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All Journal International Journal of Advances in Applied Sciences Jurnal Buana Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan InComTech: Jurnal Telekomunikasi dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Computer Science and Technology (JACOST) Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Nusantara Hasana Journal Jurnal Informatika Terpadu Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Informatika Polinema (JIP) Journal of World Future Medicine, Health and Nursing Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications Journal of Informatics and Advanced Computing International Journal of Computer Science and Information Technology Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Risqy Siwi Pradini
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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SECURITY ANALYSIS OF COLLEGE WEBSITES USING VULNERABILITY ASSESSMENT METHOD Dio Wahyu Saputra; Risqy Siwi Pradini; Mochammad Anshori
International Journal of Computer Science and Information Technology Vol. 2 No. 1 (2025): IJCOMIT Vol 2 No 1
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v2i1.13206

Abstract

Web application security, especially on college websites, is a critical aspect in maintaining data integrity and protecting users from cyber threats. This research evaluates the security of college websites using the vulnerability assessment method. By utilizing tools such as OWASP ZAP and penetration testing techniques, this research identifies weaknesses at various security layers, including vulnerabilities to Path Traversal attacks, SQL Injection, and deficiencies in the implementation of HTTP security header settings. The analysis shows that the main vulnerabilities are caused by a lack of input validation, inadequate security configuration, and the use of outdated software libraries. This research provides practical solutions such as strengthening security configurations, system updates, and implementing modern security policies to mitigate risks. These findings aim to enhance the security of college websites and create a safer online environment.
Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke Danis Rifa Nurqotimah; Naseh Khudori, Ahsanun; Siwi Pradini, Risqy
Journal of Applied Computer Science and Technology Vol 5 No 2 (2024): Desember 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i2.817

Abstract

Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. The study used data sets from the data.world.com site with a total of 40,910 data. Using the Orange tool, the study managed to classify stroke disease well using the RBF kernel with cross validation techniques resulting in an accuracy of 94.8%. The results of this study can be concluded that the stroke classification model developed has excellent performance. Overall, these results indicate that the Stroke classification model developed is highly reliable and effective, with excellent ability to detect stroke cases and provide accurate predictions. Making better and quicker medical judgments can be aided by using this approach to diagnose strokes.
Peningkatan Akurasi Rekomendasi Dokter pada Kondisi Data Sparsity Menggunakan Algoritma Content-Based Filtering Prasetya, Alwan; Khudori, Ahsanun Naseh; Pradini, Risqy Siwi
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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

Abstract

The growth of healthcare applications such as Halodoc, Alodokter, and Klikdokter has enabled easier access to doctor recommendations. However, generating relevant recommendations remains challenging. One key issue is data sparsity, where limited doctor attributes reduce the system’s accuracy. This study develops a doctor recommendation system using a Content-Based Filtering (CBF) approach based on five main attributes: specialization, rating, consultation fee, years of practice, and gender. Data imputation and attribute weighting techniques are applied to enhance accuracy. Results show that the proposed method reduces the Mean Absolute Error (MAE) from 0.142 to 0.102 and the Root Mean Squared Error (RMSE) from 0.205 to 0.150. These findings indicate that the implemented techniques improve the recommendation system under sparse data conditions.
Prediction of Sleep Disorder: Insomnia Using AdaBoost Ensemble Learning Algorithm with Grid Search Optimization Anshori, Mochammad; Kusuma, Wahyu Teja; Pradini, Risqy Siwi
InComTech : Jurnal Telekomunikasi dan Komputer Vol 14, No 1 (2024)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v14i1.19306

