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All Journal Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi Journal of Information System, Applied, Management, Accounting and Research Jurnal Teknologi Informasi dan Komunikasi Journal of Computer Science and Informatics Engineering
Mundirin Mundirin
Institut Sains Dan Teknologi Al-Kamal
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences

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The tidal station management application (PAS) Mundirin Mundirin; Joko Prasetiana
Journal of Information System, Applied, Management, Accounting and Research Vol 6 No 1 (2022): JISAMAR: February 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v6i1.696

Abstract

The development of a geospatial information system is very necessary in providing basic data to be used as a single reference in national development planning and disaster management such as InaTEWS (Indonesia Tsunami Early Warning System. Tide Gauge Stations or Indonesia Tide Gauges (InaTides) is a collection of tide stations that produce data and managed by the Geospatial Information Agency (BIG), formerly known as Bakosurtanal. In 2019, 138 tidal stations were built, but they are still recorded manually in the form of maintenance during high tide. This is an obstacle in finding problems that arise in tidal operations. Therefore, to realize the availability of an ideal data series, a more structured, systematic and efficient management of tidal stations is needed. Use the PHP and MySql programming languages ​​in managing the database. With this application, it is expected to be a solution tidal management becomes more structured, systematic, efficient which is well documented.
FORECASTING JUMLAH MAHASISWA BARU MENGGUNAKAN METODE AUTOMATIC CLUSTERING AND FUZZY LOGIC RELATIONSHIP MARKOV CHAIN (STUDI KASUS : FAKULTAS TEKNOLOGI INFORMASI DAN KOMUNIKASI VISUAL INSTITUT SAINS DAN TEKNOLOGI AL-KAMAL) Mundirin Mundirin
Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi Vol. 4 No. 1 (2020): Produktif: Jurnal Ilmiah Pendidikan Teknologi Informasi
Publisher : Program Studi Pendidikan Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/produktif.v4i1.752

Abstract

Abstraction Forecasting or forecasting is a calculation analysis technique that is done by carrying out qualitative and quantitative approaches to think about future events using reference data in the past. The purpose of this study is to predict the number of new students at the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021. Prediction of the number of new students in the Faculty of Information and Visual Communication Technology of the Al-Kamal Institute of Science in the future accurately is very important to do, because many decisions can be taken by the Leaders of the Al-Kamal Institute of Science and Technology from these predictions. Markov Chain Automatic Clustering and Fuzzy Logic Relationship Method was chosen because it has a better level of accuracy among other Fuzzy Logic methods. The data used in this study are secondary data obtained from the Academic Information System of the Al-Kamal Institute of Science and Technology. Based on this research it was found that the predicted results of the number of new students of the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021 amounted to 64 with a MAPE of 8.25%
SISTEM INFORMASI PERPUSTAKAAN (SIPUSTAKA) MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT (RAD) Mundirin, Mundirin; Adistira, Muhammad Dezan
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 15 No 2 (2024): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v15i2.868

Abstract

Library Information System at SMKN 13 Jakarta will be built to overcome various existing problems, such as the lack of information providers, difficulties in accessing books and magazines and lack of ease in searching bookshelf data. So it is necessary to create a Digital Library information system. This library information system was built using the PHP programming language, Sublime Text, Bootstrap, Java Script, CSS and MySQL database. The development method used is the Rapid Application Development (RAD) method. The presence of this library information system is expected to make it easier for library staff, school principals and library members in managing data about the library, and also for members to make reading and borrowing books easier.
Classification of Nutritional Status in Toddlers Based on Anthropometric Data Using Random Forest Imung, Mundirin; Idawati, Idawati; Latief, Ibrahim
Journal of Computer Science and Informatics Engineering Vol 4 No 4 (2025): October
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i4.1175

Abstract

Stunting is a chronic nutritional problem that remains a major challenge in improving child health in Indonesia. This condition has long-term impacts on physical growth, cognitive development, and future productivity of children. Early detection of toddlers' nutritional status is crucial for effectively preventing and addressing stunting cases. This study aims to develop a machine learning-based classification model for toddlers' nutritional status using simple anthropometric data, namely age (in months), sex, and height (in cm). The dataset used in this study was sourced from the 2022 historical records of the Health Department and the Community-Based Nutrition Recording and Reporting System (E-PPGBM), comprising 120,999 entries categorized into four nutritional status classes: normal, tall, stunted, and severely stunted. Data preprocessing included label encoding and feature standardization. The model employed is the Random Forest Classifier, evaluated using accuracy, precision, recall, and F1-score metrics. The training results show that the model achieved a classification accuracy of 99.93% on the test data, with F1-scores for each class as follows: Normal = 0.9998, Severely Stunted = 0.9985, Stunted = 0.9975, Tall = 0.9997. Feature importance analysis indicates that height is the most influential feature in the classification task. These findings demonstrate that machine learning algorithms, particularly Random Forest, are effective for predicting toddlers’ nutritional status and have strong potential to be integrated into digital applications that support Indonesia’s stunting reduction programs. However, the model's limitation lies in its use of only basic anthropometric features—age, sex, and height—without considering additional variables such as weight, disease history, dietary patterns, socioeconomic status, or immunization history, which may also influence a child's nutritional status. To improve the model's accuracy and relevance, it is recommended to incorporate other related features, such as body weight, nutritional intake, health history, and social-economic indicators, in future research.
Co-Authors Adistira, Muhammad Dezan Idawati Idawati Joko Prasetiana Latief, Ibrahim