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All Journal Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Informatics and Communication Technology (JICT) Informatics and Computer Engineering Journal Internet of Things and Artificial Intelligence Journal Akademika
Nindy Raisa Hanum
Universitas Jambi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mathematics

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Implementation of the Design Thinking Method in Designing a Laboratory Room Scheduling System Nindy Raisa Hanum; Hasanatul Iftitah; Yogi Perdana
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

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

Abstract

Laboratories are essential facilities in higher education institutions, particularly in supporting practical learning and research activities. However, manual management often leads to scheduling conflicts, inefficient room utilization, and limited access to real-time information. This study aims to develop a web-based, integrated laboratory room scheduling system for the Faculty of Science and Technology at the University of Jambi. The research adopts the Design Thinking methodology, which consists of five stages: Empathize, Define, Ideate, Prototype, and Test. Stakeholder needs were identified through interviews with lecturers, students, lab staff, and laboratory heads. The primary issue identified was the lack of a centralized scheduling system. A prototype was designed using Figma, incorporating features such as real-time schedule viewing, room booking management, and usage reporting. Usability testing with five respondents revealed high satisfaction in terms of learnability (84%), memorability (85%), and efficiency (87%). The results confirm that the system meets user needs and improves laboratory management. This study contributes to the digital transformation of academic services by offering a user-centered, context-aware solution tailored to the needs of the Faculty of Science and Technology.
Geographic Information System for Drought Potential Areas in Kampar District Hanum, Nindy Raisa
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.960

Abstract

Drought is a natural disaster that has an impact on various sectors. Drought occurs during the dry season, which is from May to October. So that there is a need for spatial information in the Kampar Regency area for drought potential so that it can help overcome the problem of drought. The data consists of Landsat 8 OLI / TRIS images and rainfall data. The transformation method used is the Tasseled Cap Transformation to get the wetness and revision index and NDVI to get the vegetation index. The results of each index will be weighted and overlaid so that a map of the area with drought potential is obtained. The results of this study are Kampar District which has an area of 7% located in Tapung Hilir District, while 33% of Kampar District has a very low drought potential. In general, the Kampar Regency is classified as having moderate drought potential.
ANALISIS PERBANDINGAN MODEL GRU DAN LSTM UNTUK PREDIKSI HARGA SAHAM BANK RAKYAT INDONESIA: Deep Learning, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), Stock Price Prediction Perdana, Yogi; Raisa Hanum, Nindy; Rabiula, Andre; Anzari, Yandi
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1692

Abstract

This research implements and compares two deep learning architectures, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for predicting the stock price of Bank Rakyat Indonesia (BRI) using historical data from February 2023 to October 2024. Through systematic hyperparameter tuning and comprehensive evaluation, the study finds that GRU consistently outperforms LSTM across all regression metrics, with a 10.7% improvement in R² and an 18.5% reduction in MAPE. The optimal GRU configuration (100 units, 100 epochs, batch size 32, learning rate 0.001) achieves an MSE of 6517.5 and MAPE of 1.3764%. Visual analysis confirms GRU's superior ability to capture stock price fluctuations and adapt more quickly to trend changes. The simpler architecture of GRU with fewer parameters proves more effective for handling the high-noise characteristics and varying volatility of stock price data. While both models face challenges in predicting extreme market events, GRU demonstrates better resilience and faster recovery after such occurrences. This research contributes to the understanding of recurrent neural network applications in financial time series forecasting and provides practical insights for developing more accurate stock price prediction systems.
PEMODELAN PREDIKTIF TRAFIK WEBSITE BERDASARKAN VOLUME KONTEN: PENDEKATAN REGRESI: Web performance, content strategy, linear regression model, page view analysis, digital content optimization Hasanatul Iftitah; Nindy Raisa Hanum
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1694

Abstract

In today's digital landscape, a website's performance serves as a key metric of an institution’s online presence and communication strategy. This research focuses on forecasting website performance by analyzing the relationship between the number of published articles and the volume of page views using a simple linear regression approach. Monthly data was obtained from the official website of the Faculty of Science and Technology at Universitas Jambi, comprising content publication frequency and corresponding traffic. The analysis reveals a strong positive correlation, where each additional published article contributes to a notable increase in page views. The regression model yields a coefficient of 103.75 with an R² value of 0.7278, indicating that over 72% of traffic variation is attributable to content volume. These results emphasize the importance of consistent content production in enhancing web visibility and provide valuable insights for content strategy development.
Parameter-Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images Akhiyar Waladi; Hasanatul Iftitah; Nindy Raisa Hanum; Yogi Perdana; Fitra Wahyuni; Rahmad Ashar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2558