Abstract

Human health is an important thing to keep. Health has to be maintained with appropriate rest. Lack of rest has a bad impact on the body such as hormonal imbalances. One of the causes of lack of rest is insomnia. Insomnia is a phenomenon that describes someone's difficulty sleeping. Insomnia is often considered trivial, but chronic insomnia puts the sufferer at risk of serious illness physically and psychologically. Some people sometimes don't realize that they have insomnia because they feel like they have trouble sleeping. Therefore, early detection of insomnia is necessary to do. This study uses a machine learning approach to make predictions, namely the AdaBoost + grid search method. AdaBoost is used because of its reliability in making strong classifiers and grid search is applied to tuning parameters from AdaBoost. Parameters that are optimized are the n estimator and learning rate. Parameter tuning by grid search gives n – estimator = 76 and learning rate = 0.1. Some preprocess technique is done, there are normalization and ordinal encoding then data splitting based on the determined ratio. There are 80% for training data and 20% for testing data. On training data, the result is 98% percentage for each accuracy, precision, recall, and f1 score. This value is better than the comparison method, it is LogRegression that only reaches 97% value on each evaluation measure. The model implemented on test data and AdaBoost + grid search obtained 100% accuracy, precision, recall, and f1 score. However, LogRegression only gives 98% result. This study proved that AdaBoost with grid search is sustainable to do early prediction of insomnia.
Analisis Usability Website Berbinar Insightful Indonesia Menggunakan USE Questionnaire dan Performance Test Shaktyanti, Frenchyani Anggi; Pradini, Risqy Siwi; Kusuma, Wahyu Teja
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1387

Abstract

This research analyzes the usability of the Berbinar Insightful Indonesia website using a use questionnaire and performance test to test the suitability of this website. Testing was carried out by giving 5 task scenarios and 30 questionnaires to 20 participants. Testing using task scenarios is very important for measuring the performance of a website through user experience. The research instrument used is a use questionnaire used to calculate the usability value of a website. Based on the results of the use questionnaire, the usability value was found to be 79% in the good category, apart from that, the value of the 4 aspects of the use questionnaire, namely, usefulness was equal to, ease-of-use was equal to, ease-of-learning was equal to and satisfaction was equal to. Furthermore, the results of the performance test show the participant's time to complete the task, the number of participant errors and mistakes, the participant's success rate as well as a time efficiency value of 79% and an overall relative efficiency of 98%. These findings contribute to usability testing on the Berbinar Insightful Indonesia website based on user experience.
ANALISIS PENERIMAAN SISTEM PENDAFTARAN ONLINE PASIEN RAWAT JALAN DI RS RADJIMAN WEDIODININGRAT MENGGUNAKAN TEKNOLOGY ACCEPTANCE MODEL Wisam Syahputra, Kelvin; Haris, M. Syauqi; Siwi Pradini, Risqy
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13917

Abstract

Perkembangan teknologi informasi dalam layanan kesehatan telah mendorong digitalisasi sistem pendaftaran pasien untuk meningkatkan efisiensi dan kenyamanan. Penelitian ini menganalisis penerimaan sistem pendaftaran online pasien rawat jalan di RS Radjiman Wediodiningrat menggunakan Technology Acceptance Model (TAM) dengan menambahkan aksesibilitas sebagai variabel eksternal. Metode kuantitatif dengan desain cross-sectional diterapkan pada 100 responden. Hasil pengujian hipotesis menunjukkan bahwa enam variabel yang diuji Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), dan Behavioral Intention (BI), Actual System Use (ASU), dan Accessibility (AC) berpengaruh signifikan terhadap penerimaan sistem (nilai sig. < 0,05). Aksesibilitas juga terbukti berpengaruh positif terhadap PU (koefisien 0,646) dan PEOU (koefisien 0,759). Temuan ini menunjukkan bahwa peningkatan kemudahan akses dan antarmuka pengguna yang baik dapat mendorong adopsi teknologi oleh pasien. Implikasi praktis dari penelitian ini menyarankan RS Radjiman Wediodiningrat untuk memperbaiki infrastruktur sistem guna meningkatkan pengalaman pengguna dan efisiensi layanan.
SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student Anshori, Mochammad; Siwi Pradini, Risqy; Teja Kusuma, Wahyu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13521

