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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Komtika (Komputasi dan Informatika) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Share : Journal of Service Learning JURIKOM (Jurnal Riset Komputer) JURTEKSI Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Ilmiah Edunomika (JIE) INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics G-Tech : Jurnal Teknologi Terapan Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Journal Computer Science and Informatic Systems : J-Cosys Jurnal Dinamika Informatika (JDI) Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Comprehensive Science Duta Abdimas: Jurnal Pengabdian Masyarakat Biner : Jurnal Ilmiah Informatika dan Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Journal Of Artificial Intelligence And Software Engineering SmartComp CSRID Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknik Informatika dan Teknologi Informasi Edcomtech: Jurnal Kajian Teknologi Pendidikan
FAULINDA ELY NASTITI
Universitas Duta Bangsa Surakarta
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Kesiapan Pendidikan Indonesia Menghadapi Era Society 5.0 Faulinda Ely Nastiti; Aghni Rizqi Ni'mal 'Abdu
Edcomtech: Jurnal Kajian Teknologi Pendidikan Vol. 5 No. 1 (2020)
Publisher : Universitas Negeri Malang in collaboration with APSTPI and IPTPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um039v5i12020p61

Abstract

The development of information technology is currently reaching all areas of people's lives, including education. In the era of the industrial revolution 4.0 required three literacies namely data literacy, human literacy, and technological literacy. Learning in the revolutionary era 4.0 can apply hybrid / blended learning and Case-base Learning. Even education in the era of society 5.0, allows students or students in learning activities side by side with robots that have been designed to replace the role of educators. So what about the education system in Indonesia? This paper examines the readiness of Indonesian education in welcoming the era of society 5.0. Thus, activists and education stakeholders get a picture of the urgency of the presence of technology era society 5.0 in the world of education
Perbandingan Kinerja Model Forecasting Nilai Perdagangan Komoditas HS pada Evaluasi Time-Based Ridwan Dwi Irawan; Marta Ardiyanto; Ringgo Ismoyo Buwono; Faulinda Ely Nastiti
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9843

Abstract

Global economic uncertainty, trade-regime shifts, and supply-chain disruptions have made export-import trade-value forecasting increasingly complex. This study compares the performance of Random Forest, Extra Trees Regression, and SARIMA in predicting monthly trade values of HS commodities in the apparel and footwear sector, covering HS 61, HS 62, HS 63, and HS 64. The dataset was obtained from Indonesia?s Central Bureau of Statistics (BPS) and organized as a monthly time series using a leakage-safe workflow through a time-based train-validation-test split. The modeling stage employed 19 predictive features consisting of historical, local statistical, calendar-seasonal, and exogenous variables, and each model was tuned on the validation set before being evaluated on the holdout test. Performance was assessed using MAE, RMSE, MAPE, sMAPE, and wMAPE, with MAPE as the primary ranking metric. The main contribution of this study lies in providing a fair and replicable comparison of three forecasting models under a time-based evaluation protocol for HS commodity trade data, making the model selection results more representative of real implementation settings. The results show that Random Forest achieved the best MAPE at 22.7746%, slightly outperforming Extra Trees Regression at 22.9469%, while SARIMA recorded 28.9794%. These findings indicate that tree-based ensemble models are more adaptive to volatile trade data, whereas SARIMA remains relevant as a statistical baseline for structured seasonal patterns.
Bridging hybrid deep learning detection and lightweight handcrafted features for robust single sample face recognition Faulinda Ely Nastiti; Sopingi Sopingi; Dedy Hariyadi; Sri Sumarlinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp888-900

Abstract

Single sample face recognition (SSFR) remains a challenging task due to the limitation of having only one reference image per identity, which reduces embedding diversity and decreases robustness under variations of pose, expression, and illumination. This study proposed a hybrid framework that integrates deep learning-based detection through anchor box optimization and non-maximum suppression (NMS) with lightweight handcrafted feature extraction using local binary pattern (LBP). The detection stage leverages deep learning to ensure robust face localisation, while LBP maintains computational efficiency under limited-sample conditions. The training process showed accuracy improvement from 47.5% at the initial epoch to 98.0% at epoch 72, while testing accuracy stabilized at 85-88% with the best value of 87.9%. Evaluation on 48 new facial images achieved 89.6% accuracy, 95.3% precision, 91.1% recall, 93.1% F1-score, and 0.94 area under the receiver operating characteristic curve (AUC ROC). Real-world implementation on Android and iOS-based attendance applications further validated the model, reaching 88.46% accuracy across 52 tests under 50-400 lux illumination. The findings proved that the proposed hybrid design provides improved accuracy and stability compared with previous approaches.
Hybrid LSTM Forecasting Framework with Mutual Information and PSO–GWO Optimization for Short-Term SARS-CoV-2 Prediction in Indonesia Nastiti, Faulinda Ely; Musa, Shahrulniza; Riadi, Imam
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5485

