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All Journal Semantik Jurnal Informatika Upgris Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang IJISTECH (International Journal Of Information System & Technology) Journal of Technopreneurship and Information System (JTIS) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Infotekmesin Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Teknik Informatika (JUTIF) Community Empowerment IJISTECH Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Publika Pengabdian Masyarakat J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Pelita: Jurnal Pengabdian kepada Masyarakat Scientific Journal of Informatics
Noora Qotrun Nada, Noora Qotrun
Universitas PGRI Semarang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Empowering communities through IoT-enabled fish cultivation in Kembangarum Subdistrict, Semarang City Rizky Muliani Dwi Ujianti; Noora Qotrun Nada; Slamet Budirahardjo; Mohamad Fajarianditya Nugroho; Setyoningsih Wibowo; Mutiara Fitri Rahmaningtyas; Nurul Hidayah; Yanuar Noor Wicaksono; Aldo Kamadi
Community Empowerment Vol 9 No 10 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.12337

Abstract

This community service activity aims to improve the welfare of the people of Kembangarum Subdistrict, Semarang, through the development of catfish cultivation based on Internet of Things (IoT) technology. The implementation of the activity includes needs analysis, pond construction, IoT installation, training, and assistance in fish cultivation and post-harvest processing. The results of the activity show an increase in community understanding of IoT technology and its application in catfish cultivation. Additionally, a grant was provided in the form of a catfish pond equipped with an IoT system for real-time water quality monitoring. This activity also successfully improved community skills in processing harvested fish into various products, thus potentially increasing community income. Overall, this community service activity has contributed to the development of the local economy and opened up opportunities for the formation of a thematic catfish village in Kembangarum Subdistrict.
Pengenalan Gestur Bahasa Isyarat Indonesia dengan Mediapipe Keypoints Dewanto, Febrian Murti; Harjanta, Aris Tri Jaka; Nada, Noora Qotrun; Herlambang, Bambang Agus
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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

Abstract

Difficulty in communication is an obstacle for deaf friends who cannot learn the language orally or acquire normal speech skills. The development of sign language gesture recognition technology is an important step to improve accessibility and social integration for the deaf community. The use of MediaPipe Holistic Keypoints and deep learning techniques provides significant potential in recognizing and understanding sign language gestures. The main objective of this study is to classify Indonesian Sign Language (BISINDO) gestures using MediaPipe Holistic Keypoints and a deep learning approach to identify basic words in sign language. By extracting features using mediapipe holistic and sending them to the LSTM 6 hidden layer model with 70:30 split train test and 250 epochs, an accuracy of 68% was produced. This is due to the limited number of datasets taken for the study.
PENERAPAN ALGORITMA REGRESI LINIER DAN RANDOM FOREST UNTUK MEMPREDIKSI PENJUALAN DI UCHI PARFUME Falah, Syamsul; Jaka, Aris Tri; Nada, Noora Qotrun
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 3 (2025): EDISI 25
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i3.6464

Abstract

Persaingan yang semakin ketat di sektor ritel parfum memerlukan pengelolaan persediaan dan strategi promosi yang tepat serta terukur. Di Uchi Parfume cabang Bumiayu, proses tersebut masih dilakukan secara manual melalui perkiraan, sehingga berisiko memicu ketidakseimbangan stok dan menurunkan kinerja penjualan. Penelitian ini mengembangkan model prediksi penjualan berbasis data historis untuk memperbaiki ketepatan perencanaan dan mengoptimalkan strategi promosi. Dua algoritma machine learning, yaitu Regresi Linier dan Random Forest, diterapkan dengan metode CRISP-DM yang mencakup tahap pemahaman bisnis, pengolahan data, pemodelan, evaluasi, dan implementasi. Variabel yang dianalisis meliputi bulan, harga, diskon, varian parfum, metode pembelian, dan lokasi cabang. Hasil evaluasi berdasarkan metrik MAE, RMSE, MAPE, dan R² menunjukkan bahwa Random Forest memiliki kinerja lebih unggul dibandingkan Regresi Linier, dengan kesalahan prediksi yang lebih rendah dan kemampuan menjelaskan variasi data yang lebih tinggi. Model kemudian diterapkan ke dalam aplikasi berbasis Streamlit, yang memungkinkan pengguna melakukan analisis penjualan dan perencanaan stok secara interaktif. Penelitian ini diharapkan dapat meningkatkan efisiensi operasional Uchi Parfume dalam membuat keputusan yang lebih akurat dan efisien.
Sistem Informasi Manajemen Praktek Kerja Lapangan Dengan Fitur Location Base Service (LBS) Nada, Noora Qotrun
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.33711

