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All Journal Jurnal Sistem Cerdas Infotek : Jurnal Informatika dan Teknologi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Elektronika dan Teknik Informatika Terapan Journal of Applied Informatics Science
Gina Khayatun Nufus
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Perancangan dan Implementasi Sistem Informasi Manajemen Operasional dan Penggajian Berbasis Web Menggunakan Model Waterfall Suhadi Parman; Gina Khayatun Nufus; Sokid; Kahfi Gunardi; Muhammad Iszul Wilsa
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35288

Abstract

Micro, Small, and Medium Enterprises such as E-Donuts face challenges in daily operational management, sales recording, packaging and donut stock management, and employee payroll because they still rely on manual processes. This study aims to design and implement EdoBest, a web-based operational management and payroll application developed using the Waterfall model. The development process includes planning, requirement analysis through interviews and observation, UML-based system design and responsive interface design, implementation using Laravel 12 and MySQL, black-box testing, and maintenance. The novelty of EdoBest lies in the integration of culinary MSME operational modules into a single system, including delivery batch management, multi-outlet management, packaging and donut stock management, employee activity recording, cash advance management, report validation, and automatic payroll calculation based on daily operational data. The quantitative testing results show that 16 functional test scenarios, consisting of 10 Admin scenarios and 6 Employee scenarios, were successfully executed with a 100% success rate. This research contributes a functionally tested design and implementation of an integrated information system that improves recording accuracy, reporting efficiency, real-time monitoring, and decision-making support for multi-outlet culinary MSMEs.
Analisis Ancaman Perang Siber terhadap Keamanan Nasional Indonesia: Tinjauan Eskalasi dan Mitigasi Tahun 2025 Rizki Dewantara; Gina Khayatun Nufus; Eko Jhony Pranata; Fariz Noor Djati
Journal of Applied Informatics Science Volume 2 Issue 1 (2026)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i1.80

Abstract

Kemajuan teknologi internet telah menciptakan interkoneksi global yang memicu ancaman perang siber terhadap keamanan nasional. Masalah utama yang dihadapi Indonesia adalah tingginya kerentanan terhadap serangan digital, dengan catatan tiga koma enam puluh empat miliar anomali trafik pada awal dua ribu dua puluh lima. Penelitian ini bertujuan untuk menganalisis berbagai jenis, tingkat bahaya, dan dampak serangan siber dalam mengganggu stabilitas kedaulatan negara. Metode penelitian yang digunakan adalah kualitatif non interaktif melalui pengkajian dokumen sekunder dari jurnal ilmiah dan laporan resmi otoritas siber. Hasil penelitian menunjukkan adanya fluktuasi anomali trafik yang signifikan dengan puncak tertinggi mencapai enam ratus lima belas koma empat juta kejadian pada Juni dua ribu dua puluh lima. Tren serangan mulai bergeser dari eksploitasi teknis menuju rekayasa sosial yang menyasar celah psikologis pengguna. Kesimpulannya, Indonesia masih berada dalam kategori negara rentan sehingga diperlukan penguatan regulasi serta peningkatan kapasitas sumber daya manusia untuk menghadapi evolusi ancaman siber.
XSentiment-HS: Hierarchical CNN-BiGRU-SVM with Explainable for Indonesian Multi-Level Hate Speech Detection Gina Khayatun Nufus; Rizki Dewantara; Ardi Susanto; Sokid; Lia Farhatuaini; Jaka Septiadi; Mohammad Raihan Akbar
Journal of Applied Informatics Science Volume 2 Issue 1 (2026)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i1.81

Abstract

Deteksi ujaran kebencian pada media sosial menuntut interpretasi teks yang kompleks karena sifatnya yang spontan dan ambigu, terutama dalam bahasa Indonesia yang kaya akan slang. Tantangan utama saat ini adalah keterbatasan penelitian sebelumnya yang mayoritas hanya melakukan klasifikasi biner tanpa mendeteksi tingkat keparahan konten. Penelitian ini mengusulkan XSentiment-HS, sebuah model deep learning hierarkis dua tahap untuk deteksi multi-tingkat hate speech. Arsitektur model menggabungkan Convolutional Neural Networks (CNN) untuk ekstraksi fitur lokal dan Bidirectional Gated Recurrent Unit (BiGRU) untuk menangkap ketergantungan kontekstual jangka panjang. Model ini juga diperkuat dengan mekanisme Multi-Head Attention dan Support Vector Machine (SVM) sebagai classifier final. Melalui integrasi ini, XSentiment-HS diharapkan mampu mengatasi tantangan ekstraksi fitur dan polisemi secara lebih efektif dibandingkan metode konvensional.
Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy Ardi Susanto; Heru Purnomo Kurniawan; Lia Farhatuaini; Muhammad Iszul Wilsa; Gina Khayatun Nufus; Ananda Sathria Maulana Amri
Journal of Applied Informatics Science Volume 1 Issue 2 (2025)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v1.i2.85

Abstract

Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.
Elite-Refined Genetic Algorithm with Hill Climbing Local Search for University Course Scheduling Heru Purnomo Kurniawan; Lia Farhatuaini; Nurul Bahiyah; Ardi Susanto; Muhammad Iszul Wilsa; Gina Khayatun Nufus
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.584