Abstract

SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.
SISTEM INFORMASI REKOMENDASI PARIWISATA KABUPATEN SRAGEN MENGGUNAKAN ALGORITMA HYBRID FILTERING Benaya Chessa Sarmanela; Faulinda Ely Nastiti; Eko Purwanto
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Pencarian informasi pariwisata di Kabupaten Sragen saat ini masih bersifat parsial dan tersebar di berbagai platform, menyulitkan wisatawan dalam merencanakan perjalanan secara terpusat. Penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi Rekomendasi Pariwisata Kabupaten Sragen berbasis website guna memberikan referensi destinasi yang dipersonalisasi. Pengembangan sistem menggunakan metodologi Agile Scrum dengan kerangka kerja Astro untuk frontend dan Supabase sebagai basis data backend. Inti penyelesaian masalah pada sistem ini adalah implementasi algoritma Hybrid Filtering yang mengintegrasikan Content-Based Filtering (CBF) dan Collaborative Filtering (CF). CBF menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan Cosine Similarity untuk mengekstraksi serta mencocokkan kategori fitur destinasi. Sementara itu, CF menerapkan pendekatan Item-Based dengan korelasi Cosine Similarity untuk menganalisis matriks rating antar pengguna. Penggabungan kedua metode ini menggunakan rasio pembobotan dinamis (basis awal 70:30) yang diarsiteki khusus untuk menanggulangi anomali cold-start pada pengguna baru. Hasil dari perancangan ini adalah sebuah purwarupa sistem komputasi yang mampu mengkalkulasi prediksi kedekatan secara presisi untuk menyajikan daftar peringkat rekomendasi pariwisata sesuai dengan riwayat interaksi pengguna. Kesimpulannya, integrasi Hybrid Filtering dalam pendekatan adaptif Agile Scrum menghasilkan sistem yang terpusat dan berpotensi kuat untuk meningkatkan visibilitas sektor pariwisata lokal.
Implementasi Gaussian Naive Bayes untuk Klasifikasi Permintaan dan Simple Additive Weighting untuk Pemilihan Supplier Pengadaan Barang Arya Kusumadewa; Faulinda Ely Nastiti; Eko Purwanto
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Proses pengadaan barang memerlukan pengambilan keputusan yang tepat untuk memastikan ketersediaan barang sesuai dengan kebutuhan serta pemilihan supplier yang mampu memenuhi kriteria organisasi. Pengambilan keputusan yang hanya didasarkan pada pengalaman atau pertimbangan subjektif berpotensi menyebabkan ketidaksesuaian jumlah persediaan maupun pemilihan supplier yang kurang optimal. Penelitian ini bertujuan mengimplementasikan algoritma Gaussian Naive Bayes untuk mengklasifikasikan tingkat permintaan barang serta metode Simple Additive Weighting (SAW) untuk menentukan supplier terbaik berdasarkan beberapa kriteria. Dataset yang digunakan merupakan Indonesia E-Commerce Sales and Shipping Dataset 2023-2025 yang diperoleh dari Kaggle dengan jumlah data awal sebanyak 20.848 transaksi. Setelah melalui tahap preprocessing, diperoleh 16.239 data yang digunakan dalam proses klasifikasi. Kategori permintaan dibentuk menjadi tiga kelas, yaitu Low, Medium, dan High, menggunakan metode kuantil (quantile). Model Gaussian Naive Bayes dievaluasi menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan nilai accuracy sebesar 60,07%, precision 65,95%, recall 60,07%, dan F1-score 57,44%. Selanjutnya, hasil klasifikasi dimanfaatkan sebagai dasar dalam proses pemilihan supplier menggunakan metode SAW berdasarkan kriteria harga, ketepatan waktu pengiriman, kualitas, dan responsivitas. Hasil perhitungan SAW menunjukkan bahwa PT. Mahkota Bisnis memperoleh nilai preferensi tertinggi sebesar 0,9000, sehingga direkomendasikan sebagai supplier terbaik. Hasil penelitian menunjukkan bahwa integrasi Gaussian Naive Bayes dan SAW dapat mendukung proses pengambilan keputusan pengadaan barang secara lebih objektif dan sistematis.
Rancang Bangun Sistem Informasi Manajemen Berbasis Web Dengan Sistem Rekomendasi Pengadaan Bahan Baku Menggunakan Metode Weighted Scoring Manase Rezata Purba; Faulinda Ely Nastiti; Eko Purwanto