Abstract

Permasalahannya adalah belum adanya Sistem Informasi Manajemen Praktek Kerja Lapangan (PKL) pada Program Studi Informatika Universitas PGRI Semarang yang masih menggunakan sistem manual sehingga menyebabkan banyak kesalahan administrasi, kehilangan data dan jalur birokrasi yang panjang dalam setiap pelaksanaan PKL. Tujuan dari penelitian ini adalah membangun Sistem Informasi Manajemen PKL pada Program Studi Informatika Universitas PGRI Semarang dengan menggunakan metode penelitian Research and Development (R&D) dengan metode pengembangan System Development Life Cycle (SDLC) dengan model Iteratif yang merupakan gabungan model Waterfall dan Iterative pada model Prototype. Tahapan awal dari metode ini yaitu tahap Analisis dan Perancangan telah dilakukan pada penelitian pendahuluan yang berjudul Perancangan Sistem Informasi Manajemen Praktek Kerja Lapangan Program Studi Informatika Universitas PGRI Semarang dengan keluaran pemodelan Use Case Diagram, Activity Diagram, Sequence Diagram , Diagram Kelas dan Kamus Data. Pada penelitian ini melanjutkan dua tahap yang tersisa yaitu tahap Implementasi dan Evaluasi. Pada tahap implementasi, peneliti membangun sistem dengan menggunakan bahasa pemrograman PHP, MySQL Database Management System dan Sistem Informasi Manajemen Praktek Kerja Lapangan (PKL) yang terintegrasi dengan Location Based Service (LBS) untuk menentukan lokasi PKL mahasiswa. Selama tahap evaluasi (pengujian), metodologi pengujian black-box digunakan, sehingga semua pengujian dan skenario diterima 100%. Ini berarti sistem Anda bekerja seperti yang diharapkan. Pengujian Kotak Putih Setelah menghitung kompleksitas siklomatik, menentukan jalur independen, dan menjalankan uji nilai untuk menguji uji kode, kita menemukan bahwa kompleksitas siklomatik yang dihasilkan adalah 3. Artinya juga terdapat 3 lintasan independen, dan 3 lintasan nilai lintasan. keluaran yang diharapkan.
“Branket” Design as a Safe Deposit Box Security System using Arduino-Based Tap Sensor Trikusuma, Arsha Raulnadi; Rizqa, Mona; Wardhana, Dhimas Aria; Nada, Noora Qotrun
Advance Sustainable Science, Engineering and Technology Vol 3, No 1 (2021): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i1.8475

Abstract

Safe is a safe place to store valuables or documents. Because they are usually made of strong and hard materials, a safe is a place to store valuables and important documents in the event of a natural disaster or fire. In addition, the safe is also equipped with a locking system so that it can also be used to secure valuables or documents from theft. Usually, safes are used by agencies or companies and the general public who have valuable items or documents. Safe security systems that have been used generally use either a manual lock, a rotary lock, or a digital lock. There are several security system developments in the safe, including using a microcontroller-based password and fingerprint code, a fingerprint sensor and an Arduino UNO-based RF remote control, using a microcontroller via SMS and FSK facilities, and other developments in the safe security system. “Branket” (Tap Safe) is a safe with a smart lock system using a knock pattern. The bracket is composed of several electronic components, mainly a microcontroller, a solenoid lock, and a piezoelectric knock sensor. The workflow for using the bracket begins by pressing the power button to turn on the bracket. Then the user sticks his hand into the small space to store or opens the safe by tapping the sensor according to the pattern. Increased security on the bank account includes a locking system with a secret knock pattern, easy to remember by the owner, faster opening of the safe, and the process of opening the safe is difficult for others to know. It is hoped that “branket” will become a new innovation in a unique locking system that still has a high level of security.
Penerapan Algoritma Random Forest Untuk Prediksi Biaya Kontruksi Berbasis Web Dui Puspitasari; Noora Qotrun Nada; Aris Tri Jaka Harjanta
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 2 (2025): Juli
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i2.385

