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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Format : Jurnal Imiah Teknik Informatika Sinergi: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nasional JURNAL ILMIAH SATYA NEGARA INDONESIA
Fajriah, Riri
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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PEMANFAATAN WEBSITE BANK SAMPAH SEBAGAI SARANA EDUKASI LINGKUNGAN DI KELURAHAN DURI KEPA Fajriah, Riri; Meiyanti, Ruci; Jumaryadi, Yuwan; Hakim, Lukman; Zuli, Faizal; Santoso, Teguh Budi
Jurnal Pengabdian Masyarakat Nasional Vol 5, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v5i1.34509

Abstract

Kelurahan Duri Kepa sebagai salah satu Kelurahan di Kecamatan Kebon Jeruk Jakarta Barat berencana untuk meningkatkan kesadaran masyarakat dalam hal ketertiban lingkungan seperti pengolahan sampah melalui bank sampah yang sudah disediakan oleh pihak kelurahan. Akan tetapi, permasalahan yang dihadapi saat ini adalah manajemen administrasi pengelolaan bank sampah masih dilakukan secara manual dan beresiko terjadi kesalahan pencatatan data dan laporan manajemen bank sampah di Kelurahan Duri Kepa menjadi tidak akurat. Oleh karena itu diperlukan upaya edukasi mengenai pemanfaatan website bank sampah untuk meningkatkan proses administrasi tata kelola bank sampah secara digital. Adapun bentuk edukasi tersebut dilaksanakan dalam kegiatan pengabdian masyarakat oleh tim dosen dan mahasiswa dari Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Mercu Buana. Peserta kegiatan pengabdian masyarakat diwakilkan oleh tim PKK, PPSU, Staf Kelurahan, Karang Taruna dan perwakilan masyarakat di Kelurahan Duri Kepa. Selain itu, kegiatan ini bertujuan menghasilkan peningkatan kesadaran masyarakat di Kelurahan Duri Kepa terhadap pengelolaan sampah dan memberikan pengalaman belajar langsung di luar kampus bagi mahasiswa dalam merancang dan mengimplementasikan solusi nyata berbasis teknologi, mendorong kolaborasi multidisiplin, serta meningkatkan keterampilan teknis dan sosial. Melalui keterlibatan aktif dalam proyek pengembangan website bank sampah di Kelurahan Duri Kepa, tim dosen dan mahasiswa tidak hanya mengaplikasikan ilmu komputer dalam konteks nyata, tetapi juga membangun jejaring dengan pemerintah daerah dan komunitas, yang memperkuat kontribusi akademisi dalam memberdayakan masyarakat secara langsung. 
EDUKASI PEMBELAJARAN BAHASA INGGRIS DASAR, DENGAN PEMANFAATAN AI UNTUK WARGA DURI KEPA Rifqi, Muhammad; Fajriah, Riri
JURNAL SINERGI Vol. 7 No. 1 (2025): SINERGI
Publisher : FT-USNI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/sinergi.v7i2.699

Abstract

Meningkatnya kebutuhan akan keterampilan berbahasa Inggris dalam kehidupan sehari-hari dan dunia kerja telah mendorong perlunya metode pembelajaran yang mudah diakses dan efektif. Menanggapi tantangan ini, kegiatan pengabdian kepada masyarakat dilakukan di Kelurahan Duri Kepa, Jakarta Barat, dengan fokus pada pendidikan bahasa Inggris dasar yang diintegrasikan dengan perangkat Kecerdasan Buatan (AI). Inisiatif ini bertujuan untuk meningkatkan literasi digital di kalangan warga sekaligus meningkatkan kemampuan bahasa Inggris mereka. Program ini melibatkan sesi pelatihan tentang penggunaan platform berbasis AI seperti Google Translate, Grammarly, dan Duolingo untuk mendukung pembelajaran mandiri dan meningkatkan tata bahasa, kosakata, dan pengucapan. Pesertanya termasuk anggota PKK, PPSU, staf desa, Karang Taruna, dan warga setempat. Hasilnya menunjukkan peningkatan antusiasme dan keterlibatan di antara para peserta, yang melaporkan peningkatan kepercayaan diri dalam menggunakan bahasa Inggris melalui pembelajaran berbantuan AI. Kegiatan ini tidak hanya memberdayakan masyarakat dengan keterampilan bahasa praktis tetapi juga memberi siswa pengalaman berharga dalam menerapkan solusi pendidikan berbasis teknologi..
Kinerja Komparatif LSTM dan XGBoost untuk Peramalan Radiasi Matahari Perkotaan Tropis Indriyanti, Prastika; Fajriah, Riri
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/10.22441/format.2025.v14.i2.010

