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All Journal Jurnal Teliska International Journal of Artificial Intelligence Research Indonesian Journal of Artificial Intelligence and Data Mining
Afifah, Luthfia
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PERANCANGAN SISTEM MONITORING KUALITAS UDARA BERBASIS MIKROKONTOLLER ESP32 Perdana, Ahmad Satrio; Afifah, Luthfia; Azizah, Putri Nur
JURNAL TELISKA Vol 18 No III (2025): TELISKA November 2025
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17768271

Abstract

Aktivitas pembakaran lahan yang terjadi di Sumatera Selatan, akibatnya sejumlah wilayah di sekitarnya dilanda kabut asap termasuk Kota Palembang, Hal tersebut dapat berdampak pada kualitas udara yang semakin memburuk. Udara merupakan unsur penting bagi kehidupan sehingga udara harus memiliki indeks yang baik agar tidak menimbulkan dampak negative bagi kesehatan tubuh. Selain asap pembakaran, terdapat factor lain yang berpengaruh pada kualitas udara seperti asap asap rokok, asap pembakaran sampah, asap kendaraan bermotor, asap pabrik dll. Karna itu sangat diperlukan nya sebuah sistem yang dapat digunakan untuk memantau kualitas udara disekitar. Atas dasar hal tersebut maka penulisan kali ini akan membahas tentang alat pemantauan kualitas udara yang portable dan mempermudah bagi penggunanya untuk monitoring kualitas udara secara berkala. Dalam project ini digunakan mikrokontroler ESP32 sebagai komponen utama yang dapat menghubungkan ke bahasa pemrograman, Selain itu digunakan tiga sensor untuk mengukur kualitas udara yaitu Sensor DHT11 untuk suhu dan kelembapan, Sensor MQ-135 untuk gas dan Dust Sensor untuk partikel debu. Kemudian komponen buzzer yang digunakan sebagai komponen output. Project ini akan menghasilkan suatu alat pemantau kualitas udara berbasis IoT yang dihubungkan ke aplikasi blynk sehingga keluaran nya akan menampilkan notifikasi dari aplikasi tersebut dan juga memiliki hasil keluaran bunyi yang dikeluarkan oleh buzzer ketika pembacaan sensor yang diukur melewati batas normal atau standarnya
Field-Level AES-128 Encryption in Laravel-based E-Commerce for MSME Data Protection Afifah, Luthfia; Nurdin, Ali; Handayani, Ade Silvia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37675

Abstract

The increasing digitization of micro, small, and medium enterprises (MSMEs) in e-commerce brings critical challenges in protecting customer data. Despite the widespread use of encrypted communication protocols such as HTTPS and TLS for secure data transmission, many MSMEs still fail to implement encryption at the data storage level. This means that once the data reaches the server, it is often stored in unencrypted form within the database. This study implemented AES-128 encryption at the field-level in a Laravel-based e-commerce system to protect MSME customer data. The encryption was applied to sensitive data fields and tested through black-box testing and benchmark analysis. A dataset of 10,000 records was used to compare performance between plaintext and encrypted operations. Results showed an average encryption overhead of 0.0409 seconds, indicating minimal impact on performance. The encryption-decryption process consistently returned correct outputs across all trials. This solution offers an affordable and scalable encryption model for MSMEs, enhancing customer data security without relying on external tools or infrastructure.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Co-Authors Ade Silvia Handayani Agung, Muhammad Zakuan Ali Nurdin Asriyadi Asriyadi Emilia Hesti Kaila, Afifah Syifah M. Ardiansyah Perdana, Ahmad Satrio Putri Nur Azizah, Putri Nur Rosita, Ella suzan zefi