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Industri coffee shop di Indonesia berkembang pesat, namun banyak pelaku usaha masih menghadapi tantangan dalam pengelolaan bahan baku secara efisien. SKA eRCe Cafe Manahan, Surakarta, merupakan salah satu coffee shop yang pengelolaan stok bahan bakunya masih dilakukan secara semi-manual menggunakan spreadsheet, sehingga keputusan pengadaan bersifat subjektif dan sering mengakibatkan kondisi overstock maupun stockout. Penelitian ini bertujuan merancang dan membangun sistem informasi manajemen coffee shop berbasis web yang dilengkapi sistem rekomendasi pengadaan bahan baku menggunakan metode Weighted Scoring (Simple Additive Weighting/SAW). Sistem dibangun menggunakan framework Laravel, MySQL, Tailwind CSS, dan Alpine.js dengan pendekatan model Waterfall. Metode Weighted Scoring diterapkan dengan empat kriteria yaitu tingkat konsumsi bahan baku, jumlah stok minimum, harga beli, dan lead time supplier, yang masing-masing diklasifikasikan sebagai atribut benefit atau cost dan diberi bobot berdasarkan hasil wawancara dengan pemilik. Sistem menghasilkan peringkat prioritas pengadaan bahan baku secara objektif melalui proses normalisasi SAW dan pembobotan terhadap seluruh kriteria, serta dilengkapi informasi pendukung berupa Reorder Point (ROP) dan estimasi kuantitas pengadaan. Pengujian dilakukan menggunakan Black Box Testing untuk memverifikasi fungsionalitas sistem. Hasil penelitian menunjukkan bahwa sistem mampu mengintegrasikan pengelolaan data operasional coffee shop secara real-time dan menghasilkan rekomendasi pengadaan bahan baku yang objektif, terukur, dan berbasis data sebagai pendukung pengambilan keputusan bagi pengelola SKA eRCe Cafe Manahan. Kata kunci : Sistem Informasi Manajemen, coffee shop, Pengadaan Bahan Baku, Weighted Scoring, Simple Additive Weighting
PENERAPAN THRESHOLD-BASED NOTIFICATIONS Umar Choirul Hadi; Faulinda Ely Nastiti; Eko Purwanto
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Perkembangan teknologi informasi mendorong lembaga pendidikan untuk meningkatkan kualitas pengelolaan aset melalui sistem yang terintegrasi. SMP IT AR-RISALAH masih melakukan pengelolaan inventaris barang secara manual sehingga menimbulkan berbagai permasalahan, seperti kesalahan pencatatan, duplikasi data, kesulitan pencarian informasi, serta keterlambatan dalam penyusunan laporan. Penelitian ini bertujuan merancang dan membangun Sistem Informasi Inventaris Barang berbasis web yang dapat meningkatkan efektivitas dan efisiensi pengelolaan inventaris. Metode pengembangan sistem yang digunakan adalah Rapid Application Development (RAD) yang terdiri dari tahapan Requirement Planning, Design Workshop, dan Implementation. Sistem dirancang menggunakan Unified Modeling Language (UML) dan Entity Relationship Diagram (ERD), serta dikembangkan menggunakan bahasa pemrograman PHP dan basis data MySQL. Salah satu fitur utama yang diterapkan adalah Threshold-Based Notification, yaitu mekanisme notifikasi otomatis yang memberikan peringatan ketika jumlah stok barang mencapai atau berada di bawah batas minimum yang telah ditentukan. Fitur ini membantu petugas humas dalam melakukan pemantauan ketersediaan barang secara lebih cepat dan akurat sehingga dapat meminimalkan risiko kekurangan stok. Hasil pengujian menggunakan metode Black Box Testing menunjukkan bahwa seluruh fungsi sistem berjalan sesuai kebutuhan. Sistem yang dikembangkan mampu meningkatkan keakuratan data, mempercepat proses pencarian informasi dan pelaporan, serta mendukung pengambilan keputusan dalam pengelolaan inventaris barang secara efektif.
PENERAPAN METODE K-MEANS CLUSTERING DAN SUPPORT VECTOR MACHINE (SVM) BERBASIS MODEL RFM UNTUK KLASIFIKASI TIER PELANGGAN Tariq; Nurmalitasari; Faulinda Ely Nastiti
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5958

