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All Journal Jurnal Sarjana Teknik Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Riset Informatika Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Pamenang (JIP) Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Sistem Informasi Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Jurnal Informatika Polinema (JIP) Akademika
Nia Nuraeni
STMIK Nusa Mandiri
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences

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Pendekatan Machine Learning untuk Deteksi Dini Kanker Paru-Paru: Mengoptimalkan Sensitivitas dan Akurasi Nuraeni, Nia; Astuti, Puji
Jurnal Informatika Polinema Vol. 11 No. 3 (2025): Vol. 11 No. 3 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i3.7011

Abstract

Kanker paru-paru merupakan salah satu penyebab utama kematian akibat kanker di dunia. Deteksi dini yang akurat sangat penting untuk meningkatkan peluang kesembuhan pasies. Penelitian ini membandingkan kinerja enam algorithma data mining, yaitu Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree Classifier (DTC), dan K-Nearest Neighbors (K-NN) dalam memprediksi kanker paru-paru. Hasil evalusi menunjukan bahwa DTC memiliki sensivitas tertinggi (0.558) dan F1- Score terbaik (0.543), sehingga lebih efektif dalam mendeteksi sebanyak mungkin kasus positif. Random Forest memiliki akurasi tinggi (90%), menunjukan keseimbangan antara prediksi yang benar secara keseluruhan dan performa disemua aspek. Model lainnya, seperti Logistic Regression dan SVM memiliki akurasi tinggi (89%) tetapi dengans ensitivitas yang lebih rendah dibandingkan Decision Tree Classifier. Berdasarkan hasil ini, model dengan sensitivitas tinggi lebih sesuai untuk skrining awal agar tidak melewatkan kasus kanker, sedangkan model dengan akurasi tinggi dapat digunakan untuk diagnosis yang lebih seimbang. Dengan demikian pendekatan machine learning dapat menjadi alat bantu yang potensial dalam deteksi dini kanker paru-paru.
IMPLEMENTASI SISTEM INFORMASI PENERIMAAN PESERTA DIDIK BARU (PPDB) BERBASIS WEBSITE DI SMPN 5 TAYAN HILIR Liniwati; Nuraeni, Nia; Destiana, Henny
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 2 (2025): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v16i2.1225

Abstract

The rapid advancement of information technology has brought profound transformations across various sectors, including education. One notable manifestation of this change is in the New Student Admission (PPDB) process, which was traditionally conducted manually but is now progressively transitioning to digital platforms. At SMPN 5 Tayan Hilir, however, the PPDB process remains conventional, resulting in several challenges such as slow data retrieval, potential loss of important documents, and inefficiencies in time management. This study aims to design and implement a web-based New Student Admission Information System (SIPPDB) as a digital solution to address these issues. The research methodology comprises field observations (primary data collection), literature review, and the development of a web-based system. The implementation results demonstrate that the proposed system significantly improves the PPDB process, making it faster, more accurate, and more efficient for both prospective students and school administrators. Moreover, the system is anticipated to serve as a catalyst for broader digital transformation in data and information management within the school environment.
Application of XGB Classifier for Obesity Rate Prediction Cahya Putri Buani, Duwi; Nuraeni, Nia
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.260

Abstract

According to the Ministry of Health, the percentage of the population in Indonesia who are overweight is 13.5% for adults aged 18 years and over, while 28.7% are obese with BMI>=25 and obese with BMI>=27 as much as 15.4%. Meanwhile, at the age of children 5-12 years, 18.8% were overweight and 10.8% were obese. From these data, early detection of obesity levels is needed. From these data, prevention is needed so that the percentage of the population who experience obsediness can decrease, one of the efforts that can be done is to do early detection of obesity, to do early detection of obesity can be done using Machine Learning. In this study, it was discussed about the prediction of obestias levels using 7 (seven) models, namely Naive Bayes (NB), Random Forest (RF), K-NN, Decision Tree Classifier (DTC), SVM, XGB Classifier (XGB), Logistic Regression (LR) from the seven models used to predict the obesity level of XGB Classifier (XGB) which has the highest accuracy, namely Accurasy 0.96, with an f1-score of 0.96,  Precission and recall 0.96.
PEMANTAUAN STATUS GIZI MELALUI INDEKS MASSA TUBUH UNTUK MENDETEKSI DINI MASALAH GIZI PADA ANAK USIA DINI DI TKQ MATLAUL KHOIR : MONITORING OF NUTRITIONAL STATUS THROUGH BODY MASS INDEX TO EARLY DETECT NUTRITION PROBLEMS IN EARLY CHILDHOOD AT MATLAUL KHOIR KINDERGARTEN Nuraeni, Nia; Yulianty , Nadya
Jurnal Ilmiah Pamenang Vol. 7 No. 2 (2025): Jurnal Imiah Pamenang (JIP)
Publisher : Stikes Pamenang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53599/jip.v7i2.317

