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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Information System and Computer
Nely Ayu Verda
Universitas Islam Nahdlatul Ulama Jepara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Pengaruh Durasi Live Selling Produk Fashion terhadap Total Penjualan Menggunakan Decision Tree Nely Ayu Verda; Heru Saputro; Joko Minardi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 11 No 1 (2026): JANUARY
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v11i1.7629

Abstract

The growth of live selling on e-commerce platforms has encouraged sellers to understand the factors influencing sales performance. However, many sellers are still unaware of which variables most significantly affect total sales during live sessions. This study aims to develop a web-based visualization system called RekomLive to analyze the influence of broadcast duration, number of viewers, and interactions on total fashion product sales using the Decision Tree algorithm. The dataset consists of 122 data collected through direct observation of live selling sessions on the Shopee platform. Numerical data were transformed using the Equal Width Binning method into three categories, namely low, medium, and high, then split at a 70:30 ratio into 85 training data and 37 testing data. The results show that the viewers variable is the most influential factor, with the highest information gain value of 0.1288 and a contribution of 84.3%, followed by interaction at 15.7%, while duration does not have a significant influence. The model achieved an accuracy of 89.19%, precision of 91.67%, and recall of 97.06%. The RekomLive system successfully presents the analysis results visually and recommends a minimum broadcast duration of 240 minutes to maximize high sales opportunities. These findings can be used as an evaluation basis for sellers in developing live selling strategies for fashion products.
SUPERVISED LEARNING UNTUK KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN DECISION TREE Septya Dwi Lestari; Winda Fatikhatul Munawaroh; Nely Ayu Verda; Heru Saputro; Joko Minardi
Journal of Information System and Computer Vol. 6 No. 1 (2026): Juli 2026
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v6i1.2076

Abstract

Deteksi dini penyakit jantung sangat penting untuk menurunkan angka kematian serta mendukung penanganan medis yang tepat dan cepat. Dalam praktiknya, proses diagnosis penyakit jantung masih banyak dilakukan secara manual oleh tenaga medis, sehingga membutuhkan waktu yang cukup lama dan berpotensi menimbulkan ketidakkonsistenan dalam pengambilan keputusan, terutama ketika jumlah data pasien semakin besar. Penelitian ini bertujuan untuk mengembangkan dan menerapkan model klasifikasi berbasis supervised learning dalam memprediksi penyakit jantung menggunakan algoritma Decision Tree. Dataset yang digunakan terdiri dari 1.025 data pasien dengan 13 atribut klinis, meliputi usia, jenis kelamin, tipe nyeri dada, tekanan darah istirahat, kadar kolesterol, gula darah puasa, hasil elektrokardiogram, detak jantung maksimum, angina akibat olahraga, depresi ST, kemiringan segmen ST, jumlah pembuluh darah utama, dan status thalassemia, dengan status penyakit jantung sebagai kelas target. Data dibagi menjadi 80% data pelatihan dan 20% data pengujian. Model Decision Tree dibangun dengan kedalaman maksimum lima tingkat untuk menjaga interpretabilitas model dan menghindari overfitting. Hasil penelitian menunjukkan bahwa model yang diusulkan mampu mencapai akurasi sebesar 98,54% dalam mendeteksi pasien yang benar-benar menderita penyakit jantung. Model klasifikasi ini diharapkan dapat membantu tenaga medis dalam melakukan deteksi dini risiko penyakit jantung serta mendukung pengambilan keputusan melalui aturan keputusan yang mudah dipahami.
Co-Authors Heru Saputro Heru Saputro Joko Minardi Septya Dwi Lestari Winda Fatikhatul Munawaroh