Abstract

Self-esteem plays a crucial role in students' psychological well-being, influencing their academic performance and personal development. Despite its importance, self-esteem is challenging to measure due to its abstract and subjective nature. This study aims to develop a predictive model to classify students’ self-esteem levels as high or low using machine learning and tabular data obtained through questionnaires. A dataset comprising 47 student responses, with 19 features consisting of social, emotional, demographic aspects, were analyzed. Five machine learning models were evaluated: Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM). To address the class imbalance in the dataset, the study applied SMOTE for data balancing and min-max normalization for feature standardization. Model performance was assessed using accuracy and F1-score. The results reveal that SVM, particularly with an RBF kernel, outperformed other models across all scenarios. On raw data, SVM achieved 66% accuracy and an F1-score of 57.3%. After applying SMOTE, the performance improved to 80% accuracy and a 79.9% F1-score. Further enhancement with normalization resulted in the best performance, with SVM achieving 83.33% accuracy and an F1-score of 83.3%. These results demonstrate how well preprocessing methods work to enhance machine learning models for datasets that are unbalanced. The proposed SVM-based model offers promising applications in educational and psychological settings, enabling early interventions to support students’ mental health.
KOMPARASI ALGORITMA BOOSTING UNTUK PREDIKSI GANGGUAN TIDUR Mawardi, Ade Bagus; Pradini, Risqy Siwi; Haris, M. Syauqi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7281

Abstract

Gangguan tidur merupakan salah satu permasalahan kesehatan yang dapat berdampak pada kualitas hidup seseorang. Dalam upaya meningkatkan akurasi prediksi gangguan tidur, teknologi kecerdasan buatan telah banyak dimanfaatkan, khususnya melalui pendekatan algoritma machine learning. Penelitian ini bertujuan untuk melakukan komparasi terhadap lima algoritma boosting, yaitu AdaBoost, CatBoost, LightGBM, Gradient Boosting, dan XGBoost menggunakan dataset Sleep Health and Lifestyle. Adapun tahap penelitian yang dilakukan meliputi pengumpulan data, prapemrosesan data, normalisasi, serta evaluasi model. Berdasarkan hasil evaluasi, algoritma CatBoost menunjukkan performa paling unggul dibandingkan dengan algoritma lainnya. Hasil evaluasi menunjukkan bahwa algoritma CatBoost memberikan performa terbaik dengan akurasi sebesar 97,37%, presisi 96,29%, recall 95,83%, dan F1-score 95,82%. Hasil analisis menunjukkan bahwa keunggulan CatBoost berasal dari kemampuannya dalam menangani fitur kategorikal secara langsung tanpa memerlukan encoding tambahan, serta kemampuannya dalam mengurangi overfitting dibandingkan dengan metode boosting lainnya. Temuan ini menunjukkan bahwa model prediksi berbasis boosting khususnya CatBoost dapat dijadikan alat bantu yang efektif dalam deteksi gangguan tidur secara lebih akurat.
IMPLEMENTASI INTERNET OF THINGS (IoT) DENGAN PROTOKOL KOMUNIKASI MQTT PADA SISTEM KONTROL LAMPU RUANGAN Makhrus, Moh. Ali; Makhrus, Moh Ali; Pradini, Risqy Siwi; Rikatsih, Nindynar
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 1 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/yxq35q60