Abstract

The increasing reliance on clean energy has accelerated the development of solar energy infrastructure. However, its intermittent nature—especially in tropical urban climates—poses significant challenges to maintaining grid stability. This study compares the performance of two machine learning algorithms, Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost), for hourly solar irradiance forecasting in two climatically distinct tropical cities: Jakarta and Bogor. Using a 10-year historical dataset from NASA POWER that includes solar irradiance and relevant meteorological variables, this research addresses the gap in comparative analysis of deep learning versus ensemble models within high-granularity tropical data settings. The methodology involves data acquisition, preprocessing, feature engineering, model development, hyperparameter tuning, and evaluation using RMSE, MAE, and R² metrics. The results show that LSTM consistently outperforms XGBoost in both cities. In East Jakarta, LSTM achieved a RMSE of 29.24, MAE of 15.63, and R² of 0.9875, compared to XGBoost with RMSE of 38.65, MAE of 18.92, and R² of 0.9782. Similarly, in Bogor Regency, LSTM achieved RMSE of 30.73, MAE of 16.89, and R² of 0.9862, outperforming XGBoost which recorded RMSE of 38.41, MAE of 18.68, and R² of 0.9785. These findings highlight LSTM's superior ability to capture complex temporal dependencies and nonlinear trends in solar irradiance time-series data, especially under the fluctuating weather patterns characteristic of tropical urban environments. The results provide strong empirical support for implementing LSTM-based forecasting in solar energy management systems across similar geographic regions.
Machine Learning Approaches to Sentiment Analysis of Mental Health Discussions on Platform X Jumaryadi, Yuwan; Fajriah, Riri; Salamah, Umniy; Priambodo, Bagus; Lystha, Arie
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11350

Abstract

Sentiment analysis on mental health issues is crucial for understanding public perceptions of healthcare services. This study analyzed tweets related to mental health on platform X in 2025 using SVM, Random Forest, and Naive Bayes algorithms. Data was collected through web scraping with Python, then evaluated using a confusion matrix with accuracy, precision, f1-score, and recall metrics. The classification results showed a distribution of sentiment: positive (3,667 tweets), neutral (838 tweets), and negative (704 tweets). A comparative analysis of the three algorithms revealed that SVM achieved the highest accuracy (78.69%), followed by Random Forest (75.04%) and Naive Bayes (70.44%), proving the superiority of SVM in classifying mental health sentiment. These findings provide valuable insights for stakeholders in improving mental healthcare services based on public feedback, while also offering a reference for effective sentiment analysis methods for social media data.
UTILIZATION OF BIG DATA IN IMPROVING MARKETING STRATEGIES Fajriah, Riri
Jurnal Ilmiah Universitas Satya Negara Indonesia Vol. 2 No. 2 (2024): May - October 2024
Publisher : Lembaga Penelitian, Publikasi, & Pengabdian kepada Masyarakat, Universitas Satya Negara Indonesia (LP3M-USNI)

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

Abstract

Big Data is data characterized by very large, very varied, fast-growing of data and may not need to be treated specially structured with innovative technology to obtain in-depth information and can help in making a better decision. The important things of Big Data is not just about technical capability to process data, but the benefits of its that can be realized by the company by using Big Data Analytics Terminology and it is believed come from web search company which process data the aggregation of distributed very large and unstructured. The challenge of Big Data includes the acquisition, accuracy of data, storage, search (search), the distribution, transfer, data analysis, and data visualization. The trend of Big Data is increasingly enlarged set of data caused by the increase of information from large sets of inter-related, compared with other small associations with the total number of the same data. One of the utilization of Big Data is a boost in sales with improved marketing strategy in matters such as: product campaigns, consumer segmentation and markets analysis, location-based marketing, sentiment analysis, personalized marketing and lure customers.
Co-Authors Bagus Priambodo Faizal Zuli, Faizal Indriyanti, Prastika Jumaryadi, Yuwan Lukman Hakim Lystha, Arie Muhammad Rifqi Ruci Meiyanti Salamah, Umniy Santoso, Teguh Budi