Abstract

Abstrak   Pemantauan status gizi pada anak usia dini merupakan salah satu aspek yang sangat penting karena status gizi pada anak usia dini akan menentukan kualitas hidup anak di awal kehidupannya. Penelitian ini bertujuan untuk melihat pertumbuhan fisik, perkembangan otak dan fungsi tubuh yang optimal berdasarkan status status gizi agar anak tumbuh dengan sehat dan berkembang sesuai usianya. Jenis Penelitian ini adalah kuantitatif. Metode penelitian yang digunakan adalah kuantitatif deskriptif dengan melakukan studi lapangan. Pendekatan penelitian menggunakan metode antropometri dalam menilai status gizi. Sample penelitian ini pada anak usia dini di TKQ Matlaul Khoir yang berusia 4-5 tahun berjumlah 5 orang anak. Teknik pengumpulan data dalam penelitian ini adalah menggunakan tiga metode utama yaitu, pengukuran berat dan tinggi badan anak, wawancara  dan dokumentasi. Berdasarkan hasil observasi tersebut kemudian menghitung Indeks Massa Tubuh (IMT) dan membandingkannya dengan standar pertumbuhan anak dari CDC (Centers for Disease Control and Prevention). Hasil pengukuran menunjukkan bahwa dari 5 anak yang diteliti, sebanyak 100% anak memiliki IMT yang tergolong dalam kategori normal, yakni berada dalam rentang IMT yang sesuai dengan standar WHO. Berdasarkan hasil pengukuran IMT yang telah dilakukan, dapat disimpulkan bahwa seluruh anak yang berada di kelas A TKQ Matlaul Khoir berada pada rentang -2 SD sampai dengan +1 SD yang artinya memiliki status gizi yang normal atau gizi baik.  Berdasarkan hasil penelitian ini dapat menunjukkan pentingnya pemantauan status gizi pada anak usia dini untuk mendukung pertumbuhan dan perkembangan yang optimal dan perlunya perhatian lebih terhadap pola makan dan kebiasaan hidup sehat bagi anak-anak di lingkungan pendidikan   Abstract   Monitoring the nutritional status of early childhood is one of the most important aspects because the nutritional status of early childhood will determine the quality of life of children in their early life. This study aims to see physical growth, brain development and optimal body function based on nutritional status so that children grow healthily and develop according to their age. The research method used is quantitative descriptive by conducting field studies. The research approach uses anthropometric methods in assessing nutritional status. The sample of this study was early childhood at TKQ Matlaul Khoir aged 4-5 years totaling 5 children. The data collection technique in this study uses three main methods, namely, measuring the weight and height of children, interviews and documentation. Based on the results of the observation, the Body Mass Index (BMI) is then calculated and compared with the child growth standards from the CDC (Centers for Disease Control and Prevention). The measurement results show that of the 5 children studied, 100% of children have a BMI that is included in the normal category, namely in the BMI range according to WHO standards. Based on the results of the BMI measurements that have been carried out, it can be concluded that all children in class A TKQ Matlaul Khoir are in the range of -2 SD to +1 SD which means they have normal nutritional status or good nutrition..Based on the results of this study, it can be seen that it is important to monitor nutritional status in early childhood to support optimal growth and development and the need for more attention to eating patterns and healthy living habits for children in educational environments.
Co-Authors ariyanto, devi Atmoko, Agus Tri Budiman, Ade Surya Duwi Cahya Putri Buani Henny Destiana Liniwati Lumban Batu, Sahat Parluhutan Puji Astuti Ratih Yulia Hayuningtyas Sa’id, Nur Sahadan, Rijan Sahat Parluhutan Lumban Batu Suci Putri Lestari Yulianty , Nadya Yusnita, Rina Tri