Abstract

Abstrak: Estimasi biaya proyek konstruksi sangat penting untuk menjamin efektivitas dan ketepatan perencanaan anggaran, kompleksitas proyek dan banyaknya variabel yang terlibat seringkali menyulitkan perusahaan konstruksi dalam menghasilkan estimasi biaya yang akurat. Tujuan dari penelitian ini adalah menggunakan data historis dan algoritma random forest regression untuk memperkirakan biaya proyek bangunan. Karena kapasitasnya untuk mengelola data yang rumit, meminimalkan overfitting, dan meningkatkan akurasi prediksi, pendekatan random forest dipilih. Model Random Forest digunakan untuk mengumpulkan, membersihkan, dan melatih data proyek sebelum dimasukkan ke dalam sistem informasi daring. Hasil pengujian menunjukkan tingkat akurasi model yang tinggi dalam esti masi biaya, tim proyek dan manajemen dapat mengakses data estimasi dengan cepat dan efektif berkat teknologi ini. Secara keseluruhan, penggunaan algoritma random forest dalam sistem berbasis web menawarkan cara yang fleksibel dan tepat untuk mendukung proses estimasi biaya proyek konstruksi.Kata kunci: Konstruksi, Machine Learning, Prediksi, Proyek, Random ForestAbstract: Construction project cost estimation is crucial to ensure the effectiveness and accuracy of budget planning. Project complexity and the numerous variables involved often make it difficult for construction companies to produce accurate cost estimates. The purpose of this study is to use historical data and the random forest regression algorithm to estimate the cost of a building project. Due to its capacity to handle complex data, minimize overfitting, and improve prediction accuracy, the random forest approach was chosen. The random forest model was used to collect, clean, and train project data before being input into an online information system. Test results demonstrated a high level of model accuracy in cost estimation, and project teams and management were able to access the estimated data quickly and effectively thanks to this technology. Overall, the use of the random forest algorithm in a web-based system offers a flexible and appropriate way to support the cost estimation process of construction projectsKeywords: Construction, Machine Learning, Prediction, Project, Random Forest.
Pembelajaran “Projek IPAS” Berbasis STEAM sebagai Kajian Implementasi Kurikulum Merdeka di SMK Muhammad Syaipul Hayat; Sumarno Sumarno; Mahmud Yunus; Noora Qotrun Nada
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.6005

Abstract

The implementation of the Independent Curriculum in SMK requires science learning to be carried out across disciplines through projects. This study aims to examine the potential and implementation of learning "IPAS project" oriented to the STEAM approach as an implementation of the Merdeka curriculum in SMK. The research was conducted using a survey method with a qualitative descriptive approach involving 48 respondents representing 34 out of 163 vocational schools in Central Java. The instrument used is a questionnaire consisting of three open questions about expectations, obstacles and efforts to overcome them; as well as a five-aspect questionnaire on the profile of teachers' understanding of the application of STEAM-based "IPAS Project" learning in the Merdeka curriculum in SMK. Qualitative descriptive analysis is carried out by data reduction, data display, and conclusion drawing. The results showed that although we have understood aspects of the independent curriculum in learning "IPAS Project", there are still obstacles, especially in the application of the STEAM approach. As for the profile of teachers' understanding of the application of STEAM-based "IPAS project" learning in the Merdeka curriculum in SMK, most teachers already have an understanding of the learning concept of "IPAS project" and in its application have involved many students in the implementation of the Merdeka curriculum. But in understanding the concept of STEAM, most teachers still don't master it well. Likewise, in terms of experience in applying the STEAM approach in "IPAS project", not many teachers have applied it in a structured and systematic manner. Based on this, it is recommended that to reduce the gap in expectations and constraints for vocational teachers, an LMS platform is needed to bridge the implementation of STEAM-based "IPAS Project".
Prediksi Risiko Depresi Berdasarkan Data Demografis dan Psikososial menggunakan Metode Ensemble Learning dengan Pendekatan Stacking Arwan Mangli; Noora Qotrun Nada; Mega Novita
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3102

Abstract

Depression is a mental health problem with high prevalence that requires accurate and reliable computational-based prediction systems to support early detection. This study proposes a depression risk prediction architecture based on a stacking ensemble approach incorporating an out-of-fold (OOF) mechanism to prevent data leakage during meta-feature generation. The model combines Support Vector Machine and XGBoost as base learners, with Logistic Regression employed as the meta-learner. A public Depression Professional Dataset is processed using a stratified split strategy, class balancing on the training data through SMOTE, and feature standardization to enhance training stability. Experimental results demonstrate that the proposed approach achieves superior performance with an accuracy of 0.99, precision of 0.91, recall of 1.00, and an F1-score of 0.95, along with consistent detection capability for the minority class. These findings confirm that the systematic integration of OOF stacking and SMOTE improves model sensitivity while reducing false negative errors, making it suitable for the development of artificial intelligence–based mental health screening systems.
Banking Stock Price Prediction Dashboard Using Long Short-Term Memory Navila, Ilma; Nada, Noora Qotrun; Renaldy, Ramadhan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5802

