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All Journal Jurnal Buana Informatika Jurnal Informatika dan Teknik Elektro Terapan InComTech: Jurnal Telekomunikasi dan Komputer Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Informatics and Advanced Computing
Pradini, Risqy Siwi
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health

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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

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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.
PENINGKATAN KOMPETENSI PELAKU UMKM KOTA BATU DALAM BRAND AWARENESS MELALUI PELATIHAN BERBASIS ARTIFICIAL INTELLIGENCE Pradini, Risqy Siwi; Haris, M. Syauqi; Anshori, Mochammad
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025): Volume 6 No 3 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i3.44159

Abstract

Program Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi pelaku UMKM di Kota Batu dalam memperkuat brand awareness produk melalui pelatihan berbasis Artificial Intelligence. Pelatihan ini dilakukan dengan menggunakan metode Participatory Learning and Action yang mengedepankan interaksi aktif dan praktik langsung dari para peserta pelatihan. Materi pelatihan mencakup konsep dasar branding, pemanfaatan media sosial, serta aplikasi teknologi AI seperti Canva dan ChatGPT dalam pembuatan desain visual untuk meningkatkan brand awareness produk UMKM. Evaluasi yang dilakukan melalui pre-test dan post-test menunjukkan peningkatan signifikan rata-rata pemahaman peserta, dari skor awal 63 menjadi 90,33. Hasil ini membuktikan bahwa metode pelatihan yang digunakan efektif dalam meningkatkan kompetensi para peserta. Pelatihan ini juga mendorong peserta untuk lebih percaya diri dalam memanfaatkan teknologi untuk pemasaran digital, sehingga mampu bersaing di pasar yang semakin kompetitif.
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.
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

Perkembangan aplikasi layanan kesehatan seperti Halodoc, Alodokter, dan Klikdokter telah menyediakan sistem rekomendasi yang memudahkan pasien untuk menentukan dokter yang relevan. Namun, rekomendasi dokter yang relevan masih menjadi tantangan. Salah satu permasalahannya adalah data sparsity, yaitu kelangkaan atribut data yang menyebabkan akurasi sistem rekomendasi bekerja kurang akurat. Penelitian ini mengembangkan sistem rekomendasi dokter menggunakan pendekatan Content-Based Filtering (CBF) untuk melakukan rekomendasi dokter sesuai dengan preferensi pasien dengan mempertimbangkan lima atribut utama: spesialisasi, rating, biaya konsultasi, lama praktik, dan jenis kelamin. Aturan imputasi data dan pembobotan atribut telah diimplementasikan untuk meningkatkan akurasi sistem rekomendasi. Hasil dari analisis data menunjukan teknik tersebut telah menurunkan Mean Absolute Error (MAE) dari 0,142 menjadi 0,102 dan Root Mean Squared Error (RMSE) dari 0,205 menjadi 0,150, sehingga teknik yang diimplementasikan meningkatkan sistem rekomendasi dokter dengan kondisi data sparsity.
Co-Authors Ahsanun Naseh Khudori Haris, M. Syauqi Indra Dharma Wijaya, Indra Dharma Makhrus, Moh Ali Makhrus, Moh. Ali Mawardi, Ade Bagus Mochamad Anshori Prasetya, Alwan Rikatsih, Nindynar Shaktyanti, Frenchyani Anggi Wahyu Tejakusuma, Wahyu