Abstract

Abstract— This paper proposes a hybrid optimization approach combining Genetic Algorithm (GA) and Hill Climbing (HC) to address the university course scheduling problem in the Informatics Study Program at Universitas Islam Negeri Siber Syekh Nurjati Cirebon. The hybrid GA-HC model integrates GA’s global exploration capability with HC's local refinement strategy to minimize hard and soft constraint violations while achieving balanced timetables. The dataset includes 56 course classes, 18 lecturers, and three rooms, with scheduling over five working days and 11 time slots per day. Experimental results demonstrate that GA-HC outperforms pure GA and pure HC in convergence speed, average fitness, and stability of feasible solutions. Parameter tuning analysis further shows that moderate mutation rates and limited HC iterations yield optimal trade-offs between runtime and solution quality. The proposed hybrid framework effectively enhances convergence, reduces conflicts, and improves overall timetable quality, confirming its robustness for large-scale academic scheduling problems.
PERBANDINGAN MODEL LSTM GRU BiLSTM CNN-LSTM DAN TRANSFORMER UNTUK PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG) Ardi Susanto; Gina Khayatun Nufus; Layli Hardiyanti
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.8071

Abstract

Prediksi Indeks Harga Saham Gabungan (IHSG) merupakan tantangan penting bagi investor dan analis di pasar modal Indonesia. Berbagai arsitektur deep learning telah dikembangkan untuk peramalan deret waktu finansial, namun studi komparatif komprehensif pada konteks IHSG masih terbatas, khususnya yang membandingkan lima arsitektur utama dengan hyperparameter tuning sistematis dan validasi statistik. Penelitian ini bertujuan membandingkan performa LSTM, GRU, BiLSTM, CNN-LSTM, dan Transformer untuk prediksi return harian dan arah pergerakan IHSG. Data historis IHSG periode 2015–2025 diperoleh dari Yahoo Finance dengan 32 fitur teknikal stasioner. Setiap model dituning menggunakan Optuna dengan 25 trial, dan performa divalidasi menggunakan Diebold-Mariano test. Hasil menunjukkan LSTM Tuned mencapai performa deep learning terbaik dengan RMSE 0,01109 dan MDA 55,83%, sementara Lasso Regression sebagai baseline mencapai RMSE 0,01094 dan MDA 56,67%. Uji Diebold-Mariano mengonfirmasi bahwa perbedaan Lasso dan LSTM Tuned tidak signifikan secara statistik (p=0,356), sedangkan LSTM secara signifikan mengungguli Transformer (p=0,047). Temuan ini menunjukkan bahwa LSTM Tuned mampu mencapai performa yang setara secara statistik dengan baseline linear regularized terbaik pada prediksi return IHSG. Penelitian menyediakan empirical benchmark yang berguna bagi peneliti dan praktisi pasar modal Indonesia
Analisis Usability Website Oceanography ITB Hub Menggunakan System Usability Scale Berdasarkan Karakteristik Pengguna Kahfi Gunardi; Iwan Pramesti Anwar; Suhadi Parman; Gina Khayatun Nufus; Layli Hardiyanti
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1529

Abstract

Oceanography ITB Hub is a scientific dashboard that presents real-time meteorological and oceanographic observation data from an Automatic Weather Station (AWS) installed at the Marine Technology and Coastal Research Center (MTCRC) pier, Cirebon. As a dashboard with high information density and technical terminology, usability becomes a critical aspect, particularly because its users come from diverse academic backgrounds. This study aims to measure the usability level of the website using the System Usability Scale (SUS) and to analyze differences in usability perception based on user characteristics. The study involved 68 respondents divided into five groups: STMKG Lecturers, Science and Technology Lecturers, STMKG Students, Science and Technology Students, and Social Science Students. Data were analyzed using descriptive statistics, the Kruskal–Wallis test, and the Dunn–Bonferroni post hoc test. The results show a mean SUS score of 67.13, classified as Grade D, Marginal Acceptability, and an “OK” Adjective Rating, with all items proven valid and the instrument reliable (Cronbach's Alpha = 0.851). The Kruskal–Wallis test revealed a statistically significant difference in usability across user groups (H = 17.126; p = 0.0018), with STMKG Students rating usability significantly higher than Science and Technology Students (p = 0.0059) and Social Science Students (p = 0.0040). Item-level analysis identified learnability as the main weakness, particularly users' need to learn extensively or seek help before being able to use the website effectively. These findings emphasize the importance of considering user characteristics in the usability evaluation of scientific dashboards and provide a basis for interface improvement recommendations grounded in Nielsen's Usability Heuristics
Co-Authors Ananda Sathria Maulana Amri Ardi Susanto Ardi Susanto Eko Jhony Pranata Farhatuaini, Lia Fariz Noor Djati Iwan Pramesti Anwar Jaka Septiadi Kahfi Gunardi Kurniawan, Heru Purnomo Layli Hardiyanti Mohammad Raihan Akbar Muhammad Iszul Wilsa Muhammad Iszul Wilsa Nurul Bahiyah Rizki Dewantara Sokid Suhadi Parman