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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Maryam, M
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Perancangan Sistem Informasi Registrasi KK Dan KTP Berbasis Web Di Kecamatan Kiaracondong Bandung Emalia, Lilis; Yanuar, Yudhi; Maryam, M
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.536

Abstract

This research is aimed to find out the design of registrative information system KK and KTP using PHP and MySql in Kecamatan Kiaracondong, Bandung, which is intended for facilitating the process of registration services to the population. In data collection, the author used method of observation, interviews, and literature study, and for research the author used the method of qualitative and for software development method was used Waterfall method. The current system is still using manual system, therefore some problems arise such as difficult to find data and loss of data at any time in the registrative process. The use of information systems facilitates the completion of each job, hence this information system can be useful in the agency in handling its duties and in handling the registrative activities of population services.
Implementasi Metode Decision Tree pada Sistem Prediksi Status Gizi Balita Kurniawan, Dasilva Nike Aria; Maryam, M
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.681

Abstract

Examination of the nutritional status of toddlers is one way to monitor and identify toddlers who are at risk of experiencing nutritional problems. Implementation of the method in predicting nutritional status using relevant parameters. The Decision Tree method is used as a predictive model, using a dataset with parameters such as age, sex, height, weight, and nutritional status as labels. At the mining stage, data processing starts from preprocessing, namely the cleansing process to clean up incorrect data and data transformation to change the data type so that it is easy to process during the classification process. Furthermore, the Decision Tree model will be trained, tested and measured based on accuracy. The model is described in the form of a decision tree so that it can be used as a rule in system implementation. The implementation results provide accurate predictions with an accuracy value of 92.73%. The prediction system is designed to assist health workers in supporting decisions on predicting the nutritional status of children under five, as well as facilitating the community to carry out independent checks. This prediction can help identify toddlers at risk of nutritional disorders so that early intervention steps can be taken appropriately..
Implementasi Metode Decision Tree pada Sistem Prediksi Status Gizi Balita Kurniawan, Dasilva Nike Aria; Maryam, M
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.681

Abstract

Examination of the nutritional status of toddlers is one way to monitor and identify toddlers who are at risk of experiencing nutritional problems. Implementation of the method in predicting nutritional status using relevant parameters. The Decision Tree method is used as a predictive model, using a dataset with parameters such as age, sex, height, weight, and nutritional status as labels. At the mining stage, data processing starts from preprocessing, namely the cleansing process to clean up incorrect data and data transformation to change the data type so that it is easy to process during the classification process. Furthermore, the Decision Tree model will be trained, tested and measured based on accuracy. The model is described in the form of a decision tree so that it can be used as a rule in system implementation. The implementation results provide accurate predictions with an accuracy value of 92.73%. The prediction system is designed to assist health workers in supporting decisions on predicting the nutritional status of children under five, as well as facilitating the community to carry out independent checks. This prediction can help identify toddlers at risk of nutritional disorders so that early intervention steps can be taken appropriately..
Co-Authors Emalia, Lilis Kurniawan, Dasilva Nike Aria Yudhi Yanuar