Abstract

The increasing need for automation of electronic devices encourages the use of Internet of Things (IoT) technology to improve energy efficiency, ease of control, and flexibility of access. This study designs and implements an IoT-based lighting control system using the Message Queuing Telemetry Transport (MQTT) protocol, which is efficient and lightweight in data communication. The system utilizes the NodeMCU ESP8266 microcontroller, the DS3231 Real Time Clock (RTC) module for automatic scheduling, and the Wi-Fi Manager for network configuration via a web interface without re-uploading code. Testing includes evaluating response time, bandwidth consumption, and connection stability when switching networks. The results show that the system has an average response time of 0.05–0.08 seconds and a minimum bandwidth consumption of 70 bytes per second. The system can also switch networks automatically without any disruption of function, making it a reliable and efficient solution for lighting control on a household, institutional, and commercial scale.
Klasifikasi Spesies Jamur Beracun Agaricus Xanthodermus dan Amanita Muscaria Menggunakan Transfer Learning dengan Arsitektur MobileNetV2 Ali Hasan, Hildan Adisqi; Siwi Pradini, Risqy; Anshori, Mochammad
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.785

Abstract

Jamur memiliki peran penting dalam keanekaragaman hayati, namun beberapa spesies seperti Agaricus xanthodermus dan Amanita muscaria bersifat beracun dan dapat menyebabkan risiko kesehatan serius jika dikonsumsi. Identifikasi jamur beracun secara akurat menjadi tantangan karena kemiripan morfologinya dengan spesies non-beracun. Penelitian ini mengusulkan model klasifikasi jamur beracun menggunakan metode transfer learning dengan arsitektur MobileNetV2, yang dikenal efisien dalam memproses data visual. Dataset terdiri dari 632 gambar, masing-masing 304 gambar untuk Agaricus xanthodermus dan 328 gambar untuk Amanita muscaria, yang diperoleh dari platform Kaggle dan dibagi menjadi 80% data latih dan 20% data validasi. Augmentasi data seperti rotation, shift, flipping, dan rescaling diterapkan untuk meningkatkan generalisasi model. Eksperimen dilakukan dengan menguji pengaruh jumlah epoch terhadap performa model, menggunakan rentang 10 hingga 100 epoch dengan interval 10. Hasil menunjukkan bahwa akurasi model meningkat seiring bertambahnya jumlah epoch, dengan performa optimal pada epoch ke-60. Pada epoch ini, akurasi validasi mencapai 99.21% dengan nilai loss validasi terendah sebesar 0,0447, menunjukkan bahwa model mampu mengklasifikasikan kedua spesies jamur secara akurat dan efisien. Selain itu, tren akurasi dan loss pada data pelatihan menunjukkan bahwa model mampu belajar secara stabil dan tidak mengalami overfitting, bahkan ketika menggunakan dataset yang relatif kecil. Penelitian ini berkontribusi pada pengembangan metode klasifikasi jamur beracun yang lebih akurat dan efisien, yang memiliki implikasi penting dalam kesehatan masyarakat dan konservasi keanekaragaman hayati.
Co-Authors ACHMAD JAELANI RUSDI Agung Setia Budi, Agung Setia Agus Widodo Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Alwan Prasetya Apriyani Puji Hastuti Bintang Pramudya Cantika Nur Previana Danis Rifa Nurqotimah Dinda Chesar Putri Ramadhani Dio Wahyu Saputra Fery Fajar, Muhammad Geraldo, Tostes Henokh Kristiawan Hilda Nuzulul Mujaddidah Indra Dharma Wijaya Irnanda Septian Ika Putri Jenike Gracelya Noke Kurniawan, Ardhiles Wahyu M. Syauqi Haris Makhrus, Moh Ali Makhrus, Moh. Ali Marilia, Busatto Mawardi, Ade Bagus Mayang Anglingsari Putri, Mayang Anglingsari Mochamad Anshori Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Nugroho Teguh Yuono Prasetya, Alwan Putri Salsabila, Dhea Putri, Irnanda Septian Ika Putriana, Rena Rahmania Ambarika Rika, Faurika Roesardhyati, Ratna Salsabila, Dhea Putri Saputra, Dio Wahyu Saputra, Zainal Setia Budi, Agung Shaktyanti, Frenchyani Anggi Trihapningsari , Denisha Trihapningsari Wahyu Tejakusuma, Wahyu Wisam Syahputra, Kelvin