Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Information Systems and Informatics Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat
Diana Laily Fithri
Muria Kudus University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Student Performance Classification Using Academic, Socioeconomic, and Digital Behavior Features: A Comparative Study Muhammad Arifin; Fajar Nugraha; Diana Laily Fithri
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1460

Abstract

Accurate prediction of student academic performance is essential for universities seeking to improve learning outcomes and deliver timely, data-driven support. Prior work commonly uses regression to estimate Grade Point Average (GPA), yet numeric predictions can be difficult for administrators to translate into actionable risk levels. This study reframes the task as binary classification, categorizing students as good (GPA ≥ 3.00) or poor (GPA < 3.00) performers. Using 2,423 records from multiple programs at an Indonesian university, we combine academic indicators from the learning management system (login frequency, assignment submission, and forum activity) with socio-economic and digital behavioral variables (parental income, extracurricular participation, study-group involvement, and social media use). Seven machine learning models—Naïve Bayes, Generalized Linear Model, Logistic Regression, Deep Learning, Decision Tree, Random Forest, and Gradient Boosted Trees (GBT)—are benchmarked under a consistent evaluation design. Results indicate that integrating academic, socio-economic, and digital behavioral features improves classification performance, and ensemble methods outperform single, traditional models. GBT yields the best accuracy of 0.75, offering a practical basis for early-warning dashboards and targeted interventions. The study provides comparative evidence from Indonesian higher education and highlights the value of incorporating digital engagement signals alongside conventional academic data for more effective student support services.
Sequential Requirements Prediction in Synthetic Fintech-Like Backlogs Using an Interpretable Hybrid of Transition Rules and Transformer Models Diana Laily Fithri; Soni Adiyono; Muhammad Arifin
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1469

Abstract

Fintech software development is characterized by rapid product iteration and stringent regulatory requirements, resulting in changing requirements as an interrelated set rather than individual elements. In this research, Sequential Requirements Prediction is proposed as a decision support task in Requirements Engineering, where the time-ordered prefix of completed backlog items is used to predict the next likely canonical requirement type as Top-k ranked output. To mitigate noise and inconsistency in backlog data, an LLM-aided semantic normalization step maps diverse requirement descriptions to a closed set of fintech requirement types. The research compares an interpretable rule-based Markov-1 predictor with Transformer-based sequential predictors under a case-level time-aware split. The proposed method is evaluated on a synthetic fintech-like backlog dataset consisting of 900 cases, 5,252 events, and 18 canonical requirement types. The best-performing model, Transformer + normalization + augmentation (M4), achieved Recall@5 = 0.638889, MRR@5 = 0.536806, and NDCG@5 = 0.566667. These results surpassed the rule-based predictor and non-normalized Transformer model. In addition, augmentation further improved Recall@5 from 0.493056 to 0.527778 in the rare-type subset. These findings suggest the methodological promise of the proposed framework for sequence-aware and compliance-conscious backlog analytics in synthetic fintech-like settings.
Digitalisasi Layanan Koperasi Syariah melalui Penerapan Sistem Informasi Simpan Pinjam Berbasis Web pada KSPPS Artha Bahana Syariah astriana putri; Diana Laily Fithri
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 1 (2025): Volume 7, No 1, Desember 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v7i1.7235

Abstract

Kegiatan pengabdian ini bertujuan untuk mendukung digitalisasi layanan keuangan koperasi syariah melalui penerapan sistem informasi simpan pinjam berbasis web. Mitra dalam kegiatan ini adalah KSPPS Artha Bahana Syariah, yang sebelumnya masih menggunakan pencatatan manual dalam pengelolaan data anggota, transaksi simpanan dan pembiayaan, serta pelaporan keuangan. Sistem dirancang dan dikembangkan menggunakan framework Laravel dan basis data MySQL, dengan fitur utama meliputi manajemen anggota, pencatatan transaksi, pembuatan laporan, serta dashboard monitoring. Metode pelaksanaan dilakukan melalui tahapan observasi, analisis kebutuhan, perancangan sistem, pengembangan, uji coba, dan pelatihan. Hasil pengabdian menunjukkan bahwa sistem ini mampu meningkatkan efisiensi kerja pengurus, mempercepat akses terhadap data keuangan, serta memperbaiki akurasi pencatatan. Sistem juga dirancang dengan pendekatan antarmuka yang ramah pengguna agar mudah dioperasikan oleh pengurus koperasi. Temuan ini memperkuat urgensi digitalisasi dalam pengelolaan koperasi syariah serta memberikan dasar pengembangan sistem koperasi yang lebih adaptif dan terintegrasi ke depan.
Penerapan Sistem E-commerce Sebagai Solusi Digitalisasi Penjualan di PR Oemega Dwipa Ananda Rifqi Pradhana; Diana Laily Fithri
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 2 (2026): Volume 7, No 2, Juni 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v7i2.7688

Abstract

PR Oemega Dwipa merupakan UMKM yang masih menggunakan penjualan konvensional via WhatsApp/Facebook, menyebabkan pencatatan tidak efektif dan jangkauan pasar terbatas. Tujuan utama kegiatan ini adalah merancang dan menerapkan sistem e-commerce berbasis web untuk digitalisasi proses bisnis mitra. Metode pelaksanaan kegiatan menggunakan pendekatan System Development Life Cycle (SDLC) model Waterfall yang disesuaikan untuk pengabdian, meliputi tahap analisis kebutuhan (observasi, wawancara), perancangan sistem, implementasi (coding), dan diakhiri dengan sosialisasi serta pelatihan kepada mitra. Hasil kegiatan ini adalah sebuah website e-commerce yang fungsional dan telah disosialisasikan kepada 1 (satu) mitra sasaran, yaitu pemilik usaha. Manfaat utama yang diperoleh adalah peningkatan efisiensi penjualan, proses pencatatan transaksi yang kini terpusat dan rapi, serta manajemen stok yang terintegrasi secara real-time. Kesimpulan dari kegiatan ini adalah penerapan e-commerce berhasil memberikan solusi digital yang praktis, meningkatkan efisiensi operasional, dan membuka potensi jangkauan pasar yang lebih luas bagi PR Oemega Dwipa.
Sentiment Classification of MyTelkomsel Reviews Using SVM and Logistic Regression Rijal Bagus Adinata; Supriyono Supriyono; Diana Laily Fithri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.110409

Abstract

The development of digital technology has encouraged increased user participation in expressing opinions through review platforms, such as the Google Play Store. MyTelkomsel's application, a digital service from Indonesia's leading telecommunications provider, has received various responses, from appreciation to complaints related to app performance and customer service. This study aims to evaluate sentiment in user reviews using Support Vector Machine (SVM) and Logistic Regression algorithms. Data was collected from the Google Play Store and underwent a series of pre-processing stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The feature extraction process uses the TF-IDF approach, while model performance evaluation is based on accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results showed that the performance of both models was relatively balanced, but SVM exhibited an advantage in recall for positive sentiment (82%), accuracy (93.36%), and AUC (0.9680). Logistic Regression excels in precision (99%) in the positive class. WordCloud visualization illustrates consistency of dominant words in each sentiment class, reflecting the model's ability to identify patterns in user opinion. These findings are expected to contribute to the improvement of digital services based on user input.
Co-Authors Ananda Rifqi Pradhana astriana putri Fajar Nugraha Muhammad Arifin Rijal Bagus Adinata Soni Adiyono Supriyono Supriyono