needs of innovation in the health sector. SEDRA represents a breakthrough in health technology, providing a practical tool for early RA detection that can be integrated into primary healthcare systems. Its implications include improving patient outcomes by enabling early intervention and monitoring. Future research should focus on enhancing the diagnostic accuracy of SEDRA, expanding its applicability to diverse populations, and integrating it with mobile or wearable technologies to increase accessibility and usability in remote or underserved areas.
Kebaruan Parameter EEG Kuantitatif Sinyal Stres pada Mahasiswa Cynthia, La Febry Andira Rose; Purnaningtyas, Sri Rahayu Dwi; Syafaah, Lailis; Hasani, Mohammad Chasrun; Basri Noor Cahyadi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3820

Abstract

Otak merupakan organ yang kompleks yang bisa mengontrol pikiran, ingatan, emosi, indra peraba, kemampuan motorik, penglihatan, pernafasan, suhu dan segala sesuatu yang meregulasi tubuh kita. Gelombang tersebut hanya bisa direkam dan dilihat aktifitasnya melalui alat Elektroensefalogram (EEG). Stres adalah perasaan ketegangan emosional atau fisik. Hal tersebut datang dari segala peristiwa atau pemikiran yang membuat seseorang merasa frustrasi, marah, atau gugup. Stres adalah reaksi tubuh terhadap tantangan atau permintaan. Dalam waktu singkat, stres bisa menjadi positif, seperti saat membantu seseorang menghindari bahaya atau memenuhi tenggat waktu. Tetapi ketika stres berlangsung lama, itu dapat membahayakan kesehatannya. Stress dapat diukur dengan menggunakan kuesioner, namun penggunaan kuesioner bisa dimanipulasi. EEG dapat dikombinasikan sebagai pengukur stress seseorang. Tujuan penelitian ini untuk menganalisis parameter sinyal kuantitatif EEG pada penderita stress. Sehingga harapannya parameter ini dapat digunakan sebagai upaya pencegahan kesalahan interpretasi deteksi stress yang berakibat penurunan produktifitas seseorang. Parameter kuantitatif saat sedang stress diharapkan bisa menjadi keterbarun di dalam penelitian ini.
PELATIHAN MANAJEMEN SISTEM INFORMASI UNTUK MENINGKATKAN PRODUKTIVITAS DALAM EKONOMI DIGITAL PADA PRODUK AIR MINUM KEMASAN Q-MAS Lestandy, Merinda; Syafaah, Lailis; Hidayat, Khusnul; Irfan, M.; Hakim, Lukman
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Volume 6 No. 1 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i1.40765

Abstract

PT. TASAMA melalui produk Q-MAS menghadapi berbagai kendala dalam pengelolaan operasional, seperti pencatatan produksi yang masih manual dan pemasaran yang kurang terintegrasi secara digital. Untuk mengatasi tantangan tersebut, dilakukan program pengabdian berupa pelatihan manajemen sistem informasi berbasis aplikasi web tasamaqmas.com. Pelatihan ini dirancang dalam tiga tahap: pengenalan teori, praktik langsung, dan monitoring implementasi, dengan pendekatan interaktif. Hasil menunjukkan adanya peningkatan efisiensi waktu pencatatan hingga 62%, akurasi data operasional, dan pengambilan keputusan yang lebih efektif. Sistem informasi ini mendukung transformasi digital Q-MAS, memperkuat daya saing, dan memastikan keberlanjutan operasional di pasar ekonomi digital. Penerapan sistem berbasis web untuk pencatatan data produksi menggantikan metode manual yang rentan kesalahan, memberikan proses yang lebih cepat, akurat, dan dapat diakses kapan saja. Program ini menjadi model strategis untuk meningkatkan produktivitas melalui integrasi teknologi dalam manajemen operasional.
Prediction of flood-affected areas based on geographic information system data using machine learning Faruq, Amrul; Syafaah, Lailis; Irfan, Muhammad; Abdullah, Shahrum Shah; Mohd Hussein, Shamsul Faisal; Yakub, Fitri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4675-4683

Abstract

Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models, including random forest (RF) and support vector machine (SVM), to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.
Co-Authors Achmad Fauzan Hery Amrul Faruq Basri Noor Cahyadi Fitri Yakub Khusnul Hidayat La Febry Andira Rose Cynthia Lukman Hakim M. Irfan Merinda Lestandy Mohammad Chasrun Hasani MOHD HUSSEIN, SHAMSUL FAISAL Muhammad Irfan Noor Cahyadi, Basri Novendra Setyawan Octavio, Abi Mufid Purnaningtyas, Sri Rahayu Dwi Shahrum Shah Abdullah Vhirdausia, Nuri Wijaya, Frenischa Yincenia Zulfatman Zulfatman