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All Journal Jurnal Ilmiah Informatika Komputer Indonesian Journal of Applied Informatics Jurnal Informatika dan Rekayasa Perangkat Lunak
Rini Astuti
STMIK IKMI Cirebon
Computer Science & IT Decision Sciences, Operations Research & Management

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IMPLEMENTASI DATA MINING ALGORITMA DECISION TREE UNTUK KLASIFIKASI STATUS GIZI BALITA DI KECAMATAN CILEDUG Siti Bulkisah Bulkisah; Rini Astuti; Agus Bahtiar
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10346

Abstract

The nutritional intake plays a crucial role in supporting the physical development of toddlers; however, not all toddlers in Ciledug District receive adequate nutrition. The number of toddlers experiencing nutritional status disorders or nutritional problems fluctuates annually, influenced by the fluctuation in the total number of toddlers. Currently, 2.9% of toddlers in Ciledug District are experiencing nutritional status disorders. This study aims to implement a classification process to determine the nutritional status of toddlers in Ciledug District using the decision tree algorithm. The achieved accuracy of the results is 99.18%, with detailed predictive outcomes as follows: 2298 instances correctly predicted as normal nutrition, 23 instances correctly predicted as abnormal nutrition, 2290 instances correctly predicted as abnormal nutrition, and 15 instances correctly predicted as normal nutrition. The classification results based on age indicate that infants aged 2 weeks have normal nutrition, toddlers aged 1 to 11 months exhibit both normal and abnormal nutrition, toddlers aged 12 months have normal nutrition, toddlers aged 13 to 58 months show both normal and abnormal nutrition, and toddlers aged 59 to 61 months have normal nutrition.
Analisis Sentimen Opini Supporter Pengguna Youtube terhadap Sistem Pembelian Tiket Pertandingan Persib menggunakan Metode Naïve Bayes Adam Arifian Alamyah; Rini Astuti; Fadhil Muhamad Basysyar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10310

Abstract

Reporting about Persib cannot be separated from the role of the media from the era of the union until today. The first news about Persib in the media was at least in November 1904, when the Priangan Association (PVB) was recorded as the first association in Bandung. Using Descriptive Analysis, in the form of a word cloud, which is used in this research to identify and form word patterns that can be associated with other words that are considered important. Naïve Bayes Classifier Method. used in this research to identify and form word patterns that can be associated with other words to obtain information that is considered important. YouTube has become one of the largest platforms for sharing visual content on the internet. One of the topics that is being widely discussed is the ticket purchasing system for Persib Bandung matches. This has invited a lot of reactions, especially from the community, especially residents of West Java. This causes the controversy to become a polemic. Therefore, a method is needed to classify reviews automatically by conducting sentiment analysis. In this research, 2129 comment data in several contents discussed the Persib Bandung match ticket system. The aim of this research is to classify the analysis. review of the polemic of the match ticket system using the Naïve Bayes algorithm.
Menentukan Nilai Gizi pada Balita Menggunakan Algoritma Support Vektor Machine (SVM) di Posyandu Kelurahan Ciherang Silvia Dini Widianti; Rini Astuti; Fadhil Muhamad Basysyar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10274

Abstract

Determining nutritional status in toddlers is based on age, weight and height. The process is still done manually, resulting in the resulting data being less relevant. This research serves to provide information about determining the nutritional status of toddlers so that the community and officers at Posyandu Ciherang Village. The problem of this study is to determine the growth and development of nutritional status in toddlers at Posyandu Ciherang Village. Data obtained from Posyandu at the village level whose activities are carried out once a month by cadres under the technical guidance of the puskesmas. Based on the existing problems, a system for determining the nutritional status of toddlers is needed to make it easier to get the right results. The method to be used is Support Vector Marchine (SVM) which is a method of classifying data and providing a basis for early preventive action in overcoming nutritional problems in toddlers. The purpose of this study is to determine the nutritional status of toddlers there are 3 criteria needed, namely the age of toddlers, weight and height. The Support Vector Marchine (SVM) algorithm is considered more optimal because it is able to analyze the best results. The results of this study are expected to provide better insight into determining nutritional values in toddlers. Based on the results show True Less (TK) on pred.NORMAL is 31 records classified as malnutrition and True Normal (TN) on pred.NORMAL is 267 records classified as normal nutrition with the smallest result of class recall 76.52% and the smallest result of class precision 76.52%. From these results it can be concluded that the accuracy rate with the Support Vector Marchine (SVM) algorithm is 85.58%.
Penerapan Algoritma C4.5 untuk Optimalisasi Manajemen Stok Obat di Apotek Nafa Farma Khairunnisa Amarullah; Rini Astuti; Willy Prihartono; Ryan Hamonangan
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96224

Abstract

Abstrak : Manajemen stok obat menjadi tantangan utama di apotek Nafa Farma untuk mencegah kelebihan atau kekurangan stok. Penelitian ini mengaplikasikan algoritma C4.5 sebagai metode klasifikasi untuk mendukung pengelolaan stok yang optimal. Data stok obat dari Januari sampai Desember 2023 dianalsis menggunakan pendekatan Knowledge Discovery in Database (KDD) dengan software RapidMiner. Penelitian ini menunjukkan bahwa algoritma C4.5 dapat meningkatkan efisiensi manajemen stok obat dengan akurasi 80,67% dan F1-score rata-rata 80.52% ini memberikan rekomendasi strategis untuk pengadaan obat. Obat kategori laku direkomendasikan untuk diutamakan dalam pengadaan, sementara obat tidak laku dapat dikurangi pembeliannya untuk menghindari pemborosan. Algoritma C4.5 efektif untuk meningkatkan efisiensi pengelolaan stok obat====================================================Abstract : Drug stock management is a major challenge at Nafa Farma pharmacy to prevent excess or shortage of stock. This research applies the c4.5 algorithm as a classification method to support optimal stock management. Drug stock data from January to December 2023 was analyzed using a Knowledge Discovery in Database (KDD) approach with RapidMiner software. This study shows that the C4.5 algorithm can improve the efficiency of drug stock management with an accuracy of 80.67% and an average F1-score of 80.52%, providing strategic recommendations for drug procurement.  Sellable category drugs are recommended to be prioritized in procurement, while unsellable drugs can be reduced in purchase to avoid waste. The C4.5 algorithm is effective in improving the efficiency of drug stock management.
Co-Authors Adam Arifian Alamyah Fadhil Muhamad Basysyar Khairunnisa Amarullah Ryan Hamonangan Silvia Dini Widianti Siti Bulkisah Bulkisah Willy Prihartono