Abstract

Malaria diagnostic automation faces critical challenges, including severe class imbalance with ratios of up to 54:1, limited datasets containing 200 to 500 images, and computational inefficiency resulting from the need to train separate models for each detection-classification combination. This study developed a multi-model framework with a shared classification architecture that trains classification models once on ground-truth crops and reuses them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved mAP@50 scores ranging from 70.84% to 96.27%, with high recall values of 71.05% to 93.12% minimizing missed parasite detections. Classification results demonstrated the importance of dataset-dependent model selection, with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved accuracies of 91.51% on the IML Lifecycle dataset and 98.28% on the MP-IDB Species dataset, while EfficientNet-B0 achieved 86.45% on the multi-patient MD-2019 dataset. ResNet50 achieved 96.13% accuracy on severely imbalanced MP-IDB Stages dataset. Focal Loss optimization with alpha = 1.0 and gamma = 1.5 enabled robust minority-class performance, achieving F1-scores between 0.44 and 1.00 on ultra-minority classes and demonstrating effective handling of class imbalance. The compact models, with sizes ranging from 46 MB to 89 MB, enable practical deployment on resource-constrained hardware.
ANALISIS PERUBAHAN TUTUPAN LAHAN KOTA JAMBI BERBASIS KLASIFIKASI NDVI DAN EKSTRAKSI POLA SPEKTRAL MENGGUNAKAN DECISION TREE PADA CITRA LANDSAT 8 Ayu Friska Purba; Daniel Arsa; Nindy Raisa Hanum
Informatics and Computer Engineering Journal Vol 6 No 2 (2026): Periode Juli 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i2.12969

Abstract

-Kota Jambi sebagai wilayah dengan pertumbuhan penduduk dan pembangunan yang pesat mengalami tekanan terhadap tutupan lahan. Penelitian ini menganalisis perubahan tutupan lahan di Kota Jambi selama periode 2016–2025 menggunakan citra satelit Landsat 8 OLI/TIRS, indeks vegetasi Normalized Difference Vegetation Index (NDVI), dan algoritma Decision Tree. Data citra diperoleh melalui platform Google Earth Engine (GEE) dengan tahapan pra-pemrosesan meliputi cloud masking menggunakan band QA_PIXEL, koreksi reflektansi, dan penyusunan komposit tahunan menggunakan metode median composite. Nilai NDVI dihitung di QGIS menggunakan formula (Band 5 − Band 4) / (Band 5 + Band 4), kemudian diklasifikasikan berdasarkan threshold NDVI menjadi enam kelas tutupan lahan, yaitu badan air, lahan terbuka, rumput, pertanian/taman, semak belukar, dan hutan lebat. Hasil klasifikasi NDVI tersebut digunakan sebagai label target untuk melatih model Decision Tree, sedangkan tujuh band spektral Landsat 8 digunakan sebagai fitur input dengan pembagian data temporal split 70:30. Model Decision Tree selanjutnya digunakan untuk mengekstraksi feature importance dan decision rules sebagai representasi karakteristik spektral setiap kelas tutupan lahan. Hasil evaluasi menunjukkan Overall Accuracy sebesar 89,61%, Kappa Coefficient sebesar 0,85457, dan weighted F1-Score sebesar 0,89. Hasil ekstraksi feature importance menunjukkan bahwa Band 4 (Red) dan Band 5 (Near Infrared), sebagai band penyusun NDVI, merupakan fitur spektral yang paling relevan dalam membedakan kelas tutupan lahan. Analisis perubahan menunjukkan bahwa luas hutan lebat menurun sebesar 1.393,92 ha (−16,1%), sedangkan pertanian/taman meningkat sebesar 721,53 ha (+19,2%). Hasil penelitian mengindikasikan adanya tekanan terhadap vegetasi Kota Jambi akibat perkembangan wilayah
Perancangan dan Pembangunan Sistem Informasi Persuratan Berbasis Microsoft Access pada Perum Bulog Regional Jambi Purba, Ayu Friska; Hanum, Nindy Raisa
Bahasa Indonesia Vol 12 No 2 (2025): Bina Insani ICT Journal (Desember) 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v12i2.3761

