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All Journal Jurnal Pendidikan dan Ilmu Sosial FingeR: Journal of Elementary School Jurnal Ilmiah Research Student
Inneke Putri
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Religion Arts Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences

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IMPLEMENTASI APLIKASI STOK BARANG PERANGKAT JARINGAN BERBASIS WEB DI PT ZATHCO Inneke putri; Dwi prapita sari; Mhd ikhsan rifki
JURNAL ILMIAH RESEARCH STUDENT Vol. 1 No. 3 (2024): Januari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v1i3.835

Abstract

The system currently used experiences problems in handling recipient and delivery data, customer data, and inventory data which are recorded on paper and only copied by the admin to the company computer. These problems can result in product calculation errors, problems in recording and reporting product recipients and deliveries, and in several months the product in and out can reach the target. There are often differences in inventory. This is caused by a helper or admin error. The warehouse department is recording, receiving and sending products. In addition, the accumulation of large numbers of files can make it difficult to find the product data you need, and searching files can take time and interfere with other tasks. The aim of this research is to develop an inventory management application that can manage recipient or delivery data, inventory data, and delivery data using visual modeling used in building object-oriented systems and waterfall system development methods. This is about developing a website that is created to simplify incoming goods data and outgoing goods data so that it can help business processes in the company.
Implementasi Model Project Based Learning Berbantuan LKPD Interaktif Berbasis Liveworksheets untuk Meningkatkan Hasil Belajar Matematika Siswa Kelas V SDN 238 Palembang Redi Firmansyah; Ernalida; Inneke Putri; Imelda Sari
FingeR: Journal of Elementary School Vol. 4 No. 1 (2025): Edisi: Juni
Publisher : Universitas Nurul Huda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/hez7x808

Abstract

Penelitian ini bertujuan meningkatkan hasil belajar Matematika siswa kelas V SDN 238 Palembang melalui implementasi model Project Based Learning berbantuan LKPD Inetraktif Berbasis Liveworksheets. Metode penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK) dengan model Kemmis dan McTaggart, dilaksanakan dalam dua siklus, dengan setiap siklus yang mencakup tahapan perencanaan, pelaksanaan, pengamatan, dan refleksi. Subjek penelitian berjumlah 25 peserta didik. Data dikumpulkan melalui pre-test, post-test, dan lembar observasi keaktifan peserta didik. Hasil penelitian menunjukkan peningkatan yang signifikan dalam hasil belajar peserta didik dari siklus I hingga siklus II. Rata-rata skor post-test meningkat dari 59 pada siklus I menjadi 80,2 pada siklus II, dengan rata-rata peningkatan keseluruhan sebesar 42,4 poin. Selain itu, indikator keberhasilan tercapai dengan 95,5% peserta didik menunjukkan keterlibatan aktif dalam pembelajaran. Berdasarkan hasil tersebut dapat ditarik kesimpulan bahwa implementasi model Project Based Learning berbantuan LKPD Interaktif berbasis Liveworksheets mampu meningkatkan hasil belajar Matematika siswa kelas V SDN 238 Palembang.
Penerapan Deep Learning dalam Analisis Citra Gigi Supiyandi Supiyandi; Wahyu Eka Judistira; Sepriana Nurliani; Rondi Sahputra Darmono; Inneke Putri
JURNAL PENDIDIKAN DAN ILMU SOSIAL (JUPENDIS) Vol. 2 No. 4 (2024): Oktober : JURNAL PENDIDIKAN DAN ILMU SOSIAL
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jupendis.v2i4.2165

Abstract

Testing in dental medical recognition and recording is still done manually, causing it to take a long time. In this study, an object detection method was applied to assist doctors in identifying patient conditions. Convolutional Neural Network (CNN) method was trained with an intraoral image dataset that includes five categories of tooth conditions: normal, filling, caries, and residual roots. CNN performance evaluation was conducted using evaluation metrics, and the results showed that the best CNN model achieved an mAP of 84% and a testing accuracy of 82%. This research successfully achieved its main goal, which is to build a reliable deep learning model for dental disease detection and recognition in humans.
Co-Authors Dwi Prapita Sari Ernalida Imelda Sari Mhd Ikhsan Rifki Redi Firmansyah Rondi Sahputra Darmono Sepriana Nurliani Supiyandi Supiyandi Wahyu Eka Judistira