Abstract

The high volatility of banking sector stocks (BBCA, BBRI, BMRI) and the limitations of conventional forecasting methods in handling non-linear data necessitate robust and adaptive predictive models. This study aims to develop an integrated stock price prediction system utilizing a Stacked Long Short-Term Memory (LSTM) architecture embedded within a Flask-based interactive web dashboard. Adopting the CRISP-DM framework, the model was trained using daily and hourly historical data from Yahoo Finance to accommodate both short-term and medium-term forecasting. Backtesting evaluation demonstrated that the LSTM model achieved Mean Absolute Percentage Error (MAPE) values below 2% for daily single-step predictions and below 0.5% for hourly intraday predictions. Furthermore, in a 7-period recursive projection, the proposed LSTM proved highly robust in mitigating error accumulation compared to Linear Regression and Support Vector Regression (SVR), successfully maintaining MAPE values below 5% for all issuers. The implementation of this dashboard system provides a significant impact on financial informatics by bridging advanced deep learning predictive algorithms into a practical, real-time decision support system for investment analysis.
Real-Time Multi-Class DoS Attack Detection on Proxmox VMs UsingLightGBM with MikroTik Integration Danu Candra Saputra; Bambang Agus Herlambang; Noora Qotrun Nada
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.57873

Abstract

Purpose: The adoption of virtualization increases the dependence on service availability, making Denial of Service(DoS) attacks a serious threat, while rule-based detection is poorly adaptive to evolving attacks. Many previous studies also rely on outdated public datasets, are evaluated offline, rarely measure inference latency, and lack automatic mitigation. This study aims to build a real-time multi-class DoS detection system on Proxmox virtual machines using LightGBM integrated with MikroTik.Methods: A controlled testbed based on Proxmox and MikroTik was built to generate normal and attack traffic. The dataset was collected from the real infrastructure at a one-second granularity and labeled into six classes, namely the normal condition and five DoS attacks. LightGBM was proposed as the detection model, while XGBoost, Random Forest, Decision Tree, and SVM served as baselines, compared using a temporal holdout to prevent data leakage, with SMOTE applied only to the training data.Findings: LightGBM was selected as the best model with an accuracy of 96.22%, a macro F1-score of 96.25%, and an inference latency of 1.369 ms. The four flooding attacks were detected almost perfectly, whereas Slowloris was the hardest class because it resembles normal traffic. Its PR-AUC dropped to 0.9271, and the system performed automatic mitigation at a median latency of 56.2 ms.Originality: This study integrates lightweight real-time detection with automatic firewall-based mitigation in a closed loop on real infrastructure, emphasizing the balance between accuracy and efficiency rather than the highest accuracy alone. Future work can extend it to distributed (DDoS) attacks.
Co-Authors Aldo Kamadi Ari Tri Jaka Harjanta Aris Tri Jaka Harjanta, Aris Tri Aris Tri Joko Harjanto Arsha Raulnadi Trikusuma Arwan Mangli Bagus Priyatno Bambang Agus Herlambang, Bambang Bayu Kurniawan Bimo Sunarfri Hantono Chadyan Fathurachman Danu Candra Saputra Dui Puspitasari Dwijayanti, Alina Indra Sakti Eko Nugroho Fatmawati, Maylia Febrian Murti Dewanto Fitria, Alfina Hayat, Muhammad Syaipul Heri Prabowo Iffah Muflihati, Iffah Jaka, Aris Tri Kayat, Diana Nurul Khoiriya Latifah, Khoiriya Mahmud Yunus Mega Novita Mega Novita Mega Novita Mohamad Fajarianditya Nugroho Mukaromah, Nisfiyatul Mustofa, Faizal Gani Mutiara Fitri Rahmaningtyas Navila, Ilma Nizaruddin Nizaruddin Nurina Happy Nurul Hidayah Rahmat Sudrajat, Rahmat Renaldy, Ramadhan Rizky Muliani Dwi Ujianti Rizqa, Mona Setyoningsih Wibowo Setyoningsih Wibowo Setyoningsih Wibowo2, Setyoningsih Shentika Ayu Wulandari Slamet Budirahardjo Slamet Budirahardjo Sumarno Sumarno Sutrisno Sutrisno Syamsul Falah Trikusuma, Arsha Raulnadi Wardhana, Dhimas Aria Wibawa, Bayu Arie Wulandari, Shentika Ayu Yanuar Noor Wicaksono