Abstract

Saat ini teknologi informasi berkembang pesat dan mendukung digitalisasi tata kelola administrasi organisasi, termasuk pengelolaan surat. Permasalahan di Perum Bulog Regional Jambi Divisi Bisnis adalah pengelolaan surat masuk, surat keluar, dan disposisi masih dicatat manual di buku agenda. Hal ini menyebabkan pengulangan pencatatan data, waktu pencarian lama, dan risiko kehilangan file. Tujuan penelitian ini adalah membangun sistem informasi pengelolaan surat berbasis Microsoft Access untuk membantu pengelolaan surat yang lebih efektif, efisien, dan sistematis. Penelitian menggunakan model waterfall yang terdiri dari tahapan analisis kebutuhan, perancangan, implementasi, pengujian, hingga pemeliharaan. Metode pengumpulan data dilakukan melalui wawancara dan observasi di Perum Bulog Divisi Bisnis Regional Jambi. Diagram use case digunakan untuk mendeskripsikan aktivitas pengguna. Implementasi sistem menggunakan Microsoft Access, meliputi tabel untuk penyimpanan data, form sebagai antarmuka input, query untuk pengolahan data, dan reports untuk pelaporan. Sistem yang dihasilkan memiliki fitur login, navigation form, laporan surat masuk, surat keluar, disposisi, dan pencarian surat. Hasil dari pengujian Black Box 100% menunjukkan semua fitur berfungsi dengan baik. Hasil wawancara menunjukkan sistem memiliki tampilan jelas, mudah dimengerti, memperlancar penginputan data, dan laporan sesuai kebutuhan. Hasil pengujian menunjukkan bahwa sistem yang dikembangkan berfungsi dengan baik dan mampu mengatasi masalah pencatatan manual. Sistem ini membantu Perum Bulog dalam mengoptimalkan proses administrasi persuratan melalui digitalisasi dan berkontribusi pada peningkatan efisiensi operasional.
Dashboard Monitoring Penjualan Berbasis Tableau untuk Mendukung Data-Driven Decision Making pada Perum Bulog Regional Jambi Nizam, Muhammad Syahrul; Hanum, Nindy Raisa
Bahasa Indonesia Vol 12 No 2 (2025): Bina Insani ICT Journal (Desember) 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v12i2.3777

Abstract

: Perum Bulog Kantor Wilayah Jambi memiliki peran strategis dalam menjaga ketahanan pangan melalui pengelolaan distribusi dan penjualan produk komersial. Namun, proses monitoring penjualan masih menggunakan sistem konvensional dengan data yang tersebar pada berbagai file spreadsheet, sehingga menyulitkan analisis data, identifikasi tren penjualan, serta pengambilan keputusan yang cepat dan akurat. Penelitian ini bertujuan untuk mengembangkan dashboard monitoring penjualan berbasis Tableau yang mampu mengintegrasikan dan memvisualisasikan data penjualan secara interaktif. Metode penelitian yang digunakan adalah metode kuantitatif dengan data sekunder penjualan produk komersial Perum Bulog Kanwil Jambi pada periode Juni hingga Agustus 2025. Hasil penelitian menunjukkan bahwa dashboard yang dikembangkan mampu mengintegrasikan data penjualan dari berbagai sumber ke dalam satu platform terpadu dengan visualisasi berupa grafik tren penjualan, analisis kinerja produk, serta peta distribusi geografis. Dashboard ini dilengkapi dengan fitur interaktif seperti filter dinamis dan drill-down analysis yang memudahkan pengguna dalam melakukan eksplorasi data secara mandiri. Dashboard ini diharapkan dapat menjadi alat bantu dalam proses monitoring penjualan dan mendukung pengambilan keputusan berbasis data di Perum Bulog Kantor Wilayah Jambi.
Co-Authors Abda Abda Akhiyar Waladi Andre Rabiula Arsa, Daniel Ayu Friska Purba Fitra Wahyuni Hasanatul Iftitah Nizam, Muhammad Syahrul Perdana, Yogi Purba, Ayu Friska Rahmad Ashar Yandi Anzari Yogi Perdana