Abstract

Suboptimal management of large-scale transaction data can lead to marketing inefficiencies, particularly in determining promotional strategies that do not align with customer characteristics. This study aims to map the customer loyalty of CV Ekasa's client partners, by segmenting its customers using an integrated Recency, Frequency, Monetary (RFM) model, K-Means Clustering, and Support Vector Machine (SVM) classification. The dataset comprises 287,512 raw point-of-sale transaction records collected between October 2022 and September 2025, which after preprocessing yielded 341 valid customers for RFM modeling. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, RFM features were log-transformed and standardized before clustering. Silhouette Score evaluation across k = 1–10 identified two customer segments (k = 2, Silhouette Score = 0.482) as optimal, labeled Passive Tier and Active Tier. These cluster labels were then used as classification targets for a linear-kernel SVM, evaluated under two data-splitting scenarios (80:20 and 70:30). The model achieved 97.10% accuracy with the 80:20 split and 98.06% with the 70:30 split, with precision, recall, and F1-scores above 0.97 for both tiers in both scenarios. These findings indicate that the integrated RFM–K-Means–SVM pipeline classifies customer loyalty tiers reliably and stably. The resulting model was deployed as an interactive Streamlit dashboard, giving CV Ekasa's client partner a practical, data-driven basis for designing more targeted and efficient marketing and retention strategies.
Co-Authors Aghni Rizqi Ni'mal 'Abdu Aghni Rizqi Nimal Abdu Agung Wicaksono Agustina Srirahayu Al Ayyubi, Raihan Abdurrahim Anisatul Farida Aprilisa Arum Sari Ardian Pamungkas Ardiyanto, Marta Arif Wicaksono Septyanto Arya Kusumadewa Aziiz, Dear Whizkid Benaya Chessa Sarmanela Bondan Wahyu Pamekas Dedy Hariyadi Dwi Hartanti Eko Purwanto Erfanti Fatkhiyah Ety Meikhati Fauziah Fanny Fazlurrahman Bima Fazlurrahman Fazlurrahman Fazlurrahman Hadi, Muhammad Heri Gunawan Husniati Mafatihus Solehah Ilyas, Daffa Imam Riadi Indraswari, Elsa Fachrisa Irawan, Ridwan Dwi Jayawarsa, A.A. Ketut Khotimah, Agil Husnul Kusumawati, Novi Manase Rezata Purba Margaretha Evi Yuliana Marta Ardiyanto Maulindar, Joni Mei Purweni Mira Erlinawati Moh. Muhtarom, Moh. Muftiyanto, Taufiq Nur Muhammad Alwan Nurdin Muhammad Frasha Candra Perdana Muhammad Ikhsanudin, Muhammad Musa, Shahrulniza Nindya Dwi Anggana Nur Muftiyanto, R. Taufiq Nurdin, Muhammad Alwan Nurmalitasari Nurmalitasari Nurohman, Nurohman Oktaviani, Intan Permatasari, Hanifah Pipin Widyaningsih Pradana, Rico Yoga Prasetyo, Yoga Andrian Prita Haryani Prita Haryani Purweni, Mei Ramadhan, Navin Rifal Bayu Ardi Rina Arum Prastyanti Ringgo Ismoyo Buwono Riska Rosita Rudi Susanto Setyawan, Yanuar Anggit Singgih Purnomo Sopingi SRI SUMARLINDA Suryani, Fajar Tariq tino, Valen Toni Iksanudin Triana Triana Udhata Swardana, Avila Umar Choirul Hadi Uning Kristiana Utomo, Diva Reihan Ferdian Vihi Atina Virgian Galang Sasongko Vita Aryadi Vita Sofia Prihatini Wijaya, Muhammad Krisna Wijiyanto Wijiyanto, Wijiyanto Yafi, Eiad