Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Management Science and Information Technology (IJMSIT) Teknofile, Jurnal Sistem Informasi
Tsirwatun Nisail Khasanah
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IMPLEMENTASI DASHBOARD BUSINESS INTELLIGENCE MENGGUNAKAN LOOKER STUDIO UNTUK EVALUASI KINERJA PENJUALAN ELEKTRONIK Tsirwatun Nisail Khasanah; Najwa Hanindya Putri; Syifa Amalia; Revanda Putri Rahmadani; Bagus Sujarwo; Muhammad Arifin
TEKNOFILE : Jurnal Sistem Informasi Vol. 3 No. 12 (2025): Desember 2025
Publisher : PT. ZIVANA CENDEKIAWAN BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Business Intelligence memiliki peran penting dalam mendukung pengambilan keputusan manajerial melalui penyajian informasi yang akurat dan real-time. Sektor penjualan elektronik menghasilkan data transaksi dalam jumlah besar yang sering kali belum dimanfaatkan secara optimal untuk evaluasi kinerja. Penelitian ini bertujuan untuk mengimplementasikan dashboard Business Intelligence menggunakan Looker Studio guna mengevaluasi kinerja penjualan elektronik secara efektif. Metode penelitian yang digunakan adalah metode deskriptif dengan pendekatan studi kasus, di mana data transaksi penjualan dikumpulkan, diolah, dan divisualisasikan ke dalam sebuah dashboard interaktif. Dashboard yang dibangun menyajikan indikator kinerja utama seperti total penjualan, tren pendapatan, produk terlaris, serta distribusi penjualan berdasarkan wilayah. Hasil penelitian menunjukkan bahwa dashboard yang diimplementasikan mampu membantu manajemen dalam memantau kinerja penjualan secara lebih cepat, mengidentifikasi tren, serta mendukung pengambilan keputusan berbasis data. Implikasi dari penelitian ini menunjukkan bahwa Looker Studio dapat dimanfaatkan sebagai alat Business Intelligence yang praktis dan ekonomis dalam evaluasi kinerja penjualan elektronik
Under-Five Children's Nutritional Status Prediction Using Naïve Bayes and Decision Tree Based on Anthropometric Data and Mother–Child Class Participation Tsirwatun Nisail Khasanah; Fajar Nugraha; Yudie Irawan
International Journal of Management Science and Information Technology Vol. 6 No. 2 (2026): July - December 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i2.8128

Abstract

Nutritional status is an important indicator of the health and development of children under five, making early identification essential for supporting appropriate nutritional interventions. This study aimed to develop and compare the performance of the Naïve Bayes and Decision Tree algorithms in classifying the nutritional status of children under five based on anthropometric measurements and participation in the mother and Children Under Five Class program in Mlonggo District, Indonesia. A quantitative approach was applied using the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The initial dataset contained 4,588 records, of which 4,563 valid records remained after the preprocessing stage. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, ROC curve, and McNemar’s test. The results showed that the Decision Tree algorithm achieved a higher cross-validation accuracy of 90.90% compared with 87.59% for Naïve Bayes. The testing results also demonstrated that Decision Tree consistently outperformed Naïve Bayes across the evaluation metrics. Therefore, Decision Tree was selected as the most suitable model for nutritional status classification. The model was subsequently implemented in a web-based application supporting individual and batch prediction, along with the presentation of prediction results and recommended health interventions. The system can support healthcare workers in conducting nutritional status assessments more efficiently and objectively.
Co-Authors Bagus Sujarwo Fajar Nugraha Muhammad Arifin Najwa Hanindya Putri Revanda Putri Rahmadani Syifa Amalia Yudie Irawan